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Adaptive Task Scheduling in Fog Computing Using Federated DQN and K-Means Clustering

IEEE Access 2025年 13卷 75462-75488页

作者： Choppara, Prashanth Mangalampalli, S. Sudheer VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati522237 India Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Karnataka Manipal576104 India

Task scheduling in distributed cloud and fog computing applications must be efficient to optimize resource utilization, minimize latency, and comply with strict service level agreements. The dynamic and heterogeneous of fog computing challenges such as balancing performance with variable task loads, resource constraints, and energy efficiency. This paper introduces a new Federated Learning Deep Queue-Learning (FLDQN) framework that integrates reinforcement learning with federated learning to overcome these challenges. The FLDQN approach enables decentralized task scheduling because fog nodes can train their local DQN models on localized data, merging their experiences to form a global model without sensitive data sharing. It further improves task prioritization by classifying tasks based on their execution time and deadlines, ensuring that high-priority tasks are scheduled first, which reduces SLA violations and task rejection rates. The proposed model dynamically adapts to the changing conditions of the fog environment through iterative learning that continuously improves work allocation. Substantial experiments conducted on large and small sizes of dataset show that the proposed FLDQN outperforms others, including standalone DQN and graph-based GGCN. For all dataset sizes small, medium, and large datasets, it reduces makespan by up to 30%, improves throughput, and reduces energy by distributing tasks on the most efficient node based on current system states. The results demonstrate the ability of FLDQN to optimize task execution in real time, while addressing the scalability and privacy issues inherent in fog computing. Combining federated and reinforcement learning proposed framework provides a flexible solution for distributed task scheduling, which is well suited for latency-sensitive applications in industrial automation, healthcare monitoring, and smart city deployments. © 2013 IEEE.

关键词： Fog computing

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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

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Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Adaptive Attention Mechanisms Based Multicriteria Recommender Systems for Improved User Preference Discovery

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SN computer science 2025年第4期6卷 1-16页

作者： Rajput, Ishwari Singh Tewari, Anand Shanker Tiwari, Arvind Kumar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science & Engineering Kamla Nehru Institute of Technology Sultanpur (UP) India

Personalized recommender systems are becoming more popular to reduce the issue of information overload. It is also observed that the recommendations provided by multi-criteria recommender system (MCRS) are more accurate and precise in comparison to conventional single-criteria recommender systems. MCRS incorporates customers’ preferences based on multiple criteria while recommending any item. Recent research has shown that MCRS can efficiently use implicit and explicit input across various dimensions to make exact recommendations across multiple domains. Due to use of multiple criteria, existing MCRS methods often struggle with effectively aggregating and interpreting these criteria, making it hard to accurately determine user preferences. In this article, we introduce a novel adaptive attention mechanism that dynamically assigns weights to each criterion based on user preferences. Each criterion is initially weighted based on its importance to a user. An attention mechanism is used to understand the exact user’s preference for each criterion. Later, the nearest-neighbor method finds users with similar interest to predict unknown ratings which is further used for Top-N recommendations. The performance of the proposed approach (AM-MCRS) is validated on Yahoo! movies multi-criteria dataset having 1716 unique users and 965 different movies and a comparative analysis is performed with existing single and multi-criteria recommender systems. The results clearly show that the AM-MCRS methodology outperform baseline methods, achieving the highest precision (0.9412), F-score (0.9343), accuracy (0.929), and coverage (0.9407), while also observing a significant reduction in MAE (0.7623) and RMSE (0.9145). These findings highlight the capability of AM-MCRS to improve recommendation accuracy and dependability across various domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention mechanism Deep learning Dynamic user preference Multi-criteria recommender system Nearest-neighbor

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A hybrid blended stacking disease prediction system based on symptoms

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Multimedia Tools and Applications 2025年第7期84卷 3439-3457页

作者： Indupalli, Manjula Rani Pradeepini, G. Research Scholar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522 302 India Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522 302 India

Traditional disease diagnosis methods often struggle with symptoms-based datasets containing categorical data, leading researchers to favor boosting algorithms like CatBoost for their computational efficiency;despite potential limitations in accuracy. To overcome these challenges, this study proposes a novel approach integrating ensemble boosting with traditional naive Bayesian techniques. Recognizing the risk of overfitting with pure ensemble methods, the model strategically blends non-linear models, including XGBoost, to assign weights to symptoms based on disease impact. The blending process, guided by XGBoost decision-making, significantly improves model accuracy compared to single-boost strategies. Preprocessing involves handling categorical data and missing values, enhancing the model's robustness. During the process of blending, the decision of XGBOOST plays an important role. The proposed model's accuracy of "97.2%" is significantly higher than the single-boost strategy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Accuracy Blended stacking CATegorical Boost Ensemble Multi classification Naive Bayesian

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Semantic-Aware Architecture Design for a Lifelong Swarm Metaverse

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IEEE Internet of Things Journal 2025年第9期12卷 12468-12482页

作者： Wang, Ning Wu, Yinxuan Lorenzo, Beatriz Liu, Bing University of Massachusetts at Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

