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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Bolisetty, Prashanth Kumar Midhunchakkaravarthy Lincoln University College Faculty of Ai Ai Computing and Multimedia Selangor Darul Eshan Malaysia Lincoln University College Faculty of Ai Computing and Multimedia Selangor Darul Eshan Malaysia

ISBN: (纸本)9798331529246

Software reliability plays a crucial role in assessing the overall quality of software systems. Developing highly reliable software presents challenges, including ensuring usability, maintaining consistent performance, offering reliable service, and facilitating smooth maintenance. To overcome these limitations, researchers have turned to intelligent prediction and optimization algorithms. This paper presents a comprehensive review of software reliability prediction, emphasizing the application of machine learning techniques. It also provides an analytical evaluation of existing methods to identify areas for improvement and future research opportunities in software reliability prediction. © 2025 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Software Reliability Prediction and Optimization using Machine Learning Algorithms

Comparative Analysis of Software Reliability Prediction and ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Prashanth Kumar Bolisetty Midhunchakkaravarthy Faculty of AI AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia Faculty of AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

关键词： Machine learning algorithms Reviews Software algorithms Machine learning Software systems Prediction algorithms Software reliability Maintenance Usability Optimization

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SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition

SVFormer: A Direct Training Spiking Transformer for Efficie...

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4th International Workshop on Human Brain and Artificial Intelligence, HBai 2024

作者： Yu, Liutao Huang, Liwei Zhou, Chenlin Zhang, Han Ma, Zhengyu Zhou, Huihui Tian, Yonghong AI Department Pengcheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819640003

Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Consequently, it has long been a research spot in the computer vision field. As artificial neural networks (ANNs) are flourishing, convolution neural networks (CNNs), including 2D-CNNs and 3D-CNNs, as well as variants of the vision transformer (ViT), have shown impressive performance on VAR. However, they usually demand huge computational cost due to the large data volume and heavy information redundancy introduced by the temporal dimension. To address this challenge, some researchers have turned to brain-inspired spiking neural networks (SNNs), such as recurrent SNNs and ANN-converted SNNs, leveraging their inherent temporal dynamics and energy efficiency. Yet, current SNNs for VAR also encounter limitations, such as nontrivial input preprocessing, intricate network construction/training, and the need for repetitive processing of the same video clip, hindering their practical deployment. In this study, we innovatively propose the directly trained SVFormer (Spiking Video transFormer) for VAR. SVFormer integrates local feature extraction, global self-attention, and the intrinsic dynamics, sparsity, and spike-driven nature of SNNs, to efficiently and effectively extract spatio-temporal features. We evaluate SVFormer on two RGB datasets (UCF101, NTU-RGBD60) and one neuromorphic dataset (DVS128-Gesture), demonstrating comparable performance to the mainstream models in a more efficient way. Notably, SVFormer achieves a top-1 accuracy of 84.03% with ultra-low power consumption (21 mJ/video) on UCF101, which is state-of-the-art among directly trained deep SNNs, showcasing significant advantages over prior models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd International Conference on Intelligent Systems and Sustainable computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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Traffic Sign Recognition for Autonomous Vehicle with YOLOs: A Comparative Study 12

Traffic Sign Recognition for Autonomous Vehicle with YOLOs: ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Lim, Wan Han Lee, Chin Poo Lim, Kian Ming Lim, Jit Yan Mogan, Jashila Nair Multimedia University Faculty of Info. Science & Technology Melaka Malaysia Xi'an JiaotongLiverpool University School of AI and Advanced Computing Jiangsu China University of Nottingham Ningbo China School of Computer Science Zhejiang China

ISBN: (纸本)9798350391398

In the rapidly evolving field of autonomous driving, proficient detection and recognition of traffic signs are fundamental for ensuring the safety and efficiency of selfdriving vehicles. This research aims to identify the most suitable algorithms for this critical task by evaluating the performance of three prominent algorithms: YOLOv5, YOLOv8, and YOLONAS. The evaluation is conducted across three diverse datasets: Kaggle Self-Driving Cars Image Datasets, Kaggle Road Sign Image Datasets, and CeyRo Datasets. YOLO-NAS emerges as the standout performer across these datasets, showcasing commendable results. Additionally, the prediction results highlight YOLO-NAS's capability to accurately detect small and distant traffic signs. These findings collectively underscore YOLO-NAS as the optimal choice for traffic sign detection and recognition, particularly in situations characterized by challenging conditions. © 2024 IEEE.

