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10 results for “Deep Reinforcement Learning”
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Research ArticleOpen accessPhrase in the titleEfficient Data Encryption for Securing HDFS Using DQN-Enhanced Deep Reinforcement Learning TechniquesShivani Awasthi, Narendra KohliIn the era of big data, ensuring the security of large-scale distributed storage systems like the Hadoop Distributed File System (HDFS) is critical. Traditional encryption methods often struggle to balance robust security with system performance, leading to vulnerabilities and inefficiencies. This study presents the design and implementation of an efficient data encryption algorithm for securing HDFS using Deep Q-Network (DQN) … …Journal of Computer Science, Vol. 21, Issue 4 · 4 Mar 2025 · 6,376 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleMCWDRL: Multi-Cloud Workflow Scheduling Using Deep Reinforcement Learning and Improved Workflow Segmentation for Multi-Cloud EnvironmentsS. Gowri, A. SumathiThe rapid growth of cloud computing has led to complex workflow scheduling challenges in multi-cloud environments, where efficient resource utilization, minimized makespan, and reduced costs are paramount. Traditional scheduling algorithms often struggle to adapt to the dynamic nature of cloud resources and workflows, leading to suboptimal performance. To overcome this issue, the paper proposed a novel Multi-Cloud Workflow Scheduling using … …Journal of Computer Science, Vol. 22, Issue 6 · 1 Jul 2026 · 933 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPrioritized Experience Replay-Based Deep Deterministic Policy Gradient for Reliable Path Selection in SDN-IoT NetworksGaurav Kumar, G. S. Girisha, N. ShamanthRouting optimization is becoming prominent in Software-Defined Networks (SDN) due to the exponential growth of network traffic demands and the requirement for Quality of Service (QoS). However, reliable routing that satisfies the QoS requirements, such as end-to-end delay, packet loss, and bandwidth, remains a difficult task in SDN. To overcome this limitation, a Deep Reinforcement Learning (DRL)-based Prioritized Experience Replay-based … …Journal of Computer Science, Vol. 22, Issue 8 · 4 Aug 2026 · 1,001 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOptimized Cloud Load Balancing Using Deep Q-Learning With Inverted S-Shaped Hierarchical Reverse Superb Fairy-Wren OptimizationB. R. Yogeetha, S. P. AnandarajCloud computing offers a scalable and cost-effective platform by providing on-demand access to shared computational resources. However, effective load balancing is essential to maintain optimal performance and maximize resource utilization for ensuring even distribution of network traffic across servers, preventing overload, improving response time and enhancing the system reliability. This manuscript proposes the Deep Q-Network with Inverted S-shaped Hierarchical Reverse … …Journal of Computer Science, Vol. 22, Issue 6 · 4 Jun 2026 · 1,497 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEnhancing Telecommunication Network Management with Autonomous Optimization Agents and Machine LearningDanny Adil Sibarani, Evaristus Didik MadyatmadjaThis study explores the integration of Machine Learning (ML) and Autonomous Optimization Agents (AOAs) in the management and optimization of Radio Access Networks (RAN). The research addresses the growing challenges posed by the need for skilled network experts capable of managing and analyzing large-scale network data, including Key Performance Indicators (KPIs) and thousands of network configuration parameters. To overcome these … …Journal of Computer Science, Vol. 21, Issue 2 · 4 Mar 2025 · 7,041 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Dynamic Lifelong Learning Network for Real-Time Stock Prediction With Reinforcement TuningTalent Mawere, Selvaraj Rajalakshmi, Venu Madhav Kuthadi, Otlhapile DinakanyaneCompanies and investors require accurate market forecasting to make more informed decisions. Traditional methods for predicting stock market performance have become less useful considering the dynamic and volatile nature that characterizes stock markets today. In this paper, we propose an innovative Dynamic Lifelong Learning Network for stock market prediction. The model incorporates a hybrid convolutional long short-term memory with an … …Journal of Computer Science, Vol. 22, Issue 2 · 20 Feb 2026 · 1,822 accessesView PDFCite
Research ArticleOpen accessIn the abstractArtificial Intelligence in Quality Control: Transforming Manufacturing ProcessesGopinath KathiresanThe integration of Artificial Intelligence (AI) into Quality Control (QC) is transforming manufacturing by enhancing accuracy, efficiency, and productivity. Traditional QC methods, reliant on manual inspection and basic automation, often fall short in addressing the complexities of modern production environments. This paper presents a review on the application of AI, particularly machine learning, deep learning, and reinforcement learning, in manufacturing … …Journal of Computer Science, Vol. 22, Issue 1 · 12 Feb 2026 · 6,492 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Lightweight PyBullet-Based Framework for Fast Reinforcement Learning Prototyping on 6-DOF Robotic ArmsNgoc Kim Khanh Nguyen, Anh Thu Mang, Quang NguyenControlling 6-Degree-of-Freedom (6-DOF) robotic arms for precise manipulation tasks is challenging due to kinematic redundancy and the complexity of existing simulation environments like MuJoCo or ROS-Gazebo. This paper presents ArmReach6DOFEnv, a lightweight, open-source simulation framework built on PyBullet for rapid Reinforcement Learning (RL) prototyping on 6-DOF robotic arms. Using a Universal Robot Description Format (URDF) model, the environment supports a … …Journal of Mechatronics and Robotics, Vol. 9, Issue 1 · 11 Feb 2026 · 3,071 accessesView PDFCite
Review ArticleOpen accessIn the abstractWeb Scraping for Product Recommendations: A Review of Techniques and ApplicationsAmarinder Kaur, Deepak PrasharA comprehensive method for developing a reliable product recommender system leveraging sophisticated web scraping technologies, machine learning, and natural language processing techniques. The proposed system addresses key challenges in personalized product recommendation, including the difficulty of integrating diverse data from multiple e-commerce platforms, ensuring data quality, and improving recommendation accuracy to enhance user experience. Specifically, this research tackles issues related … …Journal of Computer Science, Vol. 21, Issue 6 · 21 Jun 2025 · 4,225 accessesView PDFCite
Research ArticleOpen accessIn the abstractRoute Optimization using Hybrid GRU Learning Model for SDN and Edge-Based VANET TopologySavitha Kumar, Chandrasekar ChinnasamyIntelligent Transportation System (ITS) offersoutstanding features, including security applications and emergency alerts.Unfortunately, ITS limits traffic control services, adaptability, andadjustability due to the traffic volume. Hence, expanding the standard VehicularAd hoc Networks (VANET) framework is a requirement. As a result, in the latestdays, the concept of Software-Defined Networking based Vehicular Ad hocNetworks (SDN-VANETs) has drawn considerable attention, creating VANETssmarter. The SDN-VANETs … …Journal of Computer Science, Vol. 18, Issue 8 · 26 Aug 2022 · 6,160 accessesView PDFCite