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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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The Impact of Federated Learning on IoT Security: An Empirical Analysis 7

The Impact of Federated Learning on IoT Security: An Empiric...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Mehta, Shiva Sharma, Ajay Dogra, Ayush Hariharan, Shanmugasundaram Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350372816

This paper presents how Federated Learning (FL) will be applied in multiple areas, including smart homes, work environments, and healthcare systems, to enhance the security level of Internet of Things (IoT) systems. The rising number of Internet of Things devices has put focus on obscuring and managing these connected systems as a severe security issue. Traditional centralized classifiers frequently lack the required cyber-threat vulnerability, scalability, and data protection attributes. In this way, federated learning fills the communication gap between users (aka data producers) and servers by processing data locally, and on-device study demonstrates that the FL models get a significant advantage in the performance of the false positive reduction and the threat detection accuracy in comparison with the conventional models of operation. FL, though, may not be giving 92% accurate prediction in detecting the hazards as traditional models do with 88% correct prediction. Also, FL excelled in prior models by scoring 90% in industrial settings and 89% in the hospital setting, with an accuracy of 95% in the industrial setting and 93% in a ***, the recognition rate for ties grew from 5% to 3%, with 2% for correctly matched tie types compared to the traditional methods at 10% for correctly matched ties and 8% for corresponding tie types. The contribution is made using a mixed-methods approach comprising simulations and experimental sets to quantify the performance of FL. INTERPRETATION: FL has proved to be the best (from the results) among other security measures because it reduces data exposure and compliance with data privacy laws (strictness). © 2024 IEEE.

关键词： Federated learning

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Colored Structured Light Based on Nanoscale 3D Printing

Colored Structured Light Based on Nanoscale 3D Printing

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2023 Flat Optics: Components to systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Wang, Hongtao Wang, Hao Yang, Joel K.W. Engineering Product Development Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Institute of Materials Research and Engineering Singapore138634 Singapore

Through nanoscale two-photon polymerization (TPP) 3D printing, structured optical beams can be extended to colored structured light under incoherent white light illumination, promising higher-dimensional structured li... 详细信息

ISBN: (纸本)9781957171289

关键词： 3D printing

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A Resourceful Energy Management and Improved Security in Wireless Sensor Networks by Optimized Energy-Based Security Prototype

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SN computer Science 2025年第3期6卷 1-13页

作者： Arya, Nancy Deore, Shailesh Shivaji Sharma, Mahendra Verma, Amit Patibandla, R. S. M. Lakshmi Krishna, K. Murali Pareek, Piyush Kumar Department of Computer Science and Engineering School of Engineering & Sciences GD Goenka University Sohna Gurugram India Department of Computer Engineering SSVPS B S DEORE College of Engineering Maharashtra Dhule India DoIT IMT College of Engineering UP Greater Noida India Department of Computer Science University Centre for Research and Development Chandigarh University Gharuan Punjab Mohali India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Electronics Communication Engineering Rajalakshmi Engineering College TN Chennai India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

One must admit that while using a number of remote sensing points with limited nodes, it is crucial to optimize resource usage as intensely as possible and have a secure network with the least compromises, even though WSNs are applied in increasing numbers of industries. When deploying WSN, power utilization and ability to withstand a number of threats and attacks are important factors to be considered. This work presents a technique for a routing protocol that determines the shortest path between two nodes in a network while at the same time trying to conserve energy. Lifetime and energy consumption of the networks are two important factors when designing new protocols for WSN. An important problem of finding a load-sharing routing method with less energy consumption is difficult. TBEBR routing protocol optimizes the parameters that lead to minimal energy dissipation to enhance the lifetime of the network by identifying the path with the least energy consumption possible. Throughput, overhead, packet loss and end to end delay were used as performance parameters to assess the routing protocol. The analysis of the overall efficiency of the routing concept as well as the method of its application was described. As far as the performance of network and nodes is concerned, it is clear that TBEBR outperforms both DSR and AODV. For WSNs, the proposed routing protocol aims at minimizing the energy issue, which is a disadvantage of the technology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DoS attack EWMA Unit energy requirement WSN

