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9th International Conference on Computational Science and Technology, ICCST 2022

作者： Cheng, Yun-Quan Mansor, Sarina Chin, Ji-Jian Karim, Hezerul Abdul Kar-Weng, Ban Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia

ISBN: (纸本)9789811984051

Finite state machine (FSM) is a model of computation that executes an exact finite number of states at any given time where Hierarchical Finite State Machines (HFSM) can group multiple FSMs and execute as one state. Given these techniques can be used to exclusively execute certain states, it is widely used in games. In this paper we will explore and showcase the implementation of FSMs and HFSMs to create traffic non-playing characters (NPC) in our low-cost educational driving simulators. We will also discuss possible techniques that can be use. It is also in the interest to push for more understanding in any gap for traffic NPCs in similar low-cost educational driving sims in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Costs

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Direct Current Motor Malfunction Detection Through Metastatic Optimized Audio Analysis and Classification 7

Direct Current Motor Malfunction Detection Through Metastati...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Bozovic, Aleksandra Jovanovic, Luka Desnica, Eleonora Radovanovic, Ljiljana Zivkovic, Miodrag Bacanin, Nebojsa University of Novi Sad 'Mihajlo Pupin' Technical Faculty Zrenjanin Serbia Singidunum University Technical Faculty Belgrade Serbia Singidunum University Faculty of Informatics And Computing Belgrade Serbia

ISBN: (纸本)9798350359299

This study investigates the integration of advanced signal processing techniques with the AdaBoost classification algorithm for the purpose of identifying malfunctions in brushless DC motors through audio analysis. Given the increasing significance of brushless DC motors in modern manufacturing and transportation, the timely detection of anomalies becomes crucial for maintaining consistent operational efficiency and preventing potential injuries. Despite regular maintenance practices, the occurrence of unexpected mechanical and electrical failures often eludes operators, leading to the potential escalation of minor malfunctions into catastrophic failures. Consequently, the implementation of a reliable and consistent monitoring system is imperative in practical applications. The proposed solution leverages audio analysis as an effective approach. Through the amalgamation of machine learning and advanced signal processing techniques, this research employs audio signal classification to discern malfunctions in brushless DC motors from audio recordings of their operations. The extraction of dominant frequencies using the Fast Fourier Transform (FFT) enhances the analysis, and the AdaBoost classifier is subsequently employed to assess the proper functioning of the motors. Recognizing the substantial impact of hyperparameter selections on classifier performance, a modified version of the Red Fox Optimization (RFO) algorithm is introduced to meet the specific demands of this study. Results demonstrate that the optimizers, when appropriately constructed, achieve perfect accuracy when applied to a real-world dataset, highlighting the potential practical applicability of the proposed methodology. © 2024 IEEE.

关键词： Brushless DC motors

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Knowledge augmented significant language model-based chatbot for explainable diabetes mellitus prediction 19

Knowledge augmented significant language model-based chatbot...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Elfayoumi, Mazen AbouElazm, Mohamed Mohamed, Omar Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user’s understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT-3.5, and Gemma2. This approach augments the chatbot’s responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures. © 2025 IEEE.

关键词： Prediction models

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Mix-YOLONet: Deep Image Dehazing for Improving Object Detection 31st

Mix-YOLONet: Deep Image Dehazing for Improving Object Detec...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Lim, Xin Wong, Lai-Kuan Loh, Yuen Peng Gu, Ke Lin, Weisi Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Beijing University of Technology Beijing China Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819620609