关键词： Autonomous vehicles

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PCA-ViT: Hyperspectral Image Classification using Principal Component Analysis and Vision Transformer 12

PCA-ViT: Hyperspectral Image Classification using Principal ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Lim, Kian Ming Lee, Chin Poo Zahisham, Zaim Lim, Jit Yan Mogan, Jashila Nair University of Nottingham Ningbo China School of Computer Science Zhejiang Province China Xi'an Jiaotong-Liverpool University School of AI and Advanced Computing Jiangsu Province China Multimedia University Melaka Faculty of Info. Science and Technology Malaysia Multimedia University Melaka Faculty of Info. Science & Technology Malaysia

ISBN: (纸本)9798350391398

Hyperspectral image classification is essential in remote sensing applications, aiming to accurately categorize land cover or materials depicted in hyperspectral data. This paper introduces PCA-ViT, a novel approach for hyperspectral image classification that integrates Principal Component Analysis (PCA) with the Vision Transformer (ViT) architecture. PCA serves as a spectral dimension reduction technique to alleviate the curse of dimensionality inherent in hyperspectral data, transforming the original data into a more manageable format. The transformed data are then input into the ViT model, which utilizes self-attention mechanisms to capture spatial dependencies among image patches, avoiding traditional convolutional layers. Extensive experiments on benchmark hyperspectral datasets, including Indian Pines, University of Pavia, and Salinas Scene, demonstrate PCA-ViT's superior performance. It achieves 99.95% accuracy on Indian Pines, 100% on University of Pavia, and 100% on Salinas Scene, showcasing the effectiveness of transformer-based architectures in hyperspectral image classification tasks. © 2024 IEEE.

关键词： Image analysis

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Question Answering with Language Models 12

Question Answering with Language Models

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Wong, Jia Yin Lee, Chin Poo Lim, Kian Ming Lim, Jit Yan Mogan, Jashila Nair Multimedia University Faculty of Info.Science & Technology Melaka Malaysia Xi'an Jiaotong Liverpool University School of AI and Advanced Computing Jiangsu Province China University of Nottingham Ningbo China School of Computer Science Zhejiang Province China

ISBN: (纸本)9798350391398

With the significant increase in the number of resources providing various types of information, ranging from thousands to millions of sources including research papers, blogs, and more, extracting and providing meaningful insights has become a challenge. This study develops a Question Answering (QA) system using the Haystack framework, capable of retrieving relevant documents and extracting answers from lengthy texts. The implemented QA system consists of four main components: an indexing pipeline, a document store, a searching pipeline, and an evaluation module. Additionally, this study investigates how different model architectures affect performance in retrieving and extracting answers. Two different retrievers, BM25 Retriever and Dense Passage Retriever, and five different readers, BERT, Roberta, albert, MiniLM, and ELECTRA, are examined. The models are tested and evaluated using the SQuAD datasets. The combination of BM25 Retriever and RoBERTa Reader achieved the best performance, with an F1-score of 0.9301 and an Exact Match score of 0.8956 on the SQuAD2.0 dataset. © 2024 IEEE.

