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

共 3654 条记录，以下是21-30 订阅

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Comparative Analysis Between K-Nearest Neighbor (KNN) and Deep Learning Classifiers for Emotion Classification in Virtual Reality Using Electrodermography (EDG) and Heart Rate 4th

Comparative Analysis Between K-Nearest Neighbor (KNN) and De...

引用

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

作者： Bulagang, Aaron Frederick Mountstephens, James Teo, Jason Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Evolutionary Computing Laboratory Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9789819729760

Virtual Reality as a stimulus to evoke emotion in emotion recognition studies has been emerging in recent years, the objective of this research is to do a comparative analysis between classifiers such as Deep learning and KNN with Heart Rate and electrodermography signals. The difference in classifiers applied plays an important part when it comes to generating the result in accuracy. In this research, 30 participants volunteered in the experiment of using VR as stimuli while their HR and EDG signals were recorded. The recorded signals are then classified using KNN and Deep Learning classifier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Heart

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Utilizing Color Space Information as Features for Deep Learning for a Heterogeneous Food Recognition System 1

引用

13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Shamsu, Izyan Uzma Binti Moung, Ervin Gubin Farzamnia, Ali Rui, Tiong Lin Yahya, Farashazillah Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Food recognition serves as one of the most promising applications of visual object recognition, as it can be used to calculate food calories and analyze people's eating habits and dietary practices for health-care purposes. It forms the primary and most vital step in developing an application capable of providing nutritional recommendations. In this work, the CNN, ResNet, and AlexNet architectures have been proposed, given their popularity among researchers. A merged dataset, comprising Food-101 and UEC-Food256, was employed to evaluate the proposed models. To ascertain the optimal color spaces for food recognition, eight selection criteria were proposed. Among the various color space selection criteria, RGB showed superior performance when used with the Custom CNN model. To enhance performance, an upsampling method was implemented to increase the number of samples in the minority classes. This was achieved by duplicating existing samples using an augmentation process, thereby balancing the merged dataset. The final results suggest that EfficientNetB0, a CNN-based pre-trained model, performs better with the RGB color space, increasing accuracy from 40.48% (Custom CNN) to 73.11% (EfficientNetB0). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Implementation of Machine Learning-Based QoS Traffic Classification In Software-Defined Networking 10

Implementation of Machine Learning-Based QoS Traffic Classif...

引用

10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Fauzi, Nur Fatin Nabilah Mohd Chin, Tan Saw Zaki, Fatimah Audah Md Multimedia University Faculty of Computing & Informatics Persiaran Multimedia 63100 Malaysia

ISBN: (纸本)9798350364163

The concept of Software-Defined Networking (SDN) in network management involves controlling network behaviour through software applications. The implementation of SDN centralizes network management, ensuring efficient performance and reducing computational costs. With the vast amount of network traffic today, traffic classification is crucial in SDN, improving performance by prioritizing applications that require higher Quality of Service (QoS). The occurrence of imbalanced datasets, which focus more on the majority class rather than the minority class, leads to poor performance in SDN. This paper explores the implementation of feature selection using filter and wrapper methods to tackle imbalanced datasets and enhance network management performance. Using a real-time dataset containing 930 features from the University of Cambridge, the Symmetrical Uncertainty and C4.5 algorithm achieve a classification accuracy of 97.69% and reduced the dataset size by 50%. These findings demonstrate that the proposed methods significantly improve computational efficiency and classification performance, making them suitable for real-time SDN applications. © 2024 IEEE.

关键词： Quality of service

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Deep or Shallow Networks: Coral Types Classification Using Residual Network Models 13th

Deep or Shallow Networks: Coral Types Classification Using R...

