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170 results for “Resource Performance”
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Research ArticleOpen accessIn the keywordsImpacts of Job Resources on Nurses’ Performance Working in Public Sector HospitalsRabiul Islam, Abdullah Mohammad Al-HomayanJob resources refers to the physical, psychological, social or organizational aspects of the job which are necessary in the achievement of goals and objectives, necessary for the reduction of the negative effects of job demands including the associated psychological and psychological costs and which promote personal growth, learning and development. The aim of the study is to analysis the impacts … …American Journal of Applied Sciences, Vol. 10, Issue 10 · 6 Sep 2013 · 7,162 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Fog Resource Provisioning and Resource Allocation using MKFCM and FPANagarjun E, Dharamendra Chouhan, Dilip Kumar SMThe expansion of Internet of Things (IoT) deployments has motivated the Fog Computing (FC) paradigm, which brings mobile-edge and cloud resources together under a single model. Allocating and provisioning application tasks across such environments poses challenges due to constrained resource capabilities, heterogeneity, mobility, network hierarchy, and unpredictability. This paper addresses the need for additional computing devices that prioritize delay-sensitive applications … …Journal of Computer Science, Vol. 22, Issue 10 · 6 Oct 2026 · 472 accessesView PDFCite
Research ArticleOpen accessIn the abstractMCWDRL: 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 · 941 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimized 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 · 1,504 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvanced Grasshopper Optimization Scheduling Algorithm (AGOSA) for Multi-Cloud EnvironmentsS. Gowri, A. SumathiThe multi-cloud scheduling problem refers to the challenging task of efficiently allocating and managing resources across multiple cloud environments, involving the optimization of task scheduling, resource allocation to minimize makespan, reduce resource costs, and handle the complexity of multi-cloud environments while making optimal scheduling decisions. To overcome this issue. The paper proposes a novel scheduling algorithm, the Advanced Grasshopper Optimization … …Journal of Computer Science, Vol. 21, Issue 10 · 24 Nov 2025 · 2,657 accessesView PDFCite
Research ArticleOpen accessIn the abstractRK4 Differential Equation to improve QoS in an eUTRAN LTE Network: A Deterministic vs. Stochastic ApproachBrou Pacôme, Pandry Ghislain, Bodjré Aka Hugues Félix, Oumtanaga SouleymaneImproving Quality of Service (QoS) in an eUTRAN LTE network is essential to guarantee optimal performance in the face of dynamic variations in user traffic and radio channel interference. In this context, this paper explores the use of 4th-order Runge-Kutta differential equations (RK4) to model and optimize radio resource management, by comparing a deterministic approach, which assumes stable and predictable … …Journal of Mathematics and Statistics, Vol. 21, Issue 1 · 3 Jul 2025 · 5,880 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Indian Language Speech Recognition Systems with Language-Independent Phonetic Script: An Experimental ExplorationJose Stephan, Muthayyan Kamalam JayakumarIndia has a distinct linguistic profile that makes it extremely challenging to train Automatic Speech Recognition (ASR) systems correctly because most Indian languages have limited training material and are generally low-resource in nature. However, due to their similarity in phonemes, these languages offer an opportunity to create a single speech recognition technology. This study proposes an optimized phonetic script which … …Journal of Computer Science, Vol. 21, Issue 5 · 9 May 2025 · 6,545 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Sentiment Analysis for Malayalam With mBERT: A Profoundly Transparent and Accurate Approach Using LIMEAnitha R., K. S. Anil Kumar, Rajeev R. R. et al.The overlapping sentiment boundaries, intensifiers, and intricate morphological structures in Malayalam present particular difficulties for sentiment analysis, making it hard for traditional machine learning techniques to produce consistent results. We present an explainable sentiment analysis framework in this paper that refines a Multilingual Bidirectional Encoder Representations from Transformers (mBERT) model on a novel constituency-level dataset that has been manually curated … …Journal of Computer Science, Vol. 22, Issue 5 · 2 Jun 2026 · 1,065 accessesView PDFCite
Research ArticleOpen accessIn the abstractIoT-Based Smart Hydroponic System for Nutrient Management Using Predictive Machine Learning AlgorithmsPalash Gourshettiwar, K. T. V. ReddyHydroponics, an efficient cultivation method, benefits significantly from the precision and adaptability that Machine Learning (ML) algorithms can offer, along with the real-time monitoring facilitated by the devices using the Internet of Things (IoT). This study summarises the latest research and discusses how Machine Learning and the IoT work together, focusing on nutrient optimization, plant development, and resource efficiency. The … …Journal of Computer Science, Vol. 22, Issue 3 · 11 Mar 2026 · 3,056 accessesView PDFCite
Research ArticleOpen accessIn the abstractReinforcement Learning in Financial Services: Modelling Payment Switching as a Multi-Armed Bandit ProblemIshaya Gambo, Christopher Agbonkhese, Segun Aina et al.The ever-evolving landscape of digital payments demands continuous innovation and self-improvement. This study addresses this imperative by simulating a model for payment routing, a crucial aspect of the digital payment ecosystem. To achieve this, industry professionals were interviewed to inform the approach, emphasizing data randomization for effective data collection. Using Python, a randomized dataset is created and three Reinforcement Learning … …Journal of Computer Science, Vol. 20, Issue 11 · 12 Oct 2024 · 4,717 accessesView PDFCite