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A dynamic simulation model to improve the livability of transportation systems

Public Transport 2025年第1期17卷 177-220页

作者： Suryani, Erma Hendrawan, Rully Agus Adipraja, Philip Faster Eka Widodo, Basuki Chou, Shuo-Yan Az-Zahra, Alifia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Informatics Engineering Institut Teknologi Dan Bisnis Asia Malang Surabaya65113 Indonesia Department of Mathematics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Civil Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Transportation users often face shortcomings, such as unreliable service causing uncertainty in transit, low quality of service, and loss of time due to road and highway congestion. This study aims to improve the livability of the transportation system by considering several factors. System dynamics is used as a model development method to analyze the relationship between complex and nonlinear variables affecting the livability of the transportation system. The system structure of a valid model can be modified by adding multiple feedback loops, new parameters, and changing the feedback loop structure to see how it affects other variables. The scenario model is built from a valid model by changing the model structure to improve the livability of the transportation system through improvements in the perception, safety, security, and health level of public transport, land use accessibility, and mitigation of air pollution. These strategies are expected to increase the livability of the transportation system by 71%, driven by the improvement in perception, safety, security level, and health level of public transport, land use accessibility, and decreased air pollution. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Land use

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

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IDS: Robust Network Intrusion Detection via Temporal Contrastive Graph Learning

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IEEE Transactions on information Forensics and Security 2025年 20卷 1475-1486页

作者： Wu, Cong Sun, Jianfei Chen, Jing Alazab, Mamoun Liu, Yang Xiang, Yang Wuhan University School of Cyber Science and Engineering Wuhan430072 China Singapore Management University School of Computing and Information Systems Bras Basah Singapore188065 Singapore Charles Darwin University College of Engineering IT and Environment DarwinNT0810 Australia Nanyang Technological University College of Computing and Data Science Jurong West Singapore639798 Singapore Swinburne University of Technology Department of Digital Research MelbourneVIC3122 Australia

In the era of zero trust security models and next-generation networks (NGN), the primary challenge is that network nodes may be untrusted, even if they have been verified, necessitating continuous validation and scrutiny. Effective intrusion detection systems (IDS) are crucial for continuously monitoring network traffic and identifying potential threats. However, traditional IDS approaches often struggle to keep pace with evolving threats, requiring extensive supervised training on labeled datasets. This limitation leads to high false positive rates, low detection accuracy, and a failure to provide real-time detection, thereby undermining the security of NGNs. This paper proposed the first self-supervised learning-based IDS, designed on temporal contrastive graph neural network (GNN), namely TCG-IDS. It innovatively integrates three contrastive learning strategies: temporal contrasting to capture temporal dependencies, asymmetric contrasting to account for the diverse interactions within network data, and masked contrasting to enhance the learning of node representations by masking parts of the data during training. Performance evaluation was conducted on two publicly available network traffic datasets, NF-CSE-CIC-IDS2018-V2 and NF-UNSW-NB15-V2. TCG-IDS achieved a balanced accuracy of 99.48% and 91.48% on two datasets respectively, significantly outperforming state-of-the-art graph learning models. In multi-class detection, TCG-IDS attained a mean false positive rate of 4.15% and 3.34% on the two datasets respectively. Besides, it exhibits high efficiency with its running time of 0.37s and 0.51s on the two datasets to predict per batch of 100 samples. Results highlight the effectiveness and efficiency of TCG-IDS in accurately detecting various types of network intrusions. This work significantly advances the field of network intrusion detection via self-supervised temporal graph learning, offering a promising solution for future network security systems. © 2005-2012

关键词： Network intrusion

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation

Collision Avoidance System using YOLO-based Object Detection...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Shiv Akash Dhinakar Raj Ashita Singh Tarakshraj Singh Shekhawat Manav Muthanna M K. Sivasankaran School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems.

