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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Azam, Ahmad Ikmal Naim Mod Ali, Azliza Mohd Latif, Wannoraini Abdul Kamil, Wan NurHazirah Wan Ahmad Zainal, Mukarramah Hamid, Nurzeatul Hamimah Abdul College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Melaka Jasin Malaysia Universiti Teknologi Mara Faculty of Dentistry Kampus Sungai Buloh Selangor Sungai Buloh Malaysia Selangor Shah Alam Malaysia

ISBN: (纸本)9798331528553

This study presents a Convolutional Neural Network (CNN) model developed to address the limitations of current diagnostic methods for oral ulcers. Specifically, it targets the classification of three types of oral ulcers: Oral Malignant Ulcer, Recurrent Aphthous Ulcer, and Traumatic Ulcer. Traditional diagnostic approaches often suffer from limitations such as subjective interpretation, variability in diagnostic accuracy, and inadequate automation. To overcome these challenges, our approach involves constructing a CNN from scratch and rigorously optimizing key hyperparameters, including batch size and learning rate. Under optimal conditions, with a batch size of 12 and a learning rate of 0.001, our CNN achieved an accuracy of 98.61% and a loss rate of 4.78%. In contrast, alternative configurations resulted in an accuracy of 90.62% and a loss of 17.12%, highlighting the model's sensitivity to parameter adjustments. These results demonstrate that careful tuning of CNN hyperparameters can significantly improve diagnostic performance and accuracy. The findings suggest that the proposed CNN model offers a promising enhancement over traditional methods, with the potential to improve diagnostic precision and clinical outcomes for oral ulcers. Future work will focus on validating the model in real-world clinical settings and exploring its applicability to other medical imaging domains, with the aim of broadening its impact and utility in healthcare diagnostics. © 2024 IEEE.

关键词： Convolutional neural networks

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CLMM:Towards a Collaborative Chinese University Laboratory Model 6

CLMM:Towards a Collaborative Chinese University Laboratory M...

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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

With universities continually producing data in their laboratories, effective data management has become increasingly crucial. To meet this demand, the application of laboratory information management systems (LIMS) is becoming increasingly widespread. LIMS is a complex computing system used to manage laboratory data. There are currently a variety of LIMS to choose from, but most LIMS use proprietary codes, so the development cost is very high. At the same time, existing LIMS models usually only focus on functional requirements and ignore the specific means of functional implementation, which leads to difficulties for users in management. In addition, since LIMS are very complex, they are often designed to meet the needs of specific laboratories, which makes their versatility and reusability very poor. Therefore, this paper presents the Collaborative Laboratory Management Model(CLMM), which introduces advanced management ideas based on the LIMS model, aiming to make the model focus on both function and technology. CLMM integrates workflows, and users can create and manage workflows themselves. This not only improves the flexibility of the model, but also increases reusability, which can meet the needs of different types of laboratories. With the development of the model, we hope to improve the management efficiency of laboratories by managing the data of university laboratory information, equipment, instruments, etc., and contribute to the complex management work of universities. © 2024 The Authors.

关键词： Information management

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A Survey on Swarm Intelligence Algorithms Employed for Optimizing Machine Learning Techniques Used in Recommendation Systems 11

A Survey on Swarm Intelligence Algorithms Employed for Optim...

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11th IEEE Conference on Systems, Process and Control, ICSPC 2023

作者： Zet, Lim Cher Jasser, Muhammed Basheer Wong, Richard T.K. Chua, Hui Na Issa, Bayan Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350340860

With the exponential growth of digital data, recommendation systems or recommender systems are widely used in various domains assisting users in filtering and decision-making on massive information. Recommendation systems are capable of delivering personalized content to enhance user experience and satisfaction through users' preferences and behaviors. The machine learning algorithms employed in the recommendation systems facilitate the effectiveness of tasks achieved by those systems among which, for example, is providing accurate prediction that matches user preferences. Swarm Intelligence offers robust optimization mechanisms that have been successfully applied in various computational problems including recommendation systems for refining recommendation algorithms. To the best of our knowledge, there is no recent comprehensive survey on swarm intelligence algorithms used for optimizing machine learning techniques when used in recommendation systems. Therefore, this survey presents a survey of the swarm intelligence algorithms used in optimizing the machine learning techniques when employed in recommendation systems. We conducted a literature survey on recommendation systems and swarm intelligence, using relevant keywords and their variants, focusing on recent publications since 2019. Our findings highlight the use of swarm intelligence algorithms primarily in clustering, classification, and feature selection for recommendation systems. Swarm intelligence has significantly enhanced recommendation systems, especially in clustering and classification. However, the balance between computational complexity and processing speed remains a challenge. Future research could focus on refining these algorithms for better efficiency and effectiveness in recommendation systems. © 2023 IEEE.

