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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Chew, Chiou Sheng Zafarin, Muhammad Nabil Shah Zain, Nurul Hidayah Mat Aminuddin, Raihah Tan, Tse Guan Chin, Kim On Universiti Teknologi MARA Melaka Branch College of Computing Informatics and Mathematics Jasin Campus Melaka Malaysia Universiti Malaysia Kelantan Faculty of Creative Technology and Heritage Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Malaysia

ISBN: (纸本)9798331510077

In secondary schools, traditional chemistry instruction often depends on lecture-based approaches and occasional hands-on experiments, which may limit students' grasp of complex concepts like chemical reactions and molecular behaviour. This study addresses the need for enhanced educational tools by introducing a Virtual Reality (VR) application designed to enrich chemistry learning through immersive, interactive experiences. The VR tool allows students to simulate chemical reactions with adjustable parameters, such as temperature and concentration, enabling safe, hands-on exploration of complex processes. Usability was assessed among 30 secondary school students in Malaysia using the System Usability Scale (SUS), a standardized measure that gauges user experience across factors like functionality and ease of use. Findings showed a strong average SUS score of 91.25, indicating high usability and positive engagement from users. These results suggest that incorporating VR into the chemistry curriculum can help make challenging topics more accessible and engaging, supporting the modernization of science education. Future studies will look to expand the sample size and investigate additional chemistry topics to validate the tool's educational benefits further. © 2024 IEEE.

关键词： Students

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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An Investigation of the Effect of Weather on Hand-Foot Mouth Disease Incidence in Sarawak 4th

An Investigation of the Effect of Weather on Hand-Foot Mouth...

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

作者： Ibrahim, Ashraf Osman Chen, Wong Yat Robert, Joel Zeolick Yi, Chiew Cheng Majid, Mazlina Abdul Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (纸本)9789819729760

Hand, foot, and mouth disease (HFMD) is consistently present in Malaysia, with a rising number of reported cases. Sabah has encountered multiple outbreaks of this disease. However, detailed information regarding the epidemiology and molecular traits of the viruses responsible for these outbreaks remains limited. This study used two data sets;one dataset was acquired from ***, containing HFMD across various Malaysian states. The second dataset was sourced from the National Center for Biotechnology Information (NCBI) and consists of HFMD statistics for Malaysia, including case counts and weather data. Both datasets were merged to create a comprehensive dataset that was specifically focused on examining the Malaysian state of Sarawak. This consolidated dataset contains a total of 20 at-tributes, forming the basis for the subsequent analyses conducted in this study. The study employed four distinct machine learning methods for regression analysis, comprising Random Forest, XGBoost, Decision Tree, and LASSO regression. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Plant diseases

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Performance Evaluation on Resolution Time Prediction Using Decision Tree, Random Forest and eXtreme Gradient Boosting

Performance Evaluation on Resolution Time Prediction Using D...

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2023 International Conference on Computer Applications Technology, CCAT 2023

作者： Tai, Tong-Ern Haw, Su-Cheng Ng, Kok-Why Naveen, Palanichamy Al-Tarawneh, Mutaz Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Mutah University Faculty of Engineering Jordan

ISBN: (纸本)9798350301540

Providing excellent customer service is always important for an organisation, and one of the biggest demands from customers is the speed of resolution. Customers anticipate a prompt, simple, and efficient response when they submit a service ticket to request service. While the customer service representatives attempt to resolve problems assigned to them while simultaneously giving clients a nice experience and upholding the company's excellent reputation, they receive several tickets each day. On-time services may boost customer satisfaction, foster client loyalty, and grow the number of prospective loyal consumers. The best option for speeding up ticket resolution in this case will be a prediction model that makes use of Machine Learning techniques. Experimental evaluations revealed that eXtreme Gradient Boosting works better than other strategies by achieving a low Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and MAE (Mean Absolute Error) scores with good model accuracy. © 2023 IEEE.

关键词： Customer satisfaction

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Monitoring Melon Manis Terengganu (MMT) by Implementation of IoT Technology

Monitoring Melon Manis Terengganu (MMT) by Implementation of...

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2024 International Conference on Future Technologies for Smart Society, ICFTSS 2024

作者： Ali, Adriana Balqis Yusof, Mohd Kamir Zakaria, Zahrahtul Amani Universiti Sultan Zainal Abidin Kampus Besut Faculty of Informatics and Computing Terengganu Besut22200 Malaysia

ISBN: (纸本)9798350373844

Melon Manis Terengganu (MMT) is of significant cultural and economic importance to the people of Terengganu and Malaysia as a whole. The demand for melon fruit is also increasing year by year. However, one of the challenges in producing a quality melon is the soil condition. The soil condition must be good to ensure that the melon can get enough water and fertilizer. The objective of this research is to develop a formulation and implement IoT technology to monitor the process of melon growth by analyzing data related to soil conditions. The purpose of Internet of Things (IoT) is to capture data using sensors and send it to a database system. In this method, a formulation is designed to calculate the amount of water and fertilizer based on soil moisture, soil electrical conductivity (EC), NPK levels, pH level, and water electrical conductivity (EC). These sensors will capture the data and send it to a database system. After that, the system will calculate the actual amount of water and fertilizer needed for each melon plant. This approach will help farmers supply the correct water and fertilizer levels for each melon plant. The implementation of IoT technology in agriculture, especially for melons, is expected to promote smooth growth and the production of high-quality melons. © 2024 IEEE.

