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International Conference on Educational and Information Technology (ICEIT)

作者： Radiah Haque Hui-Ngo Goh Choo-Yee Ting Albert Quek MD Rakibul Hasan Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350372663

ISBN: (纸本)9798350372670

The application of machine learning techniques for predicting the career trajectories of fresh undergraduate students has become a crucial strategy for evaluating their potential to secure employment post-graduation or pursue further education. However, for such applications, imbalanced data is a vital issue that needs to be addressed with proper methods. In this paper, the combination of oversampling, using Synthetic Minority Overs amp ling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), and feature selection, using Recursive Feature Elimination (RFE) and the Boruta algorithm, is applied. The results show that the SMOTE-based Boruta approach is effective to improve the performance of machine learning classification models for undergraduate student career prediction.

关键词： Machine learning algorithms Engineering profession Education Machine learning Predictive models Feature extraction Prediction algorithms

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Simulation Design of Reinforcement-Based Migration System in Software-Defined Networking Using Q-Learning 22nd

Simulation Design of Reinforcement-Based Migration System in...

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22nd Asia Simulation Conference, AsiaSim 2023

作者： Raj, Avinash Imanuel Gana Chin, Tan Saw Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9789819972395

An innovative strategy, Software-Defined Networking (SDN), provides improved network programmability, flexibility, and scalability. However, a lot of recent material on the transition from traditional network designs to SDN has recommended providing static heuristic algorithms for determining the node migration sequence in legacy networks. The approach is deemed unfeasible for use in real-world circumstances and has issues managing the changing nature of network traffic. Reinforcement learning (RL) has therefore been proposed for use in SDN domains such flow entry management, controller placement, and routing selection. In the study, we first examine the relevant research on reinforcement learning applications on SDN conducted by previous researchers and identify the difficulties in SDN migration. Finally, using Q-learning in this context, we proposed a reinforcement learning technique to get around these problems and carefully transition a network’s legacy nodes to SDN nodes. Thus, we have discussed designs for applying reinforcement learning to a hSDN deployment in a legacy network in this article. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Nutrient Deficiency Classification using AlexNet Architecture 14

Nutrient Deficiency Classification using AlexNet Architectur...

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14th IEEE Control and System Graduate Research Colloquium, ICSGRC 2023

作者： Sabri, Nurbaity Zulkifli, Zuhri Arafah Shari, Anis Amilah Halim, Faiqah Hafidzah Zaini, Hazrati Ismail, Khairul Nurmazianna Universiti Teknologi Malaysia School of Computing Faculty of Engineering Johor Skudai81310 Malaysia Universiti Teknologi Mara College of Computing Informatics and Mathematics Campus Jasin Melaka Malaysia

ISBN: (纸本)9798350346237

Oil palm significantly contributes to Malaysia's Gross Domestic Product (GDP). To maintain palm oil productivity, keeping the oil palm crop in good health, and free from nutrient deficiencies is necessary. Therefore, a method to analyze nutrient deficiencies is required. Nutrient deficiencies can be detected in the oil palm leaves. The study proposes an image-processing method that classifies three crucial nutrient deficiencies in a palm oil leaf: Nitrogen, Potassium, and Magnesium. To improve the processing rate, a convolution neural network (CNN) based on the AlexNet architecture has been implemented, which has been successful in image classification tasks and produced remarkable results in automated classification. The AlexNet architecture consists of five (5) convolved layers and three (3) fully connected layers, achieving an accuracy of 90.85%. This shows that AlexNet is an efficient algorithm for classifying nutrient deficiencies in palm oil leaves. However, a lower classification rate was observed for Magnesium compared to Nitrogen and Potassium. Due to the similarity in texture and color which may cause misinterpretation by the classifier. Further exploration of other CNN architectures will be conducted in the future to improve classification accuracy. © 2023 IEEE.

关键词： Palm oil

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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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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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Data Leakage Detection in Cloud computing Environment

Data Leakage Detection in Cloud Computing Environment

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Cyber Security and computing (CyberComp), International Conference on

作者： Hurin Haziq Bin Suhaimi H.T. Zubair Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

Insider threats pose critical security threats through the possession of privileged and access to valuable information and resources. Data leakage is one of the significant challenges faced in cloud computing environments, emphasising the criticality of data protection across various sectors such as education, finance, healthcare, and more. Cloud computing offers numerous advantages, including reliable data storage and transfer, yet it also presents significant risks, including data breaches, server failures, and malicious insider threats. This paper's primary objective is to evaluate the effectiveness of data leakage detection methods, particularly focusing on insider threats. Through the exploration of machine learning models and anomaly detection techniques, the study seeks to identify the most effective strategies for mitigating unauthorised data exposure. The research encom-passes a comprehensive analysis of current security measures, identification of vulnerabilities, and the proposition of enhanced detection methods. By examining real-world data leakage incidents and employing advanced detection tools, the findings aim to contribute to the development of more robust data protection frameworks. This research is poised to offer valuable insights and recommendations that will aid organisations in leveraging cloud services securely and efficiently, thereby enhancing their overall data security posture. The study not only underscores the importance of the CIA triad-Confidentiality, Integrity, and Availability-but also highlights the significance of innovative detection methods such as machine learning algorithms and behavioural analytics in safeguarding sensitive information in the ever-evolving landscape of cloud computing.

关键词： Cloud computing Machine learning algorithms Computational modeling Data protection Nearest neighbor methods Predictive models Data models Real-time systems Anomaly detection Random forests

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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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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

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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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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Features Based Reconstruction Technique for Torn Documents

Features Based Reconstruction Technique for Torn Documents

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2023 International Conference on Green Energy, computing and Intelligent Technology, GEn-CITy 2023

作者： Saleh, Marwan D. Salih, N.D. Isa, Wan Noorshahida Mohd Electronics and Computer Science University of Southampton Malaysia Johor Malaysia Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia

ISBN: (纸本)9781839539596

The reconstruction of torn documents that hold sensitive information or crucial historical data is of great significance in many domains. An efficient technique for hand-torn document restoration is presented in this paper. Pre-processing, feature extraction, and matching three main phases of this technique. The reconstruction is carried out by matching pairwise the feature vectors of the fragments. Using a database consists of 120 torn pages taken from books, newspapers and magazines, the performance of this technique has been measured using simple but effective criteria called error rate. The algorithm was able to accurately reconstruct 91% of the documents, with the remaining being partially reconstructed. © The Institution of Engineering & Technology 2023.

关键词： Image reconstruction

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