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Nurill-Nabilla, H. Mohd-Isa, Wan-Noorshahida Faculty of Computing and Informatics Multimedia University 63100 Cyberjaya Selangor Malaysia

ISBN: (纸本)9789819720064

Coral reefs help to maintain healthy shorelines and support the growth of mangroves and other coastal vegetation as well as becoming home to many diverse types of sea creatures. Aside from benefitting marine life, they also benefit humans by providing critical coastal protection by reducing the impact of storms and erosion. Due to anthropogenic threats, coral reef habitats are on its path to destruction. Marine scientists are currently working on ways to restore and rehabilitate coral reefs for the good of all organisms that benefit from coral reefs. These efforts by the marine scientists take a significant amount of time to execute. Thus, marine biologists and computer vision experts work together to enhance their methods of coral conservation and rehabilitation with the help of machine learning and autonomous underwater vehicles (AUV). In this paper, we propose an experiment to teach small datasets of texture corals to pre-trained deep residual networks for them to be able to classify these corals. In doing this, the machine will be able to assist humans in the maintenance and preservation of coral reef ecosystems. The models that will be used in this project are ResNet18, ResNet50, and ResNet152. These models are chosen due to their performance in recent studies in the field of coral recognition and classification. In this paper, we have achieved 96.08% as the highest accuracy of ResNet18, 98.87% for ResNet50 and 98.37% for ResNet152. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reefs

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Factors of Pre-University Study in Influencing Graduate on Time 16

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

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16th International Conference on Advanced Computational Intelligence, ICACI 2024

作者： Law, Theng-Jia Ting, Choo-Yee Goh, Hui-Ngo Ng, Hu Quek, Albert Multimedia University Faculty of Computing and Informatics Selangor Cyberjaya63000 Malaysia

ISBN: (纸本)9798350308501

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. © 2024 IEEE.

关键词： Students

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Utilisation of the Dijkstra Algorithm to Determine the Shortest Path on Universiti Malaysia Sabah's Main Campus in Kota Kinabalu 6

Utilisation of the Dijkstra Algorithm to Determine the Short...

引用

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

作者： Henri, Masitha Raziah Binti Wahab, Shaliza Hayati A. Fattah, Salmah Saad, Nordin Tambrin, Nur Athirah Maisarah Binti Jamaluddin, Nur Farah Izzati Binti Shien, Sherlyn Ong Shu Ghazalie, Zarra Soffea Binti Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

The purpose of this research is to determine the shortest path in the Universiti Malaysia Sabah main campus using the Dijkstra algorithm. It is to determine the paths from selected locations to different locations. The buildings in the institution were illustrated in the form of a graph, where the nodes are selected locations or faculties, while the edges are the paths, and their cost indicates the length. Besides, in this research, Matlab software is used to resolve the routes. The purpose of this software is to create a graph and discover the locations of the routes. Designing maps from optimal routes and the proposed routes is a complicated problem that can be solved by putting in the Dijkstra algorithm. The implementation of the Dijkstra algorithm in searching for the optimal-shortest paths of selected routes is a great choice to solve the problem and enables the validity of the results. © 2024 IEEE.

关键词： MATLAB

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Natural Language Processing Approach for Fake News Detection Using Metaheuristics Optimized Extreme Gradient Boosting 3

Natural Language Processing Approach for Fake News Detection...

引用

3rd IEEE World Conference on Applied Intelligence and computing, AIC 2024

作者： Petrovic, Aleksandar Perisic, Jasmina Jovanovic, Luka Zivkovic, Miodrag Antonijevic, Milos Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia Union - Nikola Tesla University Faculty of Informatics and Computer Science Belgrade Serbia

ISBN: (纸本)9798350384598

The problem of fake news can be dangerous, as widely spread misinformation can cause serious physical and psychological harm. In the age of digitization and artificial intelligence (AI), particularly deep learning methods, which are able to generated synthetic text, being able to distinguish between real and fake news has become even greater challenge. Therefore, this research proposes a metaheuristics optimized machine learning (ML) approach with natural language processing (NLP) application for detecting fake news. The main classification model that was used is the eXtreme gradient boosting (XGBoost), which was tuned by the modified variable neighborhood search (VNS) algorithm. The NLP technique that was applied to transform text into the meaningful form for ML is the term frequency-inverse document frequency (TF-IDF). The performance of the VNS-tuned XGBoost was evaluated and compared with other cutting edge metaheuristics, where the proposed method obtained superior performance. © 2024 IEEE.

关键词： fake news detection optimization TF-IDF VNS XGBoost

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Modified Metaheuristic Optimization Algorithm for Hyperparameter Tuning in Mental Health Applications 6

Modified Metaheuristic Optimization Algorithm for Hyperparam...

