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

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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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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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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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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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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A video data hiding technique based on pixel sequence, weight interpolation and quorum function

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Multimedia Tools and Applications 2024年第41期83卷 88881-88901页

作者： Roselinkiruba, R. Jothi, C. Saranya Koushik, C.P. Keerthika, A. Sathya, Vanlin School of Computer and Science Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India School of Computer and Science Engineering VIT Bhopal University Bhopal India Electronics and Communication Engineering Aarupadai Veedu Institute of Technology Chennai India Celona Inc CampbellCA United States

The development of multimedia technology has increased the challenge in protecting the information. This paper proposes an effective method for data hiding in videos based on the Pixel Sequence (PS), weight interpolation to protect the secret data from intruders. Initially, the noise is removed from the video with the help of median filter and Moving Objects (MO) inside the frames having different backgrounds are accurately detected using Fast Region-based Convolution Neural Network (FR-CNN). Next, the MO and Non Moving Object (NMO) pixel from each frame are mapped with the adjacent frames to determine the weights applying PS. Then, the cover video is up-scaled with interpolated pixels based on weights of MO and NMO. Finally, the secret data are hidden inside the both interpolated and non-interpolated pixels elevate Embedding Capacity (EC). In interpolated pixels, both Most Significant Bit (MSB) and Least Significant Bit (LSB) of each frames based on their weight are utilized to hide data. Whereas, in non-interpolated pixels, Quorum Function (QF) is used to embed data. Two different data embedding techniques are adopted in each frame to enhance the security. The performance evaluation of the proposed method out-performs the existing methods in terms of precision and recall. Experimental results also show that the stego-video quality is uncompromised and is resistive to security attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Performance Challenges and Solutions in Big Data Platform Hadoop

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Recent Advances in computer science and Communications 2023年第9期16卷 27-41页

作者： Singh, Balraj Verma, Harsh K. Madaan, Vishu Department of Computer Science and Engineering Dr. B.R. Ambedkar NIT Jalandhar India School of Computer Science and Engineering Lovely Professional University Phagwara India

Background: The present era demands continuous support to bring improvements in executing complex analytics on large-scale data and to work beyond traditional systems. Objective: The need for processing diverse data types and solutions for different domains of the industry is rising. Such needs increase the requirement for sophisticated techniques and methods to enhance the existing platforms and mechanisms further. It provides an opportunity for the research community to investigate further into the existing systems, find potential issues, and propose new ways to improve the current systems. Hadoop is a popular choice to manage and process Big data. It is an open-source platform and a front-runner in the batch processing of large-scale jobs. The economy associated with the cluster in scaling is low as compared to other platforms. However, this popularity by no means guarantees high performance in all scenarios. With the continuous evolution in data development and industrial requirements, it is imperative to investigate and look into new methods and techniques to bring advancements to the existing system. Method: A systematic review is represented in this paper to have an insight into the current progress in this field. Research publications from various sources are taken and analyzed. The performance of a cluster largely depends upon the different job processing mechanisms and policies associated with it. Conclusion: While extensive studies and solutions are proposed, the performance bottlenecks in terms of load balancing, resource utilization, content management, and efficient processing prevail. Not many of the solutions are there on scheduling about the trade-off between different parameters, the process of content splitting and merging is not explored to a large extent and the skew mitigation solutions are more focused on Reduce side of the MapReduce while the Map side is not utilized much for load balancing. © 2023 Bentham science Publishers.

关键词： Batch data processing

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Privacy-Preserving Publishing Mechanism for Real-Time Vehicle Trajectory in Vehicle-to-Grid Networks 5

Privacy-Preserving Publishing Mechanism for Real-Time Vehicl...

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Wang, Yulong Xu, Jianbo Gong, Bo School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798331533694

The Vehicle-to-Grid (V2G) network is a smart grid technology generated under the background of the rapid development of new energy technology, which allows mobile energy storage vehicles (MESVs) to realize bidirectional energy exchange with the grid system. As distributed energy storage units within the V2G network, MESVs help meet the grid's load-balancing demands. To accurately schedule grid loads, V2G networks rely on MESVs' real-time trajectory data. After collecting this data, the central authority (CA) publishes it to the grid enterprise (GM) for load-balancing decisions. However, before publication, the CA must process the data to protect the location privacy of MESVs and prevent GM from extracting sensitive information. To address this privacy protection challenge during data publication, we propose a differential privacy-based mechanism. Our mechanism uses the unscented Kalman filter to predict and correct the published data at the sampling point and intelligently adjusts the sampling interval. Additionally, we introduce a privacy budget allocation method based on regional access frequency and travel time, enhancing privacy protection of trajectory in high-access areas and peak hours. Through security analysis and experimental validation, our mechanism effectively preserves the privacy of real-time vehicle trajectory data while ensuring data validity. © 2025 IEEE.

关键词： Vehicle-to-grid

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MeVs-deep CNN: optimized deep learning model for efficient lung cancer classification

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Multimedia Tools and Applications 2024年 1-32页

作者： Sewatkar, Ranjana M. Y, Asnath Victy Phamila School of Computer Science and Engineering VIT Chennai Vandalur-Kelambakkam Road Chennai Tamil Nadu600127 India

Lung cancer is a dangerous condition that impacts many people. The type and location of cancer are critical factors in determining the appropriate medical treatment. Early identification of cancer cells can save numerous lives, making the development of automated detection techniques essential. Although many methods have been proposed by researchers over the years, achieving high prediction accuracy remains a persistent challenge. Addressing this issue, this research employs Memory-Enabled Vulture Search Optimization based on Deep Convolutional Neural Networks (MeVs-deep CNN) to develop an autonomous, accurate lung cancer categorization system. The data is initially gathered from the PET/CT dataset and preprocessed using the Non-Local Means (NL-Means) approach. The proposed MeVs optimization approach is then used to segment the data. The feature extraction process incorporates statistical, texture, and intensity-based features and Resnet-101-based features, resulting in the creation of the final feature vector for cancer classification and the multi-level standardized convolutional fusion model. Subsequently, the MeVs-deep CNN leverages the MeVs optimization technique to automatically classify lung cancer. The key contribution of the research is the MeVs optimization, which effectively adjusts the classifier's parameters using the fitness function. The output is evaluated using metrics such as accuracy, sensitivity, specificity, AUC, and loss function. The efficiency of the MeVs-deep CNN is demonstrated through these metrics, achieving values of 97.08%, 97.93%, 96.42%, 95.88%, and 2.92% for training phase;95.78%, 95.34%, 96.42%, 93.48%, and 4.22% for testing percentage;96.33%, 95.20%, 97.65%, 94.83%, and 3.67% for k-fold train data;and 94.16%, 95.20%, 93.30%, 91.66%, and 5.84% for k-fold test data. These results demonstrate the effectiveness of the research. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 202

关键词： Lung cancer

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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