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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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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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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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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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Quantitative Assessment of Generative Large Language Models on Design Pattern Application

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computers, Materials & Continua 2025年第3期82卷 3843-3872页

作者： Dae-Kyoo Kim Department of Computer Science and Engineering Oakland University115 Library Dr.RochesterMI 48309USA

Design patterns offer reusable solutions for common software issues,enhancing *** advent of generative large language models(LLMs)marks progress in software development,but their efficacy in applying design patterns is not fully *** recent introduction of generative large language models(LLMs)like ChatGPT and CoPilot has demonstrated significant promise in software *** assist with a variety of tasks including code generation,modeling,bug fixing,and testing,leading to enhanced efficiency and *** initial uses of these LLMs have had a positive effect on software development,their potential influence on the application of design patterns remains *** study introduces a method to quantify LLMs’ability to implement design patterns,using Role-Based Metamodeling Language(RBML)for a rigorous specification of the pattern’s problem,solution,and transformation *** method evaluates the pattern applicability of a software application using the pattern’s problem *** deemed applicable,the application is input to the LLM for pattern *** resulting application is assessed for conformance to the pattern’s solution specification and for completeness against the pattern’s transformation *** the method with ChatGPT 4 across three applications reveals ChatGPT’s high proficiency,achieving averages of 98%in conformance and 87%in completeness,thereby demonstrating the effectiveness of the *** RBML,this study confirms that LLMs,specifically ChatGPT 4,have great potential in effective and efficient application of design patterns with high conformance and *** opens avenues for further integrating LLMs into complex software engineering processes.

关键词： Design patterns large language models pattern application pattern-based refactoring quantitative assessment

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Improvised Spectral Efficiency and Channel Estimation Parameters in Visible Light Vehicular Communication by Integrating Simulation of Urban Mobility Data

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IEEE Access 2025年 13卷 70828-70848页

作者： Wheeb, Ali H. Maurya, Sudhanshu Karimullah, Shaik Shaik, Fahimuddin Chowdhury, Subrata Siva Prasad, B.V.V. Sinha, Anurag Sood, Adamya Hussain, Syed Abid University of Baghdad College of Engineering Department of Aeronautical Engineering Baghdad10071 Iraq Department of Computer Science and Engineering Pune412115 India Annamacharya University Department of ECE Rajampet516126 India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Chittoor517127 India Anurag University School of Engineering Department of Computer Science and Engineering Hyderabad500088 India ICFAI University Tech School Computer Science Department Ranchi835222 India Amity University Amity School of Engineering and Technology Computer Science Department Noida201313 India Bakhtar University Department of Computer Science and Engineering Karta-e-Chahar Kabul1001 Afghanistan Lovely Professional University Division of Research and Development Phagwara144001 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India

Visible Light Communication (VLC) has emerged as a promising technology for vehicular communication due to its high data rates, low latency, and immunity to electromagnetic interference. However, optimizing spectral efficiency and channel estimation in dynamic vehicular environments remains a key challenge. This paper proposes an OFDM-QAM-based VLC system to enhance spectral efficiency and improve channel estimation accuracy in vehicular networks. To achieve this, we leverage Simulation of Urban Mobility (SUMO) data, which provides detailed insights into vehicular movement and traffic patterns. By integrating SUMO with VLC system simulations, we optimize modulation schemes, multiple access techniques, and spatial reuse strategies to adapt to varying traffic conditions. Additionally, a mathematical framework is developed to evaluate the impact of vehicular mobility on spectral efficiency and bit error rate (BER) performance across different QAM modulation schemes. Simulation results demonstrate that the proposed VLC system significantly improves spectral efficiency, achieving values up to 1.4 bits/s/Hz, while maintaining low BER across varying Signal to Noise Ratio (SNR) levels. These findings confirm the effectiveness of the proposed methodology in enhancing Visible Light Communication (VLC) performance for vehicular applications, making it a viable solution for next-generation intelligent transportation systems. © 2013 IEEE.