关键词： YOLO Training Road accidents Accuracy Vehicle safety Estimation Size measurement Road safety Reliability Collision avoidance

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Observation of multiple surface states in naturally cleavable chiral crystal PdSbSe

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Physical Review Materials 2025年第3期9卷 L031201-L031201页

作者： Zhicheng Jiang Zhengtai Liu Chenqiang Hua Xiangqi Liu Yichen Yang Jianyang Ding Jiayu Liu Jishan Liu Mao Ye Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China National Synchrotron Radiation Laboratory and School of Nuclear Science and Technology University of Science and Technology of China Hefei 230026 China National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 China Zhejiang Province Key Laboratory of Quantum Technology and Device Department of Physics Zhejiang University Hangzhou 310027 China State Key Lab of Silicon Materials School of Materials Science and Engineering Zhejiang University Hangzhou 310027 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Yuhang District Hangzhou 310023 China School of Physical Science and Technology ShanghaiTech University Shanghai 200031 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

Chiral multifold fermions in solids exhibit unique band structures and topological properties, making them ideal for exploring fundamental physical phenomena related to nontrivial topology, chirality, and symmetry breaking. However, the challenge of obtaining clean, flat surfaces through cleavage has hindered the investigation of their unique electronic states. In this study, we utilize high-resolution angle-resolved photoemission spectroscopy and density functional theory calculations to investigate the low-energy electronic structure of the cleavable single-crystal PdSbSe. Our combined experimental and theoretical analysis reveals the presence of multifold-degenerate fermions within this chiral crystal. We also observe multiple chiral Fermi arc surface states and spin-splitting behavior in the associated bulk bands. These findings provide unique insights into chiral, multifold fermionic states in easily cleavable crystals and offer a robust platform for further research into their unique electronic properties and potential applications in novel electronic devices.

关键词： Photoelectron spectroscopy

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Semantic Communication with Entropy-and-Channel-Adaptive Rate Control over Multi-User MIMO Fading Channels

arXiv

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arXiv 2025年

作者： Chen, Weixuan Yang, Qianqian Chen, Yuhao Huang, Chongwen Wang, Qian Xiong, Zehui Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Although significant improvements in transmission efficiency have been achieved, existing semantic communication (SemCom) methods typically use a fixed transmission rate for varying channel conditions and transmission contents, leading to performance degradation under harsh channel conditions. To address these challenges, we propose a novel SemCom method for wireless image transmission that integrates entropy-and-channel-adaptive rate control mechanism, specifically designed for multi-user multiple-input multiple-output (MU-MIMO) fading channels. Unlike existing methods, our system dynamically adjusts transmission rates by leveraging the entropy of feature maps, channel state information (CSI), and signal-to-noise ratio (SNR), ensuring optimal communication resource usage. It incorporates feature map pruning, channel attention, spatial attention, and multi-head self-attention (MHSA) to effectively prioritize critical semantic features while minimizing unnecessary transmission overhead. Experimental results demonstrate that the proposed system outperforms separated source and channel coding and deep joint source and channel coding (Deep JSCC), in terms of rate-distortion performance, flexibility, and robustness, particularly in challenging scenarios such as low SNR, imperfect CSI, and inter-user interference. Copyright © 2025, The Authors. All rights reserved.

关键词： Fading channels

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Trustworthy deep learning for encrypted traffic classification

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Soft Computing 2025年第2期29卷 645-662页

作者： Li, Zheng Liu, Yanbei Zhang, Changqing Shan, Wanjin Zhang, Haifeng Zhu, Xiaoming School of Electronic and Information Engineering Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems Tianjin300387 China College of Intelligence and Computing Tianjin University Tianjin300072 China Science and Technology on Communication Networks Laboratory Shijiazhuang050081 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China

Network traffic classification refers to the identification of collected network traffic data of various applications, which is widely used in research fields such as network resource allocation, traffic scheduling and intrusion detection systems. With the widespread application of encryption technology in the network, encrypted traffic classification has become a hot research topic. At present, most existing methods only focus on the accuracy of network traffic classification. Yet, few work studies the reliability of the classification model, which plays an important role in network regulation and network security. In this paper, we propose a novel traffic classification method based on trustworthy deep learning model, which can effectively improve the reliability of encrypted traffic classification models by correcting the confidence of model output. Specifically, we firstly perform data preprocessing on the original network traffic, and then adopt a ConvNet for feature learning and a ClassifyNet for traffic classification in the initial stage. At the same time, we utilize a trustworthy confidence criterion to design a ConfidNet trained according to the probability of the true class. The ConfidNet can provide a reliable confidence measure for the prediction of the classification model. Finally, we demonstrate the effectiveness of our framework through comprehensive experiments on two benchmark datasets ISCX VPN-nonVPN and USTC-TFC2016, and show that our method can improve the reliability of the classification model and has a good ability to identify misclassified samples compared with state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Network intrusion