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A Generalized ChatGPT-Based Collaborative Multi-Objective Decision-Making Framework for Robust Vehicle Platoon Collision Avoidance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7212-7225页

作者： Zhang, Hanwen Hou, Chenfei Chen, Jicheng Zhang, Hui Wang, Fei-Yue Beihang University Hangzhou Innovation Institute Hangzhou310056 China Beihang University School of Transportation Science and Engineering Beijing100191 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China Obuda University Physiological Controls Research Center University Research and Innovation Center Budapest1034 Hungary Macau University of Science and Technology Macau Institute of Systems Engineering Macau999078 China

In this work, we propose a ChatGPT-based framework that collaborates with human expert instructions for safe and efficient vehicle platoon control. The collaborative framework integrates multi-objective decision-making to address challenges such as robust collision avoidance in complex traffic environments. To incorporate human- like reasoning, ChatGPT is firstly used to interpret human expert driving instructions, which often involve linguistic variables. It generates behavioral decisions and computes feasible trajectories for each vehicle to ensure driving safety. A distributed model predictive controller is then utilized for accurate, real-time path tracking. To efficiently handle ChatGPT response failures, a receding instruction utilization strategy is proposed. For critical response failures, hindsight experience replay is used to supplement ChatGPT's knowledge database and improve response accuracy in complex scenarios. Simulation tests and experimental validations demonstrate the framework's effectiveness and generalizability across various traffic conditions, highlighting its potential for broader applications in platoon control. © 1967-2012 IEEE.

关键词： Reinforcement learning

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An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

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Journal of Bionic engineering 2022年第5期19卷 1504-1521页

作者： Saeid Raziani Sajad Ahmadian Seyed Mohammad Jafar Jalali Abdolah Chalechale Department of Computer Engineering Razi UniversityKermanshah ***Iran Faculty of Information Technology Kermanshah University of TechnologyKermanshah ***Iran Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsMelbourne 3217Australia

Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex *** models such as stochastic gradient descent have been developed to train ***,they mainly suffer from falling into local optima leading to reduce the accuracy of ***,the convergence speed of training process depends on the initial values of weights and biases in ***,these values are randomly determined by most of the training *** deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of ***,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local *** addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis *** medical diagnosis datasets are utilized to evaluate the efficiency of the proposed ***,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification *** experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets.

关键词： Feed forward neural network Meta-heuristic algorithm Whale optimization algorithm Optimization Classification Bionic algorithm

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Advancing IoT Security through Federated Learning: A Comprehensive Approach 3

Advancing IoT Security through Federated Learning: A Compreh...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Mehta, Shiva Khurana, Meenu Dogra, Ayush Hariharan, Shanmugasundaram Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350375190

IoT devices have changed into one of the mainstays in areas like houses, industry and education, resulting in a level of convenience and efficiency that has never been achieved before. Even though the most important advantage of the distributed network is raising the level of security, the problem of security vulnerabilities with the extended networks remains. This paper is devoted to a novel strategy of Federated Learning (FL) that could become an applied solution for enhancing the safety of the IoT. Therefore, based on its decentralized knowledge process, FL allows IoT devices to cooperate in achieving common purposes without endangering data pri vacy. The paper uses this thinking method to deal with scalability, model personalization, and non-IID data that aren't IID distributing, suggesting solutions through model aggregation techniques or Artificial Intelligence (AI). Experimental evidence highlights that FL is very useful in enhancing IoT security irrespective of the diversity of its applications. To this end, residents of smart homes experienced a 17% increase in detecting the anomalies through the setting up of FL. This situation was replicated in the industrial Internet of Things, where the false positive rates were reduced from 20% to 5%, thus improving the system's efficiency. Furthermore, in the FL context in healthcare IoT ecosystems, this blazes a trail for privacy-preserving data analytics, preserving patient confidentiality and systematic profiling of noteworthy health trends. These findings thus demonstrate that FL is a feasible way of dealing with the security challenges of IoT scalable, promptly and in a manner that maintains user privacy. The article takes part in a conversation about ways to strengthen the security of IoT networks. Such a framework is based on the computational power of IoT devices. It allows us to detect and prevent threats in time. Nonetheless, it urges extensive R&D attempts to be introduced to develop better security solutio

关键词： Internet of things

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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