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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 ...

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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 ...

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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...

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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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Human Motion Recognition Based on Kinect Sensor and Leap Motion Controller 1

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8th International Conference on Computational Science and Technology, ICCST 2021

作者： Wong, Chun Yung Ng, Giap Weng Ibrahim, Ag Asri Ag Faculty Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9789811685156

ISBN: (纸本)9789811685149

Augmented reality (AR) is an interactive experience of a real-world environment where objects reside in the real world are enhanced by computer-generated perceptual information. In essence, emulating and altering reality that include, in relatively real-time and precision, position and motion tracking (sensors like Kinect and Leap Motion Controller), match moving (techniques allowing insertion of computer graphics into live-action footage with correct position, scale, orientation and motion), and finally motion capturing (process of recording movements of objects or people). This review is dedicated to the question of what object recognition (motion tracking, match moving and motion capture) is and how this technique can be identified, thus synthesizing knowledge in the field. The former is further clarified, elaborated, and compared using input sensors like Kinect and Leap Motion Controller. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms

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Multimedia Tools and Applications 2024年第37期83卷 85103-85138页

作者： Seyyedabbasi, Amir Tareq Tareq, Wadhah Zeyad Bacanin, Nebojsa Computer Engineering Deptartment Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu India Faculty of Informatics and Computing Singidunum University Belgrade Serbia MEU Research Unit Middle East University Amman Jordan

Recently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms. © The Author(s) 2024.

关键词： Benchmarking

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Analytical Model for Underwater Wireless Sensor Network Energy Consumption Reduction

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Computers, Materials & Continua 2022年第7期72卷 1611-1626页

作者： Huma Hasan Rizvi Sadiq Ali Khan Rabia Noor Enam Kashif Nisar Muhammad Reazul Haque Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan Sir Syed University of Engineering and Technology Karachi75300Pakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMS88400Kota KinabaluSabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran Multimedia63100CyberjayaSelangorMalaysia

In an Underwater Wireless Sensor Network(UWSN),extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor *** based on clustering strategies,instead of each node sending information by itself,utilize cluster heads to collect information inside the clusters for forwarding collective information to *** can effectively minimize the total energy loss during *** environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission *** base clustering strategy works in rounds,where each round comprises three fundamental stages:cluster head selection,grouping or node association,and data aggregation followed by forwarding data to the *** UWSN,the energy consumed during the formation of clusters has been considered casually or completely evaded in the previous *** this paper,the cluster head setup period has been considered the main contributor to extra energy utilizer.A numerical channel model is proposed to compute extra *** is performed by using a UWSN broad *** results have shown that extra maximum energy consumption is approximately 12.9 percent of the system total energy consumed in information transmissions.

关键词： Analytical model submerged acoustic sensor systems energy loss round base clustering UWSN extra energy

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Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study

Chaotic Binary Ant Lion Optimizer Approach for Feature Selec...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Zivkovic, Miodrag Bacanin, Nebojsa Rakic, Andjela Arandjelovic, Jelena Stanojlovic, Stefan Venkatachalam, K. Singidunum University Faculty of Informatics and Computing Belgrade Serbia Bangladore India

ISBN: (纸本)9781665489621

Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently. © 2022 IEEE.

关键词： Feature Selection

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