关键词： Swarm intelligence

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Tangible Narrative: The Intersection of Performance, Interactivity, and Narrative - A Design Case 5

Tangible Narrative: The Intersection of Performance, Interac...

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5th European Tangible Interaction Studio, ETIS 2022

作者： Echeverri, Daniel Department of Visual Computing Faculty of Informatics Masaryk University Brno Czech Republic

This paper expands on previous work on tangible narratives. It briefly reviews the design case of Letters to José, from which stemmed a narrative architecture for tangible narratives and a typology that frames the qualities of tangible artifacts with narrative meaning. Based on this architecture and exemplified in the typology, the paper introduces The Non-myth of the Noble Red, a tangible narrative which expands upon the learnings gained from the design of Letters to José. This paper presents some of its technological considerations and discusses how it integrates Brechtian theatre concepts to explore alternative digital storytelling approaches. © 2022 Copyright for this paper by its authors.

关键词： Brechtian theatre interactive storytelling tangible interaction Tangible Narratives The Nonmyth of the Noble Red

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An Overhead Analysis of MPI Profiling and Tracing Tools 2

An Overhead Analysis of MPI Profiling and Tracing Tools

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2nd Workshop on Performance Engineering, Modelling, Analysis, and Visualization Strategy, PERMAVOST 2022

作者： Hunold, Sascha Ajanohoun, Jordy I. Vardas, Ioannis Träff, Jesper Larsson Faculty of Informatics Research Group for Parallel Computing TU Wien Vienna Austria

ISBN: (纸本)9781450393140

MPI performance analysis tools are important instruments for finding performance bottlenecks in large-scale MPI applications. These tools commonly support either the profiling or the tracing of parallel applications. Depending on the type of analysis, the use of such a performance analysis tool may entail a significant runtime overhead on the monitored parallel application. However, overheads can occur in different stages of the performance analysis with varying severity, e.g., the overhead when initializing an MPI context is typically less problematic than when monitoring a high number of short-lived MPI function calls. In this work, we precisely define the different types of overheads that performance engineers may encounter when applying performance analysis tools. In the context of performance tuning, it is crucial to avoid delaying individual events (e.g., function calls) when monitoring MPI applications, as otherwise performance bottlenecks may not show up in the same spot as when running the applications without applying a performance analysis tool. We empirically examine the different types of overheads associated with popular performance analysis tools for a set of well-known proxy applications and categorize the tools according to our findings. Our study shows that although the investigated MPI profiling and tracing tools exhibit a rather unique overhead footprint, they hardly influence the net time of an MPI application, which is the time between the Init and Finalize calls. Performance engineers should be aware of all types of overheads associated with each tool to avoid very costly batch jobs. © 2022 ACM.

关键词： MPI overhead performance analysis profiling tracing

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Interactive Data Visualization to Optimize Decision-Making Process

Interactive Data Visualization to Optimize Decision-Making P...

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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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An Overview for Computer-Assisted Automated Spinal Scoliosis Measurement 23

An Overview for Computer-Assisted Automated Spinal Scoliosis...

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Proceedings of the 3rd International Conference on Electronic Information Technology and Smart Agriculture

作者： Aishu Xie Ervin Gubin Moung Faculty of Computer and Artificial Intelligence Huaihua University China and Faculty of Computing and Informatics University Malaysia Sabah Malaysia Faculty of Computing and Informatics University Malaysia Sabah Malaysia

ISBN: (纸本)9798400716775

Spinal X-rays are widely used for screening and preliminary diagnosis of adolescent scoliosis due to their low cost and minimal radiation exposure. To enhance the accuracy of scoliosis measurements and alleviate the burden on physicians, the goal in this field has been to achieve fully automated spinal curvature measurements under the assistance of computer technology. In this paper, an analysis is conducted on existing computer assisted methods for measuring scoliosis across various image modalities. These methods are categorized into three groups based on their contributions to the automation of scoliosis measurement: automated scoliosis localization, automated curvature feature extraction, and automated classification of scoliosis severity levels. In the end, the problems existing in the current research progress are highlighted and possible solutions are discussed for achieving fully automated measurement of spinal curvature on X-rays.

关键词： Curvature feature extraction Scoliosis localization Spinal curvature

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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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