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Parallel computing and Distributed Systems (PCDS), International Symposium on

作者： Zheng-Bin Phang Su-Cheng Haw Tong-Ern Tai Kok-Why Ng Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

The increasing volume and complexity of data in today's business environment present significant challenges for traditional data analysis methods. Static reports and spreadsheets often fall short in providing comprehensive insights necessary for strategic decision-making. This paper addresses the critical issue of enhancing data comprehension and utilization through interactive data visualization. The primary objectives of this paper are to explore various visualization techniques, design and implement an interactive dashboard with a content-based recommender system, and evaluate its effectiveness in optimizing business processes. The content-based recommender system utilizes TF-IDF vectorization and cosine similarity to offer personalized product recommendations. The process includes data cleaning, preprocessing, and integrating the recommender system into the dashboard. Comprehensive evaluation through user testing and performance analysis ensures the vizualisations and recommender system's accuracy, responsiveness, and effectiveness. In addition, by leveraging descriptive analytics, the paper aims to create a user-friendly tool to explore data, uncover hidden patterns, and facilitate decision-making. The expected outcome of this paper is an interactive dashboard that significantly enhances data comprehension and strategic decision-making for businesses.

关键词： Accuracy Decision making Data visualization Prediction algorithms Real-time systems Performance analysis Predictive analytics Recommender systems Business Testing

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A Systematic Literature Review on Agile Requirements Engineering and Artificial Intelligence

A Systematic Literature Review on Agile Requirements Enginee...

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International Conference on ICT and Knowledge Engineering

作者： Albertus M. M. Antaputra Lim Tek Yong Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350391435

ISBN: (纸本)9798350391442

The iterative nature of agile requirements engineering often requires considerable time and effort. Research on automation processes with artificial intelligence for agile requirements engineering practices has gained popularity in recent years. The objective of this study is to conduct a systematic literature review and report the current state of artificial intelligence integration in agile requirements engineering. 24 papers were selected for the analysis. From the papers included in the study, it was found that the most popular requirements engineering activity currently being researched with artificial intelligence is requirement analysis and documentation, with natural language processing tools combined with machine learning as the most used artificial intelligence tool. The levels of automation were still mostly at level 3 and level 4. Similar to related works, this indicates that the implementation of agile requirement engineering largely depends on humans.

关键词： Support vector machines Automation Machine learning Documentation Knowledge representation Natural language processing Planning Requirements engineering Reliability Iterative methods

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Addressing the Limitations of News Recommendation Systems: Incorporating User Demographics for Enhanced Personalization

Addressing the Limitations of News Recommendation Systems: I...

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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Zerihun Olana Asefa Alemisa Endebu Ergosa Faculty of Computing and Informatics Jimma University Jimma Ethiopia

ISBN: (数字)9798331527792

ISBN: (纸本)9798331527808

News recommendation schemes use features of the news and user information to suggest relevant news items based on their interests. However, existing schemes are not very effective for new users who have little activity history. To address this issue, we have developed a hybrid news recommender system that combines content-based and collaborative filtering approaches, along with user demographic data. We conducted an extensive experiment using a dataset of news articles, user ratings, and demographic data to evaluate the system's effectiveness. We assessed the system's performance through two experiments. The first experiment focused on individual user similarity and achieved a precision of 68.05%, a recall of 42.46%, and an average F1 score of 52.1%. The second experiment examined user similarity within the same category, resulting in a precision of 93.75%, a recall of 40.25%, and an F1 score of 56.31%. These results indicate that the proposed model performs better in the second experiment compared to the first.

