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Dreyfus (United States) https://doi.org/10.31449/inf.v48i1.5058 Informatica 48 (2024) 1–10 1 An Overview on Robot Process Automation: Advancements, Design Standards, its Application, and Limitations Rajkumar Palaniappan College of Engineering, Department of Mechatronics Engineering, University of Technology Bahrain, Salmabad, Kingdom of Bahrain E-mail: r.palaniappan@utb.edu.bh Overview Paper Keywords: RPA, design standard, hyperautomation, cognitive, cloud, machine learning Received: July 23, 2023 In a variety of areas, including healthcare, banking, and manufacturing, repetitive and rule-based processes are automated using robotic process automation (RPA), a fast developing technology. An overview of RPA's, its uses, limitation, and applications are given in this paper. RPA can lower costs, increase process speed, accuracy, and efficiency, and free up staff to concentrate on jobs of higher value. RPA is frequently used for tasks including data input, billing, and customer care. RPA can't, however, execute activities that call for human judgment, decision-making, or creativity, for instance. The adoption of RPA also needs a sizable initial investment and continual maintenance. This paper also touches on a few RPA-related ethical issues, like employment displacement and data privacy. While RPA has a great deal of promise to alter sectors, its deployment can only be successful if its limitations and ethical implications are carefully considered. Povzetek: Narejen je obsežen pregled RPA, avtomatizacije robotskih procesov, kot hitro razvijajoce se tehnologije, vkljucno z njenimi aplikacijami, standardi, omejitvami in uporabami. of automation. This drive to expand their automation 1 Introduction capabilities is motivated by the highly sought-after Robot Process Automation (RPA) serves as a valuable rewards that come with adopting and implementing these instrument that empowers organizations with the ability to innovative technological advancements. automate tiresome tasks typically executed by humans. One of the primary reasons organizations actively pursue Software robots within the realm of RPA exhibit RPA is the promise of increased efficiency and astonishing skills which enable them to flawlessly emulate productivity. RPA technology enables the automation of human actions including button clicks, data inputting as repetitive and time-consuming tasks, freeing up valuable well as system navigation[1]. These tireless bots work human resources to focus on more strategic and value-diligently throughout all hours of the day aiming for added activities. By automating mundane and rule-based accuracy in task completion with both speed and processes, businesses can significantly reduce manual efficiency. Undoubtedly there lies in abundance a errors and increase the speed at which tasks are completed. tremendous potential embedded within RPA; potential This increased efficiency leads to cost savings and capable of vastly reducing costs firsthand whilst improves operational performance. simultaneously enhancing overall efficiency along with In addition to efficiency gains, RPA technology offers the raising operational standards by eliminating any scope for potential for scalability and agility. As organizations grow error or faults thereby Allowing relieved allocation for and evolve, RPA can be easily scaled to accommodate human resource towards activities holding relatively increased workloads and changing business requirements higher value [2]. However partial success shall [4], [5]. RPA solutions can be quickly deployed and accompany those who embark on such implementation integrated with existing systems and applications, endeavors unto successful execution of RPA merely via allowing businesses to adapt to market demands and seize reservations shall fail upon laying strong emphasis upon new opportunities more rapidly. This flexibility and meticulous planning commencing from identification agility give organizations a competitive edge in dynamic associated with suitable processes up for automation and fast-paced industries. followed closely thereby alongside selection relevant tools Furthermore, RPA can enhance accuracy and compliance associated with processes enlisted prior plus provision put within organizations. By automating processes, forth ensuring suitable governance in addition to businesses can ensure consistent adherence to established appropriate oversight [3]. The rapid advancements in RPA rules and standards. RPA software can be programmed to technology have opened a world of limitless possibilities follow predetermined workflows and perform tasks withfor organizations. With each progressive improvement in precision, minimizing the risk of human error [6]. This RPA capabilities, businesses are compelled to actively level of accuracy is particularly beneficial in industries pursue and explore new use cases to harness the potential 2 Informatica 48 (2024) 1–10 that require strict compliance with regulations and standards, such as finance, healthcare, and legal sectors. Moreover, RPA technology enables organizations to gain valuable insights from data. By automating data collection, processing, and analysis, businesses can extract meaningful information and make data-driven decisions more efficiently. RPA can integrate with other analytics and business intelligence tools, allowing organizations to uncover patterns, trends, and correlations that can inform strategic planning and optimize business processes. Overall, the possibilities presented by RPA technology are virtually limitless. As organizations witness the transformative power of RPA in streamlining operations, reducing costs, improving accuracy, and enabling data-driven decision-making, they are driven to actively pursue and explore new use cases. By expanding their automation capabilities, businesses can attain the highly sought-after rewards that come with adopting and leveraging these innovative technological advancements. 2 Advancement in RPA Robot Process Automation (RPA) is a rapidly developing discipline, and RPA technology has made several strides in recent years. The following are some significant RPA advancements: 2.1 Machine learning-based RPA: Robotic process automation (RPA) is a sophisticated technique that uses machine learning techniques to enable intelligent automation. Traditional RPA uses pre­established rules and workflows to automate repetitive tasks, but RPA based on machine learning can gain knowledge from the past and continuously improve [7]. Machine learning algorithms are trained on big datasets to identify patterns and generate predictions in RPA that is machine learning-based. Then, these algorithms can be included into RPA systems to automate difficult activities that ordinarily call for human involvement. When processing invoices, for instance, machine learning-based RPA can be used to train the system to recognize various invoice types, extract pertinent data, and verify it against existing data [8]. Compared to standard RPA, machine learning-based RPA has a number of benefits, including better accuracy, more efficiency, and the capacity to manage unstructured data. Additionally, it is better able to manage exceptions and adapt to new circumstances. For the algorithms to be trained, machine learning-based RPA needs a lot of high-quality data, which can be difficult in particular sectors. RPA based on machine learning is still in its infancy, and there are worries about data privacy, prejudice, and the possibility that automation may replace human workers [9]. In general, machine learning-based RPA is a promising advancement in the field of automation, and it has a wide range of possible uses. However, for it to be implemented successfully, much thought must be given to its constraints and moral consequences. R. Palaniappan 2.2 Cognitive RPA Natural language processing (NLP), machine learning (ML), and computer vision are examples of cognitive technologies that are combined with traditional robotic process automation to create cognitive RPA (Robotic Process Automation). Intuitive and adaptable automation systems that can carry out complicated and varied tasks that were previously challenging or impossible to automate are what cognitive RPA aims to build [10],[11]. To comprehend natural language inputs and extract meaning from unstructured data sources like emails, documents, and social media postings, cognitive RPA systems use NLP. In order to learn from data and increase the precision of forecasts and decision-making, machine learning algorithms are used. To recognize and analyze visual data, such as pictures and movies, computer vision is employed [12]. Cognitive RPA systems may automate a larger range of jobs and give users more individualized and knowledgeable responses by combining these technologies. For instance, a cognitive RPA system can be used to identify and prioritize customer support requests, classify, and extract data from invoices automatically, or analyze social media data to find trends and sentiment [13]. Overall, cognitive RPA has the potential to revolutionize a variety of industries by enhancing productivity, accuracy, and efficiency while lowering costs and mistakes. 2.3 Hyperautomation Hyperautomation is a method of automation that combines several technologies in order to automate as much of a business process as possible. These technologies include Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Process Mining, and other cutting-edge technologies [14]. While hyperautomation is based on the same principles as regular automation, it employs a larger variety of tools and technology to take a more thorough approach to automation. Hyperautomation is locating and automating every routine, repetitive operation in a business process, including those that are usually done by people [15]. The creation of an end-to-end automation process that can be managed and optimized with little to no human involvement is the main objective of hyperautomation. This strategy can increase productivity, decrease manual errors, and free up resources for firms to concentrate on more difficult jobs that need for human involvement [16]. Many company activities, including customer service, finance and accounting, HR, and supply chain management, can benefit from hyperautomation. Businesses can embrace hyperautomation to streamline their operations, lower expenses, and boost productivity, which will ultimately raise their success and competitiveness in their particular marketplaces. An Overview on Robot Process Automation: Advancements… 2.4 Cloud-based RPA Cloud based Robotic Process Automation (RPA) systems present an opportunity for users to conveniently access RPA tools and services through the internet [17]. The utilization of cloud based RPA has the potential to bring about cost reductions and enhanced scalability by granting users a flexible and scalable platform for automating their processes. Tasks like data entry, data processing, and report generation can be efficiently automated through the implementation of cloud-based RPA solutions [18]. Typically hosted in the cloud and reachable via a web browser, cloud-based RPA systems. Users can access the automation platform from any location with an internet connection and do not need to install any software on their local computers. Scalability is one of the main advantages of cloud-based RPA. Without having to spend money on extra hardware or infrastructure, businesses can quickly scale their RPA deployment up or down based on their needs. This makes it the perfect option for companies whose customers' demands fluctuate seasonally or who need to expand quickly. The ability of cloud-based RPA to be linked with other cloud-based services and applications, including Customer relationship management (CRM), Enterprise resource planning (ERP), and other business applications, is another benefit. Without having to manually move data between separate apps, this enables enterprises to automate end-to-end business operations that include several different systems [19]. In general, firms wanting to automate their business operations can gain a lot from cloud-based RPA. It offers a scalable and adaptable solution that can boost productivity, lower errors, and free up human resources for work with higher added value. 2.5 Process mining Process mining is a valuable tool used to analyze event logs or data from IT systems with the aim of uncovering, monitoring, and enhancing business processes [20]. By automating vital tasks such as process discovery, analysis, and optimization, process mining plays a significant role in driving operational efficiency. Moreover. It enables the identification of bottlenecks and inefficiencies within processes. Ultimately leading to improved levels of automation and effectiveness [21]. Process mining is a method for examining corporate procedures to find inefficiencies, bottlenecks, and potential areas for change. In order to see and evaluate the process flow, data must first be extracted from multiple sources, including databases, transaction logs, and other systems, using data mining algorithms. Robotic process automation, or RPA, automates routine, rule-based processes using software robots [22]. RPA may assist firms in locating opportunities for automation and putting automated solutions into place fast and effectively when used in conjunction with process mining. Process mining can be used to identify repetitive and time-consuming operations that can be automated using RPA. RPA can be used to automate these tasks and lower the risk of errors, for instance, if process mining indicates that a certain process comprises numerous data Informatica 48 (2024) 1–10 entry tasks that are prone to errors. Additionally, the process mining tool may be updated with the data gathered throughout the RPA installation, offering insights into the efficiency of the automation and pointing out potential areas for development. Organizations can gradually enhance their operations with the aid of this continuous feedback loop, leading to increased productivity and efficiency [23]. Overall, process mining and RPA are a potent duo that can aid businesses in streamlining their procedures, lowering errors, and boosting productivity. Organizations may increase the accuracy and speed of their operations while allowing staff to focus on more value-added duties by automating repetitive jobs. 3 Design standards in RPA RPA design guidelines are essential for developing dependable, effective, and maintainable automation solutions. Here are some of the main design standards for RPA that have been suggested in various studies: 3.1 Modularity Modularity in RPA refers to breaking down automation processes into smaller, reusable components. This approach simplifies the maintenance and updating of the automation solution over time. By dividing the tasks and functionalities into independent modules, each module can be developed and tested separately, reducing complexity. Modularity also promotes reusability, as modules can be used in different automation processes or projects, saving time and effort in development. Additionally, it enhances scalability, allowing for the integration of new modules as automation needs grow or change without affecting the entire system. Modularity in RPA fosters collaboration among developers, enabling parallel development and facilitating code reusability and version control. Overall, modularity provides a structured and efficient approach to building automation solutions, making them easier to maintain, update, and scale, while promoting collaboration and reusability among developers [24]. 3.2 Error handling In the context of RPA, it is important to design solutions that can handle mistakes graciously. This means incorporating mechanisms to detect errors and take appropriate actions to resolve them. RPA solutions should have robust error detection capabilities, allowing them to identify errors at different stages of the automation process, such as data validation, system errors, or unexpected behavior. Once an error is detected, the solution should be programmed to respond in a way that fixes the error or minimizes its impact. This may involve retrying actions, alternative approaches, data validation, or escalation to human operators for resolution. Additionally, logging and reporting mechanisms should be implemented to capture and track errors for analysis and improvement of the automation process. By designing RPA solutions to 4 Informatica 48 (2024) 1–10 handle mistakes graciously, organizations can ensure the reliability and resilience of their automated processes, reducing manual intervention and improving overall efficiency [25]. 3.3 Security In order to protect sensitive information and prevent unauthorized access, RPA systems should adhere to recognized security standards and best practices. This includes implementing strong access controls, such as user authentication and role-based access, to ensure that only authorized individuals can access the RPA systems and the data they handle. Encryption should be applied to data at rest and in transit to maintain confidentiality and prevent unauthorized interception or access. Regular updates and patching should be performed to address any discovered vulnerabilities, and logging and monitoring mechanisms should be in place to detect and respond to suspicious activities. Conducting regular security audits and assessments helps ensure ongoing compliance and identifies areas for improvement. By following these security measures, RPA systems can maintain the integrity and security of sensitive information, mitigating the risk of unauthorized access and data breaches [26]. 3.4 Scalability Scalability is a fundamental requirement for RPA solutions, as they need to handle growing workloads as businesses expand. Scalability in RPA refers to the ability of the solution to accommodate increased demands without sacrificing performance or efficiency. To achieve scalability, RPA solutions should be designed with flexibility and modularity, allowing for the addition of new components or replication of existing ones to handle larger workloads. Dynamic resource allocation and intelligent load balancing mechanisms are essential to optimize resource utilization. Additionally, the architecture and workflows of RPA solutions should be designed with scalability in mind, considering factors like data storage, transfer capabilities, and compatibility with different systems and platforms. By prioritizing scalability, RPA solutions can effectively meet the automation needs of growing businesses while maintaining optimal performance [27]. 3.5 Documentation Documentation is a critical aspect of well-designed RPA solutions. It involves detailing the goals, inputs, outputs, and dependencies of automation processes. Clear and detailed documentation provides a shared understanding among stakeholders, including developers, business users, and management, about the purpose and expected outcomes of the automation. It also facilitates troubleshooting and debugging by providing a comprehensive view of the data and information flows. Documenting dependencies helps identify any external systems or integrations that the RPA solution relies on, ensuring that all necessary components are in place for R. Palaniappan successful execution. Furthermore, well-documented RPA solutions serve as a reference for future enhancements, updates, and maintenance, enabling efficient collaboration and reducing dependence on specific individuals. They also support compliance and audit requirements by providing an audit trail of the automation processes. Overall, thorough documentation is essential for clarity, transparency, and maintainability of RPA solutions [24]. Following these five design guidelines will enable RPA developers to produce dependable, effective, and maintainable automation systems that will aid enterprises in achieving their automation objectives and streamlining their business procedures. 4 Applications of RPA Robot Process Automation (RPA) is used in a wide variety of industries. Here are a few of the main RPA applications: 4.1 Finance The finance industry is embracing the use of Robotic Process Automation (RPA) to automate crucial tasks like claims processing, invoice processing, and account reconciliation. By automating repetitive tasks that were typically performed by humans RPA can significantly reduce errors and enhance efficiency [28]. In addition. RPA aids in ensuring consistent execution of processes in accordance with regulatory requirements. Thereby helping improve compliance [29], [30]. 4.2 Healthcare RPA is now being used by the healthcare sector to automate a number of tasks, including patient scheduling, claims processing, and disease detection [31]. RPA implementation can have significant benefits, such as cost savings and improved patient outcomes, by automating tasks that were previously handled by human employees [32]. By ensuring that procedures are consistently followed in accordance with legal standards, RPA can be very helpful in improving compliance [33]. 4.3 Manufacturing In the manufacturing sector, robotic process automation (RPA) is being utilized more and more to automate crucial processes including inventory management, supply chain management, and quality control [34]. RPA has the power to significantly save expenses and increase overall efficiency by automating repetitive processes that were previously performed by people. Moreover. By routinely ensuring adherence to well defined procedures and rigorous quality standards, RPA may also dramatically improve product quality [35]. 4.4 Retail The retail sector uses RPA now for automating several processes, including order processing, inventory control, and customer support [36]. By automating processes that were previously done by people, this technology can An Overview on Robot Process Automation: Advancements… dramatically save costs while also improving the entire customer experience. RPA may also be very helpful in increasing compliance because it makes sure that jobs are completed consistently and in accordance with legal norms [37]. 