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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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A new technology perspective of the Metaverse:Its essence,framework and challenges

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Digital Communications and Networks 2024年第6期10卷 1653-1665页

作者： Feifei Shi Huansheng Ning Xiaohong Zhang Rongyang Li Qiaohui Tian Shiming Zhang Yuanyuan Zheng Yudong Guo Mahmoud Daneshmand School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Jinzhong University Shanxi030619China Department of Business Intelligence and Analytics and the Department of Computer Science Stevens Institute of TechnologyHobokenNJ07030USA

The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.

关键词： Metaverse Technical framework Ubiquitous connections Space convergence Virtuality and reality interaction Human-centered communication

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Multi-objective Cluster-head-based Energy-aware Routing using a Separable Convolution Neural Network in a Wireless Sensor Network

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IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 105-112页

作者： Ather, Danish Mary, J. Prisca Singh, Pooja Garg, Kanika Priyadharshini, T.R. Princy, B. Anni Tiwari, Mohit Department of IT and Engineering Amity University in Tashkent Uzbekistan Department of Computer Science and Engineering NPR College of Engineering and Technology Tamil Nadu Dindigul India Department of Physics Faculty of Science SGT University Haryana Gurugram India Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Ghaziabad Modinagar India Department of Information Technology Hindusthan College of Engineering and Technology Pollachi main road Coimbatore India Department of Computer and Communication Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

Wireless sensor network (WSN) applications are added day by day owing to numerous global uses (by the military, for monitoring the atmosphere, in disaster relief, and so on). Here, trust management is a main challenge. Sensor nodes are important in wireless sensor networks, but they are easily depleted because of their short lifespan from continuous sensing activity and low battery capacity. So efficient energy utilization is a challenging task in a WSN. To minimize energy loss, clustering with an optimum path selection process is needed to retain energy in sensor nodes. This manuscript proposes multi-objective Pelican Optimization Algorithm (POA) routing to maintain energy efficiency and minimize transmission distances in wireless sensor networks. A cluster head (CH) is selected by using a Separable Convolution Neural Network (SCNN). Simulation outcomes prove the proposed technique attains 22.3% and 25.04% improvements in energy consumption when compared to the Multi-Objective CH Energy-aware Optimized Routing Approach at WSN (MOCH-EORA-WSN) and Multiple Optimum Cluster Head Multi-Objective Grasshopper Optimization with Harmony-search at WSN (MOCH-MOGOH-WSN), respectively. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Energy utilization

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Hyperelliptic curve signcryption based privacy preserving message authentication and fuzzy TOPSIS-based trust management scheme for fog enabled VANET

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International Journal of Information Technology (Singapore) 2025年第1期17卷 247-261页

作者： Govindaraju, Kaladi Sreenath, N. Department of Computer Science and Engineering Puducherry Technological University Puducherry 605014 India Department of Computer Science and Engineering Aditya University Surampalem 533437 India

In open-access and self-organized network like Vehicular Ad Hoc Network (VANETs), privacy and trust are two dimensions of security which need to be enforced for preventing and mitigating the possible compromization of attacks. Trust is an important aspect which aids in assessing the degree of cooperation rendered by each vehicular node in the fog-enabled VANETs. In specific, the selfish behavior of vehicular nodes degrades the network performance and prevents the cooperative vehicle from attaining potential merits from the network using the process of false information broadcasting. In this context, a reliable and efficient model of security that helps in handling the impact of existing selfish node and malicious attacks in the network becomes necessary. In this paper, hyperelliptic curve signcryption based privacy preserving message authentication and Fuzzy TOPSIS-based trust management (HCSPPMA) scheme is proposed for tackling the influence of malicious attacks and selfish nodes for attaining maximized data dissemination process. This HCSPPMA scheme utilized the potentialities of hyperelliptic curve signcryption mechanism for determining the vehicular nodes legitimacy and for attaining the process of node authentication. It included the benefits of Fuzzy TOPSIS-based trust management scheme for exploring different impact factors that aids in determining the trust established between vehicular nodes during message exchanges in fog-enabled VANETs. This approach is deployed over fog nodes instead of Road Side Units (RSUs) for improving the throughput and reducing the latency. The results of this HCSPPMA scheme confirmed low false positive rates of 22.19%, minimized communication overhead of 24.12%, compared to the benchmarked approaches. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Fog enabled Vehicular Ad Hoc Network (VANETs) Fuzzy TOPSIS Hyperelliptic curve signcryption Message authentication Node authentication Road side units (RSUs)

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Enhancing Prediction Accuracy with an Innovative Hybrid Fuzzy Time Series Framework

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SN computer science 2025年第2期6卷 1-11页

作者： Gogoi, Abhijit Borah, Bhogeswar Department of Computer Science and Engineering Tezpur University Naapam Assam Tezpur 784028 India Department of Computer Science and Engineering Dibrugarh Uiversity Dibrugarh India

