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EACR-LEACH:Energy-Aware Cluster-based Routing Protocol for WSN Based IoT

computers, Materials & Continua 2022年第8期72卷 2159-2174页

作者： Sankar Sennan Kirubasri Youseef Alotaibi Digvijay Pandey Saleh Alghamdi Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Technical Education Department of Electronics EngineeringInstitute of Engineering and TechnologyDr.A.P.J.Abdul Kalam Technical UniversityLucknowIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Internet of Things(IoT)is a recent paradigm to improve human ***,number devices are connected to the Internet ***,the people can control and monitor the physical things in real-time without *** IoT plays a vital role in all kind of fields in our world such as agriculture,livestock,transport,and healthcare,grid system,connected home,elderly people carrying system,cypher physical system,retail,and intelligent *** IoT energy conservation is a challenging task,as the devices are made up of low-cost and low-power sensing devices and local *** networks have significant challenges in two areas:network lifespan and energy ***,the clustering is a right choice to prolong the energy in the *** LEACH clustering protocol,sometimes the same node acts as CH again and again *** overcome these issues,this paper proposes the Energy-Aware Cluster-based Routing(EACRLEACH)protocol in WSN based *** Cluster Head(CH)selection is a crucial task in clustering protocol inWSN based *** EACR-LEACH,the CH is selected by using the routing metrics,Residual Energy(RER),Number of Neighbors(NoN),Distance between Sensor Node and Sink(Distance)and Number of Time Node Act as CH(NTNACH).An extensive simulation is conducted on MATLAB *** accomplishment of EACR-LEACH is compared to LEACH and *** proposed EACR-LEACH protocol extends the network’s lifetime by 4%-8%and boosts throughput by 16%–24%.

关键词： Cluster head clustering protocol internet of things routing metrics wireless sensor networks

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Leveraging ChatGPT for enhanced stock selection and portfolio optimization

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Neural Computing and Applications 2025年第8期37卷 6163-6179页

作者： Huang, Zhendai Liao, Bolin Hua, Cheng Cao, Xinwei Li, Shuai College of Computer Science and Engineering Jishou University Jishou416000 China School of Business Jiangnan University Wuxi214122 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland VTT-Technology Research Center of Finland Kaitovayla 1 Oulu90570 Finland

The public release of ChatGPT represents a significant milestone in generative AI technology, enabling the autonomous generation of content based on pre-training. This breakthrough presents new opportunities for advancements in the field of portfolio selection. This paper aims to introduce a comprehensive portfolio selection method by applying ChatGPT for stock selection and combining it with optimization algorithms to jointly optimize portfolio selection. Compared to randomly selected stocks, the portfolios optimized using ChatGPT-selected stocks and solved with the egret swarm optimization algorithm (ESOA) demonstrate higher diversification and lower volatility, leading to superior portfolio optimization results. Additionally, to validate ESOA’s superiority, its performance is compared against genetic algorithm (GA) and particle swarm optimization (PSO) on five metrics: risk, expected return, Sharpe ratio, objective value, and penalty term. Under equivalent experimental setting, ESOA exhibits a better ability to balance the relationship between risk and return. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Optimization algorithms

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Cost-Effective Task Offloading and Resource Scheduling for Mobile Edge Computing in 6G Space-Air-Ground Integrated Network

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IEEE Internet of Things Journal 2025年第12期12卷 19428-19442页

作者： Zhu, Wenwu Deng, Xiaoheng Gui, Jinsong Zhang, Honggang Min, Geyong Central South University School of Electronic Information Changsha410075 China School of Electronic Information Shenzhen Research Institute Central South University Changsha410075 China Umass Boston Engineering Department BostonMA02125 United States University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

With the advent of the sixth-generation (6G) wireless communications, transmission speeds are projected to exceed tenfold those of 5G, reaching theoretical peak download speeds of up to 1 Tbps. Data transmission capacity and speed will be significantly enhanced, enabling emerging applications, such as mixed reality, federated learning, and digital twins, driving exponential data traffic growth. To address this, the space-air-ground integrated network (SAGIN) combines satellite, aerial, and ground communication technologies, offering seamless global coverage and high-speed connectivity. In this article, we proposes an SAGIN framework integrated with mobile edge computing (MEC) to jointly optimize system energy consumption and delay costs. Specifically, we decompose the optimization problem into three subproblems: 1) uncrewed aerial vehicle (UAV) computational resource allocation;2) satellite computational resource allocation;and 3) task offloading and channel allocation. The subproblems are then transformed and addressed using Newton's interior point method and the deep reinforcement learning DQN algorithm to derive optimal allocation strategies for UAV and satellite computing resources, along with task offloading and channel resources, that our proposed algorithm effectively reduces system energy consumption and delay costs compared to other algorithms. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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MTOClus: Multi-Type Objects Clustering in Heterogeneous information Networks 3

MTOClus: Multi-Type Objects Clustering in Heterogeneous Info...

