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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Cross-View Graph Consistency Learning for Invariant Graph Representations 39

Cross-View Graph Consistency Learning for Invariant Graph Re...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Jie Mao, Hua Woo, Wai Lok Liu, Chuanbin Peng, Xi College of Computer Science Sichuan University China Department of Computer and Information Sciences Northumbria University United Kingdom Center for Scientific Research and Development in Higher Education Institutes Ministry of Education China National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation Sichuan University China

ISBN: (纸本)157735897X

Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a coupled graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Quantum Graph Neural network for Resource Management in Wireless Communication

Quantum Graph Neural Network for Resource Management in Wire...

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7th International Conference on Artificial Intelligence in information and Communication, ICAIIC 2025

作者： Giang, Le Tung Tung, Nguyen Xuan Hwang, Won-Joo Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of School of Computer Science and Engineering Pusan National University Center for Artificial Intelligence Research Busan46241 Korea Republic of

ISBN: (纸本)9798331507022

Effective resource management in wireless communication systems is a critical requirement, given the interference challenges and the need for efficient allocation strategies. Graph Neural networks (GNNs) have emerged as powerful tools for tackling this problem due to their scalability and ability to generalize over complex graph-structured data, like that found in wireless networks. However, the computational complexity of GNNs on large-scale systems limits their real-time deployment. Recent advances in quantum computing and quantum machine learning (QML) offer a promising solution by leveraging the unique properties of quantum systems to reduce computational overhead. This paper proposes a Quantum Graph Neural network (QGNN) model specifically designed for a wireless communication scenario, implemented as a variational quantum circuit (VQC) to realize the message-passing mechanism integral to GNNs. Applied to a supervised resource management task in a wireless network, the proposed QGNN demonstrates encouraging results, showcasing its potential as a scalable and efficient approach for real-time resource management in future wireless systems. © 2025 IEEE.

关键词： Resource allocation

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm for Single Objective Optimization

3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Abdel-Nabi, Heba Ali, Mostafa Z. Awajan, Arafat Alazrai, Rami Daoud, Mohammad I. Suganthan, Ponnuthurai N. Reynolds, Robert Princess Sumaya University for Technology Department of Computer Science Amman Jordan Jordan University of Science & Technology Faculty of Computer & Information Technology Irbid Jordan Mutah University Computer Science Department Karak Jordan German Jordanian University Department of Computer Engineering Amman Jordan School of Electrical & Electronic Engineering Nanyang Technological University Singapore Kindi Center for Computing Research College of Engineering Qatar University Doha Qatar College of Engineering Wayne State University Detroit United States

ISBN: (纸本)9798350314588

This paper proposes a novel hybridization of two metaheuristic algorithms to solve the real-parameter single objective numerical optimization problems. The proposed Three-stage Cyclic Hybrid SFS and L-SHADE (3-sCHSL) algorithm integrates the high-level interactions of Stochastic Fractal Search (SFS) and L-SHADE algorithms in a single framework. Furthermore, a guided control population initialization strategy is injected in 3-sCHSL to address the stagnation and diversity loss issues as the evolution process evolves. The performance of the proposed algorithm is tested under different complexity levels of different transformations of the CEC 2021 benchmark suite with dimension 20. The experimental results demonstrated the efficiency and competitiveness of the proposed algorithm against recent state-of-the-art algorithms. In addition, 3-sCHSL achieved a superior performance when evaluating two engineering design problems. © 2023 IEEE.

关键词： Optimization

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Return-Aligned Random Graph Diffusion with Dual-Channel Temporal Convolutional network-Based Classification of Epithelial Ovarian Cancer on T2W-MRI

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-21页

作者： Lakshmi, D. Venkata Madhusudhan, Akku Muniyandy, Elangovan Preethi, S. School of Computer Science and Engineering VIT - AP University Andhra Pradesh Amaravati India Department of Electronics and Communication Engineering Anurag University Telangana Hyderabad India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai 602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Science and Engineering Cambridge Institute of Technology KR Puram Karnataka Bengaluru 560036 India

This study aims to develop a highly accurate and efficient deep-learning framework for the automated classification of epithelial ovarian cancer (EOC) subtypes using T2-weighted MRI (T2W-MRI) images. The objective is to overcome limitations such as poor contrast, high inter-class variation, dataset imbalance, and computational complexity that hinder current diagnostic methods. To address these, we propose the return-aligned random graph diffusion with dual-channel temporal convolutional network (RA-RGD-DCTCNet) model, evaluated on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. Image quality is first enhanced using Discrete Wavelet Transformation with Pre-Gaussian Filtering (DWT-PGF), followed by precise tumor segmentation via the return-aligned decision transformer (RADT). The random graph diffusion dual-channel temporal convolutional network (RGD-DCTCNet) performs feature extraction and classification, with accuracy further boosted by the Secretary Bird Optimization Algorithm (SBOA). Experimental results demonstrate that the RA-RGD-DCTCNet model achieves 99.9% accuracy and 99.8% sensitivity, significantly outperforming existing methods and showing promise for clinical application in reliable, automated cancer diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Epithelial ovarian cancer Gene Expression Omnibus Return-aligned decision transformer Secretary Bird Optimization Algorithm The Cancer Genome Atlas

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POFM: Profit-Oriented Flow Management in SDN-Based Data center networks