4.5 Human resources Currently, RPA is being utilized in the human resources industry to automate a range of tasks, such as recruiting new employees, handling payroll, and administering benefits [38]. By automating repetitive operations that have historically been done by people, RPA has the potential to decrease errors and boost efficiency [39]. RPA can assist compliance initiatives by ensuring that tasks are routinely carried out in accordance with legal requirements. 5 Challenges and limitations of RPA While Robot Process Automation (RPA) provides many advantages, there are also several difficulties and restrictions that come with using it. Here are some of the main obstacles and restrictions facing RPA: 5.1 Complexity of processes RPA is most effective when automating repetitive and rule-based processes. However, it may have limitations when it comes to managing complex operations that require analysis and decision-making [40], [41]. In various industries, there are procedures that involve intricate workflows, data analysis, strategic planning, and subjective decision-making, which may be beyond the capabilities of RPA. These complexities can limit the use of RPA in certain sectors, as the software typically follows predefined rules and lacks the cognitive abilities to interpret unstructured data or make nuanced judgments. While RPA may not be well-suited for handling complex processes, it can still be valuable in augmenting human work and streamlining specific aspects of these operations. By automating repetitive and well-defined subtasks within a larger complex process, RPA can free up human workers to focus on the more intricate and value-added aspects that require critical thinking and creativity. It is important for businesses to carefully evaluate their processes and determine where RPA can provide the most value based on the complexity and nature of the tasks involved. In some cases, a combination of RPA with other technologies such as AI or machine learning may be necessary to tackle the challenges posed by complex operations and achieve a more comprehensive automation strategy. 5.2 Integration with legacy systems The utilization of antiquated software poses challenges for companies integrating RPA into their existing systems. Many businesses still rely on legacy systems that lack integration capabilities, making the integration process time-consuming and expensive [42]. Custom development Informatica 48 (2024) 1–10 work is often required to establish communication between the RPA platform and the legacy software, adding complexity and resource requirements. Additionally, extensive testing and validation are necessary to ensure smooth interaction and avoid disruptions. These factors can constrain RPA adoption as businesses assess the cost and benefits of integration and may need to prioritize system modernization efforts alongside RPA implementation. Collaboration with experienced professionals, leveraging pre-built connectors or APIs, and conducting system assessments can help streamline the integration process and overcome challenges associated with antiquated software. By carefully considering trade-offs and employing strategies to address integration complexities, companies can successfully integrate RPA into their existing systems and harness the benefits of automation. 5.3 Security concerns RPA systems that have access to confidential data pose significant security challenges for companies. To prevent data breaches and other security issues, organizations must prioritize RPA system security [43], [44]. This involves implementing stringent access controls to ensure that only authorized personnel can interact with the RPA system and access sensitive data. Measures such as multifactor authentication, role-based access controls, and encryption of data at rest and in transit help safeguard the confidentiality and integrity of critical information. Regular security assessments and vulnerability testing should be conducted to identify and address potential weaknesses or vulnerabilities. Additionally, organizations should prioritize data privacy and compliance with relevant regulations, implementing measures to anonymize or pseudonymize data and ensuring adherence to privacy requirements. By taking a proactive approach to RPA system security, businesses can mitigate the risks associated with data breaches and protect sensitive information. 5.4 Economic concerns The implementation and upkeep of RPA systems can be costly, which may present a challenge for smaller organizations with limited financial resources. RPA implementation involves various expenses, including software licensing, infrastructure setup, process analysis, development, and testing. Ongoing maintenance and support also add to the overall cost. For smaller organizations, these expenses can be prohibitive and act as a barrier to adopting RPA fully. However, it is worth noting that the cost of RPA has been decreasing over time as technology becomes more accessible and competitive. Cloud-based RPA solutions, for example, provide a more cost-effective option by eliminating the need for extensive infrastructure investment. Additionally, partnering with RPA service providers or consultants can offer expertise and support without the need for significant upfront investments. These approaches can help smaller organizations 6 Informatica 48 (2024) 1–10 overcome the cost limitations associated with RPA and still benefit from its potential to enhance operational efficiency. 5.5 Ethical concerns The deployment of RPA raises ethical concerns regarding its impact on jobs. Analysts predict that widespread adoption of RPA could lead to job losses, especially in sectors heavily reliant on human labor [44]. To address these concerns, businesses must carefully consider the ethical implications of RPA and develop plans to mitigate negative effects on employment. This may involve strategies such as retraining and upskilling affected employees, job rotation, and fostering transparent communication with workers. Additionally, businesses should consider broader societal impacts and invest in initiatives that support job creation and skill development, ensuring a balanced approach to automation that prioritizes the well-being of employees and society. 6 Overcoming RPA challenges and limitations RPA (Robotic Process Automation) can pose a number of obstacles and challenges. The following advice will help you get through them: • Select the appropriate processes for automation: Not all processes can be automated. Find the procedures that are repetitive, rule-based, time-consuming, high volume, and have a lot of room for automation. This will aid in deciding which processes should be automated initially. • Choose the appropriate RPA tool: It's critical to pick an RPA tool that can handle the complexity of the operations you wish to automate. Pick a tool that gives solid support and training, is scalable, and is easy to use. • • A strong business case is necessary to justify the investment in RPA. It should clearly demonstrate the return on investment and list the benefits, such as improved accuracy, cost savings, and higher productivity. • • Include business users, IT, and management in the RPA implementation process as well as any other interested parties. The likelihood that everyone will agree and the implementation will proceed well will increase as a result. • RPA implementation might cause a lot of organizational change. Create a change management strategy to make sure that every employee is aware of the changes and equipped to cope with them. • Test the RPA implementation carefully to make sure it functions as expected and has no unwanted effects. • Monitoring and optimization are necessary to make sure that RPA continues to provide the anticipated benefits. Making adjustments to the procedures or the RPA implementation itself may be necessary for this. R. Palaniappan In summary, robotic process automation (RPA) is a technique that automates routine, rule-based, and high-volume processes using software robots. RPA technology provides a number of advantages, such as greater productivity and cost savings, but it also has certain drawbacks, such as the need for organized data and the absence of decision-making capabilities. It's critical to adhere to design principles, such as modular architecture and error handling, to ensure the efficacy of RPA installations. RPA technology can be used in a variety of sectors and processes, but success requires careful design, testing, and continuing optimization. 7 Conclusion Process mining, cognitive automation, machine learning, and hyperautomation are a few RPA breakthroughs that are revolutionizing business processes. By increasing automation and rising productivity, this swiftly evolving technology has the potential to completely transform how company activities are carried out. RPA has the ability to boost output, reduce costs, and free up employees' time for more difficult work. Utilizing cloud technology enhances RPA's capabilities further, making it a stronger tool for enterprises overall. To realize its full potential, RPA must overcome a variety of challenges, such as those related to security, cost, and how it interacts with legacy systems, ethics, and process complexity. Businesses must keep up with the latest RPA developments and investigate how they might be used in their particular industry. Acknowledgement I want to express my sincere gratitude to the University of Technology Bahrain's Mechatronics Engineering department for providing the necessary library information services for carrying out this research. References [1] W. M. P. van der Aalst, M. Bichler, and A. 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Palaniappan https://doi.org/10.31449/inf.v48i1.4086 Informatica 48 (2024) 11–20 11 Application of Agent-Based Modelling in Learning Process Natasha Stojkovikj, Limonka Koceva Lazarova, Aleksandra Stojanova, Marija Miteva, Biljana Zlatanovska and Mirjana Kocaleva Faculty of computer science, Goce Delcev University, Stip, Republic of North Macedonia Email: natasa.maksimova@ugd.edu.mk, limonka.lazarova@ugd.edu.mk, aleksandra.stojanova@ugd.edu.mk, marija.miteva@ugd.edu.mk, biljana.zlatanovska@ugd.edu.mk, mirjana.kocaleva@ugd.edu.mk Keywords: agent-based modelling, simulation, education Received: March 23, 2022 With advances in information and communication technologies and rapid computing and technological progress, modelling, and simulation of real problems, has become the most important teaching and learning method in educational process. Representing and explaining processes through simulations can enable students to easier understand these processes and discover the essential properties of a system. In many situations, in learning different subjects it is not possible to experiment with real objects to find the right solutions, therefore modelling and simulation can be used to build models that represent the real systems. Agent-based modelling (ABM) is a powerful simulation modelling technique, that can be easily incorporated in learning and teaching processes. Agent based modelling (ABM) is a relatively new method compared to system dynamics and discrete event modelling. In ABM a system is modelled as a collection of autonomous decision-making entities called agents, that can interact among each other’s. In this paper, the agent-based modelling simulation is considered as a tool in educational process for learning and teaching different subjects. Anylogic software is used for some simulation examples of agent-based modelling that can be used in educational process. Povzetek: Programska oprema Anylogic se uporablja za nekatere simulacijske primere modeliranja na podlagi agentov, ki se lahko uporabljajo v izobraževalnem procesu. Introduction Simulation is an imitation of the operations of real-world processes or a system over time. The behaviour of the system over time is studied by developing a simulation model. It is common for models to be represented as a set of assumptions about the system itself. These assumptions are expressed through mathematical logical or symbolic relations between entities that are objects of interest in the system. Simulations can be used at the design's stage before the system is built but also on existing systems to determine whether potential changes will have an impact on system performance. Therefore, simulations can be used either as a tool to predict how changes will affect an existing system or as a tool to predict the performance of a new system under a different set of conditions. Sometimes the evolving model can be solved mathematically. Then, the solution can be obtained by using differential equations, probability theory, algebraic models, or other mathematical techniques. This solution usually consists of one or more numeric parameters called the system performance measure. However, most real systems cannot be solved mathematically because they are too complex. In this system, numerically based simulations can be used to imitate system's behaviour over time. The simulation data are collected through system monitoring. The data generated during the simulation is used to evaluate the performance of the system [1-4]. For simulation, there are three main methods: Discrete Event Simulation, System Dynamics, and Agent Based Simulation. The Discrete Event Simulation (DES) models a process as a series of discrete events. Each event occurs at a particular point in time and represent a change of state in the system. Discrete event simulations are entity driven. The entities represent customers arriving in the system for servicing [5,6]. System dynamics (SD) are used to understand the nonlinear behaviour of complex systems over time. In SD three main objects are considered; stocks, flows and delays. Stocks are basic stores of objects; flows define the movement of items between different stocks in the system and delays are the delay between the system measuring something and then acting upon that measurement [5,6]. Agent based simulation (ABS) is a relatively new method compared to system dynamics and discrete event modelling. With agent-based modelling, the entities known as agents must be identified and their behaviour defined. The agents may be people, cells, households, vehicles etc [5-10]. In this paper, agent-based modelling and simulations and their application in the educational process is considered. It is shown how the agent-based simulation methods can help for easily and better understanding the basics of some processes, that otherwise are difficult to imagine and understand. Also, it is shown that this type of simulation can be used in education process, especially in learning math’s 12 Informatica 48 (2024) 11–20 subjects for math students and students of computer science also for learning and teaching science for medical students, or subjects connected to business and organization sciences and students learning that problematics. In the paper are described models which are implemented in AnyLogic Simulation and Modelling Software. We choose this simulation software because of is its availability and its simplicity for use because it is free simulation software originally intended for educational process. First, we describe epidemiological model SEIR-D and its usefulness for learning and understanding to the medical students, math students and students of computer science. The next model considered, is a model of Hospital Emergency Department. This model is important for the business students, medical students, and math students. And at the end, we consider example from a real world, that also can be used for easily understanding of different subjects used for math students, computer science students and business students. Precisely, we describe model of market that starts selling a new consumer product. With considering of these simple models, the students can easily understand the basic concepts of subjects connected to this simulation and can visually and dynamically present something that can be difficult to present or imagine otherwise. 2 Agent based modelling There are many definitions from different experts about what agents in systems are, but all agree that agents are a software component that is autonomous and aims to act as a human agent (collect data, process data, and interpret data) [6]. Agent -based modelling (ABM) is one of the newer approaches in computer simulation. This type of simulation is mainly used to model complex system, and it is based on autonomous agents and their interactions. Agent-based models (ABMs) are computational structures where system-level behaviour can be obtained by the behaviour of individual agents. ABM basically contains three elements: agents, an environment, and rules governingeachagent’sbehaviour and its local interactions with other agents and with the environment. Agents have their own characteristics, rules of making decision, ability to interact with other agents in the system and environment based on which they can change and adjust their behaviour [6, 7]. This method of modelling must identify active entities, or agents and their behaviour must be defined. Agents can be people, households, vehicles, equipment, products, or companies, more precisely anything, that is related to the system [7]. In recent years, ABM has been used in various branches of science, and the largest application they found in the social sciences. It is often used to simulate the phenomenon in economic and technical sciences. Until the appearance of these models, modelling of phenomena in N. Stojkovikj et al. society were most often reduced to a simplified presentation of social phenomena, and very often they were only verbal models. In ABM models, which models social phenomena and processes; agents represent people, and through their mutual communication and rules of conduct are modelled social processes and social communication. The main assumption is that people and their social skills can be realistically modelled. Agent-based modelling and simulations provide more realistic models and lead to new possibilities in modelling and simulations. Agents used in these simulation models originate from the fields of robotics and artificial intelligence. Today, ABM agents are not more related to the design and understanding of artificial intelligence. The basic application is in modelling of human social behaviour, social phenomena and individual decision making. With the obvious benefits that agent-based modelling and simulations bring, the number of simulation models of different social behaviours are increased. Today, it can be done many micro simulations that could not be done before a few years. We are using ABM for modelling different models that can help students to easily learn and teachers to better teach different natural, social and math subjects. 3 Agent based modelling in education process Knowledge application in realistic situations has been shown to be verry important in the process in developing complex skills. Students can acquire high level of expertise in complex real problem-solving tasks if they have enough previous knowledge and enough practice. Practice can be obtained with facing real problems which correspond to a professional field. In educational programs, the opportunity to engage in real-life problem solving is very limited. These limitations make practice in real-life situations often inaccessible especially for novice learners. Therefore, simulations can often be used in education settings. In STEM (science, technology, engineering, and mathematics) education, modelling and simulation can be used to facilitate a deeper understanding of concepts and relationships between objects and problems, easily problem solving, and decision making. Agent based modelling and simulation can be used in medical education, where simulations are used to enhance diagnostics competence, technical skills for future doctors and nurses. Agent based simulation can also be used in other fields, such as teaching education, engineering, and management, also can be used by the students of economic, biology, political science [11 -18]. Some of the most used simulation software in education are: EcoBeaker, SimBio, NetLogo, MIMOSE, AgentScript, Swarm, JAS-mine and Anylogic. NetLogo is a multi-agent programmable modelling environment for simulating natural and social phenomena. Application of Agent-Based Modelling in Learning Process… Informatica 48 (2024) 11–20 13 It is especially well suited for modelling systems that are developing over the time. NetLogo allows sophisticated modelling and allows the experienced programmers to add their own Java extensions. This software is used by many hundreds of thousands of students, teachers, and researchers from whole world [19]. MIMOSE consists of a model description language and an experimental frame for simulation of the described models. The main purpose of MIMOSE simulation software was the development of a modelling language that considers special demands of modelling in social science, especially the description of nonlinear quantitative and qualitative relations, stochastic influences, birth and death processes, and micro and multilevel models [20]. EcoBeaker is an ecological simulation program. This program is designed primarily for education goal but can be used and for research models. EcoBeaker gives a two-dimensional computer world into which agents are placed and their behaviours are designed [21]. Swarm is a simulation software package for multi-agent simulation of complex systems developed at the Santa Fe Institute. It is made to be a useful tool for researchers and students in many disciplines. The basic architecture of Swarm is the simulation of collections of concurrently interacting agents: with this architecture, a large variety of agent-based models can be implemented [22]. SimBio is a simulation software for teaching biology. The software can be used for biological systems such as cardiac cells, epithelial cells, and pancreatic ß cells. With this software can be simulated experiments in evolution, cell biology, genetics, and neurobiology. SimBio is written in Java, uses XML and can solve ordinary differential equations [23]. AgentScript is a minimalist agent-based modelling framework. This tool is based on NetLogo agents' semantics. Its goal is to promote an agent-oriented programming model in a deployable CoffeeScript /JavaScript implementation [24]. JAS-mine is a Java-based computational platform that features tools to support the development of large-scale, data-driven, discrete-event