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Multiple unmanned ship coverage and exploration in complex sea areas

Autonomous Intelligent Systems 2024年第1期4卷 210-223页

作者： Feifei Chen Qingyun Yu Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai200092China Shanghai Municipal Economic and Information Technology Commission Shanghai200125China

This study addresses the complexities of maritime area information collection,particularly in challenging sea environments,by introducing a multi-agent control model for regional information *** on three key areas—regional coverage,collaborative exploration,and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection *** regional coverage,a multi-objective optimization model considering effective area coverage and time efficiency is proposed,utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning,achieving a 99.67%effective coverage rate in *** exploration is tackled through a comprehensive optimization model,solved using an improved greedy strategy,resulting in a 100%static target detection and correct detection *** obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm,ensuring autonomous obstacle avoidance without communication or *** confirm zero collaborative *** research offers practical solutions for multi-agent exploration and coverage in unknown sea areas,balancing workload and time efficiency while considering ship dynamics constraints.

关键词： Multi-agent control model Regional coverage Collaborative exploration Agent obstacle avoidance

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina Department of Computer and Network Engineering University of JeddahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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Heterogeneous Ensemble Feature Selection Model(HEFSM)for Big Data Analytics

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computer Systems science & engineering 2023年第5期45卷 2187-2205页

作者： M.Priyadharsini K.Karuppasamy Department of Information Technology Sri Ramakrishna Engineering CollegeCoimbatoreTamilnaduIndia Department of Computer Science&Engineering RVS College of Engineering and TechnologyCoimbatoreTamilnaduIndia

Big Data applications face different types of complexities in *** and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed *** existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution *** ensemble methods have made a mark in classification tasks as combine multiple results into a single *** comparing to a single model,this technique offers for improved *** based feature selections parallel multiple expert’s judgments on a single *** major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple *** major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple ***,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this ***(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning *** results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.

关键词： PSO(Particle Swarm Optimization) GWO(GreyWolf Optimization) EHO(Elephant Herding Optimization) data mining big data analytics feature selection HEFSM classifier

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Vehicle-Edge Collaborative Intelligent Driving Task Processing With Task Complexity and Service Availability Awareness

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IEEE Internet of Things Journal 2025年第13期12卷 24503-24517页

作者： Wang, Nuanlai Pang, Shanchen Ji, Xiaofeng Wang, Min He, Xiao Gui, Haiyuan Lu, Xiaoping College of Computer Science and Technology Qingdao266580 China Yangzhou University College of Information Engineering Jiangsu Yangzhou225127 China COSMO Industrial Intelligence Institute Qingdao266580 China

As wireless communication technology advances, the convergence of the Internet of Vehicles (IoV) and edge computing is increasingly underpinning the progression of intelligent driving systems. Despite these advances, challenges persist in the stability and efficiency of vehicle-edge collaborative computing frameworks. This study introduces a Service Availability and Task Complexity-Aware Scheduling Framework for Intelligent Driving Tasks (SATCAS-IDT), ensuring the reliability of task scheduling between user vehicles and In-Vehicle Computing Platforms (IVCP) during high-speed mobility. Initially, a task complexity quantification model for intelligent driving is constructed, incorporating metrics such as information entropy, image contrast, energy, and color space. Subsequently, a vehicle mobility model tailored for high-speed multi-lane environments is designed, upon which the Mobility and Service Availability Aware IVCP Initial Screening Algorithm (MSAIF-IVCP) is developed, pre-selecting IVCPs that meet mobility and service availability requirements. Lastly, the Double Deep Recurrent Reinforcement Learning Task Scheduling Algorithm with Multi-observation Hybrid Encoder (DDRRL-MOHE) is proposed. By intelligently balancing task delay and processing accuracy, this algorithm dynamically adapts edge computing resources, effectively reducing response times and enhancing overall system performance. Experimental validation shows that our proposed method improves the task processing accuracy by about 1.70% on average and reduces the average latency by about 1.97% over the baseline method. © 2025 IEEE.