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Multimodal Gas Detection Using E-Nose and Thermal Images:An Approach Utilizing SRGAN and Sparse Autoencoder

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Computers, Materials & Continua 2025年第5期83卷 3493-3517页

作者： Pratik Jadhav Vuppala Adithya Sairam Niranjan Bhojane Abhyuday Singh Shilpa Gite Biswajeet Pradhan Mrinal Bachute Abdullah Alamri Artificial Intelligence and Machine Learning Department Symbiosis Institute of TechnologySymbiosis International(Deemed)UniversityPune412115India Symbiosis Centre of Applied AI(SCAAI) Symbiosis International(Deemed)UniversityPune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringUniversity of Technology SydneyUltimoNSW 2007Australia Department of Geology and Geophysics College of ScienceKing Saud UniversityRiyadh11362Saudi Arabia

Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time *** fusion of these modalities can result in the development of highly accurate diagnostic *** low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the *** objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal *** dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature *** Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal *** autoencoder was trained on the grayscale and colorized thermal *** SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of *** autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of *** multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,***,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.

关键词： Thermal imaging gas detection multimodal learning generative models autoencoders

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Multi-VM-Workloads Learning-based Forecasting Model for Resilient Server Management in Industry Clouds

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Procedia Computer science 2025年 260卷 923-929页

作者： Deepika Saxena Ashutosh Kumar Singh Division of Information Systems University of Aizu Aizuwakamatsu 965-0006 Fukushima Japan Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal 462003 Madhya Pradesh India

Forecasting server contention in large-scale cloud data center networks is crucial for efficient cloud resource management. The existing research primarily focuses on single-task machine learning methods for predicting resource-constrained VM failures, which often results in lower accuracy and higher computational costs. To overcome these challenges, this paper introduces a novel approach aimed at enhancing resilient server management in industry clouds. The proposed approach develops Multi-tasks-Learning-based Long Short-Term Memory for multiple VMs workload forecasting model named MVL-LSTM to simultaneously learn resource requirements of all VM hosted on a common server. It facilitates a single-step forecasting of workload resource requirements for all VM segments hosted on a server within a cluster. Experimental results using real-world Google Cluster VM datasets demonstrate that MVL-LSTM significantly improves forecasting accuracy while achieving faster execution and higher energy efficiency compared to existing single-task machine learning algorithms. Specifically, MVL-LSTM model achieves a 15% reduction in processing time and a 50% decrease in energy consumption compared with LSTM-based approach.

关键词： Cloud computing VM failure prediction MVL-LSTM Server management

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Heterogeneous Graph Structure Learning Based on Feature and Topology information Extraction 1st

Heterogeneous Graph Structure Learning Based on Feature and...

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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Li, Chao Li, Xin Zhu, Xiangkai Zeng, Qingtian Duan, Hua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9789819794119

Heterogeneous Graph Structure Learning (HGSL) aims at jointly learning optimized graph structure and representation. Its purpose is to enhance the performance and robustness of Heterogeneous Graph Neural Networks (HGNNs) by solving the problems of redundancy, bias, noise, incompleteness, and unreliability in graph structures. However, the existing research is still insufficient in selecting the multi-order neighborhood information aggregation method, which leads to increased computational complexity and over-convergence of multi-layer semantics. Meanwhile, when dealing with rich node features and complex topology, previous research exhibits the problem of insufficient information utilization. To solve these problems, we propose a novel model named Heterogeneous Graph Structure Learning Based on Feature and Topology information Extraction (HGSL-FTIE). First, we propose an n-hop meta-path extraction strategy. Based on this strategy, we fuse the node features to construct the feature structural subgraph and fuse the original graph structure to construct the topology structural subgraph. Then, we creatively adopt a structural subgraph fusion method based on the More Confident Fusion (MCF) strategy to guide the fusion of the feature structural subgraph and the topology structural subgraph to obtain the final graph structure and the corresponding node representation. We have conducted comprehensive experiments on three real datasets, and the results demonstrate that HGSL-FTIE outperforms the existing state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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