simulations. JAS-mine is specifically designed for both agent-based and microsimulation modelling, anticipating a convergence between the two approaches [25]. AnyLogic is a multimode simulation modelling tool developed by AnyLogic (formerly XJ Technologies). Supports simulation methodologies based on agents, discrete events, and system dynamics. AnyLogic is a cross-platform simulation software running on Windows, macOS and Linux. AnyLogic is used to simulate markets and competition, healthcare, manufacturing, supply chain and logistics, retail, business processes, social and ecosystem dynamics, defense, asset management, pedestrian dynamics, and road traffic. AnyLogic models can be based on any of the three methods in simulation modelling: discrete events, system dynamics or agent-based systems [26]. .he comparison of different simulation software used in education, mentioned before, and their main features are given in .able 1 . Table 1: Comparation of different simulation software used in education Operating system Programming language User support and License Model development effort Models’ scalability level Subjects covered User friendliness EcoBeaker Windows and Mac No programming skills required CD with tutorial/ Proprietary, not free for use Simple, easy Small scale Ecology, conservation biology, and evolution high Tutorials, SimBio Windows and Mac. Java Interactive Chapters, Workbook Labs, Frequently Asked Questions / General Public Licence, free for Moderate Medium scale Ecology, Evolution, Env Science, Cell Biology. Genetics, Conservation, Biology, Physiology Medium to high use NetLogo Cross-platform: JVM, (difficult to install on Windows) NetLogo Documentation; FAQ; selected references; tutorials; third party extensions; defect list; mailing lists /General Public License, free for use Simple, easy to moderate Medium to high scale Different natural and social sciences medium 14 Informatica 48 (2024) 11–20 N. Stojkovikj et al. MIMOSE Linux, Windows (difficult to install on Windows) Java Tutorial for installation and use/ Open sourced, free for use Moderate Small scale Social science poor AgentScript All OS with Browsers Javascript, NetLogo Tutorials, Example Models /Open source free for use, GPLv3 license Simple, easy Small scale Primary for social sciences but usable for natural sciences too. medium Swarm Cross-platform Java; Objective-C Wiki; tutorials; examples; documentation; FAQ; selected publications; mailing lists/ General Public License, free for use Hard, Complex Extreme scale Primary for social sciences . poor JAS-mine Cross-platform: JVM Java Tutorials, presentations, videos/ Eclipse plugin, free for use Simple, easy to moderate Medium scale Social and natural sciences, primary social, discrete-event simulations, including agent-based and microsimulation models medium Anylogic Linux, macOS, Windows Java Demos; training; online community; ask a question; online help; tutorials; consulting services/ Free Personal Learning Edition available Moderate High scale Different natural and social sciences, discrete events, system dynamics or agent-based systems medium to high In continuation in this paper, we give examples of the use of agent-based modelling and simulations in different areas to facilitate the study and understanding of certain problematics. We used AnyLogic as a software for implementing these examples. We chose AnyLogic because as mentioned above and as given in Table 1, AnyLogic compared to other tools has the best features in terms of ease of use, free to use, use in multiple areas, adaptability, utility in natural and social sciences and user friendliness. And considering all these features, we decided to use AnyLogic for agent-based modelling in education. We have selected three models that can be used by students of social and natural sciences, or more specifically, medical students, biology students, mathematics and computer science students, economics and business students. Examples of agent-based modelling in anylogic used for education A. Epidemiological models Epidemics of infectious diseases are triggering interest in predicting epidemic dynamics. Agent-based simulations can be used for education process for the medical students involved in public health and epidemiology. For this goal, universities and research centres are using simulations as teaching tools for these students. Simulation of spread on infectious disease is playing a central role in controlling spread of infection and making prediction that can help monitoring of epidemic [27]. Some of important epidemiologic models are SI, SIS, SIR, SIRS, and SEIR, SEIR -D model without vital dynamics and with vital dynamics. Here is given an example, where SEIR-D model without vital dynamics is explained. In the SEIR-D model, the total population of N individuals are divided in 5 categories: susceptible (S), exposed (E), infected (I), recovered (R), and death (D). • Susceptible – the started population people who are not infected by the virus. • Exposed -people who are infected but who can’t infect others • Infectious -people who are infected and who can infect others • Recovered – people who have recovered from the virus. • Death – people who death as consequence from infectious disease. Application of Agent-Based Modelling in Learning Process… Informatica 48 (2024) 11–20 15 This model relies on the assumption of a totally susceptible population at time t0 as a starting point of the pandemic. The goal of considering of SEIR-D is to explain the variation of S(t), E(t), I(t), R(t), D(t). This model can help medical students in public health and epidemiology, for easy understanding of a spread of the any infectious disease. The SEIRS-D model in Anylogic simulation software is represented in Fig 1. In Anylogic, stocks are used to represent real-world processes (material, knowledge, people, money, etc) and it define static part of the system. Flows define their rate of change -how stock values change in the time, and it define the dynamics of the system. Figure 1: SEIR-D model in AnyLogic AnyLogic automatically generates a stock’s formula accordingtotheuser’sstock-and-flow diagram. AnyLogic automatically created these formulas when the flow is added. This process can be easily done by students or teachers to visually represent real situation of spreading the disease. Next step is defining the parameters and dependencies. Seven Parameters are defined: Total Population, Infectivity, ContactRate, AverageIncubationTime, AverageIllnessDuration, AverageImmunizationperiod, FalalityRate, with their default values (As shown in Fig. 2). • Total Population=2000000 • Infectivity=0.01 • ContactRateInfectious=1 • AverageIncubationTime= 5.1 days • AverageIllnessDuration= 21 days • AverageImmunizationperiod = 90 days • FatalityRate = 0.03 Figure 3: Output from SEIR-D model 16 Informatica 48 (2024) 11–20 The medical student, using this model, has powerful tools for prediction of the spread of the infectious disease. This model can be modified by students to track some epidemic spread (for example COVID-19 pandemic) [28]. After discussing death rates, prevention and treatment options and genetic and age-related variation in host susceptibility, the students can decide to focus on transmission into their model. Through discussion with the professors, they can realize how the transmission of infection disease can occur. This exercise with extending a model to reflect specific biological assumptions helps students understand the iterative process by which models are developed. Also, students can understand the utility of simpler models to understanding key features of the system’s behaviour [29]. On the other hand, this model can be important for math and computer science students, because the model is given by the system of the following differential equations: This model can be used as a good example of how differential equations can be implemented in epidemiological models. Advantages of using agent -based simulation in epidemiology are in the fact that the mathematical representation of processes enables transparency and accuracy regarding the epidemiological assumptions. This allows students with their professors to test understanding of the epidemiology disease by comparing model results and results obtained from observation. Also, mathematical models can help predicting outcomes and adjustment of measures for stopping the spread of infections, as well as taking new appropriate measures. B. Hospital emergency department simulation This model is important and can be applied in process of education for the business students, and for a math and computer science’s students. For business students, the model can be used for well organizing of the healthcare systems. For the math and N. Stojkovikj et al. computer science’s students is good example for hybrid model that integrates methods of discrete event simulation and agent-based simulation. Overcrowding in the Emergency Department (ED) is one of the most important issues in healthcare systems. This situation leads to an increase in length of stay, a decrease in the quality of care and the burnout of nursing staff. Two major causes of this congestion are identified, the first one is unjustified Emergency Department visits and the second one a lack of downstream beds. An unjustified emergency visit concerns a patient who have no health problem or a non-emergency health problem. This situation creates a work overload for the medical staff. The lack of downstream beds increases the length of stay in the Emergency Department because patients must wait for a bed in a relevant medical unit. Sometimes patients are admitted to a medical unit that is not adapted to their pathology to decrease the ED congestion. This situation is problematic because it reduces the quality of care. First the patients come to the emergency department of the hospital, in the department they are checked whether they are emergency cases or not. In case of an emergency and in relation to the condition of the patient with an emergency, some mandatory medical tests are performed, such as different X-rays of certain parts of the body or other diagnostics tests. For the emergencies, there must always be beds available in the hospital and after the medical tests are performed, it is decided whether to keep the patient and to determine his diagnosis or just to determine the diagnosis and patient can leave the hospital. If the case is not urgent, the patient's vital signs such as pulse, temperature, blood pressure and respiratory rate are checked. After checking patient's vital signs, his treatment is determined. Because these patients are not urgent from high degree, additional medical tests may not be needed, therefore they can only be diagnosed if necessary and discharged from the hospital, but still, some can leave without the need for a diagnosis. For the successful development of this simulation, a Discrete event simulation model and agent-based simulation model in Any Logic program is used. In classic discrete event tools, the entities are passive and can only have attributes that affect the way they are handled. In AnyLogic multimethod simulation software, entities and resources can be modelled as agents with individual behaviour and state changes. In this simulation triangular distribution is used because the exact rate of patient arrival is not known, therefore, a minimum, a most probable, and a maximum value for a triangular distribution are set. The model in Anylogic is presented in Fig 4. Application of Agent-Based Modelling in Learning Process… Informatica 48 (2024) 11–20 17 Figure 4: Emergency department model in anylogic The result of simulation is given in Fig 5. Figure 5: Output from simulation This simulation can be helpful for the students of business and other organization sciences, managing and logistics, also and computer science students. The model can be helpful in the process of improving organization on the Hospital Emergency Department, to obtain optimal number of rooms, beds, and some other things for each sub-department of the ED, as well as have an estimated price for the ED, to optimally serve patients entering the ED with a known arrival rate. This simulation can be useful because students can experiment on the simulation model instead of real Hospital Emergency Department. The students can modify appropriate parameters and estimate output results from these parameters. Therefore, using this model students can easily manage with real problems like this one. C. Market models An agent-based model of a costumer cinema is considered for this example. In this model each costumer is an agent. The model includes 5000 people who have not seen the movie in the cinema, but a combination of advertising will eventually lead them to purchase the ticket to watch it. Also, advertising’s influence on consumer demand is considered, by allowing a specific percentage of them to become interested in purchasing the ticket during a given day. Advertising effectiveness = 0.1 determines the percentage of potential users that become ready to buy the product during a given day. In Fig 6 is presented diagram of Cinema model presented in AnyLogic. 18 Informatica 48 (2024) 11–20 N. Stojkovikj et al. Figure 6: Cinema model The parameters that are used represent several functions. The first parameter AdEffectiveness defines the percentage of potential users who become ready to buy the ticked and watch the movie during a given day. The second ContactRate represent how many contacts a person has per day with other PotentialUsers. The third AdoptionFraction is used to show us how much the ContactRate (the contact between two PotentialUsers) has affection. The last parameter, DiscardTime, represents how much time will the User wait to become PotentialUser again. There are two more parameters to test the impatience of the customers. MaxWaitingTime, which is the maximum time a user will wait for the product (in this case, seven days), and MaxDeliveryTime, which is the maximum time for delivery a product (in this case, 20 days). When the program is run, the 5000 population that are previously selected are obtained. Mostly there are gray Potential Users because the patience is very low and the max waiting time in this case is 7 days. The yellowGreen which are the Users are less and when they are done with watching the movie, they cannot go back for another 6 months. This Cinema model simulates how 5000 people will react if they all are PotentialUsers and waiting to purchase one ticked for the one movie in the Cinema. From this model it can be concluded that 5000 people is a lot for just one selling counter and the waiting line is too long, which means that the customers will have high impatience and most of them won’t wait, eventually quit, and go back to PotentialUsers. Therefore, if the purpose of the model is to sell tickets to 5000 people there must be more than one selling counter, therefore the waiting line won’t be too long. The output from simulation in given in Fig 7. Figure 7: Output from Cinema model This model can be good example for computer science students, economics, and business students. With making different adjustments of the parameters, students can watch changes of the behaviour of the model and can easily understand how appropriate changes reflected in consumer behaviours and whole system dynamics and can improve customer satisfaction. This model can help students to make market predictions. Students can easily apply and extend this obtained knowledge to real problems like this. 5 Conclusion Rapid advances in computing power and increasing use of ICT in all aspects of life have made agent-based modelling and simulation (ABMS) feasible and appealing tool for easily studding teaching and understanding different subjects. Simulation-based learning can offer learning with approximation of practice, overcoming limitations of learning in real-life situations. Performing modelling and simulation activities in educational environments can be an effective tool for learning complex and dynamic systems. Students using simulation can be more motivated for learning, gaining new skills, easily understanding subjects, gaining intuition, and making generalization. The opportunity to alter and adjust real life aspects and situations, in a way that facilitates learning and practicing makes simulation an effective educational tool. Simulation-based learning can start early in study programs because it can be effective for beginners and advanced learners too. Simulation models could be used as a tool in education system, from primary and secondary school and for higher education in learning and teaching subjects in undergraduate curriculums. Agent-based modelling and simulation (ABMS) is a powerful technique in simulating and exploring phenomena that includes a large set of active components represented by agents. 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Pal Department of Mathematics, Statistics and Computer Science, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, 263145, Uttarakhand, India E-mail: garimabisht98@gmail.com, ak.pal@gbpuat-cbsh.ac.in *Corresponding author Keywords: multi-criteria decision-making, rank reversal, RAFSI, triangular fuzzy numbers Received: April 30, 2022 In real-life decision-making problems, the constraints may change from time to time. Change in certain decision elements can lead to the introduction of new alternatives or the removal of old alternatives to the existing decision, resulting in rank reversal. Rank reversal is the most significant problem that can’t be ignored in multi-criteria decision-making (MCDM) methods. Ranking of alternatives through functional mapping of criterion subintervals into a single interval (RAFSI) method effectively removes the problem of rank reversal, but there are some limitations like standardized decision matrix is obtained by the assumption of supreme value as at least six times improved than the anti-supreme value, which is not always true. This paper aims to address those limitations by giving a modified form of the RAFSI (MRAFSI) method. As real-life problems are associated with uncertainty in the form of linguistic terms, a fuzzified form of the MRAFSI method has been given using triangular fuzzy numbers (TFNs) to deal with uncertainty. The effectiveness of the presented method is illustrated using a real-time case study to rank five stocks under the National Stock Exchange (NSE) for the year 2021 and is compared with other MCDM methods for validation. The supplier selection problem has been taken as an example to show the application of the Fuzzy Modified RAFSI (FMRAFSI) method. Povzetek: Študija predstavlja Fuzzy Modified RAFSI (FMRAFSI) metodo za reševanje problemov veckriterijskega odlocanja (MCDM), ki obvladuje negotovost z uporabo trikotnih mehkih števil in zmanjšuje problem obratnega razvršcanja. Introduction MCDM methods proved as a very important tool in solving most real-world problems. But one of the foremost significant problems that can’t be ignored in most of the MCDM methods is rank reversal, the matter of unpredicted modification within the ranking of alternatives with the addition of the latest alternative or removal of an old alternative. MCDM methods are also prone to rank reversal when a problem is decomposed into multiple smaller problems keeping the standard weight and alternative scores unaltered [1]. The key explanation for rank reversal is the use of normalization, which changes with the addition or deletion of alternatives. This distorts the initial data and violates the ‘Principle of Independence from Irrelevant Alternatives (PIIA). This is often true for any normalization [2]. Since differences in dimensional units of attributes can only be eliminated by normalization in most of the MADM approaches it becomes a vital part. During the utilization of the Analytic Hierarchy Process (AHP), the matter of rank reversal was initially observed by Belton and Gear [3]. The identical was also noticed by Triantaphyllou and Mann [4] in AHP during the substitution of the worst alternative with an anti-ideal alternative. Saaty and Varga [5] presented that the matter of rank reversal can happen because of the occurrence of almost identical copies within the set of alternatives. They also opined that the addition of a new alternative can practically modify the previous preference order. Fedrizzi et al., [6] presented that the possibility of rank reversal rests on the distribution of criteria weights i.e., the entropy of the weight distribution. They established that the projected possibility of rank reversal rises with the weight’s entropy. Further many authors noticed this problem in several MCDM methods because of the mutual correlations between the relevant and irrelevant alternatives, as a consequence of normalization [7]. Wang and Elhag [8] presented a technique to evade rank reversal in AHP by preserving the local significance of alternatives with the introduction of a new alternative. Mufazzal and Muzakkir [9] proposed a proximity index to minimize the rank reversal in MCDM problems. Salabun et al., [10] developed a new MCDM method called the Characteristic Objects Method (COMET). They established that it’s better than AHP concerning rank reversal. De Farias Aires and Ferreira [11] introduced an approach targeting the identification of rank reversal during the normalization process in the TOPSIS method. Yang and Wu [12] introduced a novel R-VIKOR-based method to address rank reversal problems. Majumdar et al., [13] investigated a novel form of rank reversal specifically within the Analytic Hierarchy Process (AHP), identifying the aggregation method and criteria weight 22 Informatica 48 (2024) 21–30 normalization as pivotal factors contributing to its occurrence. Similarly, Liu and Ma [14] delved into the causes of rank reversal within the ELECTRE II method, offering insights into its evaluation. Additionally, Tiwari and Kumar [15] presented a robust rank reversal technique for cloud service selection using the TOPSIS method with a Gaussian distribution. Yang et al., [16] an adapted approach to minimize rank reversal occurrences within the classic TOPSIS method. However, within the previous couple of years, a huge number of advanced MADM methods gave effective outcomes for resolving real-world problems [17]. But a maximum of those methods are not able to effectively remove the matter of rank reversal. There are abundant applications of MCDM methods in real-life problems. Some of the applications consist of construction method selection for green building projects, portfolio selection, business and marketing, supplier selection, healthcare management, wastewater management, transportation problems, site selection for solar thermoelectric power plants, infectious waste disposal, industry development, flood detection criteria, social media analysis, supply chain network design, etc. In such cases, if rank reversal exists, and that too of higher order, a non-optimal alternative gets selected, thus resulting in a big concession. Zizovic et al., [18] developed a new method referred to as Ranking of alternatives through functional mapping of criterion subintervals into a single interval (RAFSI), and its fuzzified form has been used for solving the selection problem in health organizations for COVID-19 virus pandemic [19], and for choosing a group of construction machines for enabling mobility [20]. Although this method successfully removes the problem of rank reversal, some modifications may be done to this method to make it better for solving real-life problems. This paper aims to work on the modifications that can be made to the RAFSI method. Also, since real-life problems are associated with uncertainty in the form of linguistic terms, the fuzzified form of the MRAFSI method has been given using triangular fuzzy numbers (TFNs) to deal with uncertainty persisting in the real world. To show the applicability of the presented method it has been applied to two important decision-making problems namely indices selection and supplier selection problems. For validation comprehensive analysis has been done with other well-known MCDM methods. The rest of the paper is organized as follows. Section 2 discusses the RAFSI method and its shortcomings. Section 3 presents the mathematical formulation of the modified RAFSI method with the real case study as an application along with the comparative analysis. Section 4 presents the fuzzification of the MRAFSI method with application and comparison with the traditional fuzzy MCDM methods. Section 5 discusses the theoretical basis of the proposed approach and compares it with existing approaches for rank reversal, followed by sensitivity analysis in Section 6. At last section 7 concludes the paper. G. Bisht et al. 1.1 Related work Extensive research has been conducted in the field of rank reversal, resulting in a vast body of literature. To gain insights into this domain, we conducted a comprehensive review of relevant studies and categorized them based on the approach employed, the method utilized, and the limitations identified. The classification of these studies is presented in Table 1, offering a systematic overview of the diverse research framework surrounding the rank reversal problem. Table 1: Literature review on rank reversal approaches Year Author Method Limitations 2023 Saluja et al. [21] Proximity indexed value (PIV) Struggles with a substantial prevalence of rank reversal. 