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HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion

International Journal of Security and Networks 2024年第4期19卷 188-198页

作者： Ying, Zhilei Meng, Yanan Chen, Ruoxi Lou, Jianlou School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin 132022 China Department of Automation Jilin Institute of Chemical Technology Jilin 132022 China School of Computer Engineering Northeast Electric Power University Jilin 132012 China

Timely transmission line fire inspections are vital for power system safety. Although deep learning models are widely used for flame detection, struggle with small target recognition due to background interference and are vulnerable to input data perturbations, posing security risks. This study introduces a hierarchical feature fusion method based on the you only look once Version 8 (YOLOV8) framework. It employs high-performance GPU network version 2 (HGNetV2) to enhance small-target feature extraction while reducing computational complexity. A spatial pyramid pooling-fast module with large separable kernel attention is designed to highlight key features and suppress background noise. The proposed bidirectional slim neck structure (BiSlimneck) reduces feature loss. Adversarial training enhances the model’s robustness. Experimental results show a 1.4% accuracy improvement, with a reduction in parameters and complexity by 18.3% and 21.6%, respectively. After adversarial training, the accuracy of the network in the face of attacks increased by 10% compared to the pre-training. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Premixed flames

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SpGCLF: A versatile deep graph contrastive learning framework for spatial transcriptomics analysis

SpGCLF: A versatile deep graph contrastive learning framewor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Xiang Yu, Junnan Li, Min Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

The rapid development of spatial transcriptomics (ST) has revolutionized the study of spatial heterogeneity and increased the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a critical step for downstream analysis and biological interpretation. Here, we propose a deep graph contrastive learning framework (spG-CLF) for spatial transcriptomics, which leverages contrastive learning through graph convolutional networks to effectively learn features among spots, addressing the issue of poor cross-platform generalizability. By alternately performing attribute and topological denoising, our method specifically reduces noise in ST data. We demonstrate that s p GCLF is effective for spatial domain identification, trajectory inference, multi-slice integration, and type annotation. © 2024 IEEE.

关键词： Contrastive Learning

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Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

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Recent Advances in computer Science and Communications 2024年第4期17卷 40-53页

作者： Murugan, Ramalingam Paliwal, Manish Patibandla, Rama Seetha Maha Lakshmi Shah, Pooja Balaga, Tarakeswara Rao Gurrammagari, Deepti Raj Singaravelu, Parvathavarthini Yenduri, Gokul Jhaveri, Rutvij School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India AP India Kallam Haranadhareddy Institute of Technology Andra Pradesh Guntur India Department of Computer Applications Kongu Engineering College Erode Perundurai India

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem. © 2024 Bentham Science Publishers.

关键词： Knowledge management

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Reconfiguration of vertex-disjoint shortest paths on graphs

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Journal of Graph Algorithms and Applications 2024年第3期28卷 87-101页

作者： Saito, Rin Eto, Hiroshi Ito, Takehiro Uehara, Ryuhei Graduate School of Information Sciences Tohoku University Sendai Japan School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

We introduce and study reconfiguration problems for (internally) vertex-disjoint shortest paths: Given two tuples of internally vertex-disjoint shortest paths for fixed terminal pairs in an unweighted graph, we are asked to determine whether one tuple can be transformed into the other by exchanging a single vertex of one shortest path in the tuple at a time, so that all intermediate results remain tuples of internally vertex-disjoint shortest paths. We also study the shortest variant of the problem, that is, we wish to minimize the number of vertex-exchange steps required for such a transformation, if exists. These problems generalize the well-studied Shortest Path Reconfiguration problem. In this paper, we analyze the complexity of these problems from the viewpoint of graph classes, and give some interesting contrast. © 2024, Brown University. All rights reserved.

关键词： Graph theory

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Research on Domain Text Classification Algorithm Based on Multi-Granularity Fusion 24

Research on Domain Text Classification Algorithm Based on Mu...