关键词： Soil moisture

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Crop Yield Recommendation Using Machine Learning 19

Crop Yield Recommendation Using Machine Learning

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Bagane, Pooja Amenu, Obsa Das, Lahitanshu Potdukhe, Omkar Gandhi, Manraj Singh Kothari, Sonali Symbiosis Institute of Technology Pune India Jimma Institute of Technology Faculty of Computing and Informatics Jimma Ethiopia

ISBN: (纸本)9798331504502

Crop forecasting constitutes an integral part of the modern farming practice, affecting the decisions of farmers and agricultural companies. This machine learning model is intended to produce more accurate yield forecast with multiple datasets including weather temperature, pesticides, location and other as well. The methodology that systematizes data acquirement, preprocessing, model selection, training and implementing is used by the project to overcome many obstacles in crop production. Accurate performance forecasting in this technology is therefore not only a matter of innovation, but of food security, economy and environment as well. The model training & testing is done with mean absolute error to determine its accuracy & generalization. After training the model is launched into the production environment or plenty into the existing agricultural platforms for real-time yield forecasting. This project will constantly be striving for improvement and filter knowledge down to the stakeholders, optimizes resources allocation and brings about change entrenching food security and sustainable development. © 2024 IEEE.

关键词： Crop forecasting Data preprocessing Food security Machine learning model Yield forecast

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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Puppetry in Tangible Narratives: Interactive and Collaborative Storytelling in The Non-myth of the Noble Red 24

Puppetry in Tangible Narratives: Interactive and Collaborati...

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18th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2024

作者： Echeverri, Daniel Atelier of Graphic Design and Multimedia Department of Visual Computing Faculty of Informatics Masaryk University Czech Republic

ISBN: (纸本)9798400704024

The Non-myth of the Noble Red is a tangible narrative that combines cardboard puppets with digital storytelling in an integrated physical storyworld. This narrative employs networked microcontrollers within the puppets, RFID readers, and sensors to enable interactivity. The narrative unfolds within three physical, interactive environments. Each environment can track the puppets' positions and trigger audio fragments of the story. Performers use these puppets to navigate the narrative, interact with objects, and engage in a battle. This project explores the potential of puppets as virtual and physical avatars, emphasising interactive and collaborative storytelling. It seeks to look at the impact of these puppets and the potential emerging social dynamics during the narrative experience. The work presented here contributes to tangible narratives by emphasising an integrated physical storyworld (instead of a mapped one) and utilising puppets as storytelling artefacts that serve as both input devices and virtual/physical avatars, promoting immersive and collaborative storytelling. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Interactive Storytelling Puppetry Tangible Interaction Tangible Narrative

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The Performance of Classification Method in Telco Customer Trouble Ticket Dataset

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第2期49卷 325页

作者： Yayah, Fauzy Che Ghauth, Khairil Imran Ting, Choo-Yee Multimedia University Cyberjaya Malaysia Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

A customer trouble ticketing system (CTT) is an organization’s tool to track the detection, reporting, and resolution of tickets submitted by customers. It also comprises a summary of the issue reported, the status of the ticket, the incident information, and the approach that was previously utilized to resolve the problems. The technician’s skill set and experience rely solely on completing the task without the right direction on which area to focus on first. As a result of this manual classification of a trouble ticket, it will be necessary to build methodologies for predicting future resolution codes. The research for this report is mainly focused on one of the telco companies in Malaysia. This study result assists the telco engineer, and the specialists resolve each issue in a very short amount of time. Additionally, the classification of the trouble ticket resolution code method used in this study will indicate the characteristics of each issue that is being investigated. The relationship between events is feasible to discover by exploring the root cause. It is critical to establish a link between recent events and events in the previous. Because of current data mining limitations, the study needs to be more comprehensive. Data processing methods are being implemented within big data platforms to overcome the limitation of data scalability, enhance classification accuracy, and increase computation speed. The research work will continue to progress in the direction of big data centricity. Some of the most effective approaches for big data integration and machine learning will be discussed in this paper. Throughout the experiment, any problems will be explained, as well as the solutions to each situation. A wide range of research subjects will be discussed, including construction classification models for trouble tickets. To achieve reasonable accuracy, a few customized transformations are required. The data set’s custom parameter optimization process will furthe

关键词： Classification (of information)

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