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A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

computers, Materials & Continua 2024年第4期79卷 1631-1657页

作者： Maria Gul Imran Ali Khan Gohar Zaman Atta Rahman Jamaluddin Mir Sardar Asad Ali Biabani May IssaAldossary Mustafa Youldash Ashraf Saadeldeen Maqsood Mahmud Asiya Abdus Salam Dania Alkhulaifi Abdullah AlTurkey31Department of Computer Science COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Computer Science Abbottabad University of Science and TechnologyAbbottabad22010Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Science and Technology Unit Umm Al-Qura UniversityMakkah21955Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Business Analytic Program Department of Management and MarketingCollege of Business AdministrationUniversity of BahrainSakhir32038Bahrain

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

关键词： Safe crowd evacuation public safety emergency transition probability cooperation

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

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computer Modeling in Engineering & sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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An Overview of Event Extraction Methods based on Semantic Disambiguation 24

An Overview of Event Extraction Methods based on Semantic Di...

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2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Li, Haili Wang, Xiaodong Zhou, Yunyan Liu, Weijie Pan, Shilong College of Computer Science and Technology National University of Defense Technology Hunan Changsha China Unit 63893 of PLA Henan Luoyang China Unit 63891 of PLA Henan Luoyang China

ISBN: (纸本)9798400711237

Event extraction is a fundamental and complex task in information extraction, aiming at automatically identifying and extracting structured event information from unstructured text. However, the same word may have different meanings in different contexts, necessitating semantic disambiguation during the classification process. The polysemy of triggers presents a significant challenge to accurate EE. Existing surveys primarily focus on different domains, fields, or technical paradigms, but none provide a systematic summary of the semantic disambiguation techniques employed. This study classifies these techniques into three categories based on the underlying technical frameworks and offers a comprehensive overview of the most advanced methods in each category. Finally, we summarize the application scenarios, strengths, and limitations of existing approaches, and discuss potential directions for future research. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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EVONChain: A Public Blockchain Architecture with Bi-tiered Network for Edge Computing 10

EVONChain: A Public Blockchain Architecture with Bi-tiered N...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Kong, Yihan Chen, Qinyang Li, Jing Xiong, Ting Xie, Tao National University of Defense Technology College of Computer Science and Technology Changsha China Guangdong University of Technology School of Computer Science and Technology Guangzhou China PLA Unit 96890 Jilin China

ISBN: (纸本)9798350312461

This paper introduces EVONChain, a nature-inspired evolvable blockchain that addresses scalability issues in large-scale blockchain applications. EVONChain decouples the functions of a full blockchain node into two consensus phases. A small number of high-performance cloud nodes in the inner network handle transaction validation, block packaging, and consistent replication using the 'longest chain' rule. Meanwhile, a large number of low-performance user nodes connected by Mobile Edge Computing (MEC) providers in the outer network handle the Proof of Intelligent (PoI), a novel Proof of Work (PoW) mechanism that resists Application-Specific Integrated Circuit (ASIC) computing and enables 'one-CPU-one-vote' principle. This architecture enables EVONChain to achieve perfect decentralization, high scalability and security. To evaluate EVONChain's performance, we developed a simulator. Simulation results show that EVONChain can achieve a throughput of over 10,000 transactions per second (TPS) with an inner network bandwidth of 4Gbps, a block interval of 10 seconds, and an orphaned block rate of less than 7%. This indicates that EVONChain significantly improves performance scalability for high-frequency applications, reduces confirmation time to the minute level, while maintaining a comparable level of security to the Ethereum network. © 2023 IEEE.

关键词： Blockchain

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Efficient Multi-Instance Hashing for Large-scale Image Retrieval 4

Efficient Multi-Instance Hashing for Large-scale Image Retri...

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4th International Conference on computer Vision and Data Mining, ICCVDM 2023

作者： Yang, Yao Xiao, Yajuan Liu, Yonghai Wang, Kuikui Unit 92020 of PLA Qingdao China School of Computer Science and Technology Shandong Jianzhu University Jinan China

ISBN: (纸本)9781510674448

Hashing has recently received excellent performance for similarity search on large-scale data set. In many scenarios, most image contains multi-instance features, and how to hash multi-instance image is an important task. Although some researchers focus on this problem and put forward solutions, they usually simply combine with existing hashing methods, which are not efficient methods for multi-instance hashing. To address this issue, we propose an Efficient Multi-Instance Hashing (EMIH) method. EMIH first construct bag features by multi-instance feature mapping. To improve the quality of hash codes, EMIH are both consider semantic correlation and local similarity preserving between bag features. Different from existing methods that relaxing the binary constraints, EMIH learn discrete codes directly by an alternate algorithm. Experimental results on two benchmark datasets demonstrate the superiori-ty of EMIH as compared to related multi-instance hashing methods. © 2024 SPIE.

关键词： Image retrieval

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Deep learning at the service of metaheuristics for solving numerical optimization problems

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Neural Computing and Applications 2025年 1-36页

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Thieu, Nguyen V. Gandomi, Amir H. School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Bangladesh Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 United States Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Faculty of Computer Science Phenikaa University Yen Nghia Hanoi Ha Dong12116 Viet Nam Department of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms. © The Author(s) 2025.

关键词： Contrastive Learning

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Combining Event Segment Classification and Graph Self-Encoder for Event Prediction 2

Combining Event Segment Classification and Graph Self-Encode...

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2nd International Conference on Big Data, Information and computer Network, BDICN 2023

作者： Yuan, Yewen Wang, Yongli Gong, Xiaoze Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Unit Baicheng63850 China

ISBN: (纸本)9798350321364

Scripted event prediction is the automatic learning of knowledge from unstructured text to predict subsequent events based on a given sequence of events. Existing work has mainly been based on event pairs or entire event chains for learning, which may ignore much structural and semantic information. In order to learn global dependencies between nodes in event evolution graphs, this paper proposes an event prediction method combining event segment classification and graph self-encoder, which uses a variational self-encoder of the graph to obtain the global structure of the event graph, learn and represent potential evolution patterns between events, and construct a global representation of the event graph. The method combines different rules to classify event segments, learns the features of different event segments, and predicts events by determining the scores of candidate events based on the correlation between the event segments and the candidate events. Experiments on a standard dataset demonstrate the effectiveness of the method. © 2023 IEEE.

关键词： Forecasting

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Combining Feature Fusion Link Prediction with Graph Neural Networks 2

Combining Feature Fusion Link Prediction with Graph Neural N...

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2022 2nd Conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Zheng, Yamin Wang, Yongli Gong, Xiaoze School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Unit Baicheng63850 China

ISBN: (纸本)9781510663374

At present, Graph Neural Network (GNN) methods usually follow the node centered message passing process, and rely heavily on smooth node characteristics rather than graph structure. In view of this limitation, based on the heuristic method and graph attention mechanism, a feature fusion link prediction model (SAFL) combined with graph neural network is proposed. This model extracts the enclosing subgraphs around the target, combines the attention mechanism to assign neighbor weights to learn useful structural features, considers the impact of different nodes on the link, and fuses the graph neural network with the characteristics of input nodes to predict the link. The experiment on OGB dataset shows that the proposed link prediction model based on heuristic method enhances the graph structure characteristics, effectively represents the connectivity of enclosing subgraphs, and achieves better performance in link prediction. © 2023 SPIE.

关键词： Graph neural networks

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