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22nd International Conference on ICT and Knowledge Engineering, ICT and KE 2024

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

ISBN: (纸本)9798350391411

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. © 2024 IEEE.

关键词： Requirements engineering

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Strategies for Enhancing Software Reliability: A Focus on Fault Avoidance Techniques 1

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4th International Conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2023

作者： Rajendran, Venushini Ramasamy, R. Kanesaraj Faculty of Computing Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

This paper provides an in-depth analysis of fault avoidance strategies in software applications, a critical aspect for enhancing software reliability and dependability. Fault avoidance encompasses various techniques aimed at preventing, eliminating, and predicting software faults to ensure the creation of fault-free software. The intertwined nature of software quality and reliability is a key focus of this study. The primary objective is to synthesize empirical and theoretical insights on the attributes of software fault avoidance. This is achieved through the analysis of diverse metrics, modules, and methodologies, leading to recommendations aimed at bolstering software dependability. Achieving high levels of software reliability is a challenging task due to the inherent nature of software faults, which are latent programming errors that manifest upon discovery. This review critically examines a range of strategies aimed at mitigating these issues, including Byzantine Fault Avoidance (BFA), formal and semi-formal methods, and verification and validation techniques involving fault detection. Additionally, the study delves into software reliability growth models and the concept of fault removal through relative correctness (NHCTMC), among others. Recommendations are formulated based on a comprehensive analysis of various research studies in the realm of software fault avoidance. Ultimately, this paper highlights that fault avoidance is a multifaceted approach, integral throughout the software development lifecycle, aimed at minimizing software faults and averting unnecessary failures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software reliability

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Artificial Intelligence Integration in Programming Education: Implications for Pedagogy and Practice 1

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4th International Conference on Computer Vision, High-Performance computing, Smart Devices, and Networks, CHSN 2023

作者： Rajendran, Venushini Ramasamy, R. Kanesaraj Faculty of Computing Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

In the swiftly evolving domain of programming education, Artificial Intelligence (AI) has emerged as a transformative force, prompting a reevaluation of pedagogical strategies and educational practices. This paper critically examines the integration of AI technologies in programming education and elucidates their pedagogical implications. With the accelerating development of programming languages and frameworks, traditional educational models struggle to maintain relevance. AI-driven intelligent search engines and recommendation systems are now pivotal in curating personalized educational content, thereby enhancing learning efficiency and reducing learner frustration. Furthermore, AI-facilitated platforms have redefined the conventional classroom, introducing virtual instructors and democratizing access to programming knowledge. Significantly, Natural Language Processing (NLP) technologies have reduced barriers to entry, enabling individuals with limited technical backgrounds to interact with programming interfaces in vernacular language. This has facilitated broader participation and has scaffolderd the learning process. Additionally, AI interventions have automated aspects of code generation and provided instantaneous feedback mechanisms, essential for real-time error correction and debugging, thus fostering a supportive learning environment. The collaborative aspects of AI are manifested through peer-to-peer learning ecosystems, bolstered by forums and code-sharing platforms. The synthesis presented in this paper highlights how AI not only enriches the learning experience but also promotes inclusivity and engagement in programming education. The implications of these AI-driven pedagogical innovations are vast, suggesting a paradigm shift towards more adaptive, personalized, and collaborative learning frameworks. This paper contributes to the discourse by not only charting the current landscape but also projecting the future trajectory of AI’s role in programming

关键词： Collaborative learning

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A Game-Based Learning Application for Engineering Education

A Game-Based Learning Application for Engineering Education

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Azli, Afif Dzakirin Bin Mohd Chua, Fang-Fang Lim, Amy Hui-Lan Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9798331530303

Integrating game-based learning application in engineering education provides a dynamic approach to increase student engagement and understanding. The goal of this initiative is not only to boost interest in engineering programs, but also to develop a more viable and sustainable future for the engineering workforce. This paper presents the design and development of a game-based learning application titled Electroguys, in delivering electrical engineering concepts to high school students. The teaching materials includes interactive games such as "Circuit Rush" and "Energy Expedition" that reinforce learning through problem solving and active participation. The propose work aims to equip students with the skills, knowledge and enthusiasm needed to succeed in the dynamic field of electrical engineering. Testing and feedback show significant improvements in student motivation and retention. ©2024 IEEE.