2023 Tu and Wu [22] AHP Intransitive preference and the prioritization methods cause rank reversals in single pairwise comparison matrices. 2023 Dehshiri and Firoozaba di [23] Wins in league (WIL) Sensitive to small changes, limited handling of uncertainty. 2022 Yang et al. [16] IE-TOPSIS Relies on supplementary data, potentially unable to eliminate rank reversal. 2021 Tiwari and Kumar [15] G-TOPSIS Reliance on Gaussian distribution assumptions, subjective user priority influence. 2021 Kizielewi cz et al. [24] Characteristi c Objects method (COMET) Potential sensitivity to minor variations in input data, uncertainties in handling fuzzy data representations, and a lack of robustness in maintaining consistent rankings. 2020 Stevic et al. [17] MARCOS Complex implementation, limited generalizability, A Novel Fuzzy Modified RAFSI Method and it’s Applications… Informatica 48 (2024) 21–30 23 sensitive to parameter changes. 2020 Zizovic et al. [18] RAFSI Subjective criterion interval setting, reliance on an arbitrary superiority threshold, and the potential for identical rankings among different alternatives due to its assumptions on criteria types. RAFSI method In this section, the RAFSI method given by Zizovic et al., [18] is discussed. Given the initial decision matrix with weights of criteria estimated by any of the known methods, the RAFSI method has the subsequent stages. 1) The DM describes ideal (......)and anti-ideal (......)values for individual criteria. 2) Mapping of elements of the decision matrix into criteria intervals. • .....[......,......], where ....belongs to max type criteria. • where ....belongs to min type .....[......,......], criteria. Mapping of subintervals into criteria interval [..1,..2..]by the formula­ ..1-..2..........1-........2.. ....(..)=..+ ......-............-...... It is supposed that the optimal value is six times improved than the non-optimal value i.e., ..1=1and ..2..=6. In this way, a standardized decision matrix is obtained. • for max type criteria if ......>......, then ..(......)= ..(......) • for min type criteria if ......<......, then ..(......)= ..(......) 3) Next, calculate arithmetic and harmonic mean of n1, n2k. (..1+..2..)2 ..=,..= 211..1+..2.. 4) Find a normalized decision matrix ...... • for max type criteria ..^= ....2.. .. • for min type criteria ..^= .... 2...... 5) Calculate criteria functions of alternatives V(Ai). V(Ai) = ..1..^^^ ..1+..2....2+.....+.......... Finally, alternatives are ranked in descending order of V(Ai). 2.1 Limitations of RAFSI method This section discusses the limitations of the existing RAFSI method. 1) In this method the DM’s set the interval for each criterion by assumption without the use of any standard formula. 2) In this method for forming a standardized decision matrix, it is supposed that the optimal value is at least six times better than the non-optimal value, but it is not always true. 3) This method assumes that • for max type criteria if ......>......, then ..(......)= ..(......) • for min type criteria if ......<......, then ..(......)= ..(......) but this may lead to the same ranking of two different alternatives. The following example illustrates it more efficiently. Example: Consider the initial decision matrix given below and let the criteria sub-intervals be defined as­ C1 .[2, 10], C2 .[4, 8], C3 .[0, 5] ..1..2..3..11261..21061..= ..3574..4853[ ..................] thus, according to RAFSI method ..(12)=..(10)for alternative A1, and other values being same for alternatives A1 and A2 we get same rank for alternative A1 and A2. But as it can be seen since criteria C1 is of the maximum type so A1 must be at a higher rank than A2. 3 Modified RAFSI (MRAFSI) method In this section, we have tried to overcome the shortcomings of the RAFSI method. The flow chart of the MRAFSI method is shown in Figure 1. Let the initial decision matrix consists of m alternative A1, A2, ….Am and n criteria C1, C2,…… Cn. Find the weights of criteria by any one of the known methods considering the relative importance between criteria such that ...=1. The initial decision matrix is shown as ..=1.... follows. ..11...1....=[...]....1. ...... The MRAFSI has the following steps­ 24 Informatica 48 (2024) 21–30 Step.1. Find intervals for each criterion using the mean (µ) and standard deviation (..) of the values of criteria for different alternatives as given in the decision matrix. [ µ-2×.., µ+2×..] = [ n1,n2] Step.2. Find the normalized decision matrix S = [......]..*..by the use of the following formula­ 1 ......=1+.-..(1) here, ......-..1 ..=for beneficial criteria ..2-..1 ..2-...... ..=for non-beneficial criteria ..2-..1 Step.3. Calculate the criteria functions of alternative V(Ai)= ..1....1+..2....2+.....+..........(2) where ..1,..2,............represents the weight of criteria. Finally, rank the alternatives in descending order of V(Ai). Figure 1: Block diagram of the MRAFSI method 3.1 Applications of MRAFSI multi-criteria model This section presents the application of the MRAFSI methodology for the stock selection problem. A real case example of NSE (National Stock Exchange) is shown for selecting the best indices out of the given four indices Hindustan unilever (A1), Asian paints (A2), Tata consultancy services (A3), Reliance industries (A4) with four criteria Return on equity (ROE) (C1), Earning per share (EPS) (C2), Face value (C3), P/E ratio(C4) of year 2021 downloaded from www.ratestar.in. The weights of each criterion are given by ....= (0.104445,0.13603,0.645511,0.114014) found by the entropy method. The decision matrix is demonstrated below. G. Bisht et al. ..1..2..3..4..128.6337.34156.10..227.7131.82190.83..338.55102.11134.83..49.2798.511027.87 [........................] Applying the steps of MRAFSI method­ Step.1. Find the criteria subintervals using the mean and standard deviation of each column. C1 .[1.62,50.45]; C2 .[-8.6,143.53]; C3 .[-5.75,12.25]; C4 .[-4.17,108.98]; Step.2. Find the normalized decision matrix by applying eq.1. 1 ....1(..1)== 0.634839 -(28.63-1.62)(50.45-1.62) 1+.. similarly solving other values, the normalized decision matrix can be obtained and as shown below: ..1..2..3..4..10.63480.57490.592670.6148..20.63050.56610.592670.5400..30.68050.67430.592670.6582..40.53910.66910.705780.7191 [........................] Step.3. Using eq. 2. find the criteria functions V(Ai) of alternatives and rank them in descending order of V(Ai) as shown in Table 2 and Figure 2. Table 2: Final ranking of alternatives Alternatives V(Ai) Rank Hindustan unilever 0.597184 3 Asian Paints 0.586997 4 Tata consultancy services 0.620423 2 Reliance industries 0.679521 1 Figure 2: Ranking of stocks Based on the above results, we found that Reliance industries is the best stock to invest in. A Novel Fuzzy Modified RAFSI Method and it’s Applications… 3.2 Rank reversal problem The four alternatives are ranked according to MRAFSI method, now we need to check rank if we remove one alternative from them. Let us remove the alternative Hindustan unilever from the given alternatives. We find that the on removing the alternative of rank 3rd all the alternatives, after that alternative shift one rank up, without causing any rank reversal. Thus, it is observed that MRAFSI method gives effective results in dynamic environment as shown in Table 3. Table 3: Ranking after removing one alternative Alternatives V(Ai) Rank Asian Paints 0.586997 3 Tata consultancy services 0.620423 2 Reliance industries 0.679521 1 Now let us add another alternative tata steel to the given four alternatives and check the rank. The new decision matrix formed is given below. ..1..2..3..4..128.6337.34156.10..227.7131.82190.83..338.55102.11134.83..49.2798.511027.87..510.87317.21104.3 [........................] After applying the steps of the MRAFSI method we found the rank of alternatives as shown below in Table 4. Table 4: Ranking after adding one alternative Alternatives V(Ai) Rank Hindustan Unilever 0.591028 4 Asian Paints 0.575865 5 Tata consultancy services 0.613342 3 Reliance industries 0.6429 2 Tata steel 0.681706 1 The added alternative stood first in the ranking order, so all the alternatives moved single place down in the order. Thus, the MRAFSI method is resistant to rank reversal problems on adding and removing new alternatives. 3.3 Comparative analysis For validation, the results obtained by MRAFSI method is compared with other known traditional MCDM methods. The same weights and initial decision matrix are taken in all other methods for comparison of the performance. Table 5 shows the ranking of alternatives using different methods. Table 5: Ranking obtained by different methods Method Ranking Best Worst alternati alternat ve ive MRAFSI A4>A3>A1>A2 A4 A2 TOPSIS A4>A3>A1>A2 A4 A2 Informatica 48 (2024) 21–30 25 COPRAS A4>A3>A1>A2 A4 A2 MAUT A4>A3>A1>A2 A4 A2 It is clear from the above table that there is no conflict in the ranking order of best and worst alternatives by all methods. Hence, this validates the MRAFSI method. 4 Fuzzy MRAFSI method In this section, we present the fuzzified form of the MRAFSI method. This helps in handling the uncertainty persisting in real-life problems. Fuzzification is performed by applying triangular fuzzy numbers A= (a1, a2, a3), where a1 presents the smallest likely value, a2 presents the most probable value and a3 presents the largest possible value of any fuzzy event. Triangular fuzzy numbers (TFNs), being a specialized case of generalized fuzzy numbers, offer a competent way to present ambiguous information and linguistic preferences. The easy properties of TFNs captivated our attention to design the fuzzy RAFSI method to process the ambiguous information in the form of TFNs. The fuzzy MRAFSI has the following stages­ Step.1. Formation of the fuzzy initial decision matrix. This matrix is formed by evaluating m alternatives (A1, A2,…. Am) on n criteria C1, C2, …… Cn. The decision matrix is shown below. ..11...1.. ..=[...] ....1. ...... .... where ......=(......,......,........)denotes the triangular fuzzy number. Step.2. Find the criteria interval, by finding the mean and standard deviation for each element of TFNs. After finding the ideal and anti-ideal value in form of TFN we have the fuzzy criteria interval. .....[..1,..2]..=1,2,3….. where n1 and n2 are TFN’s. Step.3. Convert the initial decision matrix into normalized matrix S = [......]..*..by applying the formula (1,1,1) ......=(1,1,1)+.-..(3) here, ......-..1 ..=for beneficial criteria ..2-..1 ..2-...... ..=for non-beneficial criteria ..2-..1 aij, n1, n2 are all TFN’s. For solving equation (3) use the operations of triangular fuzzy numbers. 26 Informatica 48 (2024) 21–30 Step.4. Calculate the fuzzy criteria functions of alternatives V(Ai) by applying the expression: (4) V(Ai)=..1....1+..2....2+.....+.......... where ....represents the weights of criteria, which an be found by applying any of the known methods of weight determination. Here weight determination is not taken into consideration, they are assumed to be already known. Step.5. Defuzzification of the fuzzy criteria functions of alternatives V(Ai) is done by applying the expression: [..(....)..+4*..(....)..+..(....)..] ..(....)=(5) 6 Now rank the alternatives in the descending order of value of V*(Ai). 4.1 Applications of Fuzzy MRAFSI multi-criteria model This section presents application of Fuzzy MRAFSI method for the supplier selection problem. An automobile company desires to select raw material suppliers. Three suppliers (S1, S2, S3) are to be selected based on five criteria: 1. Quality supplied item (C1) 2. Cost of supplied item (C2) 3. Delivery time of supplied item (C3) 4. Technology of supplied item (C4) 5. Flexibility of supplied item (C5) The linguistic variables for weights are shown in Table 6. Table 6: Linguistic variables for weights Linguistic Variables Ratings Very Low (VL) (0,0.1,0.2) Low (L) (0.1,0.3,0.5) Medium (M) (0.3,0.5,0.7) High (H) (0.6,0.8,0.9) Very High (VH) (0.8,0.9,1.0) Weights of the criteria are given as: ..1= (0.83,0.97,1) ..2= (0.63,0.83,0.97) ..3= (0.77,0.93,1) ..4= (0.57,0.77,0.93) ..5= (0.5,0.7,0.9) Applying the steps of fuzzy MRAFSI method to the given problem. Step.1. Form the Fuzzy decision matrix using linguistic variables for rating shown in Table 7. Table 7: Linguistic variables for rating Linguistic Variables Ratings Very Poor (VP) (0,1,2) Poor (P) (1,3,5) Medium (M) (3,5,7) Good (G) (6,8,9) Very Good (VG) (8,9,10) G. Bisht et al. The fuzzy decision matrix is shown below in Table 8 for the given problem. Table 8: Fuzzy decision matrix C1 C2 C3 C4 C5 S1 (8.33,9 (7.67,9. (7.67,9. (7,9,10) (7,9,10) .67,10) 33,10) 33,10) S2 (5.67,7 (3.67,5. (3.67,5. (3.67,5. (4.33,6. .6,9.3) 67,7.6) 67,7.6) 67,7.6) 33,8.3) S3 (7,8.67 (4.33,6. (4.33,6. (5.67,7. (1.67,3. ,9.67) 33,8.3) 33,8) 67,9.3) 67,5.6) max min min max max Step.2. Find the criteria interval by taking the mean and standarddeviationofeachelement ofTFN’sinthe criteria column as shown in Table 9. Table 9: Interval for first criteria 8.33 9.67 10 5.67 7.67 9.33 7 8.67 9.67 Mean(µ) 7 8.67 9.67 S. D (..) 1.08 0.82 0.27 µ-2* .. 4.84 7.03 9.13 µ+2* .. 9.16 10.31 10.21 Thus, the interval for C1 becomes: C1 .[(4.84,7.03,9.13), (9.16,10.31,10.21)] Similarly, we find intervals for all other criteria: C2 .[(1.72,3.92,6.7), (8.72,10.3,10.63)] C3 .[(1.72,3.92,6.5), (8.72,10.3,10.62)] C4 .[(2.7,4.7,7.04), (8.18,10.18,10.95)] C5 .[(0,1.98,4.43), (8.68,10.68,11.56)] Step.3. Find the normalized matrix by applying equation (3). (1,1,1) ....1(..2)= -((8.72,10.3,10.63)-(7.67,9.33,10))((8.72,10.3,10.63)-(1.72,3.92,6.7)) (1,1,1)+. (1,1,1)(1,1,1) == (1,1,1)+.-(-0.63,0.15,0.146) (2.88,1.86,1.23) = (0.35,0.54,0.81) Similarly solving other values, we get the normalized matrix as shown in Table 10. Table 10: Normalized decision matrix C1 C2 C3 C4 C5 S1 (0,0.6 9,1) (0.35,0. 54,0.81) (0.36,0.5 4,0.79) (0.49,0.6 9,0.98) (0.55,0.6 9,0.84) S2 (0,0.5 5,1) (0.53,0. 67,0.97) (0.53,0.6 7,0.96) (0.05,0.5 4,0.7) (0.49,0.6 2,0.73) S3 (0,0.6 2,1) (0.51,0. 65,0.96) (0.52,0.6 5,0.94) (0.23,0.6 3,0.93) (0.34,0.5 5,0.6) max min min max max A Novel Fuzzy Modified RAFSI Method and it’s Applications… Step.4. Using eq. (4) calculate the final fuzzy criteria functions of alternatives V(Ai). Step.5. Final ranking of alternatives is done after defuzzification of fuzzy criteria functions of alternatives V*(Ai), as shown in Table 11 and Figure 3. Table 11: Ranking of alternatives Altern ative V(Ai) V*(Ai) Ranking S1 (1.05,2.63,4.24) 2.635 1 S2 (1.01,2.57,4.21) 2.585 3 S3 (1.03,2.62,4.28) 2.630 2 Figure 3: Ranking of suppliers Based on the above results, we found that supplier 1 is the best alternative. 4.2 Comparative analysis For validation, the results obtained by the FMRAFSI method is compared with the well-known Fuzzy TOPSIS and Fuzzy VIKOR method. The same weights and initial decision matrix are taken for comparison of the performance. Table 12 shows the ranking of alternatives using different methods. Table 12: Comparison of ranking order Method Ranking Best alternat ive Worst alternat ive FMRAFSI A1>A3>A2 A1 A2 FTOPSIS A1>A3>A2 A1 A2 FVIKOR A1>A3>A2 A1 A2 FCOPRAS A1>A3>A2 A1 A2 FELECTRE A1>A3>A2 A1 A2 FPROMETHE A1>A2>A3 A1 A3 It is clear from the above table that there is no conflict in the ranking order of best alternatives by different methods. Hence, this validates the FMRAFSI method. Informatica 48 (2024) 21–30 27 5 Discussions 5.1 Theoretical basis The rationale behind the mathematical formulation of mean and standard deviation in the modified RAFSI method is explained below: Simplicity: This method offers a straightforward and easy-to-understand approach to estimate the mean and standard deviation of TFNs. By breaking down the TFN into its three values (lower, middle, upper), it simplifies the calculation process. Transparency: It provides a transparent representation of the TFN's uncertainty. By using arithmetic operations (e.g., mean calculation, standard deviation computation) on individual terms, it offers an intuitive way to understand how these terms contribute to the overall statistics of the TFN. Computational efficiency: Compared to some more complex methods like Monte Carlo simulation or PDF-based approaches, this method is computationally efficient. It avoids the need for extensive simulations or intricate mathematical formulations, making it suitable for quick estimations. Applicability: This method might be particularly useful in scenarios where simplicity and a quick estimation of the mean and standard deviation are required. It can serve as a preliminary or initial estimation method, especially when dealing with a large number of TFNs in decision-making or uncertainty analysis contexts. 5.2 Comparative analysis This section conducts a comparative analysis between the proposed approach and other methodologies for addressing rank reversal, as outlined in Table 1. It aims to elucidate the advantages inherent in the proposed approach when compared with existing methods. 1. Stability against rank reversals: Unlike methods such as Proximity Indexed Value (PIV), AHP, Wins in league (WIL), IE-TOPSIS, G-TOPSIS, and others prone to rank reversals, the Modified RAFSI method is designed to potentially mitigate the prevalence of rank reversals. It aims to produce more stable and consistent rankings, enhancing the reliability of decision-making processes. 2. Enhanced handling of uncertainty: Compared to methods like the Characteristic Objects method (COMET), which struggle with uncertainties and fuzzy data representations, Modified RAFSI offers improved handling of uncertainty. It provides a more robust means of dealing with fuzzy data representations, resulting in more reliable and consistent rankings even in uncertain scenarios. 28 Informatica 48 (2024) 21–30 3. Reduced sensitivity to small changes: In contrast to methods sensitive to small changes, such as Wins in league (WIL) and others, Modified RAFSI demonstrates lower sensitivity to minor fluctuations or variations in input data. This characteristic leads to more stable and robust rankings, less likely to be affected by insignificant changes. 4. Objective ranking: Similar to G-TOPSIS, RAFSI minimizes subjective bias. It aims to provide a more objective approach, enhancing the credibility and reliability of the rankings by minimizing the influence of subjective user assumptions. 5. Simplicity and Generalizability: Unlike complex methods like MARCOS, Modified RAFSI offers a more straightforward implementation while maintaining robustness and applicability across diverse decision-making scenarios. Its simplicity does not compromise its effectiveness in producing meaningful and reliable rankings. 6. Reduced reliance on supplementary data: RAFSI's design aims to reduce dependency on supplementary data, similar to how it is with IE-TOPSIS. This characteristic contributes to its practicality and efficiency, allowing it to generate rankings without relying heavily on additional information. 