Atmospheric haze significantly impairs the performance of computer vision tasks such as image dehazing and object detection. Existing methods often address these tasks independently, failing to provide an integrated solution that can effectively handle hazy conditions while maintaining accurate object detection. This paper presents Mix-YOLONet, a novel joint network architecture designed to tackle both image dehazing and object detection simultaneously. Mix-YOLONet leverages the powerful image restoration capabilities of U-Net-like architecture and integrates it with the detection precision of a YOLO head. Additionally, we integrated Mix Structure Blocks (MSB) to our joint network and experimented various configurations at strategic locations to enhance feature extraction and context aggregation. Through extensive experiments, we demonstrate that Mix-YOLONet achieves superior performance in both dehazing and object detection tasks under challenging visibility conditions, outperforming state-of-the-art methods on three benchmark datasets both quantitatively and qualitatively. The proposed joint network not only improves the clarity of hazy images but also ensures accurate object localization, paving the way for more robust object detection in adverse environmental conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Restoration

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Predicting game-induced emotions using EEG, data mining and machine learning

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Bulletin of the National Research Centre 2024年第1期48卷 1-10页

作者： Lim, Min Xuan Teo, Jason Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Evolutionary Computing Laboratory Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal’s changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers’ performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases’ results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm’s capabilities in precisely determining the emotion label for each participant’s EEG features’ instance. The findings in this study fill the existing gap of game-induced emotion recog

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Underwater Corrosion Recognition using Colour Illumination Mapping

Underwater Corrosion Recognition using Colour Illumination M...

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2022 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2022

作者： Ibrahim, Amalina Mohd-Isa, Wan-Noorshahida Su'Ud, Mazliham Mohd Desa, Shahbe Mat Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350332421

Visual-based corrosion detection is one of the techniques that can be utilised for performing inspection in areas where human access is impossible because of environment, safety and other concerns such as underwater. Image quality has become a great factor in underwater image analysis. Noticeable challenges are limited source of natural lighting, loss of colour underwater at depth, magnification of objects underwater and much more. These factors may affect the process of estimating the actual corroded area underwater. Due to that, this work is proposing a technique to recognize underwater corrosion based on colour illumination mapping. © 2022 IEEE.

关键词： Underwater imaging

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Data-Oriented Operating System for Big Data and Cloud

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Intelligent Automation & Soft computing 2024年第4期39卷 633-647页

作者： Selwyn Darryl Kessler Kok-Why Ng Su-Cheng Haw Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia

Operating System(OS)is a critical piece of software that manages a computer’s hardware and resources,acting as the intermediary between the computer and the *** existing OS is not designed for Big Data and Cloud computing,resulting in data processing and management *** paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud computing *** proposed algorithm utilizes the performance benefits from the improved Input/Output(I/O)*** performance engineering runs the data-oriented design on traditional data management to improve data processing speed by reducing memory access overheads in conventional data *** OS incorporates a data-oriented design to“modernize”various Data Science and management *** resulting OS contains a basic input/output system(BIOS)bootloader that boots into Intel 32-bit protected mode,a text display terminal,4 GB paging memory,4096 heap block size,a Hard Disk Drive(HDD)I/O Advanced Technology Attachment(ATA)driver and *** are also I/O scheduling algorithm prototypes that demonstrate how a simple Sweeping algorithm is superior to more conventionally known I/O scheduling algorithms.A MapReduce prototype is implemented using Message Passing Interface(MPI)for big data *** attempt was made to optimize binary search using modern performance engineering and data-oriented design.

关键词： Operating system big data cloud computing MapReduce data-oriented

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Investigating Electrodermography (EDG) and Heart Rate (HR) Signals for Emotion Classification in Virtual Reality 1

Investigating Electrodermography (EDG) and Heart Rate (HR) S...