关键词： Measurement Filters Collaborative filtering System performance Scalability Africa Data processing Information and communication technology History Recommender systems

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Zirkus Empathico 2.0: a multiplayer serious mobile game for children with autism spectrum disorder (ASD), with a focus on enhancing social and emotional development

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Multimedia Tools and Applications 2025年 1-24页

作者： Hassan, Ahmed Pinkwart, Niels Shafi, Muhammad Department of Computer Science and Informatics Berlin School of Business and Innovation Karl Marx Strasse 97 - 99 Berlin12043 Germany Institute for Informatic Humboldt University Berlin Rudower Chaussee 25 Berlin Berlin12489 Germany School of Computing Ulster University Belfast Campus United Kingdom Faculty of Computing & Information Technology Sohar University Sohar Oman

This study explores the effectiveness of the multiplayer serious mobile game "Zirkus Empathico (ZE) 2.0" in enhancing social-emotional behaviors in children diagnosed with autism spectrum disorder (ASD). It aims to assess how such games can foster emotional recognition, empathy, and prosocial behavior, thereby supporting the holistic development of children with ASD. An eight-week randomized controlled trial (RCT) was conducted between January and April 2021 at autism centers in Islamabad, Pakistan, and Berlin, Germany. The study involved 107 children aged 5–11 years, with 60 diagnosed with ASD and 47 typically developing children serving as an Active Control Group (ACG). Participants were engaged in sessions using the bilingual"ZE 2.0"game, incorporating multiplayer and emotional learning modules. The intervention required 120 min of weekly training supplemented by daily sessions. Post-intervention assessments demonstrated significant improvements in the ASD group compared to the control group in areas of empathy and emotional awareness, as evidenced by higher scores on the Griffith Empathy Measure (GEM) and multiplayer module evaluations. Notable was the ability of participants to apply learned social skills in dynamic, real-world contexts, which suggests the game’s effectiveness transcends the controlled settings typically associated with ASD interventions. ZE 2.0 adds to the social and teamwork competencies of children with ASD. Besides encouraging peers to be proactive in communications and cooperation, the multiplayer mode provides an immersion learning atmosphere for facilitating social and emotional development. Such an outcome underlines the perspectives of serious games for use as a framework for the treatment and education of children who have ASD. In this manner, serious games can be utilized as a supplementary or alternative means of treating ASD. Future research may focus on distinguishing the effects of intervention from those of other therapy modalit

关键词： Serious games

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A Review of Knowledge Graph Embedding Methods of TransE, TransH and TransR for Missing Links 8

A Review of Knowledge Graph Embedding Methods of TransE, Tra...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Asmara, Salwana Mohamad Sahabudin, Noor Azida Nadiah Ismail, Nor Syahidatul Ahmad Sabri, Ily Amalina Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9798350310931

Knowledge representation and reasoning require knowledge graph embedding as it is crucial in the area. It involves mapping entities and relationships from a knowledge graph into vectors of lower dimensions that are continuous in nature. This encoding enables machine learning algorithms to effectively reason and make predictions on graph-structured data. This review article offers an overview and critical analysis specifically about the methods of knowledge graph embedding which are TransE, TransH, and TransR. The key concepts, methodologies, strengths, and limitations of these methods, along with examining their applications and experiments conducted by existing researchers have been studied. The motivation to conduct this study is to review the well-known and most applied knowledge embedding methods and compare the features of those methods so that a comprehensive resource for researchers and practitioners interested in delving into knowledge graph embedding techniques is delivered. © 2023 IEEE.

关键词： Knowledge graph

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Collaborative Task Allocation Problem in Laboratory Equipment Maintenance in Universities 6

Collaborative Task Allocation Problem in Laboratory Equipmen...

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6th International Conference on Machine Learning and Intelligent Systems, MLIS 2024

作者： Hu, Huiying Dolah, Natrah Abdullah Ahmad, Nor Diana College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam Malaysia Faculty of Information Engineering Jiujiang Vocational University Jiangxi Jiujiang China