关键词： Engineering education

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Integrating Serious Game in Personalized Learning Environment

Integrating Serious Game in Personalized Learning Environmen...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Wee, Vivian Gek-Ting Chua, Fang-Fang Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia

ISBN: (纸本)9798331530303

Computer science skills have become very important in the careers of STEM fields in today’s digital economy. However, many students often struggle in grasping programming concepts, leading to high dropout rates in computer science courses. The students have also found traditional learning environment to be ineffective in engaging and supporting them to tackle the issue. This paper recognizes the need for a pedagogical shift towards personalized learning experience to address the shortcomings of traditional teaching approaches. It aims to develop a personalized learning system named Codecaster Odyssey that caters to the diverse needs of learners. It also aims to leverage the intrinsic appeal of gaming to enhance student engagement and foster sustained interest in learning programming by integrating serious game into the learning system. ©2024 IEEE.

关键词： Contrastive Learning

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Data Leakage Detection in Cloud computing Environment 1

Data Leakage Detection in Cloud Computing Environment

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1st International Conference on Cyber Security and computing, CyberComp 2024

作者： Bin Suhaimi, Hurin Haziq Zubair, H.T. Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350387728

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. © 2024 IEEE.

关键词： Information leakage

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Developing Deep Learning Models to Predict Benign and Malicious Attacks in IoT Networks

Developing Deep Learning Models to Predict Benign and Malici...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Zaedi, Nik Muhammad Danial Bin Nik Ram Lan, Amy Lim Hui Hui-Ngo, Goh Faculty of Computing & Informatics Multimedia University Malaysia Cyberjaya Malaysia

ISBN: (纸本)9798331530303

The Internet of Things (IoT) has afforded increased connectivity across various domains which increased cybersecurity vulnerabilities. Threats like Distributed Denial of Service (DDoS) attacks, Spoofing, and web-based attacks pose a significant danger to IoT networks, which require more advanced Network Intrusion Detection Systems (NIDS) to effectively counter these threats. The aim of this paper is to investigate the viability of DL-based classifiers for NIDS. The CICIoT2023 dataset is used in this study. Feature reduction is performed using the Pearson’s Correlation Coefficient (PCC) Matrix and features that contain all-zero values are removed. To resolve the class imbalanced issue, Synthetic Minority Oversampling (SMOTE) is applied exclusively on the training set only. For comprehensive comparison studies, a total of nine classifiers have been considered which can be categorized into machine learning (ML)based classifiers and Deep Learning (DL)-based classifiers. ML-based classifiers consist of Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN) and Logistic Regression (LR), and while DL-based classifiers consist of Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (BiLSTM), 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN). The findings revealed that the DL-based classifiers have consistently recorded high accuracy, precision, recall, F1 score and AUC of more than 0.9. Although ML-based classifiers have recorded high precision and AUC, the accuracy, recall and F1 score have a wider variation in scores. The consistency and high scores across various evaluation metrics indicate the viability of adopting DL-based classifiers for building NIDS. ©2024 IEEE.

关键词： Network intrusion

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Hybrid Deep Learning Architectures for Histological Image Segmentation 6

Hybrid Deep Learning Architectures for Histological Image Se...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Hasan, Md Jahid Ahmad, Wan Siti Halimatul Munirah Wan Ahmad Fauzi, Mohammad Faizal Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and InformaticsFaculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350344349

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (HandE) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied. © 2024 IEEE.

关键词： Image segmentation

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Imbalanced Data Classification Using Oversampling and Automatic Feature Selection Methods for Undergraduate Student Career Prediction 13

Imbalanced Data Classification Using Oversampling and Automa...

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13th International Conference on Educational and Information Technology, ICEIT 2024

作者： Haque, Radiah Goh, Hui-Ngo Ting, Choo-Yee Quek, Albert Hasan, Md Rakibul Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia

ISBN: (纸本)9798350372663

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. © 2024 IEEE.

关键词： Forecasting

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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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