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Comparative Analysis of Filtering Techniques in Eye Landmark Tracking: Kalman, Savitzky-Golay, and Gaussian 4

Comparative Analysis of Filtering Techniques in Eye Landmark...

引用

4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

作者： Nugroho, Muhammad Arief Abdurohman, Maman Erfianto, Bayu Sulistiyo, Mahmud Dwi Kasim, Anita Ahmad School of Computing Telkom University Bandung Indonesia Department of Informatics Faculty of Engineering Tadulako University Palu Indonesia

ISBN: (纸本)9798350369694

This study benchmarks Kalman, Savitzky-Golay, and Gaussian filters to improve eye landmark detection crucial for online proctoring systems. Using eye-tracking video data, we evaluated filters on Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), computation time, and jitter reduction. The Kalman filter emerged superior with an MSE of 0.49, MAE of 0.48, RMSE of 0.70, and MAPE of 15.56%, alongside a 2.39% reduction in standard deviation, 4.73% in variance, and the quickest computation time of 0.02 milliseconds. The Gaussian filter reported an MSE of 0.49, MAE of 0.45, RMSE of 0.70, and MAPE of 14.48%, with modest jitter reductions of 0.96% in standard deviation and 1.90% in variance. In contrast, the Savitzky-Golay filter, with an MSE and MAE of 0.70, increased jitter, shown by a -1.50% change in standard deviation and -3.03% in variance, and took the longest computation time at 1.44 milliseconds. The results solidify the Kalman filter’s suitability for real-time, precision-demanding eye-tracking, while the Gaussian filter serves well for scenarios with relaxed accuracy needs. The Savitzky-Golay filter’s applicability seems limited due to its propensity to amplify noise under the conditions tested. © 2023 IEEE.

关键词： Kalman filters

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Detecting Routing Attacks in WSN: A Novel Cartesian Product-Based Method

Detecting Routing Attacks in WSN: A Novel Cartesian Product-...

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2024 Arab ICT Conference, AICTC 2024

作者： Ahmed, Saad Salman Alansari, Zainab Belgaum, Mohammad Riyaz College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman G. Pullaiah College of Engineering and Technology Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350342086

Routing attacks pose a substantial security risk in Wireless Sensor Networks (WSNs), compromising network performance and reliability. Utilizing the Cartesian product concept to identify these routing attacks in WSNs is an emerging and promising approach. This strategy involves generating a Cartesian product graph from the original network graph and subsequently extracting relevant features to detect potential threats. The success of this approach is dependent on several factors, such as feature selection, the detection algorithm employed, and the prevailing network conditions. This research presents an innovative framework that leverages the Cartesian product approach to identify routing attacks in WSNs. The performance of this framework is evaluated using the Cooja simulator, yielding results that indicate high detection accuracy, low energy usage, and minimal overhead. A comparative analysis with contemporary studies illustrates the superior ability of this framework to handle a wide range of attack scenarios and network conditions. The introduced framework significantly advances the field of routing attack detection in WSNs and provides an actionable solution for easy integration into existing WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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A Descriptive Study of Factors Influencing Online Purchasing Behavior: Malaysian Consumer Perspective 9th

A Descriptive Study of Factors Influencing Online Purchasing...

引用

9th International Conference on computing and informatics, ICOCI 2023

作者： Mustakim, Nurul Ain Abdul-Rahman, Shuzlina Aziz, Maslina Abdul Hasan, Zuhairah School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Shah Alam Malaysia Faculty Business and Management Universiti Teknologi MARA Melaka Malacca Malaysia

ISBN: (纸本)9789819995912

Understanding the influence factors on online purchasing behavior from a Malaysian consumer perspective is crucial for businesses and marketers aiming to thrive in the digital marketplace. This study aims to investigate the factors that influence online purchasing behavior among Malaysian consumers. It gives detailed insights into how the factors affect consumer behavior. The questionnaire was adopted in accordance with previous research, and data were collected using a survey method. Approximately 560 respondents’ data were collected through convenience sampling, with the criteria of being Malaysian, above 18 years old, and having used an e-commerce platform at least once. Descriptive statistics, correlation coefficients, and multiple regression analyses were conducted, and the findings showed that attitude, psychology, product price, privacy, perceived benefits, and accessibility were significant factors in online purchase behavior. Meanwhile, perceived risk was found to be significant but negatively affects online purchase behavior. The results revealed that online consumers’ purchase behavior is not influenced by trust and security, hedonic motivation, emotional and promotional factors. However, the findings should be further explored by delving deeper into specific factors and exploring emerging trends, such as social commerce or live stream, to be more generalizable. This is the first study to measure the eleven influencing factors on online purchasing behavior comprehensively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Consumer behavior

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Development of Kansei-Based Visualization Pattern for E-Learning Website 4th

