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Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

computer Modeling in engineering & sciences 2023年第6期135卷 2173-2196页

作者： Weimin Zheng Mingchao Si Xiao Sui Shuchuan Chu Jengshyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative *** has good performance in global optimization fields such as *** this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)*** strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local *** paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test *** results show that PACS algorithmoutperforms other algorithms in 20 of 28 test *** to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular *** results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

关键词： Rectangular layout cuckoo search algorithm parallel communication strategy adaptive parameter

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Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

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Digital Communications and Networks 2024年第2期10卷 328-336页

作者： Bo Yi Jianhui Lv Xingwei Wang Lianbo Ma Min Huang College of Computer Science and Engineering Northeastern UniversityShenyang110169China Pengcheng Lab. Shenzhen518055China College of Software Northeastern UniversityShenyang110819China College of Information Science and Engineering Northeastern UniversityShenyang110819China

The rapid development of 5G/6G and AI enables an environment of Internet of Everything(IoE)which can support millions of connected mobile devices and applications to operate smoothly at high speed and low ***,these massive devices will lead to explosive traffic growth,which in turn cause great burden for the data transmission and content *** challenge can be eased by sinking some critical content from cloud to *** this case,how to determine the critical content,where to sink and how to access the content correctly and efficiently become new *** work focuses on establishing a highly efficient content delivery framework in the IoE *** particular,the IoE environment is re-constructed as an end-edge-cloud collaborative system,in which the concept of digital twin is applied to promote the *** on the digital asset obtained by digital twin from end users,a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention(TPA)enabled Long Short-Term Memory(LSTM)***,the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning(RL)***,a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing *** experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate,the average throughput,the successful content delivery rate and the average routing overhead.

关键词： Digital twin IoE Content delivery Caching Routing

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Performance issue monitoring, identification and diagnosis of SaaS software:a survey

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Frontiers of computer science 2025年第1期19卷 71-95页

作者： Rui WANG Xiangbo TIAN Shi YING College of Mining and Safety Engineering Shandong University of Science and TechnologyQingdao 266590China School of Computer Science Wuhan UniversityWuhan 430072China

SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and diagnosis for performance issues is typically expensive and laborious because of the complexity of the application software and the dynamic nature of the deployment environment. Recently, substantial research efforts have been devoted to automatically identifying and diagnosing performance issues of SaaS software. In this survey, we comprehensively review the different methods about automatically identifying and diagnosing performance issues of SaaS software. We divide them into three steps according to their function: performance log generation, performance issue identification and performance issue diagnosis. We then comprehensively review these methods by their development history. Meanwhile, we give our proposed solution for each step. Finally, the effectiveness of our proposed methods is shown by experiments.

关键词： SaaS software performance log generation performance issue identification performance issue diagnosis

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A Dual-Layer Attention Based CAPTCHA Recognition Approach with Guided Visual Attention

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computer Modeling in engineering & sciences 2025年第3期142卷 2841-2867页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Enhancing website security is crucial to combat malicious activities,and CAPTCHA(Completely Automated Public Turing tests to tell computers and Humans Apart)has become a key method to distinguish humans from *** text-based CAPTCHAs are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable *** this regard,we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual *** have specifically adapted the well-established image captioning task to address this *** approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance CAPTCHA *** extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and Captcha 0.3—shows the adaptability and efficacy of our *** approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for *** results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets,showcasing its robustness,reliability,and ability to handle varied challenges in CAPTCHA recognition.

关键词： Text-based CAPTCHA image recognition guided visual attention web security computer vision

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2025年第17期84卷 17951-17977页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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Early-stage stroke prediction based on Parkinson and wrinkles using deep learning

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Neural Computing and Applications 2024年第30期36卷 18781-18805页

作者： Haritha, T. Babu, A. V. Santhosh Department of Information Technology Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India

The application of noninvasive methods to enhance healthcare systems has been facilitated by the development of new technology. Among the four major cardiovascular diseases, stroke is one of the deadliest and potentially fatal, but, if detected early enough, a patient's life may be spared. Most stroke research has centered on MRI and CT scans for uncomplicated categorization. This medical approach (imaging) is costly, time-consuming and needs the utilization of complex technology. To make up for these shortcomings, however, there has been a lot of interest in adopting noninvasive, measurable EEGs. Nevertheless, the raw data should be classified before the proper characteristics can be formed, both the forecasting algorithms and the analytical techniques demand time. As a result, this work proposes a deep learning-based model that aims to predict the chance of stroke at an early stage utilizing Parkinson's disease and wrinkles as markers. A patient may have a stroke disease if they are diagnosed with both Parkinson's disease and wrinkles. To the best of our knowledge, this research is the first to use these biomarkers to predict the risk of having a stroke. The proposed model achieves a higher accuracy of 94.7% on the considered dataset. Additionally, the recommended model was evaluated and tested in terms of loss, training time, accuracy, recall, and F1-score versus the other existing models. With less price and pain than present testing approaches, these discoveries are predicted to result in major enhancements in the early detection of strokes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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A comprehensive systematic review of machine learning in the retail industry: classifications, limitations, opportunities, and challenges

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Neural Computing and Applications 2024年第4期37卷 2035-2070页

作者： Hassan, Dler O. Hassan, Bryar A. Department of Computer Science College of Science Charmo University Kurdistan Region Chamchamal Sulaimani46023 Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

Machine learning has profoundly transformed various industries, notably revolutionizing the retail sector through diverse applications that significantly enhance operational efficiency and performance. This comprehensive review examines the state-of-the-art machine learning applications in the retail sector from 2019 to 2024, focusing on supervised learning, unsupervised learning, and ensemble methods. It aims to identify and categorize recent machine learning applications in retail, evaluate the performance of machine learning algorithms, and determine the most suitable algorithms for specific retail use cases. This review article examines 56 studies and identifies 20 unique machine learning applications within the retail sector. This review also discusses the challenges and opportunities of implementing machine learning in retail, offering valuable insights to guide future research and enhance retail performance and customer satisfaction. The findings highlight the strengths and limitations of different machine learning methods, providing insights into their practical applications and future potential. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Ensemble methods Machine learning Retail Supervised learning Unsupervised learning

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A High-Speed Compression and Secure Authentication Framework for Data Sharing Cloud-Enabled Big Data Environments

A High-Speed Compression and Secure Authentication Framework...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 IEEE.

关键词： Data integrity

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An efficient integrity based multi-user blockchain framework for heterogeneous supply chain management applications

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International Journal of computers and Applications 2023年第4期45卷 337-351页

作者： Karumanchi, Mani Deep Sheeba, J.I. Devaneyan, S. Pradeep Department of Computer Science and Engineering Bapatla Engineering College Bapatla India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry India

Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of integrity computational and data security due to structured data and computational memory. As the size of the cloud data files is increasing in the public and private cloud servers, it is difficult to provide strong data security due to file format and high dimensionality. The computational time and storage space of the conventional attribute-based encryption and decryption models are high during the data integrity verification in the traditional blockchain frameworks. This paper implements a hybrid variable size data integrity algorithm on the heterogeneous cloud supply chain data files for the strong data encryption and decryption process. This work implements an optimized blockchain framework using the advanced heterogeneous integrity computation approach and integrity policy-based attribute encryption and decryption approach for better cloud data security. Experimental results proved that the proposed integrity-based encryption model has better efficiency than the traditional integrity-based encryption frameworks on cloud heterogeneous data types. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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