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检索条件"机构=Faculty of Computing and Informatics"

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Factors of Pre-University Study in Influencing Graduate on Time

Factors of Pre-University Study in Influencing Graduate on T...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Theng-Jia Law Choo-Yee Ting Hui-Ngo Goh Hu Ng Albert Quek Faculty of Computing and Informatics Multimedia University Cyberjaya Selangor

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

In education, identifying the important pre-university study factors poses a formidable challenge, due to the necessity for human intervention in the variable selection process. Determining the important factors enables the educational institutions to shape strategic planning throughout a student’s entire academic journey, with a specific focus on the pre-university factors. Researchers have investigated various approaches to identify the optimal features. One of the techniques is by leveraging Artificial Intelligence. Therefore, this study has attempted to (i) identify the important pre-university study variables towards graduate on time, and (ii) develop as well as evaluate predictive models with and without the important pre-university study variables identified. The dataset is collected from a university in Malaysia, containing 4463 student records and 27 variables. It consists of student profile, registration details, grades of Sijil Pelajaran Malaysia, and English Test. Data preprocessing was performed to remove those features with more than 75% missing values and impute other features with median. Important features was identified by applying Boruta, and BorutaShap. Among 22 variables, both methods identified permanent address district, date of birth, campus, program description, and the grades of History subject. To evaluate the influence of the important factors towards graduate on time, several Machine Learning models were constructed due to its simplicity and evaluated with and without the important features identified based on accuracy, precision, recall, F1-score, and Area under the Curve, The findings showed that Gaussian NB produced the highest recall (95.2%) when the important features identified by BorutaShap were used.

关键词： Shape Input variables Education Machine learning Predictive models Strategic planning Feature extraction

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Nutrient Deficiency Classification using AlexNet Architecture 14

Nutrient Deficiency Classification using AlexNet Architectur...

引用

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

Imbalanced Data Classification Using Oversampling and Automa...

引用

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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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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A Self-adaptive Step-size Search Algorithm for the Cardinality Constrained Portfolio Optimisation Problem 20

A Self-adaptive Step-size Search Algorithm for the Cardinali...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Loke, Zi Xuan Goh, Say Leng Likoh, Jonathan Universiti Malaysia Sabah Labuan International Campus Faculty Of Computing And Informatics Labuan Malaysia Universiti Malaysia Sabah Preparatory Centre For Science And Technology Sabah Malaysia

ISBN: (纸本)9798350382310

This paper proposes a Self-adaptive Step-size Search (SASS) algorithm to address a Cardinality Constrained Portfolio Optimisation Problem (CCPOP). The proposed methodology is tested using five datasets from OR-Library. Experiments are conducted to test different settings of the particles in the SASS algorithm. The computational results are compared in terms of performance measures. The SASS algorithm achieves a lower value for most of the performance measures when the number of particles increases. © 2024 IEEE.

关键词： Constrained optimization

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A Game-Based Learning Application for Engineering Education

A Game-Based Learning Application for Engineering Education

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computing (ICOCO), IEEE International Conference on

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

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

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.

关键词： Electrical engineering Knowledge engineering Power system dynamics Games Multilingual Time factors Servers Problem-solving Sprites (computer) Testing

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Movie Recommender System Based on Generative AI

Movie Recommender System Based on Generative AI

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

作者： Carmen Pei Ling Tung Su-Cheng Haw Wan-Er Kong Palanichamy Naveen Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Movie recommender systems have revolutionized how people discover and experience movies using sophisticated algorithms, such a system generates personalized recommendations to assist users in reaching the movies that precisely fit their tastes. The traditional movie recommender systems lacks of diversity, unable to capture complex user preferences, and suffers from the cold-start issues. In this paper, we utilize the generative AI, specifically with Variational Autoencoders (VAE), to solve these problems. By combining VAEs, the system aims to produce more rich and individualized recommendations. VAE mitigates these issues by enabling the generation of meaningful item representations, promoting diversity in recommendations, and capturing the complexity of user preferences in a continuous latent space. The implementation is structured as follows: first, review the generative AI in movie recommender systems; second, implement the chosen generative AI approach; and finally, conduct the performance metrics evaluation of the selected generative AI approach. Ultimately, this research will enable the system to offer an achievement of an interactive, changing, and more personalized movie-discovering experience through the amazing power of generative AI.

关键词： Measurement Generative AI Reviews Diversity reception Refining Prototypes Parallel processing Motion pictures History Recommender systems

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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...

引用

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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Oral Ulcer Classification System Using CNN 5

Oral Ulcer Classification System Using CNN

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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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