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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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Deep ensemble learning for osteoporosis diagnosis from knee X-rays: a preliminary cohort study in Kashmir valley

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Neural Computing and Applications 2024年第33期36卷 21041-21059页

作者： Wani, Insha Majeed Arora, Sakshi School of Computer Science and Engineering Shri Mata Vaishno Devi University J&K Katra India

Osteoporosis (OP) is the most prevalent and common bone disease, especially knee osteoporosis. It significantly disables sufferers all over the world. Although laborious and prone to user variation, manual diagnosis, segmentation, and annotation of knee joints continue to be the preferred way to diagnose OP in clinical procedures. Therefore, many deep learning algorithms, particularly the convolutional neural network (CNN), have been created to increase clinical workflow efficiency to overcome the shortcomings of the widely used method as above. Medical imaging procedures can show hidden structures in a volumetric view, particularly those that generate three-dimensional (3D) pictures like MRI. We created a dataset of 240 pictures from patients who had knee X-rays and skeletal bone mineral density assessments at the same time. Four convolutional neural networks (CNN) models were used to analyse the X-ray images and deep neural networks for clinical covariances to determine the degree of osteoporosis. Additionally, we investigated ensemble models that included each CNN with a clinical covariance. For every network, scores for accuracy and error rate were computed. ResNet and Alexnet displayed the highest levels of accuracy when the CNN models were tested using knee X-rays with normal, low BMD, and osteoporosis. An ensemble of DNN with Alexnet, ResNet, and both ResNet and Alexnet are employed resulting in improved accuracy. The ensemble of best-performing CNN and DNN is proposed to diagnose osteoporosis more accurately. The proposed method has produced a highly accurate osteoporosis diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Diseases

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Learner Phase of Partial Reinforcement Optimizer with Nelder-Mead Simplex for Parameter Extraction of Photovoltaic Models

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Journal of Bionic engineering 2024年第6期21卷 3041-3075页

作者： Jinpeng Huang Zhennao Cai Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065SichuanChina Department of Artificial Intelligence Wenzhou PolytechnicWenzhou325035China

This paper proposes an improved version of the Partial Reinforcement Optimizer(PRO),termed *** LNPRO has undergone a learner phase,which allows for further communication of information among the PRO population,changing the state of the PRO in terms of ***,the Nelder-Mead simplex is used to optimize the best agent in the population,accelerating the convergence speed and improving the accuracy of the PRO *** comparing LNPRO with nine advanced algorithms in the IEEE CEC 2022 benchmark function,the convergence accuracy of the LNPRO has been *** accuracy and stability of simulated data and real data in the parameter extraction of PV systems are *** to the PRO,the precision and stability of LNPRO have indeed been enhanced in four types of photovoltaic components,and it is also superior to other excellent *** further verify the parameter extraction problem of LNPRO in complex environments,LNPRO has been applied to three types of manufacturer data,demonstrating excellent results under varying irradiation and *** summary,LNPRO holds immense potential in solving the parameter extraction problems in PV systems.

关键词： Partial reinforcement optimizer Learner phase Nelder-Mead simplex algorithm Parameter extraction

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Digital twin empowered cooperative trajectory planning of platoon vehicles for collision avoidance with unexpected obstacles

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Digital Communications and Networks 2024年第6期10卷 1666-1676页

作者： Hao Du Supeng Leng Jianhua He Kai Xiong Longyu Zhou School of Information&Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China

Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road *** Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through cooperative sensing and ***,the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing *** this paper,we first design a platoon-based collision avoidance framework for *** this framework,we deploy a Digital Twin(DT)system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning *** addition,a DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT *** this case,the transmission frequency of kinetic states of platoon members can be reduced to ensure low-overhead ***,we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high *** further improve road safety,an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency *** results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle *** to the existing schemes,it can reduce collisions by 95%and is faster by about 10%passing by the unexpected obstacle.

关键词： Platoon driving Cooperative trajectory planning Digital twin Obstacle avoidance

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Gene selection and clustering of single-cell data based on Fisher score and genetic algorithm

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Journal of Supercomputing 2023年第7期79卷 7067-7093页

作者： Feng, Junhong Zhang, Jie Zhu, Xiaoshu Wang, Jian-Hong School of Computer and Information Engineering Guangdong Songshan Polytechnic Guangdong Shaoguan512126 China School of Computer Science and Engineering Yulin Normal University Guangxi Yulin537000 China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Huge amounts of genes in single-cell RNA sequencing (scRNA-seq) data may influence the performance of data clustering. To obtain high-quality genes for data clustering, the study proposes a novel gene selection algorithm based on Fisher score and genetic algorithms with dynamic crossover (abbreviated as FDCGA). To reduce time and space complexity, FDCGA first employs Fisher score to gain the preliminary candidate genes and then utilizes genetic algorithms with dynamic crossover to select beneficial genes to data clustering and analysis. The experimental results conducted on several publicly real-world scRNA-seq datasets demonstrate that FDCGA outperforms the other several competitors in terms of both NMI and ARI metrics and possesses significant optimization performances. The experimental convergence shows that the fitness of FDCGA can increase and converge to a fixed state versus the number of iterations. The statistical analysis demonstrates that FDCGA statistically significantly outperforms the other competing methods. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Genetic algorithms

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

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computer Systems science & engineering 2023年第7期46卷 659-670页

作者： Sanghyuck Nam Suhwan Kwak Jaehwan Lee Sangoh Park School of Computer Science and Engineering Chung-Ang UniversityDongjak-guSeoul06974Korea Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea

Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted *** autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing *** solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation ***,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their *** are also challenging to process computation tasks within 100 ms which is the time limit for driving *** this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving *** proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of *** also conduct simulations in the highway environment to evaluate the existing scheme and the proposed *** result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.

关键词： Task offloading vehicular edge computing vehicular ad-hoc network dedicated short-range communication autonomous platooning

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Multi-Objective Computation Offloading Based on Decentralized Deep Reinforcement Learning in Industrial Internet of Things

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1940-1953页

作者： Zhao, Yingjie Chai, Zhengyi Li, Yalun Huang, Hao Kang, Hongshen Tiangong University School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics and Information Engineering Tianjin300387 China

With the increasing scale of industrial equipments, delay and energy consumption have emerged as critical concerns within the Industrial Internet of Things (Industrial IoT). Mobile edge computing (MEC) offloads tasks to nearby edge servers to meet the demands of delay-sensitive applications. However, the limitations of edge computing resources can lead to significant processing delays or even task failures when offloading numerous tasks. Furthermore, it is difficult for existing centralized algorithms to acquire global information within large-scale industrial environment. To tackle these challenges, the computation offloading problem is transformed into a decentralized partially observable Markov decision process (Dec-POMDP) with rewards for delay and energy consumption, and a decentralized multi-objective computation offloading method is proposed to achieve the long-term reward maximization. Specifically, two deep neural networks, namely the delay and energy network, are designed to estimate the expected rewards for each offloading decision in terms of delay and energy consumption. Meanwhile, to achieve dynamic offloading, gated recurrent unit (GRU) is introduced to predict the occupancy of edge computing resources, and an adaptive weight network is devised to dynamically adjust the weights of optimization objectives based on historical information. Comprehensive experiments demonstrate that the proposed method effectively meets the requirements of delay-sensitive tasks, as well as minimizing long-term delay and energy consumption. © 2015 IEEE.

关键词： Deep reinforcement learning

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Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security 1

Proposed AES-based Obfuscation and Encryption Model for Clou...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 IEEE.

关键词： Cloud platforms

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