关键词： Question answering

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Acoustic Event Classification with Enhanced EfficientNet 12

Acoustic Event Classification with Enhanced EfficientNet

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Lim, Kian Ming Lee, Chin Poo Lee, Zhi Yang Lim, Jit Yan Mogan, Jashila Nair University of Nottingham Ningbo China School of Computer Science Zhejiang Province China Xi'an Jiaotong Liverpool University School of AI and Advanced Computing Jiangsu Province China DZH International Sdn. Bhd. Kuala Lumpur Malaysia Multimedia University Melaka Faculty of Info. Science & Technology Malaysia

ISBN: (纸本)9798350391398

In recent years, research into automating the recognition and classification of diverse acoustic events in audio recordings has surged. This technological advancement has profound implications for fields such as speech recognition, music information retrieval, and environmental sound monitoring. This study introduces a novel approach to acoustic event classification using a fine-tuned EfficientNet-B0 model. To mitigate overfitting, data augmentation techniques including pitch shifting, time stretching, noise addition, and time shifting are employed, thereby expanding the training dataset. Subsequently, these augmented audio signals undergo Short-Time Fourier Transform (STFT) to generate Log Mel-spectrograms, which are then integrated into the proposed fine-tuned EfficientNet-B0 architecture. Experimental results demonstrate promising performance across diverse settings, achieving validation accuracies of 89.44% and 74.23% on the ESC-10 and ESC- 50 datasets, respectively. © 2024 IEEE.

关键词： Spectrographs

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Traffic Sign Recognition for Autonomous Vehicle with YOLOs: A Comparative Study

Traffic Sign Recognition for Autonomous Vehicle with YOLOs: ...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Wan Han Lim Chin Poo Lee Kian Ming Lim Jit Yan Lim Jashila Nair Mogan Faculty of Info. Science & Technology Multimedia University Melaka Malaysia School of AI and Advanced Computing Xi’an JiaotongLiverpool University Jiangsu China School of Computer Science University of Nottingham Ningbo China Zhejiang China

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

In the rapidly evolving field of autonomous driving, proficient detection and recognition of traffic signs are fundamental for ensuring the safety and efficiency of selfdriving vehicles. This research aims to identify the most suitable algorithms for this critical task by evaluating the performance of three prominent algorithms: YOLOv5, YOLOv8, and YOLONAS. The evaluation is conducted across three diverse datasets: Kaggle Self-Driving Cars Image Datasets, Kaggle Road Sign Image Datasets, and CeyRo Datasets. YOLO-NAS emerges as the standout performer across these datasets, showcasing commendable results. Additionally, the prediction results highlight YOLO-NAS’s capability to accurately detect small and distant traffic signs. These findings collectively underscore YOLO-NAS as the optimal choice for traffic sign detection and recognition, particularly in situations characterized by challenging conditions.

关键词： YOLO Roads Prediction algorithms Safety Autonomous automobiles Character recognition

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SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition

arXiv

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arXiv 2024年

作者： Yu, Liutao Huang, Liwei Zhou, Chenlin Zhang, Han Ma, Zhengyu Zhou, Huihui Tian, Yonghong AI Department Peng Cheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Consequently, it has long been a research spot in the computer vision field. As artificial neural networks (ANNs) are flourishing, convolution neural networks (CNNs), including 2D-CNNs and 3D-CNNs, as well as variants of the vision transformer (ViT), have shown impressive performance on VAR. However, they usually demand huge computational cost due to the large data volume and heavy information redundancy introduced by the temporal dimension. To address this challenge, some researchers have turned to brain-inspired spiking neural networks (SNNs), such as recurrent SNNs and ANN-converted SNNs, leveraging their inherent temporal dynamics and energy efficiency. Yet, current SNNs for VAR also encounter limitations, such as nontrivial input preprocessing, intricate network construction/training, and the need for repetitive processing of the same video clip, hindering their practical deployment. In this study, we innovatively propose the directly trained SVFormer (Spiking Video transFormer) for VAR. SVFormer integrates local feature extraction, global self-attention, and the intrinsic dynamics, sparsity, and spike-driven nature of SNNs, to efficiently and effectively extract spatio-temporal features. We evaluate SVFormer on two RGB datasets (UCF101, NTU-RGBD60) and one neuromorphic dataset (DVS128-Gesture), demonstrating comparable performance to the mainstream models in a more efficient way. Notably, SVFormer achieves a top-1 accuracy of 84.03% with ultra-low power consumption (21 mJ/video) on UCF101, which is state-of-the-art among directly trained deep SNNs, showcasing significant advantages over prior models. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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