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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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Adjustable Model Compression Using Multiple Genetic Algorithm

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IEEE Transactions on Multimedia 2023年 25卷 1125-1132页

作者： Ople, Jose Jaena Mari Huang, Tai-Ming Chiu, Ming-Chih Chen, Yi-Ling Hua, Kai-Lung National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan

Generative Adversarial Networks (GAN) is a popular machine learning method that possesses powerful image generation ability, which is useful for different multimedia applications (e.g., photographic filters, image editing). However, typical GAN models have a large memory footprint that limits their practical applications for resource-constrained devices (e.g., smartphones). To deploy GAN models on devices with various hardware constraints, we propose our method, AdjustableGAN, which can compress a pretrained GAN model to different compression ratios. Our method compresses GAN by performing filter-wise pruning that follows these objectives: (1) deactivate convolutional filters for minimal performance decrease, (2) reactivate convolutional filters for maximal performance increase. We implement multiple Genetic Algorithms (GA) to perform each of these objectives - Downsize GA for best filter deactivations, while Upsize GA searches for best filter reactivations. By selective utilization of Upsize/Downsize GA, we could explicitly control the compression ratio of the model. For finalization, we fine-tune the compressed output model using the training dataset of the original input model. Our experimental results show that our method can reliably compress generative networks with minimal accuracy drop compared to other state-of-the-art compression algorithms. © 1999-2012 IEEE.

关键词： Generative adversarial networks

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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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Optimizing Technical Analysis and Stock Price Analysis Using: BLRM-QNDNL 21

Optimizing Technical Analysis and Stock Price Analysis Using...

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21st OITS International Conference on information technology, OCIT 2023

作者： Kalva, Sudhakar Naganjaneyulu, Satuluri Sudhakar, K. Computer Science and Engineering Department of Information Technology Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350358230

The Stock value forecast is a significant issue to determine the future direction of the financial Markets. Many research works are carried out and design many techniques to predict stock price of Individual stocks. But, the forecast precision/ of the regular methods was lower when taking the enormous size of the dataset. To address this downside, a Novel Deep Learning based Broken-Stick Linear Regressive MapReduce Based on Quasi-Newton Deep Neural Learning (BLRM-QNDNL) method is proposed right now. BLRM-QNDNL technique targeting optimizing the parameters engaged with stock profit prediction. Quasi-Newton based Deep Neural Network Learning (Q-NDNL) is applied in the BLRM-QNDNL procedure for precisely foreseeing the stock prices dependent on the news with higher accuracy. Q-NDNL used several hidden layer in order to thoroughly examine the effects of emotions conveyed in news items on stocks with the least amount of time complexity. The BLRM-QNDNL method expands the stock price forecast performance with lower time complexity when compared with cutting edge works. The BLRM-QNDNL technique has been evaluated exploratory on metrics such as prediction accuracy, prediction time, and false positive rate with regard to different numbers of data. When compared to state-of-the-art studies, the experimental results show that the BLRM-QNDNL technique can reduce the time complexity of stock price prediction while simultaneously increasing its accuracy. The suggested BLRM-QNDNL technique reduces the false positive stock price prediction rate by 41% and 57%, respectively and decreases market price prediction time complexity by 9 percent and 29 percent relative to traditional DeepClue [1] and MFNN [2], respectively. © 2023 IEEE.

关键词： MapReduce

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Hybrid energy efficient deep learning and redactable consortium blockchain-based secure framework for smart parking in smart cities

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Multimedia Tools and Applications 2024年第37期83卷 85391-85420页

作者： D, Amu Baskaran, Santhi Department of Computer science and Engineering Puducherry Technological University Puducherry India Department of Information Technology Puducherry Technological University Puducherry India

Management of vehicular parking in the crowded environment is the indispensable requirement for the smart city scenario. The advent and potential development of information Communication Technologies (ICT) and Internet of things (IoT) helped in proper thinking, operation and planning of parking management in smart city environment. But they suffer from the issue of privacy, security, integrity, energy efficiency, communication bandwidth and centralization. In this paper, Hybrid Deep learning and Redactable consortium blockchain-based Secure Framework (HDLRCBSF) is proposed for achieving the objective of energy-efficient smart parking in smart cities. This HDLRCBSF adopted the merits of Deep Bi-LSTM network for exploring the data associated with parking zones, such that optimal parking zones can be potentially allocated to the drivers with the ideal timing and location. It used the Redactable consortium blockchain network using Road Side Units (RSUs) which helped in attaining data verification and authentication at the security layer. It also implemented Threshold Chameleon Hash (TCH) algorithm for the objective of encrypting and decrypting the data related to the parking zones to establish secure communication. It adopted the significance of Deep Auto Regression Feature Augmented Bi-LSTM Networks in the analysis layer for exploring the cloud data that provides optimal parking lot to the drivers depending on the requirements that include cost, distance, and time. The quantitative and qualitative investigation conducted on the proposed HDLRCBSF confirmed its efficacy in terms of best parking space allocation rate of 21.32%, better than the baseline approaches that offered to the drivers with maximized privacy and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第6期36卷 2559-2573页

作者： D.Vidyabharathi V.Mohanraj Sona College of Technology Computer Science and EngineeringSalem636005India Sona College of Technology Information TechnologySalem636005India

For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over *** has been proved to enhance generalization as well as *** parameters,such as the network’s size,the number of hidden layers,drop-outs to avoid overfitting,batch size,and so on,are solely based on *** work has proposed Adaptive Teaching Learning Based(ATLB)Heuristic to identify the optimal hyperparameters for diverse *** we consider three architec-tures Recurrent Neural Networks(RNN),Long Short Term Memory(LSTM),Bidirectional Long Short Term Memory(BiLSTM)of Deep Neural Networks for *** evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate(CLR),Hyperbolic Tangent Decay(HTD),and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts(T-HTR)*** results have shown the performance improvement on the 20Newsgroup,Reuters Newswire and IMDB dataset.

关键词： Deep learning deep neural network(DNN) learning rates(LR) recurrent neural network(RNN) cyclical learning rate(CLR) hyperbolic tangent decay(HTD) toggle between hyperbolic tangent decay and triangular mode with restarts(T-HTR) teaching learning based optimization(TLBO)

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A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

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Recent Advances in computer science and Communications 2022年第4期15卷 620-635页

作者： Jain, Neetika Mittal, Sangeeta Department of Computer Science & Engineering and Information Technology Faculty of Engineering Jaypee Institute of Information Technology Noida India

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12

关键词： Telematics

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Image coding Image color analysis Quaternions Forensics Transform coding Color Feature extraction

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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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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