ISBN: (纸本)9781643685533

Given the increasing demand for laboratory equipment management in universities, especially the increasingly complex equipment management, the traditional equipment management system can no longer meet the management needs of universities. Therefore, it is very important to optimize the university's equipment management system. The allocation of laboratory equipment maintenance tasks in the laboratory equipment management system of universities is a very critical link. Its effective solution is crucial to ensure the normal operation of laboratory equipment and the reasonable allocation of maintenance resources. This study proposes a double coding adaptive genetic algorithm to optimize the allocation of laboratory equipment maintenance tasks in universities to achieve the optimal allocation of resources and minimize maintenance costs. The work allocation scheme is iteratively optimized by a dual-coding strategy and definition of adaptive crossover and mutation operators. The experimental results of this study show that the algorithm can find the approximate optimal task allocation scheme within a reasonable time, which improves the efficiency and accuracy of laboratory equipment maintenance. In addition, compared with the traditional allocation method, the algorithm in this paper shows stronger flexibility and robustness when dealing with large-scale complex problems. © 2024 The Authors.

关键词： Resource allocation

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Paddy Diseases Multi-Class Classification using CNN Variants 24

Paddy Diseases Multi-Class Classification using CNN Variants

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Aziz, Danial Izzat Anwar Bin Abdul Yusoff, Marina Ibrahim, Nurain Alazaidah, Raed College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798350384307

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively. © 2023 IEEE.

关键词： Agriculture Convolutional Neural Network Deep Learning Paddy Disease

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Metaverse: A Bibliometric Analysis by Internet Crawler and Big Data Technology 6

Metaverse: A Bibliometric Analysis by Internet Crawler and B...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Lim, Tek Yong Luo, Zhiqiang Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Foshan University School of Industrial Design and Ceramic Art Guangdong China

ISBN: (纸本)9798350323535

Digital platforms have been widely adopted during Covid19 pandemic, but individuals and organizations have encountered various challenges with these digital platforms. Metaverse is a new Internet evolution that addresses the challenges of supporting humans in seamlessly moving between digital spaces and the physical world. Many bibliometric studies have been published on Metaverse, but previous findings on this topic are inconsistent and contradictory. Therefore, we explored trends in Metaverse research through bibliometric analysis. An online search was conducted on 16 February 2023. A total of 706 journal documents were obtained from the Scopus database. The results revealed that China was the most productive country with three China productive institutions, two China productive authors, and four China funding sponsors. This bibliometric analysis results confirmed that the rate of Metaverse research has sharply increased. The top nine most cited documents were classified as secondary research. Taking a socio-technical design approach, the Metaverse research areas identified by these review articles can be grouped into four main levels of computing: mechanical, informational, psychological, and social. The limitations of this bibliometric study were also identified. © 2023 IEEE.

关键词： Big data

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Enhancement of Prototype Driving Simulator Using Available AI-Based Game Technology 8th

Enhancement of Prototype Driving Simulator Using Available ...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Cheng, Yun-Quan Mansor, Sarina Chin, Ji-Jian Karim, Hezerul Abdul Kar-Weng, Ban Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Faculty of Science and Engineering University of Plymouth Plymouth United Kingdom

ISBN: (纸本)9789819930906

Simulators—games that simulate the real world in a virtual environment, such as racing simulators, have been widely studied and documented. Their uses could, however, be further expanded into the field of driving education. The motivations behind this study are to dive into the trend of immersive learning and exploit artificial intelligence (AI)-based game technology to benefit driving education. The preliminary work shows that the use of a driving simulator as a teaching tool for driving education has improved the passing rate of driving training. This paper proposes several enhancements to the prototype driving simulator, by utilizing rudimentary AIs to simulate basic traffic for certain scenarios. The driving simulator is developed in Unity and is paired with a driving rig consisting of a steering wheel and pedals. The project currently is a functioning driving simulator that can accept both controller and steering wheel inputs. For prototype enhancement, three scenario-based tracks and a mock-up town with a traffic light system and AI traffic were added. The prototype now includes five circuit tracks and two on-the-road tracks based on the Standard Examination syllabus, three scenario-based tracks, and a mock-up town with AI traffic, together with an automated test mode for the syllabus tracks. The free practice mode will be available for all tracks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Virtual reality

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