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检索条件"机构=Faculty of computing and informatics"

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Constructing Ensembles of Automatically Designed Relocation Rules for the Container Relocation Problem 13

Constructing Ensembles of Automatically Designed Relocation ...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Durasević, Marko Dumić, Mateja Gil-Gala, Francisco Javier University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia School of Applied Mathematics and Informatics J. J. Strossmayer University of Osijek Osijek Croatia University of Oviedo Department of Computing Gijon Spain

ISBN: (纸本)9798350308365

Automated design of heuristics with genetic programming (G P) has, in recent years, become an intensively researched research area. One of the most recent applications of this methodology is to generate relocation rules (RRs) for the container relocation problem (CRP). CRP is an important combinatorial optimisation problem that is found in ship ter-minals and warehouses. RRs are simple constructive heuristic methods that provide a good solution quickly, thus representing an alternative to computationally expensive exact or metaheuris-tic methods. Even though the RRs designed by GP provide an improvement over existing manually designed rules, they have limited performance. An efficient way to improve the performance of RRs generated by GP is to use ensemble learning. In this study, we apply ensemble learning on RRs generated by GP for CRP to improve the performance of individual rules. We investigate how different ensemble sizes and combination methods affect the quality of the results, as well as which rules are selected to form ensembles. The experimental study shows that ensembles constructed out of automatically designed RRs significantly improve performance compared to the individual rules. © 2024 IEEE.

关键词： Combinatorial optimization

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Application of Speech Augmented Reality for Sabah Tourism Industry 1

引用

8th International Conference on Computational Science and Technology, ICCST 2021

作者： Saimon, Rianty Ng, Giap Weng Tanalol, Siti Hasnah Faculty of Computing and Informatics University Malaysia Sabah Sabah Kota Kinabalu Malaysia

ISBN: (数字)9789811685156

ISBN: (纸本)9789811685149

This work focuses on developing and researching the potential of hybrid application with speech and augmented reality technology to solve the problem that found in Sabah tourism industry. Tourism in Sabah, Malaysia in fact has been growing rapidly and becoming one of the most important industry in Sabah. Although tourism in Sabah is growing steadily, there are some problems found within the industry especially in term of technology advancement. First problem that found by the researcher is there still a few numbers of applications has been developed for Sabah Tourism. Second, lack of interaction between tourists and tourism place such as Museum Sabah, Mount Kinabalu and so on. There are two objectives involve in this research. First objective is to develop a prototype of speech AR application based on Sabah tourism industry. This include the information of the tourism places and 3D virtual object that represent the environment and landscape of tourism places in Sabah. Second objective is to evaluate the potential of developed prototype for Sabah tourism industry. To achieve the second objective, semi-structured interview was conducted to evaluate the potential of the hybrid application. The potential of application with speech and augmented reality technology determined based on the response from participants that involved in an interview. A great potential of an application can be concluded if positive response acquires from the participants. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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Oral Ulcer Classification System Using CNN 5

Oral Ulcer Classification System Using CNN

引用

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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MSSThreD: Multitask Social Media Sentiment Analysis with Sarcasm for Public Security Threat Detection 11

MSSThreD: Multitask Social Media Sentiment Analysis with Sar...

引用

11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Suhaimin, Mohd Suhairi Md Hijazi, Mohd Hanafi Ahmad Moung, Ervin Gubin Politeknik Kota Bharu College Education Department of Polytechnic and Community Kelantan Kota Bharu Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Sabah Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Faculty of Computing and Informatics Sabah Malaysia

ISBN: (纸本)9798350389289

Public security threat detection is critical for maintaining safety of citizens from possible natural or non-natural disasters. Social media, with its vast and diverse content, serves as a significant source for communication and information dissemination. Early detection of threats through social media analysis can greatly benefit in prevention before the event, or response and rescue during the event. However, the noisy textual with linguistic nature of social media content presents substantial challenges for sentiment analysis. Coupling with sarcasm can reverse the polarity of sentiments, complicating threat detection efforts. To address these challenges, we introduce an application framework for public security threat detection that embeds multitask learning, incorporating sentiment analysis and sarcasm detection. This application utilized preprocessing and feature engineering techniques based on Natural Language Processing to capture the nuance of sentiment and sarcasm, with deep learning network model of Bidirectional Long Short-Term Memory and Gated Recurrent Unit combination. The sentiment filtering and identifying threat-related keywords, producing monitoring reports that highlight threats earlier than traditional web news sources, as evidenced by our case study results. This application demonstrates the practical utility and impact of the framework in enhancing public security through timely threat detection. © 2024 IEEE.

关键词： Long short-term memory

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Intrusion Detection System for Cyber-attacks in the Internet of Vehicles (IoV) Environment

Intrusion Detection System for Cyber-attacks in the Internet...

引用

Cyber Security and computing (CyberComp), International Conference on

作者： Mueez Hanifa H.T. Zubair Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

This report presents the development of an Intrusion Detection System (IDS) designed to detect cyber-attacks in an Internet of Vehicles (IoV) environment. The IDS employs machine learning techniques, specifically Support Vector Machines (SVM), and is implemented on two low-memory, low-speed microcontroller systems connected to a CAN bus. The model is trained on real-world data, sourced from a publicly available dataset collected from a Hyundai Avante CN7, using Python-based tools on a computer. The resulting coefficients and biases are extracted and embedded into a C program, which runs on the microcontrollers. The final implementation achieved an accuracy of 83%, with a precision of 97% and a recall of 68%.

关键词： Support vector machines Microcontrollers Tiny machine learning Intrusion detection Data models Real-time systems Hardware Security Cyberattack Internet of Vehicles

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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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Copy-Move Forgery Detection Optimization Using Harmony Search with Clonal Selection Algorithm 6

Copy-Move Forgery Detection Optimization Using Harmony Searc...

引用

6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Hijazi, Mohd Hanafi Ahmad Josmin, Ezlyn Sherlyn Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Center Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

In today's world, where technology is advancing rapidly, the extensive use of digital images on social media brings up concerns about their authenticity. One common form of deception is image forgery, especially copy-move forgery, where parts of an image are duplicated and inserted elsewhere within the same image. Traditional detection methods, like randomized search algorithms, often struggle with adaptability. This study presents a new approach that uses the Harmony Search with Clonal Selection Algorithm (HSCSA) to improve the detection of copy-move forgeries by fine -tuning the hyperparameters of Convolutional Neural Network (CNN) models. Our method involves preprocessing the CoMoFoD dataset, optimizing essential hyperparameters using HSCSA, and comparing the performance of the optimized model with standard CNN models. The results showed a notable improvement, with accuracy rising from 67.72% to 78.20%. © 2024 IEEE.

关键词： Convolutional neural networks

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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...

引用

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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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 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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