关键词： Spectrum efficiency

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IMPROVEMENT OF THE ACO ALGORITHM FOR INTELLIGENT TASK SCHEDULING IN CLOUD SYSTEMS

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Scalable Computing 2025年第2期26卷 531-539页

作者： DJEBBAR, ESMA INSAF BELALEM, GHALEM Department of Computer Science Systems Engineering National Polytechnic School of Oran-Maurie Audin Oran Algeria Department of Computer Science University of Oran1-Ahmed Ben Bella Algeria

Cloud computing involves accessing and using computing resources, such as servers, storage, and software applications, over the Internet, enabling scalable access on demand. Cloud computing systems are becoming an essential platform for scientific applications. They enable task scheduling and IT resource allocation. When these resources are not enough to meet demand, planning techniques are necessary. We propose to apply an improved ACO algorithm for intelligent task scheduling and appropriate resource allocation in a cloud environment. This work proposes a modified version of the ACO algorithm that can quickly converge to the best solution to further optimize the total response time, the average response time and the total execution cost. The algorithm suggested based on artificial intelligence application of enhanced ACO is compared with the classical ACO algorithm using Cloudsim simulator. The results obtained after the experiments and simulation are very encouraging to adopt this technique. © (2025), (West University of Timisoara). All rights reserved.

关键词： Cloud platforms

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Improved security with novel M-Log chaos steganography algorithm for huffman compressed english text

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Multimedia Tools and Applications 2025年第2期84卷 665-688页

作者： Raghuvanshi, Devvrat Joshi, Kamaldeep Nandal, Rainu Sehrawat, Harkesh Singh, Surender Singh, Srishti Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Haryana India

This paper addresses the underexplored landscape of chaotic functions in steganography, existing literature when examined under PRISMA-ScR framework it was realized that most of the studies predominantly focuses on utilizing chaotic functions for either concealing or diffusing messages within cover images. This study explored a different direction of using chaotic function as guards for concealed data by proposing the M-Log chaos algorithm which employs logarithmic chaotic function to drive a unique bomb generator equation, strategically placing bomb bits in the image. These bombs act as sentinels, safeguarding the concealed message. Successful retrieval involves diffusing the bombs before extracting data from the rest of the image. Failure to defuse these bombs prior to access results in irreversible damage to concealed information, thwarting unauthorized access. Our proposed method demonstrates notable advancements across quantitative performance metrics, including a 40% increase in payload capacity, a mean PSNR of 45.8981, and a mean SSIM of 0.9931. Future avenues include exploring multi-dimensional chaotic functions and employing nature-based searching algorithms such as genetic algorithms or particle swarm optimization for optimal chaos bomb placement. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Steganography

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Detection of brain tumor using Hybridized 3D U-Net model on MRI images

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Multimedia Tools and Applications 2025年 1-29页

作者： Shreeharsha, J. Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Karnataka Ballari India

The automatic detection of brain tumor is an emerging challenge because tumors vary in mass, nature, position, and similarities between the normal and brain lesions. First, the Magnetic Resonance Imaging (MRI) images are acquired from Brain Tumor Segmentation (BraTS) 2018, 2019 and 2020 databases;here, BraTS2018, BraTS2019 and BraTS2020 databases are considered as DB1, DB2 and DB3. Next, the image denoising is carried out by employing a normalization technique to enhance contrast and intensity levels of the acquired MRI images. Then, a hybridized 3D U-Net model is introduced for Brain Tumor Detection (BTD). In the hybridized 3D U-Net model, a ResNet-50 model is incorporated as a feature extractor in the conventional 3D U-Net model. In this particular context, the ResNet-50 model extracts intricate textures and patterns from the denoised 3D brain images, and assists the 3D U-Net model in accurate segmentation and classification of four modalities of brain tumors: T1, T2, Contrast-Enhanced (CE) -T1, and flair. In the segmentation phase, the hybridized 3D U-Net model obtains maximum segmentation results with a dice coefficient of 96.82%, 98.06% and 98.80%, and Jaccard coefficient of 96.64%, 97.25%, and 98.60% on the DB1, DB2, and DB2 databases. Additionally, in the classification phase, the hybridized 3D U-Net model achieves significant classification results with accuracies of 99.32%, 99.40%, and 99.80% on the DB1, DB2, and DB3 databases, correspondingly. The implementation of this research has been uploaded to the GitHub repository at https://***/shreeharsha-revanth/Hybrid-3D-UNet/. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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