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6th International Conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Kozakijevic, Stanislava Jovanovic, Luka Tedic, Smiljana Jankovic, Ninoslava Zivkovic, Miodrag Bacanin, Nebojsa Faculty of Media and Communication Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9798331522667

The World health organization considers a mentally healthy person to be able to manage life’s stresses, work productively, and contribute to their communities. However, mental disorders, affecting 970 million people in 2019, often reduce quality of life, necessitating accurate diagnosis and therapy. Affective disorders, encompassing depressive and manic episodes, vary in severity and pattern, with conditions like bipolar disorder and recurrent mood disorders identified through ICD-10 classifications. Diagnosis, typically made by expert teams, remains challenging due to the complex and variable nature of mental disorders, leading to frequent misdiagnoses that hinder treatment progress and erode patient trust. Artificial intelligence (AI) holds promise in supporting mental health diagnosis by identifying subtle patterns in symptoms and validating expert conclusions. While AI’s specialized nature poses limitations, integrating it into clinical practice can improve diagnostic accuracy and reduce errors. High quality models can assist doctors by ruling out less likely conditions and confirming diagnoses. Optimizing AI models, however, requires effective hyperparameter selection, a complex task classified as NP-hard. Metaheuristic algorithms offer a solution, enabling efficient parameter tuning and improving AI model performance. Given the critical implications of accurate mental health diagnoses, leveraging AI and optimization techniques is essential to enhance outcomes and improve lives. This work proposed a modified metahetusirc aimed at optimizing classification accuracy. When evaluated on a genuine dataset the best performing models attain an accuracy of 0.972222 suggesting feasibility in real world situations. © 2025 IEEE.

关键词： Patient treatment

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Gold Prices Forecasting Using Recurrent Neural Network with Attention Tuned by Metaheuristics

Gold Prices Forecasting Using Recurrent Neural Network with ...

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2023 IEEE World Conference on Applied Intelligence and computing, AIC 2023

作者： Jovanovic, Andjela Dogandzic, Tea Dobrojevic, Milos Sarac, Marko Bacanin, Nebojsa Zivkovic, Miodrag Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (纸本)9798350310061

A large number of statistical studies have proven that the value of gold is highly valued nowadays, despite the numerous crises that have hit the global market. Due to the great risks that daily affect sudden changes in gold prices on the global market, it is of great benefit to develop tools that will enable automated monitoring and prediction of gold price changes. This can be quite helpful for those countries and major corporations whose economy depends on this. In this paper recurrent neural network with attention layer was used to forecast the gold price, while the hyperparameters of the network were tuned by the novel variant of the particle swarm optimization algorithm. Obtained results in terms of standard regression metrics were compared to those generated by other cutting-edge methods and proposed method proved to be very promising in this area. © 2023 IEEE.

关键词： Forecasting

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Optimizing Machine Learning for Malicious Blockchain Node Detection Using Modified Metaheuristic 1

Optimizing Machine Learning for Malicious Blockchain Node De...

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1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Bacanin, Nebojsa Vicentijevic, Marko Jovanovic, Luka Njegus, Angelina Radomirovic, Branislav Zivkovic, Miodrag Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia

ISBN: (纸本)9798350355611

The decentralized architecture of blockchain tech-nology introduces various challenges, particularly in Proof of Stake (PoS) networks, where malicious nodes can undermine the consensus process and threaten the integrity of the network. Nodes with significant stakes may disproportionately influence validation, weakening the network's decentralization. Despite ongoing advancements in blockchain security, there remains a notable gap in research on the integration of artificial intelli-gence (AI) and optimization techniques for enhancing blockchain security. This paper proposes an approach, utilizing a CatBoost classifier optimized with a modified Particle Swarm Optimization (PSO) algorithm, designed specifically for this study. A compar-ative analysis, performed on a real-world dataset, demonstrates the effectiveness of the proposed method, with the most accurate models achieving an accuracy of 87.13%. The findings highlight the potential of AI -driven optimization in improving blockchain security. Future research will focus on the application of AI and optimization techniques to further refine blockchain protocols, enhance detection and prevention of security threats, and improve overall network performance. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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