6 Sensitivity analysis Decision-making is a multifaceted process susceptible to various potential errors. Therefore, a comprehensive analysis before model adoption becomes imperative. This typically involves conducting a sensitivity analysis, which can be executed through diverse approaches such as altering weight coefficients of criteria, changing measurement units expressing alternative values, comparing with alternate methodologies, etc. [25]. Most authors commonly perform sensitivity analyses focusing on adjustments in weight coefficients of criteria [26-27], as is the case in this paper as well. The primary objective of this sensitivity analysis is to gauge the impact of the most influential criterion on the ranking performance of the proposed model [28]. For the sensitivity analysis involving changes in weight coefficients, five distinct scenarios are developed. The basis for the change in weight coefficients makes the change in the weight coefficient of the best criterion C3. The changes in the weight coefficients of this criterion are made in interval ..3.[0, 0.5]. The proportion set in this way always provides the 4 condition where .=1. The values of the weight ..=1.... coefficients in all scenarios are shown in Figure 4. G. Bisht et al. Figure 4: Weights under different scenarios To further verify the stability of the proposed approach to attribute weights obtained by different methods, we use the objective weights obtained by critic and standard deviation method in place of weights obtained by entropy weights in the example. The weights obtained by different methods are shown in Table 13. Table 13: The weight vector by different methods Methods .... .... .... .... Entropy 0.10444 0.13603 0.64551 0.11401 Critic 0.36515 0.18964 0.28223 0.16296 St. dev. 0.2186 0.28373 0.26211 0.23555 The ranking of alternatives by different scenarios and weight determination methods is shown in Table 14. It can be easily observed from Table 14 that although the weights differ greatly, a very small change in ranking results is seen. Thus, the proposed approach is stable in terms of ranking. To further verify the results the SSCs between the ranking obtained is calculated. From Table 15 it is observed that the SSCs between the ranking is greater than 0.8 under different weights. Thus, the proposed approach is stable under different weights. Table 14: Ranking of alternatives by different scenarios Alternati ve Or igi nal Cr iti c St. De v. S1 S2 S3 S4 S 5 Hindusta n unilever 3 3 3 3 3 3 3 3 Asian Paints 4 4 4 4 4 4 4 4 TCS 2 1 2 2 2 2 2 1 Reliance industries 1 2 1 1 1 1 1 2 A Novel Fuzzy Modified RAFSI Method and it’s Applications… Informatica 48 (2024) 21–30 29 Table 15: The SSCs between the ranking results Or igi na l Critic St. De v. S1 S2 S3 S4 S5 Original 1 0.8 1 1 1 1 1 0.8 Critic - 1 0.8 0.8 0.8 0.8 0.8 1 St. Dev. - - 1 1 1 1 1 0.8 S1 - - - 1 1 1 1 0.8 S2 - - - - 1 1 1 [6]0.8 S3 - - - - - 1 1 0.8 S4 - - - - - - 1 0.8 S5 - - - - - - - 1 Conclusions This paper discusses the limitations of the RAFSI method and endeavors to address these deficiencies by introducing a modified RAFSI method (MRAFSI). To assess the efficacy of the proposed method, a real case study is conducted to rank five indices of the Bombay Stock Exchange (BSE) for the fiscal year 2020-21. Comparative analysis with established MCDM methods is performed to validate the modified approach, confirming the consistency in results and affirming the validity of the modified method. In recognition of uncertainties prevalent in real-world scenarios, the MRAFSI method undergoes fuzzification using the triangular fuzzy numbers. The fuzzy modified RAFSI (FMRAFSI) is applied to a supplier selection problem. Comparative validation with traditional fuzzy methods is conducted, revealing congruent outcomes and thus affirming the validity of the FMRAFSI method. Additionally, a sensitivity analysis is carried out to showcase the resilience and reliability of the proposed approach. For the future work, the proposed framework can be integrated to leverage hybrid models [29-30], thereby achieving more effective outcomes. It would be fascinating to use the proposed method to address a variety of further real-world decision-making issues. References [1] Triantaphyllou, E. (2001). 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Informatica, 47(4). https://doi.org/10.31449/inf.v47i4.3480 https://doi.org/10.31449/inf.v48i1.4475 Informatica 48 (2024) 31–44 31 ADeepLearningModelforContextUnderstandinginRecommendation Systems NgoLe HuyHien1, Luu Van Huy2, Hoang Huu2 Manh, Nguyen VanHieu2,* 1Leeds BeckettUniversity, Leeds, United Kingdom 2The University of Danang -University of Science and Technology, Danang, Vietnam E-mail: n.hien2994@student.leedsbeckett.ac.uk, lvhuy@dut.udn.vn, hoanghuumanh54@gmail.com, nvhieuqt@dut.udn.vn *Correspondingauthor Keywords: recommendation system, context understanding, convolutional neural network, matrix factorization, deep learning,text processing Received: November2,2022 Due to the robust growth in the amount of data and Internet users, there has been a significant rise in information overload, hindering timely access to user demand. While information retrieval systems, such as Google, Bing, and Altavista have partially addressed this challenge, prioritization and personalization of information have yet to be fully implemented. Therefore, recommendation systems are developed to resolve the issue by filtering and segmenting important information from an enormous volume of data based on different criteria such as preferences, interests, and user behaviors. By collecting data on users’ interests and purchased products, the system can predict whether a particular user would enjoy an item, thus delivering an appropriate suggestion strategy. However, the increased number of Internet users and items has resulted in sparseness in increasingly vast datasets, reducing the performance of recommendation algorithms. Therefore, this study developed a model integrating Convolutional Neural Network (CNN) and Matrix Factorization (MF) to add extra product and user information, extract contexts, and add bias to the observed ratings in the training process, attempting to enhance the recommendation accuracy and context understanding. This approach can take advantage of CNN to efficiently capture an image’s or document’s local features, with the combination of MF to create relationships between 2 main entities, users and items. The proposed model obtained the highest RMSE of 0.93 when predicting favorable movies for 4,000 users, with an ability to learn complex contextual features and suggest more relevant content. The results are promising and can act as a reference for developing context understanding in recommendation systems, and future work may focus on optimizing the performance and developing more text-processing techniques. Povzetek: Razvit je nov model globokega ucenja, ki združuje konvolucijske nevronske mreže (CNN) in matricno faktorizacijo (MF) za izboljšanje natancnosti in razumevanja konteksta v priporocilnih sistemih. 1 Introduction Recommendation systems (also known as recommender systems [1]) are algorithms designed to deliver sugges­tions for the most pertinent items to a certain user by fil­tering out information from a pool of data using various factors [2]. Normally, the recommendations pertain to dif­ferent decision-making processes, including what movies to watch, books to read, products to buy, music to listen to, online news to read, or other products based on the de­sired industry [3]. Recommendation systems are substan­tially beneficialwhen a person hasto pick an item froman overwhelmingnumberofoptionsprovidedbyaservice[4]. Netflix [5, 6] and Amazon [7], for example, employ rec­ommendationsystemstoassisttheirconsumersinchoosing a suitable product or movie. The recommendation system handlesahugeamountofdatabyfilteringthemostsignifi­cantinformationfromdatagivenbyauserandothercriteria thatcorrelatetotheirinterestsandpreferences[3]. Itdeter­mines the match between the user andthe item, then infers the similarities amongthem for suggestions [4]. Recommendation systems have been proven to provide decent benefits to both users and supplied services. They werecharacterizedfromthestandpointofE-commerceasa tool that assists users in searching through a source of data associated with users’ preferences [8]. Especially, under a complex and large accumulation of information, recom­mendation systems might showcase their advantage to en­hance the quality of decision-making strategies [9]. This utilitymayresultindecreasingtransactioncostsassociated with locating and selecting products in the E-commerce sector[10]. Eveninseveralcompanies,anefficientrecom­mendationsystemcangeneratecolossalrevenue,andserve as a means todiffer considerably from their rivals[11]. It is prevailing to apply recommendation systems when having insufficient personal knowledge or expertise with Informatica 48 (2024)31–44 N.V. Hieuetal. the alternatives since the systems may support and enrich thesocialprocessofmakingdecisionsbasedonthe[9]. For instance, recommender systemsare utilized in scientific li­brariestoassistusersbyenablingthemtogobeyondcatalog searches[3]. Therefore,thesetypesofsystemscanaddress the information overloading issue, which is commonly en­countered in recent years [12], by operating accurate and efficient recommendationalgorithmstodeliverindividual­ized, distinctive service andcontentsuggestions [13]. Thereareseveralrecenttechniqueshavebeendeveloped for constructing recommendation systems, including col­laborative filtering, content-based filtering, and hybrid fil­tering[14]. Themostdevelopedandwidelyusedtechnique is collaborative filtering, which finds users who own sim­ilar preferences and utilizes their views to suggest to an­other user [15]. Contrarily, the content-based approach links user attributes to content resources. It hence often disregards inputsfromotherusersanddeliversrecommen­dations solely based on the information provided by the user [16]. Notwithstanding, hybrid filtering can improve theeffectivenessandaccuracyofrecommendationsystems, by combining two or more filtering approaches in various methods. It balances out the corresponding deficiencies of different filtering techniques while using their respective strengths. The methods can be weighted, switching, cas­cade, mixed, feature-combination, feature-augmented, or meta-levelhybrid depending on the operations of the com­binedtechniques[17]. However, the aforementioned filtering techniques retain a few drawbacks, notwithstanding their success. Overspe­cialization,limitedcontentanalysis,anddatascarcityarea few issues with content-based filtering algorithms. In ad­dition, cold-start, scalability, and sparsity issues remain to existincollaborativetechniques,reducingtheeffectiveness of recommendations [18]. It can be seen that the common problemwithsuchfilteringtechniquesisdatasparsity. Itis becauseoftheexplosivegrowthinthenumberofusersand items in the fast-growing service market, which increased thesparsenessofproductreviewdatafromusers[19]. This sparsenessdiminishesthepredictionaccuracyoftraditional filtering techniques[20]. In order to address the above data sparseness limitation, in this paper, different factors have been added to the rec­ommendation system such as user information, user in­teractions, and product description documents instead of only using review data, attempting to enhance the accu­racy of the system. Moreover, traditional information re­trievalmethodsmostlyusethebag-of-wordsmodel,which ignores the context information of the text document [21]. Toaddressthis,thestudyproposedamodeltoapplyaCon­volutional Neural Network (CNN) in the recommendation system to better understand the text document. Owing to the fact that CNN can efficiently capture local features of documentsorimagesthroughlocalreceptivefields,shared weights,andpooling[22]. However,sinceCNNisprimar­ily used in classification problems, this study proposed an approach to integrate it into Matrix Factorization (MF) to define relationships between users and items. The com­bination makes it possible to take full advantage of both CNNandMF[23]. InspiredbytheworkofDonghyunand colleagues [24], this study aims to enhance the model by adding bias for the training more objectively; and supple­menting extra information from description documents of both users and items. The research outcomes are promis­ing and can be used as a reference for further developing contextunderstandingin recommendation systems. 2 Literaturereview 2.1 Thedevelopmentofrecommendation systems Recommendation systems have gained considerable inter­estsincetheirinitialintroductionandhavebeenwidelyuti­lizedinvarioussectors,includinge-commerce[8],e-library [31],e-tourism[32],education[33],news[34],information retrieval, and digital content services [35]. Table 1 indi­cates the eminent applicationsof recommendationsystems indifferent domains. Item Type Recommendation Systems E-commerce Products Amazon [7], eBay [36], Shopify, Flipkart [37] Videos Netflix [5], YouTube [38], Dai­lymotion, Hulu [39], MovieLens, Nanocrowd, Jinni [40] Online News Google News, Yahoo! News, BBC, NewYorkTimes[41],Findory[42], Digg, Zite [43] Music Spotify,AppleMusic,AmazonMu­sic, Soundcloud, Pandora, Mufin [44] SocialNetwork­ing Contents Facebook, TikTok, Twitter, LinkedIn, Instagram [45] Table 1: Current eminent recommendation systems in dif­ferentdomains Leadinge-commercecompanyAmazonappliesacollab­orativefilteringtechniqueto addressscalability challenges byofflinegeneratingatableofrelateditemsusinganitem­to-item matrix [7]. To enhance suggestion quality, it em­ploys topic diversity algorithms. Following that, the algo­rithm suggests items that are comparable online based on the customers’ past purchases [46]. Thanks to this, items that are not among the shop’s 100,000 best-selling items have helped Amazon gain20%to 40% of sales [47]. NetflixRecommendationEngineusesalgorithmsthatfil­ter its contents using each user’s unique profile. The sys­temuses 1,300 clustersbased on user choices tofilter over 3,000 titles at once [48]. Cinematch, a proprietary recom­mendationsystemusedbyNetflix,hasarootmeansquared error (RMSE) of 0.9525. In 2009, Netflix held a compe­ ADeepLearningModelforContextUnderstandingin … Informatica 48 (2024) 31–44 33 tition called ’Netflix Prize’, attempting to produce a rec­ommender system that outperformed its algorithm, with a million-dollarprizeforthewinner[6]. Forthatreason,60% ofNetflix’sDVDsarerentedthankstorecommendational­gorithms,and47%ofNorthAmericanspreferNetflixwith aretention rateof93%. [49] TikTok, one of the most popular and rapidly expanding social media networks in the world, has its secret strength as a unique recommendation system for discovering and distributing content [50]. TikTok blends videos from new­bies and celebrities in the ‘For You’ feed, rewards high-quality creative content based on page views, and encour­ages emerging users to share videos with other viewers. Therefore, every user has the opportunity to become fa­mous on the platform, regardless of their fanbase or level of popularity. High-quality creative work may be easily shared thanks to TikTok’s recommendation system, which regularly suggests videos to individuals with similar inter­ests [51]. It can be seen that recommendation systems have been applied in numerous domains and have helped businesses notonlygeneratecolossalrevenuebutalsoserveasameans to differ considerably from their competitors. 2.2 Relatedworks For a system to deliver its customers reliable and helpful recommendations, the usage of accurate and efficient rec­ommendation algorithms is essential. Therefore, it is criti­calto clarify the advantages andlimitations ofvariousrec­ommendation approaches. There are several recent tech­niques for constructing recommendation systems, which are content-based filtering, collaborative filtering, and hy­bridfiltering, as depicted inFigure 2.1 [14]. Figure2.1: Differentrecommendationfilteringtechniques. First of all, collaborative filtering is a technique to find users who own similar preferences and utilize their views to suggest to another user. It has become the most de­veloped and widely used filtering technique in recommen­dation systems [15]. Collaborative filtering is prominent when the content cannot be accurately and simply repre­sentedbymetadata,likemusicandmovies[25]. Thistech­niqueaimstobuildadatabaseofuserpreferencesforthings calledauser-itemmatrix. Bycomparingthecommonalities between users’ profiles, it connects people with shared in­terests and preferences in a so-called neighborhood to pro­vide suggestions. The user then receives suggestions for unseen items that received favorable reviews from others in the neighborhood [26]. The suggestions can be in the form of recommendations or predictions. A recommenda­tion is a list of the top items that the user would enjoy the best, whereas a prediction is an estimated favorable score of an item for the target user [27]. In contrast, content-based filtering links user character­isticstotheattributesofitems. Ithenceoftendisregardsin­putsfromotherusersanddeliversrecommendationssolely based on the information provided by the user [16]. This filtering technique is significant when the suggested docu­mentscan bemetadata-represented,whichcouldbe books, news,andwebpages. Content-basedfilteringextractschar­acteristics from the content of items previously rated by different users and then merges them into a training set. From there, the system recommends items that are greatly related to a user’s favorability to them. The technique can deliver recommendations even when a user never offered ratings before [28]. As a result, users may receive sugges­tions without disclosing their profiles, ensuring their pri­vacy. Furthermore, content-based filtering could handle circumstancesinwhichdifferentusersmightnothaveiden­ticalitems,butonlysimilaritemsthatsharedcommonchar­acteristics[29]. Nevertheless, by integrating two or more filtering algo­rithms diversely, hybrid filtering can increase the efficacy and accuracy of recommendation systems. It compensates fortheinadequaciesofvariousfilteringsystemswhilemax­imizing their unique strengths [17]. Depending on the op­erations of the combined approaches, the methods can be weighted,switching,cascade,mixed,feature-combination, feature-augmented,ormeta-levelhybrid. Collaborativefil­tering and content-based filtering approaches can be used differently before being combined. Thereafter a unified model was formed that encompasses both content-based and collaborative filtering capabilities. Consequently, the datasparsityandcold-startissuescouldbesolvedbymerg­ingitemratings,characteristics,anddemographicinforma­tion [30]. Despitethesuccessoftheaforementionedfilteringtech­niques,theycomewithcertaindrawbacks. Issueslikeover­specialization, limited content analysis, and data scarcity posechallengesforcontent-basedfilteringalgorithms. Col­laborative techniques also grapple with problems such as cold-start, scalability, and sparsity, ultimately hampering theeffectivenessofrecommendations[18]. Acommonun­derlying problem in these filtering techniques is data spar­sity, which stems from the rapid expansion of users and itemsinthedynamicservicemarket. Thisproliferationhas increasedthesparsenessofproductreviewdatafromusers, Informatica 48 (2024)31–44 N.V. Hieuetal. leadingtoadeclineinthepredictionaccuracyoftraditional filtering methods [19, 20]. 3 Methodology To overcome the above limitation of data sparseness, this study aims to develop a model integrating Convolutional Neural Network (CNN) and Matrix Factorization (MF) to addextraproductanduserinformationandextractcontexts beforetraining,attemptingtoenhancetherecommendation accuracy. In this section, the architecture of CNN and MF isbrieflypresented. 