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4th International Joint Conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Yao, Xiaotong Wu, Yuntao Yu, Xiaopeng Huang, Yufei School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China School of Management Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798400710100

The current research on domain text classification is primarily based on word granularity to facilitate the extraction and representation of text information. However, this approach is susceptible to the influence of out-of-vocabulary words and is insufficient for inter-word and inter-sentence semantic representation. Given the capacity of sentence template to effectively summarize sentence semantics, this paper proposes a domain text classification algorithm based on multi-granularity fusion. The algorithm introduces sentence template information to enhance text features from a semantic perspective, constructs embedding vectors of characters, as well as word and sentence templates. It extracts the contextual semantic features of these vectors using BiLSTM, further identifies key features of the characters and words through the attention mechanism, and finally, the information at each granularity is fused using the weighted fusion module to obtain the final semantic feature vectors. These vectors are then input into the softmax function for classification prediction. Experimental results demonstrate that the sentence template information significantly improves the model's ability to represent text semantics. Additionally, the weighted feature fusion method effectively retains and utilizes more feature information. These two improvements enhance classification accuracy by 1.77% and 1.49%, respectively. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Information fusion

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Gesture-based mouse control system based on MPU6050 and Kalman filter technique

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International Journal of Intelligent Systems Technologies and Applications 2023年第1期21卷 56-71页

作者： Prasanth, Narayanan Shrivastava, Kaustubh Sharma, Abhishek Basu, Aritri Sinha, Ritwik A. Raja, S.P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India

The recognition of gestures with accuracy is still a widespread challenge which leads to prevention in the usage of wearable gesture recognition systems. In this paper, we proposed a gesture-based mouse, a smart glove based system for gesture recognition and remote control. To reduce the dependency of the gesture recognition system on external conditions like light and network connection, we eliminate the need of a camera and use a glove fitted with the MPU6050 sensor to detect motion accurately with minimal latency. The elimination of the camera component also reduced the overall component cost significantly. In this paper, we discuss the algorithm followed to map hand motion to cursor movement on the screen via the pseudocode and flowchart. Using this methodology, basic functionalities of the mouse and its operations were implemented with less expense and to more effectiveness. © 2023 Inderscience Enterprises Ltd.

关键词： Cameras

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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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Soft Computing Paradigms for Load Balancing in Cloud Computing 3rd

Soft Computing Paradigms for Load Balancing in Cloud Computi...

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Ghafir, Shabina Alam, M. Afshar Alankar, Bhavya Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9789819719457

This paper provides a comprehensive comparison of load-balancing algorithms employed in cloud computing, spanning from the earliest to the most recent developments. Emphasizing the significance of load balancing for system performance, resource optimization, and equitable allocation among users, the paper offers insights into various techniques, their effectiveness across workloads, and associated limitations. With an overview of each algorithm, this study caters to practitioners and researchers in the dynamic field of cloud computing, aiming to contribute valuable perspectives for enhanced system efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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TRS Scheduling for Improved QoS Performance in Cloud System

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computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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Core-Competitiveness in Partially Observable Networked Market 24

Core-Competitiveness in Partially Observable Networked Marke...

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33rd ACM Web Conference, WWW 2024

作者： Li, Bin Hao, Dong School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798400701719

In auction theory, a core is a stable outcome where no subgroup of participants can achieve better results for themselves. Core-competitive auctions aim to generate revenue that is achievable in a core. They are particularly important because they not only generate optimized revenue for the seller, but also provide an efficient and stable environment for participants. We generalize the design of core-competitive auctions to encompass partially observable networked markets (PONM). Unlike traditional auctions, which often deal with scenarios of limited trading activity, our approach to core-competitive auctions for PONM captures the nature of real-world transaction markets, which is a large linking world for the economic entities and commodities circulate among the entities in the market. Our generalizing the auction market to PONM can much improve the liquidity of the auction, and is especially meaningful for the web economics. Specifically, we quantify the upper and lower bounds of the minimum core revenue in PONM, and further prove that there does not exist any truthful auction for PONM which is efficient and core-competitive. Governed by this impossible result, we identify the criteria that the allocation rule for PONM should meet. Based on these criteria, we propose a new class of auction mechanisms for PONM that is individually rational, incentive-compatible, and core-competitive. © 2024 ACM.

关键词： Competition

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