Development of Kansei-Based Visualization Pattern for E-Lear...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Aning, Alberto Mohd Naim, Nur Faraha Baharum, Aslina Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819729760

Through their websites, a lot of businesses and people directly offer their services. The way in which these services are offered may affect users emotionally. A website that elicits pleasant emotions in its users can make them feel happy, engaged, trustworthy, or satisfied. Well-known for its ability to analyze customer emotional demands and define the connection between emotions and product design aspects, Kansei engineering (KE) is a user-centered technology. The development of e-learning software, e-learning authoring tools, learning management systems, and other related technologies is currently a major investment made by universities, training centers, and government bodies. Even though there are many advantages for educators using e-learning technology, getting students to use these services can be difficult, especially for the e-learning websites of Malaysia’s higher education institutions (HEIs). Few standards exist for user interface (UI) design that encourages emotional connection on the e-learning platforms of Malaysian HEIs. Students were disengaging from e-learning platforms in substantial numbers because of this issue. Hence, this paper aims to explore users’ needs and emotions while using an HEI website and produce a new guideline for HEIs e-learning website. Using the card sorting technique, a visualization pattern has been presented as a design guideline for an e-learning graphical user interface (GUI) website for HEIs in Malaysia. Positive outcomes have been obtained by implementing the suggested guideline on the GUI of the prototype website. The purpose of this guideline is to help HEIs as well as website developers and designers create e-learning websites that will keep students interested in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Optimizing Question-Answering Framework Through Integration of Text Summarization Model and Third-Generation Generative Pre-Trained Transformer 14

Optimizing Question-Answering Framework Through Integration ...

引用

14th International Conference on Computer and Knowledge Engineering, ICCKE 2024

作者： Moung, Ervin Gubin Tong, Toh Sin Sufian, Maisarah Mohd Liaw, Valentino Farzamnia, Ali Yahya, Farashazillah Faculty of Computing and Informatics University Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Computing and Engineering University of Huddersfield West YorkshireHD1 3DH United Kingdom

ISBN: (纸本)9798331511272

This research project addresses the growing demand for efficient data access amidst the surge in digital information. Conventional keyword-based search engines face limitations, driving the exploration of advanced natural language processing (NLP) approaches. The study introduces an algorithm that autonomously extracts data from summary reports, utilizing NLP and information retrieval as a question-answering API. Evaluated using Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L scores, PEGASUS achieved the highest average ROUGE score (0.432) with a single sample, while BART attained the highest multi-sample score (0.302) with 1000 samples. The research emphasized optimal hyperparameters in pre-trained models, specifically the impact of batch size on completion time and the relationship between maximum sequence length and ROUGE scores. The study enhances question-answering systems for efficient information retrieval, with practical applications in sectors like legal analysis, healthcare, and business intelligence. This study not only improves the efficiency and accuracy of QA systems but also offers valuable insights for future advancements in NLP-driven information extraction. The refined methodologies and enhanced performance metrics provide a promising avenue for transforming how organizations handle large-scale data, driving innovation in both computational efficiency and user experience. ©2024 IEEE.

关键词： Question answering

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Investigating the Impacts of Weather and Personalisation on Asthma Exacerbations using Machine Learning 2022

Investigating the Impacts of Weather and Personalisation on ...

引用

11th International Conference on Software and Computer Applications, ICSCA 2022

作者： Haque, Radiah Ho, Sin-Ban Chai, Ian Abdullah, Adina Faculty of Computing and Informatics Multimedia University Malaysia Faculty of Medicine University of Malaya Malaysia

ISBN: (纸本)9781450385770

This paper reports the results of investigating the impacts of weather and personalisation on asthma exacerbations. Additionally, several machine learning classification algorithms were applied and evaluated to develop an intelligent mHealth solution that can offer accurate personalised estimations of asthma exacerbations based on weather and demography and provide tailored precautionary guidelines for selfcare to each user. Consequently, the proposed solution will enhance asthma self-management by assisting patients with chronic asthma to plan their daily outdoor activities and limit exposure to weather triggers, thus controlling their asthma exacerbations. © 2022 ACM.

关键词： Machine learning Neural networks Personalisation Asthma self-management Classification algorithm

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A Systematic Literature Review of Multimodal Analysis Techniques for Malware Detection 5

A Systematic Literature Review of Multimodal Analysis Techni...