3.1 Convolutionalneuralnetwork ConvolutionalNeuralNetwork(CNN/ConvNet-proposed byFukushimaKunihiko)isavariantofafeedforwardneu­ralnetwork. ConvolutionalNeuralNetworksrepresentsig­nificantprogressandinfluenceinthedevelopmentofDeep Learning [52]. Many CNN variations, including VGGNet, MobileNet, Inceptions, ResNet, RegNet, DenseNet, and EfficientNet have been developed robustly. These variants emphasizedifferentfacetsofaccuracy,efficiency,andscal­ability. The field of computer vision is mostly dominated by ConvNets models[53]. The organization of the visual cortex and the human brain’s neural network both had an influence on CNN’s architecture [54]. Individual neurons can only respond to stimuli in the restricted visual field region known as the Receptive Field. A succession of similar fields that over­lapencompassestheentirevisualfield[55]. Therearefour maintypesoflayersforaconvolutionalneuralnetwork: the convolutional layer (to extract local features), the pooling layer(representingdataofthepreviouslayerinamorecon­ciseform,i.e.,selectonlythetypicalfeatureswiththehigh­est scores through activation functions), the ReLU correc­tion layer and the fully-connected layer [56], as indicated in Figure 3.1. Figure 3.1: TheArchitecture of CNN. [57] AsshowninFigure3.1,aCNNnormallyconsistsoftwo main components: 1. Hiddenlayersorfeatureextractionlayers:inthiscom­ponent, the network will perform a series of convolu­tion and pooling computations to detect features. For example,ifanimageofazebraisinputted,inthiscom­ponent,thenetworkwillrecognizeitsstripes,twoears, and four legs. 2. Classification: in this component, a class with full associations will act as a classifier of previously ex­tracted features. The CNN model in natural language processing often considersthelocalcontextaspectofthecorpus[58]. These contexts are extracted through filters or the kernel and ag­gregatedatthepoolinglayer[59]. However,sincetheCNN model is often used for classification problems, it is chal­lenging to apply CNN directly to the recommendationsys­tem. 3.2 Matrixfactorization Matrix Factorization (MF) is a commonly used collabora­tivefilteringmethodinrecommendationsystemsproposed bySimonFunk[60]. Matrix Factorizationdecomposesthe performance evaluation matrix into a product of two ma­trices U and V . While U represents the correlation be­tween users, V represents the relationship between items, described inFigure 3.2. Figure 3.2: Theconcept of matrix factorization. As shown in Figure 3.2, the Matrix Factorization tech­nique involves decomposing a large matrix R into two smallermatricesU andV ,suchthatthereconstructionofR from these smaller matrices is as accurate as possible, i.e., R ˜ U × V T . In which: – U isamatrixofsizem×k,whereeachrowrepresents k latent factors describinguser m. – V isamatrixofsizen×k,witheachrowbeingavector comprisingk latentfactorsdescribingitemi (typically k << m and k << n). – V T denotes the transpose matrix of V . The key challenge in theMF technique lies in determin­ing the values of the two parameters (matrices) U and V . These parametersare identifiedbyoptimizinganobjective function. In the context of rating prediction, the objective function, denoted as L, is expounded upon in the subse­quentsection. Theconceptoflatentfeaturesthatreflecttherelationship betweenobjectsandusersisfundamentalinMatrixFactor­ization for Recommendation Systems. For example, in a ADeepLearningModelforContextUnderstandingin … Informatica 48 (2024) 31–44 35 movie recommendation system, the latent features can be criminal,political,action,comedy,etc.;mayalsobeacom­bination of these features or anything that may not need to benamed[61]. Eachitemcanbringsomelatentfeaturesto some extent corresponding to the coefficients in its vector v. The higher the coefficient, the higher the possibility of having that feature. Similarly, each user will also tend to prefer certain latent features described by the coefficients in its vector u. The higher the coefficient, the more likely users prefer the movies with that latent feature. The value oftheexpressionuvwillbehighifthecorrespondingcom­ponents of v and u are both high. This means that the item has latent features that the user likes, thus the system rec­ommendsthis itemto that user. Assume that there are m users and n items, with a user-item rating matrix R, in which R . Rm×n . In Matrix Fac­torization, latent models of user i and item j can be repre­sentedask-dimensionalmodels,ui . Rk andvj . Rk. The observed rating rij of user i on item j is calculated by the innerproductofrespectivelatentmodelsofuserianditem j. A common approach to training latent models is mini­mizingalossfunctionL,whichcomprisessum-of-squared­error terms among the observed ratings and the predicted ratings. Therefore,thelossfunctioninthissituationcanbe expressed as: mn XX 2 T L = Iij(rij - ui vj )+ ij mn XX + .u ||ui||2 + .v ||vj||2 (1) ij in which: – Iij is an indicator function that becomes 1 if user i rateditem j andequals 0if not. – . denotes the regularization term. When . is ex­cessively large, the model tends to underfit the data; conversely, if . is overly small, the model may be­comeoverlycomplex,leadingtooverfitting. Thefine­tuning of the . value is a crucial aspect in optimizing the performance of theMF model. – .u istheregularizationparameterassociatedwithuser vectors ui. Regularization serves as a technique to prevent overfitting in machine learning models. It is applied in the loss function by penalizing the squared Euclidean norm (L2 norm) of user vectors. This reg­ularizationconstrainsuservectorsfrombecomingex­cessively large during the training process, mitigating the risk of overfitting to the training data and poten­tially enhancing the model’s generalization ability to unseen data. – Similarly, .v represents the regularization parameter for item vectors ui. This regularization parameter is essential for preventing overfitting in the context of itemvectors,analogoustoitsroleintheregularization of user vectors (.u). 4 Proposedmodel 4.1 Generalarchitecture As depicted in Figure 4.1, MF (Matrix Factorization, in the green box) is the decomposition of the observed rating matrixRofuser-itemintotwomatriceswithlowerweights. Matrix U represents the relationship between users, while matrixVrepresentsthecorrelationbetweentheitems. The modelaimstoaddproductfeaturestotherecommendation system. CNN in natural language processing often consid­ers the local context aspect of the text. Therefore, CNN is used to extract features with local contexts of the user and item description sets and then add the information to matrixU(matrixcontainingvectorsdescribingcharacteris­ticsoftheuser,suchasage,gender,andoccupation)andV (matrix containing vectors describing features of the item) respectively. This technique can complement and clarify the properties of the vectorsin matrix U andV. In Figure 4.1, Xu and Xv act as the set of documents describing the user and item respectively, and Wu and Wv are the weights of the CNN model for the user and item correspondingly. TheoutputsoftheCNNarelatentfeature vectors of those input documents. The difference between thoselatentfeaturevectorswithmatrixUandVistheinte­grationbetweenCNNandMFinfullyanalyzingdescriptive documents and evaluationdata. This research employs a Convolutional Neural Network (CNN) to extract local features from embedding vectors, consisting of thefollowing layers: – Input layer: receives embedding vectors describing product narratives with a length of100 tokens. – The token and position embedding layer comprises two main components: – Token embedding: transforms each word in the productnarrativeintoadensevectorrepresenta­tion. This representation captures the semantic meaning of the word as well as its relationships with other words in thevocabulary. Informatica 48 (2024)31–44 N.V. Hieuetal. – Position embedding: encodes the position of each word in the product narrative into a vec­tor representation. This representation helps the model understand the context of each word and its relationships with other words in the product narrative. – The output of the embedding layer, comprising token and position information, is a sequence of embedding vectors,whereeachembeddingvectorrepresentsato­ken (word) in the product narrative and incorporates its position in theproduct narrative. – Subsequently, the embedding vectors are fed into a CNN layer, consisting of fundamental layers such as Convolutional, pooling, and incorporating dropout techniques to extract more complex features from the text. The CNN layer learns to identify patterns and relationships among the embedding vectors, which are then utilized to predict user rankings for different products. DetailsoftheCNNmodelarchitectureareillustratedinFig­ure 4.2. TherationalebehindtheutilizationofthisCNNstructure is predicated upon the model’s input being comprised of embedded vectors used to depict products, typically of rel­atively modest dimensionality (dim = 100). Consequently, aCNNarchitecturewithfundamentallayers,asexpounded above, is employed in this study to extract local features fromtheembeddedvector. 4.2 Addingbias AsmentionedinSection3.2,theobservedratingrij ofuser i on item j is calculated by the inner-product of respective latent models of user i and item j, which can be indicated as: T rij ˜ rˆij = ui vj (2) However,toavoidoverfittingissues,thisstudyaddsbias tothe observed rating: T rˆij = ui vj + di + bj (3) inwhich: – di isacoefficientrepresentingthepleasantnessofuser i. Thehigherthecoefficient,thebettertheuseritends to rate the products. – bj is a coefficient illustrating product quality, the higherthecoefficient. Themoreuserstendtoratethat product better. 4.3 Lossfunction From there, thelossfunction now can be depicted as: mn XX Iij L(U, V, W ) = 2 )2(rij - ˆrij i j m X .U + ||vj - cnn(W v, Xvj)||2 2 j |wuk | X .Wu + ||wuk ||2 2 k |wvn X| .Wv + ||wvn ||2 (4) 2 n The loss function is minimal when the derivative of the above equation is 0. The loss function uses coordinate de­scent to find the function that updates u and v. This op­timizes having to iterate over and over one variable while correctingthe others. Assuming Wu, Wj, and V (or U) are constants, the above equation becomes a quadratic function with respect toU (or V). Therefore: -1 ui . (V IiV T + .uIk)(VRi + .unn(W u, Xui)) T di . (rij - ui vj - bj ) -1 vj . (UIj UT + .V Ik)(URj + .vcnn(W v, Xvj)) T bj . (rij - ui vj - dj) (5) ADeepLearningModelforContextUnderstandingin … Informatica 48 (2024) 31–44 37 Wu andWj willbeupdatedthroughthebackpropagation of the CNN. 5 Experimentandresults 5.1 Dataset ThisresearchutilizesMovielens1M[62],auser’smoviere­view dataset, which contains 6000 users and 4000 movies. It was released in 2003 with a rating rate of 4.6%. This dataset includes: – Movie information: id, movie name, genre, release year; – Userinformation: gender, age, occupation; – List of user reviews corresponding to movies ( 1 mil­lion samples). The training was conducted on Google Colab with the configuration specified in Table 2. Type Specifications CPU Intel(R) Xeon(R) CPU @ 2.20GHz Number of CPUs 2 RAM 12.0 GB Memory 108.0 GB [44] GPU Nvidia Tesla K80 Table 2: Device Specification. 5.2 Datasetpre-processing Theinputofthemodelistheitemdescriptiondocumentset. Particularly in this experiment, it contains 4000 movie de­scriptiontextscorrespondingto4000moviesinthedataset. AsampledatausedinthedatasetispresentedinFigure5.1. The user quantity within the dataset was partitioned for experimental purposes, comprising subsets of 1000 users, 2000users,andsoforth. Thisapproachfacilitatedtheeval­uationofthe modelacrossvaryingdatasetscales,allowing an examination of potential impacts. Statistics of the num­ber of users, items, and ratingsare presented in Table3 for referenceand analysis. From the description text of the movies, latent features were extracted to add to the training model. The input text setofmoviedescriptionshasbeenthroughdifferentprepro­cessing steps, as shown in Figure 5.2, starting with clean­ing to remove the noise in the text like HTML tags. The next step is word splitting, meaning splitting the sentences intosinglewords. Thosewordswerethennormalizedtothe same font and type. And finally, stopwords will be elimi­nated, which are words that appear frequently but contain trivial meanings, such as ‘is’, ‘that’, or ‘this’ in English. A sampleofamoviedescriptionafterthepre-processingpro­cess is presented in Figure 5.3. Figure5.1: Sample data used in the dataset. Number of Users Number of Items Number of Ratings 1000 3280 154212 2001 3452 337262 3001 3477 484775 4001 3505 660411 5001 3532 826438 Table 3: Statistics of the number of users, items, and rat­ings. 5.3 Training The dataset was divided into 3 subsets, which are training, validation,andtestingsets. Correspondingtoeachuser,the numberofuserreviewswillbedividedbytheratioof80% for the training set, 10% for the test set, and 10% for the validation set. .U .V Dimension Train. Loss Val. Loss Test. Loss 10 40 500 0.76 0.88 0.88 10 60 500 0.77 0.88 0.88 10 50 50 0.78 0.89 0.88 10 10 50 0.7 0.90 0.90 100 10 100 0.87 0.90 0.90 50 100 100 0.88 0.91 0.91 Table4: Loss results in different hyperparameters. From Table 4, it can be seen that the ratio between .U and .V significantly affects the results. If .U is much largerthan .V ,meaningahigherpriorityisgiventolearn­ingtheparametersofU,agoodresultcouldnotbeattained. Whilethegoaloftheproblemistousedatafromtheitem,it Informatica 48 (2024)31–44 N.V. Hieuetal. Figure5.2: Text Pre-processing Process. isbettertogivepreferenceto .V ,makingitslightlyhigher than .U, to obtaina better result. 5.4 Evaluation To evaluate the model’s general performance, this study usesRoot-mean-squareerror(RMSE)andmean-squareer­ror (MSE), which represent the dispersion of the predicted datarelative tothe actual data. rP m (ˆri - ri)2) i RMSE = (6) m m X 2 MSE =1 (rbi - ri) (7) m i The RMSE function evaluates the results after each it­eration for all 3 training, validation, and testing sets. The model training process was repeated for about 100-200 it­erations until the loss function gave the smallest value on the validating and testing sets. RMSE results of the model onthetraining,validating,andtestingsetsareillustratedin Figure 5.4. As can be seen from Figure 5.4, in the 8th iteration, the results began to deteriorate, and the validation RMSE in­creased while the training RMSE continued to be overfit­ting. Therefore,theresultwasobtainedinthe8thiteration. TheevaluationofresultsfortheentiredataisshowninTa­ble5. Table5evaluatestheproposedmodelusingtwometrics: Root Mean Square Error (RMSE) and Mean Squared Er­ror(MSE).Thesemetrics gaugethedisparity between pre­dictedrankingsandactualrankings. Basedonthetabulated data, it is evident that the proposed model demonstrates strong performance on the test set, yielding an RMSE of 0.89andMSEof0.78. Thissignifiesthemodel’sabilityto accurately predictuserrankings for diverse products. The RMSE and MSE values across all three sets— training, validation, and testing—indicate that the model exhibits robust predictive capabilities on the test dataset. Both RMSE and MSE values remain stable, with minimal deviationobservedbetweenthevalidationandtestdatasets. This suggests that the model does not encounter issues re­latedto overfitting or underfitting. To determine how the results correlate with the user amount, a comparison of RMSE with different numbers of usersis presented in Table 6. Evaluation metric Training Validation Testing RMSE 0.76695 0.88974 0.88563 MSE 0.58821 0.79163 0.78435 Table 5: ResultEvaluation in differentmetrics. ADeepLearningModelforContextUnderstandingin … Informatica 48 (2024) 31–44 39 No. of users Train. RMSE Val. RMSE Test. RMSE Exec. time (s ) Train. time (s) 1000 0.87865 0.91478 0.90093 0.0062 110 2000 0.87205 0.91791 0.93004 0.0052 75 3000 0.87168 0.91896 0.92671 0.0053 91 4000 0.86955 0.91383 0.92973 0.005 159 5000 0.87865 0.91478 0.90093 0.0062 110 Table 6: Comparison of the RMSE with different numbers of users. Table 6 demonstrates when increasing the number of users in the dataset, from 1000 to 5000, the accuracy in­creases, but with a longer convergence time. Therefore, in ordertoproduceappropriaterecommendations,recommen­dation systemapplicationsneed to employ a large dataset. 5.5 Utilizingthetrainingresults Theresultsobtainedaftertrainingthemodelare2matrices Uand V. An evaluation matrix Y[i,j] can be generated as: Y [i, j]= U[i] * V [j]T (8) in which: -i: i-th user -j: j-th item Figure 5.5: Using the training results for creating recom­mendations. As depicted in Figure 5.5, the evaluation matrix can be applied in the recommendation system for further usage, which outputs a list of recommended movies for the ith user. 6 Conclusionsandfuturework In this research, a deep learning model for recommenda­tionsystemsisproposedbyintegratingConvolutionalNeu­ralNetworkandMatrixFactorizationtoaddextrainforma­tion and extract contexts before training, attempting to en­hance recommendation accuracy and context understand­ing. Despite substantial previous efforts [21, 63, 64], this study adds additional information on both user and item description documents and applied Convolutional Neural Networks to efficiently capture their local features. Fur­thermore, this research adds bias to the observed ratings toavoidoverfittingissuesandusesMatrixFactorizationto createrelationshipsbetweenusersanditems. Theproposed model can be further used as a benchmark for developing contextcomprehensioninrecommendationsystems,hence delivering morerelevant recommendations for users. ItisobservedthatthemodelobtainedaverygoodRMSE of 0.89 in the testing set, which means the model can rela­tivelypredictfavorablemoviesofusersaccurately. Testing on different amounts of users reveals that the more users, the higher the accuracy, but the longer the convergence time. It is noted that this study subdivides the dataset to assess each subset independently, as opposed to providing a comprehensive evaluation of the entire dataset. Conse­quently, the rationale for refraining from comparing with othermodelsstemsfromthedivergenceindatapartitioning strategies. Hence, the evaluation process becomes inher­ently untenable due to the dissimilarity in data distribution methodologiesacross models. Future research may aim to overcome the scant user information (e.g., hobbies, location, marital status) by looking for a large dataset with more user information, including more features in the user description documents, leadingtoahigherimpactontheprediction. Moreover,the proposed model could be developed further by swapping out Matrix Factorization with more efficient techniques, such as singular valuedecomposition (SVD). Acknowledgement This research was funded and implemented for the Rising-Star project of the University of Science and Technology, The University of Danang,Vietnam. References [1] L. Lü, M. Medo, C. H. Yeung, Y.C. Zhang, Z.K. Zhang and T. 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Hieuetal. https://doi.org/10.31449/inf.v48i1.4604 Informatica 48 (2024) 45–56 45 IdentificationofStudents’ConfusioninClassesfromEEGSignalsUsing ConvolutionNeuralNetwork Rekha Sahu1,Satya Ranjan Dash2,*,and Amarendra Baral3 1Silicon Institute of Technology, Bhubaneswar, Odisha, India 2KalingaInstituteofIndustrialTechnology,Bhubaneswar,Odisha, India 3Trident Academy of Technology, Bhubaneswar, Odisha, India E-mail:sahu_r@rediffmail.com,sdashfca@kiit.ac.in, hodmath@tat.ac.in *Corresponding author Keywords: students’ confusion, predefined confusion, user-defined confusion, mismatch of confusion label, one­dimensionalconvolution neural network, electroencephalographysignals Received: January 7, 2023 For a student, classes are vital factors for gaining knowledge. The lectures may be online or offline, but getting knowledge without confusion is a major issue. The confusion labels can be measured from the elec­troencephalography signals and the confusion can be solved after knowing that students are suffering from confusion. Different machine learning approaches were implemented on electroencephalography signals to identify the suffering of students from confusion. The performance of traditional machine learning ap­proaches in predicting confusion status is found as poor. In this paper, the one-dimensional convolution neural network is implemented on the electroencephalography signals to detect confusion of the students at the time of watching video classes. Students’ attention, mediation, electroencephalography signals, delta, theta, alpha1, alpha2, beta1, beta2, gamma1 and gamma2 are taken into consideration to train a one-dimensional convolution neural network classifier. The one-dimensional convolution neural network approach has achieved better accuracy in detecting the confusion of the students. Besides finding confu­sion labels of students, the experiment is performed when understandable classes are creating confusion and the difficult classes are understandable for the students. This second experiment is also performed on electroencephalography signals of students and after identification of confusion status, the improvement of students’ deficiencies can be possible. For future work, more data and different aspects of the students can be taken into consideration for identifying confusion and different obstacles respectively which helps to improve in achieving perfect knowledge from the classes. Povzetek: Raziskava obravnava identifikacijo zmedenosti študentov med predavanji z uporabo EEG sig­nalov in enodimenzionalne konvolucijske nevronske mreže, kar omogoca boljše razumevanje in obravnavo ucnih ovir za izboljšanje pedagoškega procesa. 1 Introduction Education influences society significantly and education is an essential aspect of a better society and comfortable life. To spread education all over society, the proper way of teaching, as well as students’ perception levels, should beanalyzedandemphasizedforadoptingtheimprovement approaches in teaching procedures. The teaching proce­dures and student perceptions are vital factors in creating an educated society. Investigations show that students are facing problems when learning from lectures. They suf­fer from confusion and are unable to understand the lec­tures. It is found that students can better learn only if the teaching procedure, as well as student perception, is bet­ter [8]. Further, the teaching process influences the edu­cational system drastically and delivering a better lecture, appreciated by students, influences the educational system positively [30, 34]. By the way, students’ attitudes in per­ceiving the contents of the lecture are also influencing the learning strategy [29]. Different observation shows that student confusion level is an important factor to certify whether a class is appreciable for better education or not. Again, the lectures are delivered either online or offline. Whatevermay bethe procedure ofdeliveryofthelectures, mainly the students should understand and be clear on the conceptsbehindthelessons. Otherwise,thelecturesareun­necessary,wastageoftime,andmeaningless. Since,nowa­days, education is provided through online classes, experi­mentshavebeenperformedontheimpactofonlineclasses [6].Duringthepandemic,onlineclassesweretakentoover­come from discontinuous classes of the students. But, the students were suffering from confusion, down, sad, upset, excitement etc. in the classes [24]. Because of the online classesduringcovid-19, theperceptionofthestudents was less [7, 34]. Besides the deficiencies in students’ percep­tion of online classes, the instructors also showed their de­ficienciesinteaching,behaviours,emotions,attention,cog­ Informatica 48 (2024) 45–56 R. Sahu et al. nitive workload and trust [18]. Moreover, the relationship between the students’ and instructors’ behaviours, emo­tions,attentional,cognitiveworkloads,trustandcollabora­tionwasrequiredtoenhancetheclarity,andunderstanding of the lectures in the classes [18]. By the way, online lec­tures are more useful since the lectures can be attended at any time and anywhere according to the flexibility of stu­dents. Even after the pandemic, online classes are appreci­atedforhighereducationalongwiththecognizanceofstaff and students which is essential [10]. It is needed to ob­serve the impact on the understanding and confusion level of students automatically during classes for taking appro­priate actions. During the online classes, whether the student is in confu­sionornot,wasavitalmatter. Inanexperiment,theonline class was shown as poor in participation, emotional, skill and performance engagement in contrast to face-to-face classes [37]. But, Ram´irez-Moreno et.al. has found from electroencephalography(EEG)signalsthatonlineteaching is better than classroom teaching [26]. The EEG signals fromthefrontallobesvisualizetheconfusionlevelofahu­man. Hence,theEEGsignalsfromthefrontallobesofstu­dentscouldstate whether the studentisinconfusionornot during online teaching. Further, the experiment stated that the Fp1 channel is placed on the frontal lobe and it can be used to measure the concentration and confusion level of a subject [22]. Again, by manipulating raw EEG signals oftheFp1channel,delta,theta,alpha,betaandgammafre­quencieshavebeenextractedfordeepanalysis[1].Forclas­sifyingtheEEGsignals’pattern,traditionalmachinelearn­ing and deep learning have been applied to EEG signals datasets to find the pattern of EEG signals in recognizing thestudent state features [16]. The confusion labels of students can be measured from EEG signals and the deep learning approach implementa­tion on EEG signals can find out the specific pattern for a specific target class [22, 16]. These influence to imple­mentation of a deep learning approach on the EEG sig­nalsofstudentsfordetectingstudents’confusionlabels. In our experiment, the EEG signals of students had been col­lected at the time of watching videos in online classes[35]. Intentionally, the videos were created as confused videos andnon-confusedvideos. Thosearecalledpredefinedcon­fused and non-confused labels. After watching the videos for learning, the students labelled whether the videos are creating confusion or non-confusion in understanding the lessons. Those are called user-defined confusion and non-confusion labels. Both pre-defined labels and user-defined labels are mismatched in some cases. Hence, we have cre­ated three questions. Firstly, for which pattern of EEG signals, the students are suffering from confusion. Sec­ondly, for which pattern of EEG signals, the students are not in confusion. Since in some cases, pre-defined labels and user-defined labels are mismatching, so thirdly, we have analysed the pattern of EEG signals for which sig­nals are mismatched. The collected EEG signals are raw Fp1 EEG signals. From the raw Fp1 EEG signals, differ­ent features like, Attention, Meditation, Raw EEG signals, Deltafrequency,Thetafrequency,Alpha1,Alpha2,Beta1, Beta 2, Gamma1, and Gamma2 are extracted for confused andnon-confusedstudents. Sincedeeplearningapproaches are implemented for finding the pattern for classification tasks [16], so we have applied a one-dimensional convolu­ tion neural network (1DCNN) on our extracted dataset to classify the EEG signals for confusion, non-confusion and mismatching labels of user-defined and pre-defined labels. The overall work performed in this paper is represented in Fig.1. The rest of the paper is as follows. In section 2, related work is stated. Our experiment details are represented in section3. Thedescriptionofthedatasetispresentedinsec­tion3.1,thetechnologyappliediselaboratedinsection3.2 andtheresultanalysisispresentedinsection3.3. Finally,in section4,aconclusionandpossiblefutureworkarestated. 