POFM: Profit-Oriented Flow Management in SDN-Based Data Cent...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Wang, You-Chiun Liang, Jhong-Ting Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (纸本)9798350353464

In a data center network (DCN), there are numerous hosts connected by well-organized switches, usually involving a large amount of data transmission to offer cloud services. How to manage flows in a DCN by deciding packet routes is critical. This paper applies the software-defined networking (SDN) technique to flow management in fat-tree DCNs. Unlike previous studies, we consider that flows have different values and associate flows with profits to reflect their values. The practical profit acquired from each flow will depend on the proportion of successfully sent packets. The paper proposes a profit-oriented flow management (POFM) scheme to maximize the profit obtained while reducing packet loss in flows. In POFM, once a link becomes congested, some flows on that link may be rerouted to other paths. When no alternative path can be found, POFM adjusts the data rates of flows on the congested link based on multiple factors, including their demanded rates, profits, and transmission durations. With simulations, we show that POFM can effectively raise the overall profit and decrease the packet loss rate in the DCN. ©2024 IEEE.

关键词： Packet loss

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Trend Ratio-Based Portfolio Optimization Model Adopting Entanglement-enhanced Quantum-Inspired Evolutionary Computation in the Global Financial Markets

Trend Ratio-Based Portfolio Optimization Model Adopting Enta...

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Jiang, Yu-Chi Chang, Ming-Ho Chang, Yu-Yu Wu, Kun-Min Chen, Po-Chun Tong, Yong Feng Lai, Yun-Ting Kuo, Shu-Yu Chou, Yao-Hsin National Taiwan University Department of Electrical Engineering Taiwan National Chi Nan University Department of Computer Science and Information Engineering Taiwan National Taiwan University Center for Quantum Science and Engineering Department of Physics Taiwan

ISBN: (纸本)9798350314588

Financial management is a critical and complicated issue. People tend to invest in the stock market and expect a stable uptrend portfolio to spread the investment risk effectively. As global economies affect each other, investing in global markets is the best way to address systematic risk for effective financial management. Selecting an appropriate portfolio across global markets involves a substantial solution space;thus, an efficient and effective evolutionary computation is proposed. The novel entanglement-enhanced quantum-inspired optimization technique is proposed as an efficient mechanism to search for the relationship between stocks with high-dependency solutions. The novel indicator trend ratio aims to evaluate the investor-desired portfolio with a stable uptrend under a perfect balance between return and risk. Therefore, this study is the first attempt to evaluate the major global markets in the Group of Seven (G7) countries to expand the generality of trend ratio usage. The experiments demonstrate that the proposed novel approach has better and more precise searchability than other state-of-the-art optimization. The proposed intelligent model outperforms the Sharpe ratio, benchmark strategy, and index performance regarding the trend ratio, risk, maximum drawdown, and profit factor. The results reveal that risk dispersion substantially improves investment performance. Under the evaluation of the trend ratio, the proposed system integrates G7 markets as an expansion of a prospective avenue for global asset management © 2023 IEEE.

关键词： Financial markets

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A Lightweight Multi-domain Multi-attention Progressive network for Single Image Deraining 24

A Lightweight Multi-domain Multi-attention Progressive Netwo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhong, Junliu Li, Zhiyi Xiang, Dan Han, Maotang Li, Changsheng Gan, Yanfen School of Computer Science Guangzhou Maritime University Guangdong Guangzhou China Network and Information Service Center Guangdong University of Foreign Studies South China Business College Guangdong Guangzhou China School of Low Altitude Equipment and Intelligent Control Guangzhou Maritime University Guangdong Guangzhou China School of Computer and Information Engineering Guangdong Polytechnic of Industry and Commerce Guangdong Guangzhou China

ISBN: (纸本)9798400706868

Currently, the information processing in a spatial domain alone has intrinsic limitations that hinder the deep network's effectiveness (performance) improvement in a single image deraining. Moreover, the deraining networks' structures and learning processes are becoming increasingly intricate, leading to challenges in structural lightweight, and training and testing efficiency. We propose a lightweight multi-domain multi-attention progressive network (M2PN) to handle these challenges. For performance improvement, the M2PN backbone applies a simple progressive CNN-based structure consisting of the S same recursive M2PN modules. This recursive backbone with a skip connection mechanism allows for better gradient flow and helps to effectively capture low-to-high-level/scales spatial features in progressive structure to improve contextual information acquisition. To further complement acquired spatial information for better deraining, we conduct spectral analysis on the frequency energy distribution of rain steaks, and theoretically present the relationship between the spectral bandwidths and the unique falling characteristics and special morphology of rain steaks. We present the frequency-channel attention (FcA) mechanism and the spatial-channel attention (ScA) mechanism to fuse frequency-channel features and spatial features better to distinguish and remove rain steaks. The simple recursive network structure and effective multi-domain multi-attention mechanism serve as the M2PN to achieve superior performance and facilitate fast convergence during training. Furthermore, the M2PN structure, with a small network component quantity, shallow network channels, and few convolutional kernels, requires only 168K parameters, which is 1 to 2 orders of magnitude lower than the existing SOTA networks. The experimental results demonstrate that even with such a few network parameters, M2PN still achieves the best overall performance. © 2024 ACM.

关键词： Image enhancement

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