关键词： Deep reinforcement learning

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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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Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

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computer Systems science & engineering 2023年第9期46卷 2667-2685页

作者： I.Mettildha Mary K.Karuppasamy Department of Information Technology Sri Ramakrishna Engineering CollegeCoimbatoreTamilnaduIndia Department of Computer Science&Engineering RVS College of Engineering and TechnologyCoimbatoreTamilnaduIndia

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance ***,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution ***(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between *** problem can be overcome by the use of Wrappers as they select better features by accounting for test and train *** aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between *** proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)*** methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

关键词： Cloud analytics machine learning ensemble learning distributed learning clustering classification auto selection auto tuning decision feedback cloud DevOps feature selection wrapper feature selection Adaptive Kernel Firefly Algorithm(AKFA) Q learning

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On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

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Machine learning-based opinion extraction approach from movie reviews for sentiment analysis

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Multimedia Tools and Applications 2025年第17期84卷 18599-18624页

作者： Jassim, Mustafa Abdalrassual Abd, Dhafar Hamed Omri, Mohamed Nazih MARS Research Laboratory University of Sousse Sousse Tunisia College of Engineering Al-Muthanna University Samawah Iraq College of Computer Science and Information Technology University of Anbar Ramadi Iraq

The field of sentiment mining, also known as sentiment analysis and sentiment extraction, has shown a surge with diverse applications. Various approaches to automate the process of sentiment analysis have been introduced focusing on machine learning, data mining, and natural language processing. Existing works examine the feelings embedded in people’s opinions and beliefs that affect various areas applications, including companies’ services and products. Movie review must undergoes various processes to detect and name feelings and achieve better accuracy in decision making. Due to the structure of the language, the difficulties have been increased for the natural language processing algorithms, particularly for the grammar and dictionary management. In this paper, a new approach for extracting words from a specific text and classifying them into positive or negative opinion classes is proposed. We employ pre-processing and extraction technique for the selection of 500 to 20,000 words based on their frequency, which can be utilized using vector construction. This is performed using "Term Frequency" and "Term Frequency-Inverse Document Frequency". Four Naive Bayes models (including Complement, Multinomial, Bernoulli, and Gaussian) are implemented and evaluated over unseen samples. Our extensive simulation results indicated that the Naive Bayes multinomial model achieved the best performance with 86.46% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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One-step quantum dialogue

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Chinese Physics B 2024年第3期33卷 95-104页

作者：朱鹏辉钟伟杜明明李喜云周澜盛宇波 College of Science Nanjing University of Posts and TelecommunicationsNanjing 210023China College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China

Quantum dialogue(QD)enables two communication parties to directly exchange secret messages *** conventional QD protocols,photons need to transmit in the quantum channel for two *** this paper,we propose a one-step QD protocol based on the *** the help of the non-local hyperentanglement-assisted Bell state measurement(BSM),the photons only need to transmit in the quantum channel *** prove that our one-step QD protocol is secure in theory and numerically simulate its secret message capacity under practical experimental *** with previous QD protocols,the one-step QD protocol can effectively simplify the experiment operations and reduce the message loss caused by the photon transmission ***,the non-local hyperentanglement-assisted BSM has a success probability of 100%and is feasible with linear optical ***,combined with the hyperentanglement heralded amplification and purification,our protocol is possible to realize long-distance one-step QD.

关键词： one-step quantum dialogue hyperentanglement hyperentanglement distribution non-local Bell-state measurement

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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management

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IEEE Access 2025年 13卷 97628-97646页

作者： Rokaya, Mahmoud Hemdan, Dalia I. Alajmani, Samah Hazzaa Alyami, Raneem Yousif Elmarhomy, Ghada Hashim, Hasan Atlam, El-Sayed College of Computers and Information Technology Taif University Department of Information Technology P.O. Box 11099 Taif21944 Saudi Arabia Taif University Faculty of Science Department of Food Science and Nutrition P.O. Box 11099 Taif21944 Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia Tanta University Department Computer Science Faculty of Science Tanta31527 Egypt Taibah University College of Computer Science and Engineering Department of Computer Science Yanbu6421 Saudi Arabia

The scheme of water resources management is a necessity for reducing water scarcity in arid areas and improving water availability in general [1]. However, water leak detection and irrigation scheduling traditional AI models are often computationally intensive and require complex hyperparameter tuning, making them less scalable. This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. Moreover, ontology-based feature structuring enhances interpretability, while hyperparameter tuning (GridSearchCV, Bayesian Optimization) and model compression techniques (pruning, quantization, knowledge distillation) ensure computational efficiency. A large number of experiments on real-world IoT sensor data testify to the effectiveness of the framework. It achieves 0.992 AUC-ROC scores for leak detection, an RMSE of 0.227 hours for irrigation scheduling, and an overall accuracy of 94.8%. Additional performance measures comprise precision (89.0%), recall (95.2%), F1-score (0.92), and inference speed (0.003 ms/sample). Although quantization has reduced the computational overhead, we still see a 13.02% increase in the model size as seen in Experiment 6, leading to a trade-off that needs to be optimized further. This study offers a deployable AI-based model for sustainable water management by tackling the issues of scalability, computational cost, and limitations in benchmark evaluation. By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems. © 2013 IEEE.

关键词： Resource allocation

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