2 Relatedwork For developing teaching-learning procedures, different ex­periments and surveys are performed. Some surveys have concluded that students are suffering from academic stress drastically. Even achieving knowledge from the lectures of reputed universities is becoming hard for them [3]. Sometimesforimprovinglearning,studentswerespecially trained with some teaching-learning techniques and got good scores in comparison to direct attending the lecture [19]. Moreover, student confusion is a major factor in col­legelecturesand thedetection of confusion depends on at­tentionandmeditation[23]. Itishardtomeasuretheatten­tionofthestudentthroughself-reportorfromthebehaviour of thestudents. Thestateofthestudents’minds canbean­alyzed and found from the EEG signals [20, 9]. Sincethereportofstudentsorobserversisnotsufficient to measure mind state and the mind state of a student can be measured from EEG recording [20, 9], so we have ex­perimented with EEG recording to find out the confused students. Our survey helps to find how different factors like Attention, Meditation, Raw EEG signals, Delta fre­quency, Theta frequency, Alpha1, Alpha 2, Beta1, Beta 2, Gamma1,andGamma2areextractedfromEEGsignals. J. K. Grammer, et.al. stated that from EEG signals, the mea­surementofstudentattentioncanbequantified[13]. More­over, from the channel Fp1 EEG signals, the attentive & inattentive students are classified and Ning-Han Liu, et.al. implemented Support Vector Machine (SVM) approach to classify the EEG signals pattern to visualize the attention ofthestudent[17]. ItisfoundoutMeditationdescribesthe state of calmness and focused attention of mental activity andthiscanbeidentifiedfromEEGsignals[32]anditisob­served that Mindfulness meditation can be quantified from the frequency of EEG signals [2]. The above-mentioned Fp1channelisplacedonthefrontallobeanditcanbeused to measure the concentration of a subject. Again, mem­ory retrieval, decision-making, planning, response evalua­ Identification of Students’ Confusion in Classes… Informatica 48 (2024) 45–56 47 Figure 1: Overall workflow diagram: It is representing different attribute values generated from EEG signals taken as input. The class values as confused vs unconfused and matched vs mismatched are included. Input data are split as training dataset and testing dataset. 1DCNN classifier is trained using a training dataset and implemented on a testing dataset tofind the accuracy of prediction. tion,andreflectionofasubjectarestudiedthroughchannel frequency[22]. At the same time, EEG signals can display fivetypesofEEGwavesi.e.,gamma,beta,alpha,theta,and delta[1]. Generallyinthecaseof the gammawave, higher processing tasks and cognitive functioning are performed. The gamma waves are responsible for cognitive function­ing, learning, memory, information processing, attention, focus, consciousness, mental processing, and perception. In other sites, Betawaves are related to conscious thought, logicalthinking,stimulatingeffect,consciousfocus,mem­ory, and problem-solving. Again, Alpha waves lead to the feeling of deep relaxation and calm down whereas, Theta waves involve improving intuition, creativity and a more natural feel. Lastly, Delta waves involve feeling rejuve­nated, promoting the immune system, natural healing, and restorative/deep sleep[25]. To find out the delta, theta, al­pha, beta and gamma frequency, we manipulate raw EEG signals[1]andhencebymanipulatingFp1EEGrecording, we can find the delta, theta, alpha, beta and gamma band frequency for Fp1 EEG channel. To study the pattern of EEG signals to recognize the student state features, both traditional machine learning and deep learning can be ap­pliedto EEG signal datasets [16]. From the literature survey, we have found that machine learning and deep learning approaches are applied to EEG signalstofinddifferentpatterns[14,27,28]. TheMachine learning approaches like logistic regression, random for­est, decision tree, K-nearest neighbour (KNN) and SVM areappliedtoBrain-ComputerInterface(BCI)datasetand found out logistic regression has given better performance inthedetectionofstudents’confusioninMassiveOpenOn­lineCourse(MOOC)[5]. Again,theattentionofstudentsis studied from the EEG signals when the students were in­volved in MOOC and traditional classrooms and the SVM approach was implemented on the EEG data [32]. The ex­ perimentresultconcludedthattheMOOClearningprocess maintains higher attention. Besides, different traditional machine learning approaches like the random forest, SVM andKNNareappliedtotheEEGsignalsdatasettoclassify students’attentionlevelswheninvolveinonlineclasses[4]. Notonlytraditionalmachinelearning,butdeeplearningap­proacheshavealsogiven betterperformance inidentifying aspecificEEGsignalpattern. TheexperimentonEEGsig­nalsofnineteenstudentsisperformedtoidentifytheiremo­tions like happiness, sadness, anger, fear, disgust, and sur­prise. Inthisexperiment,thedeeplearningapproachesi.e., Long Short Term Machine (LSTM) and Convolution Neu­ralNetwork(CNN) areappliedtotheEEG signalstoiden­tify the emotions and found 99.8% classification accuracy with implementing CNN [14]. Again, the Students’ atten­ tivenesstowardsthelecturesismeasuredfromEEGsignals patterns, and it was fruitful by analysing EEG signals data using three-dimensional CNN [15]. With the above sur­vey, we also found out that Bidirectional LSTM Recurrent Informatica 48 (2024) 45–56 R. Sahu et al. Neural Networks were implemented on the EEG signals dataset to identify the confused and non-confused students when involve in online courses. It was observed that the classification accuracy was 73.3% and the gamma 1 wave can be used to identify the confusion [23]. A deep learn­ing approach can also be implemented on EEG signals to find out the attention level of a student[33]. Thus, the sur­vey concludes that the traditional machine learning, deep learningandspikingneuralnetworkanalysedandclassified the EEG signals for extracting specific patterns [27, 28]. It is observed that the one-dimensional convolution neu­ral network (1DCNN) is implemented on the EEG signals and given higher accuracy in detecting the different pat­tern EEG signals [27]. Again, the CNN approach is im­plementedontherawEEGsignalsofonechanneltodetect sleepdisorders[31]. Besides,fear,funandsademotionsare identified from the EEG signals using the CNN approach [12]. Aftergoingthroughtheaboveliterature,wehavepro­posed a 1DCNN model applied to EEG signals data set to detectconfusionofstudentswheninvolveinvideoclasses. Inthenextsection,wehavestatedourexperimentandcom­paredournovelapproachwithotherworksandalsothedif­ferent aspect, we have experimented, withis elaborated. 3 Experiment For fair teaching procedure, emphasis should be given to observinghowfairlylecturersaredeliveredandhowmuch studentscanabletoperceivefromlectures. Hence,thestu­dent’sunderstandingandconfusionstatusisessentialtoob­serve. Our experiment is performed to find out whether a studentisinaconfusedornon-confusedstatewhenwatch­ing online lectures. Therefore, EEG signals are collected from the students when they were watching the lectures. Those signals are used to train the models for classifica­tion tasks. Here, confusion and non-confusion of a stu­dentareinterpretedaccordingtopredefinedoruser-defined labels. Predefined implies the videos of the lecture are recorded intentionally as either confused or not confused lectures. User-defined impliesstudentspractically labelled that the lecture is either confusing or not confusing. With this dataset, a deep learning model is trained. The model predicts whether the student is in confusion according to the predefined or confusion according to the user-defined. Also, a model is trained to find out the pattern of signals for which predefined opinions and user-defined opinions arethesameandforwhichtheyhavemismatched. Theex­planation of experiments is as follows. We have described thedatasetinsection3.1,thedescriptionofthemethodap­plied tothe dataset isrepresented in section 3.2 and finally in section 3.3 resultof theexperiment is discussed. 3.1 Datasetanditsanalysis Forfinding whether the students suffering from confusion, the EEG signals pattern is required to study when they are involved in watching MOOC video clips. We have col­lected EEG brain wave dataset from the Kaggle database [35]. Tocollectthedatasetofstudents’EEGsignals,twenty videos were prepared and each video was of two minutes. Again,atwo-minuteclipinthemiddleofatopicischopped to make the videos more confusing. Out of twenty videos, tenvideosarepreparedtoconfuseanormalstudentandten videosarepreparedtonotconfuseanormalstudent. These videos are shown to ten students to test their confusion la­bels. However, one student is not considered for missing data due to a technical defect. Among twenty videos, ran­domly five videos of each category are picked and those are presented to a student in random sequence. This was the procedure that was followed for each student. Then, the students were instructed to learn as much as possible from the video clip. When the students were watching the videoclip,thebodylanguageofthestudentswasobserved and the confused state of the students was noted. In gen­eral, after each video, the student rated the confusion label as well as an observer of the student rated the correspond­ing confusion label. The confusion label was defined on a scaleof1-7,where1standsforleastconfusingand7stands for most confusing. EEG signals from each student were collected from the frontal lobe (Fp1) that lies between the left eyebrow and hairline. Using a wireless single-channel Mindset, EEG signalsofFp1werecollectedandthosearedepictedinfig­ure. 2. Besides, using NeuroSky’s API, the following sig­nals’information is collected. 1. The raw EEGsignal, sampled at512 Hz 2. Anindicator of signal quality, reportedat 1Hz 3. MindSet’s proprietary ”attention” and ”meditation” sig­nalsaresaidtomeasuretheuser’slevelofmentalfocusand calmness, reportedat 1Hz 4. A power spectrum, reported at 8 Hz, clustered into the standard namedfrequency bands: delta (1-3Hz), theta (4-7 Hz),alpha(8-11Hz),beta(12-29Hz),andgamma(30-100 Hz) Finally,fromtheFp1channelsrecording,theattributesAt­tention, Meditation, Raw EEG signals, Delta frequency, Thetafrequency,Alpha1,Alpha2,Beta1,Beta2,Gamma1, and Gamma2 are taken into consideration. To character­ize the overall values of the attributes, the mean statistic is calculated. We have 100 data points for 9 subjects and each watch 10 videos. The class value for the correspond­ing instance is the label based on a predefined confusion labelasthe experiment designed andthe user-defined con­fusion label as the user’s subjective rating. Hence, for one instance we have two labels one is a predefined confusion labelandanotherisuserdefinedconfusionlabel. Besides,a mismatch label is generated to differentiate the predefined confusionlabelandtheuser-definedconfusionlabel. Inthe dataset,thenumberofinstancesis12811andthenumberof attributesis16. Theattributesaretheserialnumberofsub­jects, the serial number of videos, Attention, Meditation, Raw EEG signals, Delta frequency, Theta frequency, Al­pha1, Alpha 2, Beta1, Beta 2,Gamma1, Gamma2, the pre­ Identification of Students’ Confusion in Classes… Informatica 48 (2024) 45–56 49 Figure 2: Fp1 channel locationis shown on the head whichisbetween the lefteyebrow and hairline. defined, user-defined and the mismatched labels. Atten­tion,Meditation,RawEEGsignals,Deltafrequency,Theta frequency, Alpha1, Alpha 2, Beta1, Beta 2, Gamma1, and Gamma2 are the frequency values and the pre-defined and user-definedattributescontaineither0or1,where0stands for the student is not confused and 1 stand for the student is confused. Again, the mismatchattribute contains 1 or -1 or0,where1impliesconfusedaccordingtopredefinedbut notconfusedaccordingtotheuser-defined,-1inmismatch implies not confused according to predefined but confused according to the user-defined and 0 implies both have the same label. All information about the dataset is summa­rizedin the table. 1. The graphical analysis of 11 attributes of three types of class i.e., predefined confused, user-defined confused and mismatched labels, are depicted in figs. 3, 4, 5, 6, 7, 8, 9, 10. 3.2 One-dimensionalconvolutionneural networkapproach(1DCNN) We have proposed a variant of the CNN approach called 1DCNNtoidentifytheconfusedstudentagainsttheuncon­fused. 1DCNN is a sequence of layers: convolution layer, pooling layer, flatten layer and dense layer followed by activation function [27]. The purpose of the convolution layer is to filter the data. For the convolution operation, we have the kernel, the dot product is performed between the input data and kernel. The stride and padding are performed and finally get a new filter dataset. Then, the dataset is reduced by doing the max pooling operation in the pooling layer. After pooling, we flatten the pooling data into a column. Then those column data are the input for the artificial neural network that is the dense layer of the proposed approach. On the output of the dense layer, we use the activation functions like the Re LU function and soft-max function, which are defined in equations 1 and 2respectively. { 0 when x< 0 f(x)= (1) 1 when x = 0 x e S(x)= Sn (2) x=1ex In the convolution layer, one-row data (1×n) is filtered us­ingtheconvolutionoperationwithaone-dimensionalfilter (1×m). The maximum value of one pad is taken for max pooling. Besides,theReLUfunctiongivestheoutputvalue when the value is positive otherwise it gives zero and the softmax function predicts the probability of input data be­longing to a class. The diagrammatical representation of the CNN model isrepresented in Fig.11. 3.3 Experimentresultanddiscussion 1DCNNapproachappliestopredefinedconfusionEEGsig­naldatasetsanduser-definedconfusionEEGsignaldatasets to identify the confused students according to predefined confusion and user-defined confusion of students respec­tively. Videosareintentionallyrecordedasconfusedvideos andunconfusedvideos. Someconfusedvideosareratedas unconfusedbythestudentsandsomeunconfusedvideosare ratedasconfusedbythestudents. 1DCNNisalsoappliedto findthesignalpatternforthemismatchoftheuser-defined andpredefined class labels. ForthepredefinedconfusedEEGsignalsdataset,thestruc­ture of 1DCNN is as follows. The kernel size is 1×3, the number of filters is 10, and the input shape is 1×12. The Maxpoolingsizeis4. Afterflattening,twodenselayersare structured with 500 neurons with a ReLU activation func­tionfollowedby2neuronswithaSoftMaxactivationfunc­tion. For optimization, Adam’s version of the gradient de­scent learning approach is implemented. 80% data is used fortrainingand20%isusedfortesting. Withoneepoch,we have got 100% classification accuracy in finding confused students’ EEGpatterns incontrast tounconfused ones. For the user-defined confused EEG signals dataset, the Informatica 48 (2024) 45–56 R. Sahu et al. Number of subjects 9 Number of Videos 20 (10 for confused and 10 for not confused) EEG recording duration per subject and video 2 min (total 6 hours recording) Channel recorded One channel Fp1 Number of attributes 17 Number of instances 12811 Class label Confused and not confused mismatched of pre-defined and user-defined opinion Table 1: Dataset descriptions of the students’ online classes and their confusion labels. Figure 3: Attributes value representation for userdefined non-confused labels. Figure4: Attributesvalue representation for user-defined confused labels. structure of 1DCNN is as follows. The kernel size is 1×3, function. For optimization, Adam’s version of the gradi­thenumberoffiltersis10, andtheinputshapeis1×11. entdescentlearningapproachisimplemented.80%datais The max pooling is 4. After flattening, two dense layers used for training and 20% is used for testing. With 1500 arestructuredwith1000 neuronswithaReLUactivation epochs,wehavegot99%classificationaccuracyinfinding function followed by 2 neurons with a SoftMax activation confused students’ EEG patterns in contrast to unconfused Identification of Students’ Confusion in Classes… Informatica 48 (2024) 45–56 51 Figure 5: Attributes value representation for predefined non-confused labels. Figure 6: Attributes value representation forpredefined confused labels. ones. Formismatcheduser-definedandpre-definedconfusedrate EEGsignalsdataset,thestructureof1DCNNisasfollows. The kernel size is 1×3, the number of filters is 10, and the input shape is 1×11. The max pooling is 4. After flatten­ing, two dense layers are structured with 500 neurons with a ReLU activation function followed by 3 neurons with a SoftMax activation function. For optimization, Adam’s version ofthegradientdescent learningapproachis imple­mented. 80% data is used for training and 20% is used for testing. With10000epochs,wehavegot99%classification accuracyin findingmismatches. Some works are performed on the EEG signals confused dataset [23, 21, 11]. The probability-based features ap­proachutilizestheprobabilisticoutputfromtherandomfor­estandgradient-boostingmachinetotrainmachinelearning models to detect the confused student [11]. Again, Gaus­ sian Naïve Bayes classifiers are trained with the dataset to findouttheconfusedstudents. Theaccuracyoftheclassifi­cationpattern of EEG signalsfor theconfused student was less than 70% [36]. The bidirectional LSTM Recurrent Neural Networks approach is applied to the confused EEG signaldatatodetecttheconfusedstudentandtheclassifica­tion accuracy is found to as 73.3% [23]. The experiments with different traditional machine learning approaches and deep learning approaches on the dataset have given less accuracy in comparison to our experiment except for the probability feature-based approach and the performances are summarizedin table.2. Thus,fromthesummaryintable.2,itisconcludedourpro­posed approachhasefficiencytoidentify theconfusedstu­dents. Besides, the experiments with different traditional Figure7: Attributesvalue representation for user-defined and pre-defined labels are matched (not confused). Figure 8: Attributes valuerepresentationfor user-defined and pre-defined labels are matched (confused). Approach Implemented Purpose of the Approach Accuracy 1DCNN Detect confused student (according to predefined) 100% 1DCNN Detect confused student (according to user-defined) 99% 1DCNN Detection of mismatch of user-defined and pre-defined confused label 99% The probability-based features approach utilizes the probabilistic output from the random forest and gradient-boosting method Detect confused student 99% Gaussian Naïve Bayes method Detect confused student 70% The bidirectional LSTM Recurrent Neural Networks approach Neural Networks approach Detect confused student 73.3% Table2: Summary of the performances of different approaches. Identification of Students’ Confusion in Classes… Informatica 48 (2024) 45–56 53 Figure 9: Attributes value representation for user-defined and pre-defined labels are matched (when predefined is not confused and user-defined is confused). fusedand user-defined is not confused). machinelearningapproachesanddeeplearningapproaches on the dataset have given less accuracy in comparison to ourexperiment,exceptfortheprobabilityfeature-basedap­proach. The probability feature-based approach and other machine learning approaches have emphasized the finding of confused students from the signals whereas our experi­menthasperformedonmorethanfindingconfusedstudents i.e.,whenuser-definedconfusionisfound,whenpredefined confusionisfoundandwhenpredefined&user-definedla­belsaremismatched. Forallthreecases,EEGsignals’pat­terns are trained using the 1DCNN model and have given 100%,99%and99%classificationaccuraciesrespectively. Besides, no discussion is shown in any paper still now on mismatched labels of user-defined and predefined labels. Infindingamismatch,itispossibletoanalyzemoreonthe reasonforthemismatch. Thereasonforthemismatchmay beduetomisinterpretationormoretalentedstudents. Ifthe predefined confusion level is 0 but the user-defined confu­sion level is 1, then it will be assumed the student is more talentedor hadknowledgeof thelecture before. If the pre­definedconfusion level is 1 but the user-definedconfusion level is 0, then those students should be analyzed to study thereasonforconfusionandtheirEEGsignalspatternpre­dict the student is in confusion although the lecture is very simple to understand. This issue can be analyzed more to treat the student’s deficiency. Informatica 48 (2024) 45–56 R. Sahu et al. 4 Conclusionandfuturework Students learn from the lectures in the classes and so the lectures should be understandable without confusion. Due tocovid19pandemic,classeswereonlinemodeandnowa­days also video lectures are influencing students. In this work, the confusion labels were studied when the student was watching video lectures. Twenty videos were col­lectedoutofwhich,tenwereconfusedvideosandtenwere non-confusedvideos. Ninestudents’EEGrecordingswere collected and the attributes’ values were extracted to find the patterns for confused students according to predefined, non-confused students according to predefined, confused students according to the user-defined, and non-confused students according to the user-defined. Besides, the mis­matched patterns of user-defined and predefined are ex­tracted. For extracting the patterns, 1DCNN is imple­mented and found to have better classification accuracy. Forpre-defined labels, ithasgiven 100% classificationac­curacy. Foruser-definedlabels,ithasgiven99%classifica­tion accuracy. Finally, the mismatched confusion label of user-definedandpredefinedhasshownclassificationaccu­racyas99%. Inallthreecases,80%dataisusedfortraining with 1DCNN and 20% data is used for testing. Thus, the proposeddeeplearningapproachhasgivenbetteraccuracy infindingconfusedstudentswhenpre-definedconfusedla­belsaremismatchedwiththestudent-definedconfusingla­bel. Theexperimentswereperformedtoidentifythepattern ofEEGsignalsforconfusedstudentsbutnodiscussionwas emphasizedforthepatternthatcausesmismatchedandour paper has discussed mismatch in confusion labels. By ap­plying the approach to more datasets, we can extract more information for analyzing students’ confusion. As a result, the deliberation of lectures can be improved and the stu­dentscanbetreated accordingly. More research can be performed relating to confusion and other problems of the students when involved in offline or online classes or watching videos. We have taken less amount of EEG datasets, and more experiments with more datasets can give better conclusions regarding the confu­sion of students and correspondingly we may treat the stu­dentsforbetterachievementineducation. Themajorstudy of mismatches of user-defined confusion and pre-defined confusion labels tends to analyze the different characteris­ticsofthestudentstocheckwhetherthestudentismoretal­ented (user defined is 0 but predefined is 1) or not talented (user defined is 1 but predefined is 0) or any other issues (previously know about the contains of lectures). Hence, mismatchleadstomoreanalysisonthefeaturesofstudents andthiscanbekeptasfeaturework. Moreover,iftheuser­definedlabelisthesameasthepredefinedlabel,thenthere will not require more analysis, otherwise, more analysis will require on the attribute values or some other criteria are taken into consideration to find the reason for the mis­match like a student is more talented. 