As the Metaverse evolves with developments in AI, semantic communication, edge computing, and blockchain, it encounters challenges in adapting to dynamic environments and meeting rising communication and computation needs. In this article, we propose a semantic-aware UAV-based architecture tailored to the dynamic Metaverse that leverages UAV swarms consisting of a collection UAV and edge UAV servers. By mapping different semantic features of Metaverse environments, such as amount of tasks, arrival rate, throughput, and latency requirements, we jointly optimize the mobility, task allocation, and resource allocation in a dynamic Metaverse system. First, a particle swarm optimization-based collection-edge mobility algorithm (PSO-CEMA) is designed to optimize the mobility of UAV servers. Second, to facilitate timely and stable task allocation with reduced complexity, we propose a dual-queue system and a Lyapunov drift function-based dynamic programming task allocation algorithm (LDF-DPTAA). Then, we adopt lifelong learning and design a collection-edge joint training and processing algorithm (LL-CJTPA) to optimize the dynamic allocation of computational resources in the swarm. Finally, we integrate our algorithms into a PSO-LDF-LL algorithm to serve the dynamic Metaverse system. Simulation results show that our approach effectively optimizes UAV servers’ positions and task allocation, significantly reduces the training time when facing new tasks, and enhances the stability and efficiency of the network in dynamic settings while reducing congestion. © 2014 IEEE.

关键词： Dynamic programming

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Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations

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SIAM-ASA Journal on Uncertainty Quantification 2025年第2期13卷 375-399页

作者： Vanmechelen, P. Lombaert, G. Samaey, G. Department of Computer Science KU Heverlee 3001 Belgium Department of Civil Engineering KU Heverlee Leuven3001 Belgium

We propose a multilevel Markov chain Monte Carlo (MCMC) method for the Bayesian inference of random field parameters in PDEs using high-resolution data. Compared to existing multilevel MCMC methods, we additionally consider level-dependent data resolution and introduce a suitable likelihood scaling to enable consistent cross-level comparisons. We theoretically show that this approach attains the same convergence rates as when using level-independent treatment of data, but at significantly reduced computational cost. The convergence analysis focuses on Lipschitz continuous transformations of Gaussian random fields with Mat\'ern covariance structure. These results are illustrated using numerical experiments for a two-dimensional plane stress problem, where Young's modulus is estimated from discretizations of the displacement field. © 2025 Society for Industrial and Applied Mathematics and American Statistical Association.

关键词： Markov chains

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Detection of Brain Tumor Using U-Net Model Approach

Detection of Brain Tumor Using U-Net Model Approach

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Patra, Arunima Paul, Krishnangshu Paul, Prithwineel Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India Department of Computer Science and Engineering Centre of Excellence for Quantum Computing Institute of Engineering & Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks which in turn are capable of capturing and propagation of information of the features. In this paper, for brain tumor segmentation, a U-Net Convolutional Neural Network (CNN) is employed, utilizing multimodal magnetic resonance imaging (MRI) data and skip connections for precise tumor localization and segmentation. Through comprehensive experiments, we show that our method outperforms traditional techniques and current CNN models for medical image segmentation and lesion detection in brain tumors. Moreover, the proposed method can perform the task with higher accuracy. This tailored U-Net architecture demonstrates the potential for advancing medical image analysis, enabling precise segmentation and lesion detection, ultimately leading to improved patient care and treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Climate Geoscience–Based Disaster Management in Healthcare Analysis by Markov Q-Transfer Adversarial with Cloud Computing

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 474-483页

作者： Srinivas, Jhade Rebecca, B. Bhargavi, Kovvuri N Swetha, Velma Reddy Spandana, S. Degala, Divya Priya Department of Computer Science and Engineering-Data Science KG Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE Aditya University AP Surampalem India Department of Computer Science and Engineering (AI-ML) Malla Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), which are capable of precisely identifying and locating the relevant areas of interest within satellite imagery or other types of remote sensing data. This helps with rescue planning, restoration efforts, and disaster evaluation. Difficulties in the provision of healthcare services, due to natural disasters, are made worse by the existing problems prevalent in most healthcare systems. The aim of this research is to propose a novel technique in climate change–based disaster management with their public healthcare analysis using cloud computing with deep learning techniques. In this research, the cloud network has been used in LiDAR image collection for climate change–based disaster-struck regions and their public healthcare analysis. This collected image has been classified and optimized using a deep Markov Q-transfer adversarial neural network with binary grey whale swarm optimization. Experimental analysis has been carried out for various disaster management–based public healthcare datasets in terms of classification accuracy, specificity, NSE, sensitivity, and F-1 score. The proposed technique attained a classification accuracy of 97%, F-1 score of 94%, specificity of 96%, sensitivity of 95%, and NSE of 57%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial neural network Cloud computing Disaster management LiDAR image Whale swarm optimization

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham science Publishers.

关键词： Cloud platforms

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High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm 36

High-accuracy sampling from constrained spaces with the Metr...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Srinivasan, Vishwak Wibisono, Andre Wilson, Ashia Department of Electrical Engineering and Computer Science MIT United States Department of Computer Science Yale University United States

We propose a first-order sampling method called the Metropolis-adjusted Preconditioned Langevin Algorithm for approximate sampling from a target distribution whose support is a proper convex subset of Rd. Our proposed method is the result of applying a Metropolis-Hastings filter to the Markov chain formed by a single step of the preconditioned Langevin algorithm with a metric G, and is motivated by the natural gradient descent algorithm for optimisation. We derive non-asymptotic upper bounds for the mixing time of this method for sampling from target distributions whose potentials are bounded relative to G, and for exponential distributions restricted to the support. Our analysis suggests that if G satisfies stronger notions of self-concordance introduced in Kook and Vempala (2024), then these mixing time upper bounds have a strictly better dependence on the dimension than when G is merely self-concordant. Our method is a high-accuracy sampler due to the polylogarithmic dependence on the error tolerance in our mixing time upper bounds. © 2025 V. Srinivasan, A. Wibisono & A. Wilson.

关键词： Markov chains

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