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3rd International Conference on Communications, information System, and Data science, CISDS 2024

作者： Liang, Yongjie Hu, Wujie Wu, Junjie Wu, Jinzhao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Electric Engineering Guangxi University Nanning China Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

Clustering is crucial for analyzing heterogeneous information networks (HINs). Mainly state-of-the-art algorithms often focus on single-type node clustering, overlooking the clustering of multiple node types and requiring manual setting of cutoff distances and clustering centers. This paper introduces a clustering algorithm integrating multi-type objects, named MTOClus, which generates multiple similarity matrices for different node types and assigns weights to each matrix to indicate their significance. By aggregating weighted similarity matrices, it creates a distance matrix. MTOClus automates cluster center selection through node sorting and mitigates the impact of outliers by utilizing the K-Nearest Neighbors approach to compute the cutoff distance. Experimental results across four datasets consistently show that MTOClus outperforms six other algorithms, underscoring its superior clustering efficacy. © 2025 SPIE.

关键词： Clustering algorithms

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Invasive weed optimization for multi-AGVs dispatching problem in a matrix manufacturing workshop

Invasive weed optimization for multi-AGVs dispatching proble...

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作者： Li, Zhong-Kai Sang, Hong-Yan Li, Jun-Qing Han, Yu-Yan Gao, Kai-Zhou Zheng, Zhi-Xin Liu, Li-li School of Computer Science Liaocheng University Liaocheng 252059 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

This paper studies automatic guided vehicle dispatching problem (AGVDP) with time and capacity constraints. The purpose of this problem is to find a solution with low transportation cost. To reduce the transportation cost, punishment for violation of time and AGV cost have to be considered. A mathematical model is formulated according to practical problems. A new type of AGV is proposed to reduce the AGV cost. An effective discrete invasive weed optimization algorithm (DIWO) is proposed with some new strategies and methods to solve the problem. In the proposed algorithm, a nearest-neighbor-based heuristic is used to generate initial population with high quality. Two effective neighborhood operators are proposed to generate new solutions with high efficiency. Two theorems are proposed to reduce unnecessary calculations. A local search based on insertion is proposed to enhance the local search capability. A new strategy of delaying departure is proposed to reduce the punishment for violation of time. A heuristic method is proposed to balance the delayed departure time and vehicle carrying capacity. A heuristic method is proposed to reduce the computational time complexity of local search. A comprehensive simulation experiment based on the 110 instances from an actual production factory validates the effectiveness of the proposed algorithm. © 2023

关键词： Automatic guided vehicles

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Proposed Privacy Preservation Technique for Color Medical Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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DF-CFAR: deep feature constant false alarm ratio detector based on feature game for sea-surface small target with robustness to sea states

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Neural Computing and Applications 2025年第19期37卷 14029-14048页

作者： Xiang, Houhong Zhu, Liangliang Chen, Yufeng Wang, Fengyu Zeng, Xiaolu School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Department of Early Warning Technology Wuhan Early Warning Academy Wuhan430019 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

The classical constant false alarm rate (CFAR) detector is optimal for target detection in Gaussian white noise but struggles with unknown, time-varying sea states. Data-driven target detection methods are highly sensitive to clutter distribution, leading to poor detection performance and high false alarm rates (Pfa) under unknown sea states. This paper proposes a deep feature constant false alarm ratio (DF-CFAR) detector based on a feature game model. The input to the feature extraction network is fragmented fast time-dimensional data processed by coherent integration, incorporating a feature game mechanism. This mechanism effectively mines target echo features that are uncorrelated with the background sea clutter. By transforming the target detection problem under varying sea state conditions into a regression prediction problem, we achieve constant false alarm detection through a statistical probability threshold. Simulation and real data experiments demonstrate that, compared to commonly used algorithms, the proposed method not only achieves higher detection probability and lower false alarm rates but also exhibits excellent constant false alarm characteristics and stronger robustness to unseen sea states. Results from publicly available X-band radar data processing show that the proposed algorithm can eliminate the influence of different sea clutters and improve the detection performance of sea-surface small targets by an equivalent signal-to-clutter ratio (SCR) of about 3.1 dB. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Gaussian distribution

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Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

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computers, Materials & Continua 2021年第2期66卷 1155-1174页

作者： Abdullah Baz Hosam Alhakami Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

The World Health Organization declared COVID-19 a pandemic on March 11,2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of ***-19 has now affected millions across 211 countries in the world and the numbers continue to *** information discharged by the WHO till June 15,2020 reports 8,063,990 cases of *** the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug,the nations are relentlessly working at the most ideal preventive systems to contain the *** Kingdom of Saudi Arabia(KSA)is additionally combating with the COVID-19 danger as the cases announced till June 15,2020 reached the count of 132,048 with 1,011 *** to the report released by the KSA on June 14,2020,more than 4,000 cases of COVID-19 pandemic had been registered in the *** to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease,our examination expects to assess the severity of COVID-19 pandemic in cities of *** addition,computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the ***,a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi ***,this paper has taken the Fuzzy Analytic Hierarchy Process(AHP)technique for quantitatively assessing the severity of COVID-19 pandemic in cities of *** discoveries and the proposed structure would be a practical,expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of *** these urban zones clearly emerge as the COVID-19 *** cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the *** analy

关键词： Kingdom of Saudi Arabia COVID-19 severity assessment fuzzy AHP dynamics of infection

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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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