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Atlantic Marketing Jour­nal, 11(1):9,2022. https://doi.org/10.31449/inf.v48i1.4759 Informatica 48 (2024) 57–68 57 AHybridFeatureSelectionBasedonFisherScoreandSVM-RFEfor MicroarrayData HindHamla1, Khadoudja Ghanem2 1Laboratory ofModelling and Implementation of Complex System, DepartmentofComputer Science, University of Ab­delhamidMehri Constantine 2, Constantine, Algeria 2Laboratory ofModelling and Implementation of Complex System, DepartmentofComputer Science, University of Ab­delhamidMehri Constantine 2, Constantine, Algeria E-mail: hind.hamla@univ-constantine2.dz , khadoudja.ghanem@univ-constantine2.dz Keywords: SVM-RFE, Fisherscore, gene selection,microarray data Received: March 22, 2023 Microarray data analysis has played a significant role in disease diagnosis and tumor type identification over the last two decades. However, due to the curse of dimensionality issues, microarray data classifica­tion remains a challenging task. This issue arises from a situation where the number of features is large, but the number of samples is small. As a result, dimension reduction techniques, specifically feature selec­tion methods, are critical for removing non-informative features and improving cancer classification. This paper presents a Filter-embedded hybrid feature selection method to address the gene selection challenge in microarray data analysis. First, it selects the features with the highest Fisher score to create a candi­date subset for the next embedded stage. Second, the proposed method employs support vector machine-recursive feature elimination (SVM-RFE) on the candidate subset to identify the optimal set of features to enhance cancer classification. Extensive experiments were conducted with ten high-dimensional microar­ray datasets to assess the efficacy of the proposed approach. The results show that the proposed method improves classifier performance significantly regarding classification accuracy, number of selected fea­tures, and computational efficiency. Povzetek: Predstavljena je hibridna metoda izbire znacilk z uporabo Fisherjeve ocene in SVM-RFE za izboljšanje natancnosti klasifikacije raka z analizo mikromrežnih podatkov. 1 Introduction Overthelasttwodecades,advancesinmicroarraytechnol­ogyhaveenabledresearcherstoanalyzethousandsofgenes simultaneously, which has been used in various applica­tions such as disease classification [3]. Microarray data classification is an effective tool for early disease diagno­sis and determining disease subtypes [9]. However, due to the curse of dimensionality, where the number of features is remarkably large (often thousands of features) while the number of samples is limited (often tens of samples), this task poses a significant challenge for machine learning al­gorithms [5]. In addition, a significant proportion of genes areirrelevantorredundant,affectingclassifierperformance [4]. Thus, gene selection methods have emerged as effec­tive approaches for reducing dimensionality in microarray data. Gene selection methods seek to identify and elimi­nate redundant and irrelevant features to obtain a subset of the most informative features [32]. These methods have improved classification accuracy while reducing computa­tional costsassociatedwith classifiers[34]. Gene selection methods are broadly classified as filter, wrapper, and embedded methods. Filter methods select features independently from thelearning classifier, based on statistical properties [3]. These methods are fast, but theyproducealowclassificationaccuracy[15]. Thewrap­permethodsusethelearningalgorithmtoevaluateasubset ofselectedfeatures[3].Althoughtheyproducehigherclas­sification accuracy, they are computationally expensive. Therefore,whendealingwithhigh-dimensionaldata,these methods are avoided [6]. Embedded methods select fea­tures during the learning process [31]. They are appropri­ateforanalyzingmicroarraydataduetotheirreducedcom­putational demands compared to wrapper methods and en­hanced efficiency compared to filter methods. [5]. Hybrid methods, which sequentially combine two or more feature selection methods from the same or different conceptual origins,haverecentlyemerged[6]to leveragethestrengths of diverse methodologies. Many feature selection (FS) surveys for microarray data processing have been conducted. [2] compares feature se­lection methods including information gain, twoing rule, sum minority, max minority, Gini index, sum of variances, t-statistics, and one-dimension support vector machines. This study use two publicly available glioma gene expres­sion datasets for evaluation. It was discovered that feature selection is important in the classification of gene expres­sion data. In [7], the authors examined the importance and Informatica 48 (2024)57–68 H.Hamlaetal. challenges of feature selection methods when dealing with high-dimensional data such as microarray and instruction detection. The paper emphasized the importance of effi­cienttechniquesformanagingthecomputationalcomplex­ity of high-dimensional data. Furthermore, open issues in feature selection are addressed, particularly in the context of big data and high-dimensional datasets. The authors of [8] compared five filter methods: the F test, the T-test, the signal-to-noise ratio (S/R), ReliefF, and the Pearson product-moment correlation coefficient (CC). The study used five microarray datasets: leukemia, lungcancer,lymphoma,centralnervoussystemcancer,and ovarian cancer. The results showed that combining the signal-to-noise ratio (S/R) with KNN classifiers produced thebestclassification accuracy. In[13],theresearchersin­vestigatedtheeffectofpopularfiltermethods(ReliefF,Mu­tual information, Chi-square, F-score, Fisher score, Lapla­cian, MRMR, and CMIM) on six well-known classifiers (random forest, logistic regression, K-Nearest Neighbour, decision tree, and Support Vector Machine). The experi­ment was carried out on ten high-dimensional microarray datasets, and the results revealed a distinct trend. Uni­variate filter feature selection techniques such as Mutual Information, F-score, and Fisher score outperformed mul­tivariate techniques such as MRMR and CMIM. Only a few studies on embedded methods have been conducted. [12] assessed the efficacy of five embedded feature se­lection techniques: decision trees, random forests, lassos, ridges,andSVM-RFE.Theexperimentemployedtenhigh­dimensional microarray datasets. The results highlight the SVM-RFE’s superior accuracy performance. This paper combines the embedded method’s perfor­mance with the filter method’s computational efficiency. theproposedmethodisdividedintotwostages: TheFisher score filter method is used in the first stage to select the mostrelevantfeaturesduetoitseffectiveperformancewith high-dimensional data [10]. Second, the selected subset is input for the embedded Support Vector Machine Recur­sive Feature Elimination SVM-RFE method. This com­bination improves classification accuracy while signifi­cantly reducing the number of selected features. Exper­iments were conducted on ten high-dimensional microar­ray datasets, including Colon, Central Nervous System CNS, Leukemia, Breast cancer, Lung cancer, Leukemia3­Classes, Leukemia4-Classes, Ovarian, Lymphoma, and MLL.Theexperimentalsetupconsistsofthreemajorcom­ponents: – A comparative analysis of the proposed method with other filter methods combined with the same embed­ded method, SVM-RFE, specifically ReliefF_SVM­RFEandMutualInformation(MI)_SVM-RFE.Inad­dition, we present SVM-RFE results without using a filter method. We avoid comparing the proposed method to the Minimum-Redundancy Maximum­Relevancy(MRMR)andChi-squarefiltermethodsbe­causethey have already beenstudied [19] and[4]. – Investigation the impact of employing six well-established classifiers: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-NearestNeighbour(KNN)onthefeaturesubsetse­lected by ourproposedmethod. – Finally, to highlight the effectiveness of the proposed method, we compared it with filter-wrapper methods ([30], [34], [23], [21], [24]) and with filter-embedded ones [19] and [4]. The paper is structured as follows: Section 2 examines re­latedworksonhybridfeatureselectionmethods. Section3 briefly describes the Fisher score algorithm and the SVM­RFE algorithm. Section 4 describes the proposed method in depth. Section 5 presents a comprehensive analysis of the experimental findings. Finally, Section 6 provides the conclusion and outlines potentialfuturedirections. 2 Relatedwork Numeroushybridfeatureselectionmethodshavebeenpro­posed to address the dimensionality reduction challenge and eliminate irrelevant and redundant features from mi­croarray data. While most existing studies in the literature combine filter methods and wrapper methods [1], only a fewworksinvestigatethecombinationofembedded meth­odsandfiltermethods. Inthissection,wewillreviewsome recenthybridfeatureselectionmethodsthathavebeenpub­lishedin the literature. 2.1 Hybridwrapper-filtermethods Given their adaptability and efficiency in dealing with large-scale issues, meta-heuristics methods have attracted attention for solving gene selection problems [26]. How­ever, these methods frequently necessitate a significant amount of computational time. Therefore, meta-heuristics have been combined with filter methods to narrow the searchspaceandspeedupthefeatureselectionprocess[21]. Naiketal. [20]proposedahybridfeatureselectionmethod combining the filter and wrapper methods. The Fisher score filter method was used to select a subset of features. The Binary Dragonfly Algorithm was used in the wrapper methodtosearchforaninformativesubsetoffeatures,and theRadial Basis Function NeuralNetwork was used as the learning model that evaluates the selected subset. Shukla [24]designedHMPAGA,ahybridfeatureselectionmethod that used an ensemble gene selection method to filter out noisy and redundant genes. It also used a multi-population adaptive genetic algorithm to identify high-risk difference genes. SVM and NB classifiers were used as objective functions. Shulka et al. [25] proposed a two-stage feature selec­tion method for microarray data recognition. In the first stage, noisy and redundant features were removed using a AHybridFeatureSelectionBasedonFisherScore… Informatica 48 (2024) 57–68 59 multi-layer approach and f-score filter methods. An adap­tive genetic algorithm selected the most important fea­tures in the second stage. Zhang et al. [30] proposed IG-MBKH, a hybrid feature selection method that com­bines Information Gain and Modified Binary Krill Herd. Themethodwasvalidatedusingninehigh-dimensionalmi­croarray datasets, improving classification accuracy with fewer features. Zheng et al. [34] presented the K Value Maximum Reliability Minimum Redundancy Improved Grey Wolf Optimizer (KMR2IGWO), a hybrid feature se­lection method. MRMR was used in the filter stage to se­lectKfeatures,withKdeterminedbythedataset’smessage. These features were then used as input for the IGHO algo­rithm, with theSVMclassifierused to assess classification accuracy. KMR2IGWO’sperformancewasvalidatedusing 14microarraydatasets, highlighting its superiority. MIMAGA, a combination of mutual information maxi­mizationandadaptivegeneticalgorithm(AGA),wasintro­duced by Lu et al. [17]. MIM was used to choose a subset of300features. Then,AGAwasappliedwiththeaccuracy ofELMclassifierservingasthefitnessfunction. Sadeghian et al. [23]introduced a three-stage hybrid feature selec­tion method named Ensemble Information Theory-based binaryButterflyOptimizationAlgorithm(EIT-bBOA).The method employed Minimal Redundancy-Maximal New ClassificationInformation(MR-MNCI)intheinitialphase to eliminate 80% irrelevant features. Subsequently, the Information Gain-binary butterfly optimization algorithm (IG-bBOA) optimized the first phase. In the final phase, an ensemble of ReliefF and the Fisher Score method was applied to the final feature subset. The method was eval­uated using six well-known datasets. Ouadfel et al. [21] developed a two-stage feature selection method that used the ReliefF filter method to estimate feature relevance in the first stage. The top-ranked M features where then pre­selected. The second stage combined the binary Equilib­rium Optimizer with a local search strategy based on Pear­soncoefficientcorrelation. Theproposedmethodwaseval­uatedon16UCIdatasetsandtenhigh-dimensionalbiolog­icaldatasets. 2.2 Hybridembedded-filtermethods Intermsofcomputationaltime,embeddedfeatureselection methods outperform wrapper methods. Though only a few embedded methods have been presented in the literature, [12] conducted a comparative study of the most common ones. SVM-RFE emerged as the most accurate method, withcomparableexecutiontimeandselectedfeatures,. Fur­thermore, SVM-RFE has consistently demonstrated its ef­ficacy [16]. Thus, many studies have proposed hybridiza­ tionbetweenfilterandembeddedmethodsthatconcentrate on combining SVM-RFE with filter methods. SVM-RFE has been shown to be effective in identifying informative genesinmicroarraydata[33]. Mundraetal. [19]proposed a hybrid feature selection method combining MRMR and SVM-RFE. The approach’s performance was assessed on four well-known microarray datasets. Almutiri and Saeed [4] introduced the ChiSVMRFE feature selection method based on the Chi Square Statistic and SVM-RFE. On ten microarray datasets, the proposed method was evaluated. Mishra et al. [18] combined SVM-RFE with the Bayesian T-test for gene selection, which resulted in improved clas­sificationaccuracy,fewerselectedgenes,andalowerclas­sificationerror rate. Huang et al. [14] enhanced the SVM-RFE’s perfor­mance for gene selection by incorporating feature cluster­ing, thereby reducing computational complexity and gene redundancy. Lietal. [16]proposedVSSRFE,animproved version of SVM-RFE that aimed to reduce time using a more efficient SVM classifier implementation. The results demonstratedtheproposedmethod’sefficiency in terms of time reduction. Combining wrapper or embedded meth­odswithfiltermethodsconsistentlyimprovesclassifierper­formance in terms of classification accuracy and computa­tional efficiency, according to the aforementioned works. SVM-RFE, in particular, has demonstrated its ability to improve classification accuracy while optimizing feature dataset. ThispapercombinesSVM-RFE,aleadingembed­ded method, with the best filter method to further improve the results. 3 Background This section describes the Fisher score and SVM-RFE methods. 3.1 Fisherscore The Fisher score algorithm is a well-known filter feature selection method that is applied to a subset of discrimi­native features. In summary, the algorithm works as fol­lows: It begins by calculating the average and variance of each feature for each class. Then, it calculates scatter matrices between and within classes to assess the effec­tiveness of the features in differentiating various classes. TheFisherScoresarethencalculatedusingthesematrices, allowing for comparing different features. Features with higherFisherScoresareconsideredmoreimportantfordis­tinguishing between classes. We can rank the features and selectthebestbasedontheirscores. Thegoalistominimize the distances between samples in the same class while in­creasing the distancesbetweensamplesindifferentclasses [29]. Fisher scores fi are calculatedasfollows: . c 2 j=1 nj (µi,j - µi) SCF (fi)= . (1) c j=1 nj s2 i,j where,ui isthemeanoffi feature,nj is thenumberofsam­ples in the class jth , uij is the mean of fi in the jth class, and sij is the variance of fi in the jth class. Usually, a higherFisherscoremeansthefeatureisvitalforclassifica­tion. Informatica 48 (2024)57–68 H.Hamlaetal. 3.2 SupportVectorMachineRecursive FeatureElimination(SVM-RFE) SVM-RFE is an embedded feature selection method intro­ducedbyGuyonetal. [11]. Thismethodemploysaweight vectorasa criterion for splitting,calculated as follows: n . W =(yi,xi,ai) (2) i=1 where, i represents the number of features ranging from 1 ton, yi isthelabeledclassofthesample xi. ai isthemax­imum class separation margin estimated from the training set. SVM-RFE works in a recursive manner, similar to it­erative refinement. The entire feature set is initially used to train an SVM classifier. The algorithm then iteratively eliminates features with the lowest discriminative power, reducing the risk of the curse of dimensionality and over-fitting. Thefeaturesarethenrankedaccordingtotheircon­tributiontotheclassificationtask. Theith rankingcriterion iscalculated as follows: R = W 2 (3) The higher the value of the ranking criterion, the more im­portantthefeature. Algorithm1depictsthedetailedSVM­RFEalgorithm. Algorithm 1 Pseudocode ofSVM-RFE Input: F initial feature set Output: R ranklist 1: R = Ø 2: while F .= Ødo 3: Train SVM with F 4: ComputetheweightvectorusingEquation2 5: ComputetherankingcriterionusingEquation3 6: Findfeaturewiththelowestrankingcriterion 7: UpdatetheRankedlistoffeatures 8: R = R + Fi 9: Updatesetoffeatures 10: F = F- Fi 11: endwhile 4 Proposedmethod Because of its low computational requirement, the Fisher score is a simple and efficient feature selection method thatisparticularlysuitableforhigh-dimensionalmicroarray dataclassification[28]. However,theFisherscoredoesnot achievesatisfactoryclassificationaccuracy. SVM-RFE,on the other hand, has been successfully applied to gene se­lection problems. It has consistently outperformed sev­eral other embedded methods regarding classification ac­curacywhile using a smaller feature set [12]. Nonetheless, one major disadvantage of SVM-RFE is the lengthy fea­ture selection process, especially when dealing with high-dimensional data such as microarray [16]. This work pro­poses a hybrid feature selection method that combines the computational efficiency of the Fisher score filter method and the high performance of the SVM-RFE embedded methodtocapitalizeonthestrengthsofboth. Fig. 1shows theflowchart of thehybrid filter-embeddedmethod. The followingarethe specifics of the proposed method: Figure 1: Flowchart of the proposed method. 1. Data pre-processing This first step involves replacing missing values with themean value derivedfromall known genevalues. 2. Filter stage Calculate Fisher score The Fisher score is used at this stage to eliminate re­ dundantandirrelevantfeatures. Eq. (1)calculatesthe Fisher score value for each feature, and the features are then sorted based on these values. The higher the Fisherscorevalue,themoreinformativethefeatureis for classification. Select n top features The top n features the Fisher score method indicates areselectedascandidateinputfortheembeddedstage. 3. Embedded stage SVM-RFEisappliedtothepreviouslyselectedcandi­date inputs. SVM-RFE uses all the selected features AHybridFeatureSelectionBasedonFisherScore… Informatica 48 (2024) 57–68 61 to train the SVM classifier. Each iteration removes thefeatureswiththelowestrankingcriterionfromthe features set. This process is repeated until all features havebeenremoved. Thefeaturesaresortedinreverse orderofremoval,withthemostrecentlyremovedfea­tures consideredthe most important. 4. Selectoptimalsubset Finally, SVM-RFE selects a subset of m most impor­tant features. The value of n and m is determined through experimentation, with m always being less than n (m