This study introduces a new hybrid fuzzy time series (FTS) model designed to improve forecasting abilities by independently applying forecasting techniques using both overlapping and non-overlapping partitions. By leveraging fuzzy logical relationships, the model enhances forecast reliability for decision-making. The independent application of the overlapping and non-overlapping partitions allows for a comparative analysis, demonstrating the superior forecasting accuracy of overlapping partitions. Evaluation using the Root Mean Square Error metric consistently demonstrates that the hybrid FTS model yields better forecasting results when overlapping partitions are considered. Real-world datasets across various domains, such as enrollment data from the University of Alabama, market indices, car sales figures, gold prices, lynx populations, sunspot counts, and car accidents, are employed to assess the effectiveness of the proposed FTS forecasting model. This evaluation highlights the potential of the hybrid FTS model as a dependable forecasting technique with broad applicability. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Forecasting Fuzzy logic Fuzzy logical relations Fuzzy time series Time-invariant Time-variant

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Stacked autoencoder with weighted loss function for intrusion detection in IoT application

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Multimedia Tools and Applications 2024年 1-29页

作者： Gangula, Rekha Vutukuru, Murali Mohan Kumar, M. Ranjeeth Department of Computer Science and Engineering Vaagdevi Engineering College Telangana BollikuntaWarangal506005 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur522502 India School of Computer Science & amp Artificial Intelligence SR University Anantasagar Telangana Warangal506371 India

The fast increase of network traffic in recent times causes significant detection of intrusions in Internet of Things (IoT) environments. Currently, Deep Learning (DL) models play a crucial role in cyber security for malicious identification and intrusion detection in IoT networks. The existing methods have drawbacks like overfitting, data imbalance, and not completely capturing complex dependencies and relationships among input and features that are significant for intrusion detection. To overcome these limitations, Stacked Autoencoder (SAE) with weighted loss function is proposed for effective Intrusion Detection System (IDS). The SAE includes weighted loss function to minimize the overfitting issue and finally, the One Class-Support Vector Machine (OCSVM) is used in the classification layer to classify the intrusions. The database used for intrusion detection in IoT environment are Bot-IoT and ToN-IoT databases, which undergo pre-processing by using standard scalar and min–max normalization to remove duplicates and inconsistent data in the databases. Then, the optimal features are selected by using firefly optimizer which selects active features values for classification. In the resulting phase, the stacked autoencoder obtains 99.99%, and 99.70% of classification accuracy on the Bot-IoT and ToN-IoT databases, respectively, which are superior while compared to the traditional autoencoder model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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EPDTNet + -EM: Advanced Transfer Learning and SubNet Architecture for Medical Image Diagnosis

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Cognitive Computation 2025年第2期17卷 1-22页

作者： K, Dhivya K, Sangamithrai S, Indra Priyadharshini M, Vedaraj Department of Electronics and Communication Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & d Institute of Science and Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Tamilnadu Kavaraipettai Thiruvallur601206 India

This paper presents a novel medical imaging framework, Efficient Parallel Deep Transfer SubNet+-based Explainable Model (EPDTNet + -EM), designed to improve the detection and classification of abnormalities in medical images. The model integrates Enhanced Exponential Transfer Learning (En-ETL) and the Parallel SubNet (PSNet +) architecture, addressing challenges like overfitting, high computational cost, and limited generalization across complex datasets. En-ETL accelerates learning and enhances precision, while PSNet + balances computational efficiency and performance through parallel convolutional layers and an axis attention mechanism. The proposed framework preprocesses medical images using resizing, noise removal, and contrast enhancement, ensuring high-quality inputs for analysis. For classification, the PSNet + model categorizes medical images into normal and abnormal cases. Explainable AI (XAI) is applied to highlight key features in classified images, improving interpretability and clinical decision-making. Experimental results validate the framework on various datasets, including MRI, CT, X-ray, and mammography images. The proposed model achieves a classification accuracy of 98.83%, a false positive rate of 2%, and an execution time of 5.3 ms. Comparative Analysis show superior performance over existing approaches in classification accuracy and efficiency. Ablation studies confirmed the significant contributions of the proposed model using each component in enhancing performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Transfer learning

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An efficient security testing for android application based on behavior and activities using RFE-MLP and ensemble classifier

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Multimedia Tools and Applications 2025年第15期84卷 15357-15386页

作者： Kumar, Pawan Singh, Sukhdip Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Sonipat131039 India

An enormous amount of applications that are available for download permits users to enhance the functionality of the devices with brand-new features, which is a significant factor in the growing popularity of smartphones and tablets. In reality, third-party applications for business and entertainment are abundantly available in official stores, with the majority of them being offered for free. However, vulnerable applications may expose private information (such as emails, global GPS location, financial information, and phone contacts). To overcome these issues, the log files are collected for normalizing and selecting the features related to the behaviour and activities using the Min–Max and Recursive Feature Elimination (RFE) based on the MLP. The selected features are learned by using the ensemble learning algorithm, which is a stacking of Bi-directional Long Short Term Memory (Bi-LSTM), Deep Neural Network (DBN) and Radial Basis Functional Network (RBFN) giving the collected learned features from each algorithm to the Meta classifier for predicting the malware. The ensemble learning algorithm then feeds the gathered learned features from each method to the Meta classifier for malware prediction. The suggested strategy protects users' privacy by alerting them when it detects malicious apps and removing them. Accuracy, Precision, Specificity, and Kappa performance measures are assessed and contrasted with the proposed and current models. The proposed model achieved performance metrics values are 99.78%, 99.56%, 99.85% and 98.89%. Thus, the security testing for Android applications based on behaviour and activities using RFE-MLP and Ensemble classifier performs better than the existing model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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