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1st International Visualization, informatics and Technology Conference, IVIT 2022

作者： Bulagang, Aaron Frederick Mountstephens, James Teo, Jason Universiti Malaysia Sabah Faculty of Computing and Informatics Malaysia

ISBN: (纸本)9781665486484

This paper shows the findings and results of combining Heart Rate and Electrodemography signals with KNN classifier for multimodal emotion detection using Virtual Reality (VR). The study was conducted by using a VR headset to show the participants 360 videos to elicit their emotional responses to the video. Their emotional response was captured using a wearable device that records both heart rate and skin activity in real time. A total of 5 participants took part in the experiment where the results are classified for intra-subject classification using KNN classifier. In the study, for the classification of intra-subject, the peak accuracy achieved amongst the five participants is 97.7% with HR and EDG signals combined with KNN as the classifier. These results demonstrate that by fusing HR and EDG signals, high-Accuracy results can be generated through the use of KNN as the classifier. The application and potential of this study can be useful in entertainment, VR rehabilitation, and gaming. © 2022 IEEE.

关键词： Virtual reality

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Crowd Monitoring of Hajj Pilgrimage using Video Analytics and Deep Learning *

Crowd Monitoring of Hajj Pilgrimage using Video Analytics an...

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2022 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2022

作者： Bhuiyan, Md Roman Abdullah, Junaidi Hashim, Noramiza Farid, Fahmid Al Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350332421

This paper presents developments in crowd analysis, with a specific emphasis on crowd density during the Hajj and Umrah pilgrimages. In recent years, there has been a rising interest in increasing video analytics and video surveillance to enhance the safety and security of pilgrims throughout their stay in Mecca. Crowd surveillance research and development are still in their infancy. Real-time monitoring of crowds is impeded by a lack of distinct crowd scenes or the high intensity of the crowd, which reduces its use. This paper proposed a fully convolutional neural network (FCNN) method for large crowd analysis, and it is able to classify the three classes of crowd density. This is intended to overcome existing technical challenges in video analysis in situations involving the circulation of a large number of pilgrims at a density of seven to eight per square meter. This endeavor intends to build a new dataset based on the Hajj scenario, in part to overcome the aforementioned difficulty. Manual and semi-automatic approaches are now used for video analysis, notably during Hajj. However, it is vital to automatically monitor pilgrim crowd events. Integrating FCNN classification with FCNN regression requires a strategy that is more powerful than the direct approach. Using ResNet50 and Fully Convolutional Neural Networks, a deep learning architecture is constructed to predict the density of certain crowd footage (FCNNs). The new dataset (Hajj-Crowd dataset) and using the recommended strategy, our solution obtained 100 % accuracy in crowd analysis, exceeding the state-of-the-art technology. In addition, the ResNet50 method achieved an accuracy of 98.6% for the developed crowd dataset. © 2022 IEEE.

关键词： Convolutional neural networks

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Noise-Aware Zero-Reference Low-light Image Enhancement for Object Detection

Noise-Aware Zero-Reference Low-light Image Enhancement for O...

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2022 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2022

作者： Ang, Kelvin Lim, Wan Teng Loh, Yuen Peng Ong, Simying Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350332421

Computer vision advancement has proven to be able to automate many practical tasks such as object detection and recognition in challenging environments. However, most notable computer vision models are optimized to work under environment with ideal lighting conditions. Real-world scenarios are uncontrolled and it is common to encounter quality and performance deterioration due to challenges in poor lighting, especially related to the amplification of noise signals. Consequently, there is an increase of vision enhancement related works to maintain such models' performances, however there is still a gap in exploring the practical implication that such existing enhancement work has on detection models, as well as the issue and handling of noise signals. Hence, this paper investigates the incorporation of noise information into the enhancement modelling with the specific aim to improve the performance of object recognition. Building upon the zero-reference deep curve estimation (Zero-DCE) approach, a noisy data training strategy is designed in order to introduce noise priors into the curve estimation training to produce better image structure enhancement. Furthermore, the commonly implemented post-process denoising approach is also studied in this work to find out its impact and effectiveness in the context of object detection. Experiments on the ExDark dataset show that the enhanced images produced by the proposed approach is able to improve object detection in low-light images using YOLOv5 with an increase of up to 3% in precision, and comparable performance with significantly more complex state-of-The-Art low-light image enhancement models. © 2022 IEEE.

关键词： Object recognition

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