引用

5th International Conference on Smart Sensors and Application, ICSSA 2024

作者： Ibrahim, Zul-Azri Ismail, Saiful Adli Rahim, Fiza Abdul College of Computing and Informatics Universiti Tenaga Nasional Selangor Kajang Malaysia Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350354294

As cyber threats continue to evolve in complexity, traditional malware detection methods often fall short in identifying sophisticated attacks. Multimodal analysis, which integrates various data sources and analytical methods, has emerged as a promising approach to enhance malware detection capabilities. This paper presents a systematic literature review (SLR) of existing research on multimodal analysis techniques for malware detection. We conducted an extensive search across two major databases and identified 31 studies that explore the multimodal approaches for malware detection. Our review synthesizes the methodologies, modalities, and evaluation metrics used in these studies, highlighting their strengths and limitations. The findings reveal that multimodal approaches significantly improve detection accuracy and robustness compared to single-modality methods. However, challenges such as data integration complexity, computational overhead, scalability, and transparency remain. This review also identifies research gaps and suggests directions for future work to further advance the field of malware detection. © 2024 IEEE.

关键词： Cyber attacks

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MVR-RCM: An Approach to Recommend Microlearning Videos in Informal Online Learning Environment 23

MVR-RCM: An Approach to Recommend Microlearning Videos in In...

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Proceedings of the 15th International Conference on Education Technology and Computers

作者： Fang-Fang Chua Shin-Yan Chong Tek-Yong Lim Faculty of Computing and Informatics Multimedia University Malaysia

ISBN: (纸本)9798400709111

With the rapid development of Web technologies, online learning has become mainstream. People acquire for new skills or knowledge through the Internet in formal or informal ways. As compared to the well-developed curriculum-based Learning Management Systems (LMSs), more people prefer the informal style-learning that can be customized to meet different learning habits or interests. They prefer engage with resources that are short or casual, but could give immediate solutions. In informal online learning, people acquire learning resources from open-source platforms. As the open-source learning resources grow, the searches become complex and nearly impossible to identify desired content if just have a glance at the learning resource title. This paper described the full architecture of “MVR-RCM” to help learners acquire microlearning video resources efficiently in an informal online learning environment. Instead of manually browsing through the Internet for learning resources, an automated approach named “MVR-RCM” has been proposed to deliver microlearning videos using a content-based recommender system. In “MVR-RCM”, key influence factors like learning interest and video's category are adopted to suggest relevant microlearning video resources to learners.

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Analysis of the Gamification Aspects of Digital Educational Tools Using Gamification Classification 32

Analysis of the Gamification Aspects of Digital Educational ...

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32nd International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2024

作者： Bozic, Ana Vrcelj Hoic-Bozic, Natasa Dlab, Martina Holenko Stancin, Kristian Jagust, Tomislav Civil Engineering Technical School in Rijeka Rijeka Croatia University of Rijeka Faculty of Informatics and Digital Technologies Rijeka Croatia University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9789532901382

Nowadays, gamification is increasingly being used in education to increase student motivation. The reason for this is that it is easier to design and implement in the classroom than game-based learning (GBL). Instead of developing special educational games, gamification can be implemented simply by using some game design elements provided by publicly available digital gamification tools. However, choosing the right gamification tools and their game design elements is not an easy task for teachers. This paper describes a new classification for gamification and its application in the evaluation of popular digital gamification tools. Reviewing the characteristics of these tools can help teachers not only in selecting these tools, but also in independently evaluating other tools. The classification described is the first step in developing the future technological-pedagogical framework for the design and implementation of gamified activities in schools. © 2024 University of Split, FESB.

关键词： Game design

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Implementation of Machine Learning-Based QoS Traffic Classification In Software-Defined Networking

Implementation of Machine Learning-Based QoS Traffic Classif...

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Asia-Pacific Conference on Applied Electromagnetics, APACE

作者： Nur Fatin Nabilah Mohd Fauzi Tan Saw Chin Fatimah Audah Md Zaki Faculty of Computing & Informatics Multimedia University Malaysia

ISBN: (数字)9798350364163

ISBN: (纸本)9798350364170

The concept of Software-Defined Networking (SDN) in network management involves controlling network behaviour through software applications. The implementation of SDN centralizes network management, ensuring efficient performance and reducing computational costs. With the vast amount of network traffic today, traffic classification is crucial in SDN, improving performance by prioritizing applications that require higher Quality of Service (QoS). The occurrence of imbalanced datasets, which focus more on the majority class rather than the minority class, leads to poor performance in SDN. This paper explores the implementation of feature selection using filter and wrapper methods to tackle imbalanced datasets and enhance network management performance. Using a real-time dataset containing 930 features from the University of Cambridge, the Symmetrical Uncertainty and C4.5 algorithm achieve a classification accuracy of 97.69% and reduced the dataset size by 50%. These findings demonstrate that the proposed methods significantly improve computational efficiency and classification performance, making them suitable for real-time SDN applications.

关键词： Accuracy Uncertainty Machine learning algorithms Quality of service Telecommunication traffic Filtering algorithms Real-time systems Software Computational efficiency Software defined networking

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