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20 articles for “workload prediction”
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AI-PASS: An AI-Based Predictive, Adaptive and Energy Aware Task Scheduling Framework for Heterogeneous Parallel Computing
Abstract: Heterogeneous parallel computing systems are made up of a combination of multicore CPUs, GPUs, and specialized devices to boost computational power and utilize resources. But changes in workload intensity and processor capability, energy consumption and system conditions are dynamic, making efficient task scheduling difficult. Traditional scheduling approaches are based primarily on task properties or the state of the resources employed and can be sub-optimal when workloads are evolving. This paper …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 28–34 Read article
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Artificial Intelligence-Based Workforce Analytics Framework for Predicting Employee Engagement and Continuous Performance Improvement: Evidence from Tata Steel
Abstract: In large, diversified manufacturing organizations, managing employee engagement and sustaining performance require systematic understanding of workload distribution, employee sentiment, and workplace experience. This study examines the role of AI-enabled workforce analytics as a strategic human resource management tool through a case study of Tata Steel. The research positions artificial intelligence not as a technical innovation, but as a people-analytics decision-support mechanism that assists HR leaders in identifying early indicators of …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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A Physics-Informed Graph Neural Network Framework for Real- Time Thermal-Aware Fault Prediction and Adaptive Power Optimization in Heterogeneous System-on-Chip Architectures
Abstract: Heterogeneous System-on-Chip (SoC) architectures are increasingly adopted in edge computing, artificial intelligence, autonomous systems, and high-performance embedded platforms due to their superior computational efficiency and flexibility. However, increasing integration density and workload diversity introduce severe thermal hotspots, accelerated device degradation, and unexpected hardware faults that adversely affect system reliability and energy efficiency. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for real-time thermal- aware fault prediction and adaptive …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 2, 2026 · pp. 19–28 Read article
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Cloud-based Application Development and Optimization
Abstract: As cloud computing powers today’s applications, optimizing cloud-based development is crucial to achieve performance, cost effectiveness, and scalability. This research focuses on enhancing the design, deployment, and maintenance of cloud applications, tackling challenges in resource management, scalability, and resilience. We specifically explore dynamic resource allocation algorithms that use predictive analytics for auto-scaling based on workload variations, aiming to cut costs while preserving high performance. The study also investigates cross-cloud optimization …
Published in Journal of Open Source Developments · Vol. 12, Issue 1, 2025 · pp. 37–42 Read article
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The Role of Artificial Intelligence in Enhancing Athlete Performance and Training Strategies
Abstract: Artificial Intelligence (AI) is transforming modern sports by improving athlete performance, training methods, and decision-making processes. The integration of AI technologies such as machine learning, data analytics, wearable sensors, and computer vision has enabled coaches and sports scientists to analyze large amounts of performance data with greater accuracy and efficiency. Athletes' physiological indicators, movement patterns, injury risks, and recuperation processes are all monitored by these technology. Training regimens can therefore …
Published in Recent Trends in Sports · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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VolleyNexis AI: A Multimodal Artificial Intelligence Framework for Opponent Strategy Prediction, Tactical Intelligence, and Athlete Performance Optimization in Volleyball
Abstract: The rapid advancement of Artificial Intelligence (AI) has profoundly transformed sports analytics, enabling deeper insights, real-time data analysis, and enhanced performance predictions. Noticeable results have been seen by enabling automated analysis of complex gameplay patterns along with athlete performance. Volleyball is a dynamic and strategic sport, which requires continuous tactical adjustments and constant monitoring of the player’s performance. This paper presents VolleyNexis AI, which is a multimodal artificial intelligence framework …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 2, 2025 Read article
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Automated Evaluation of Descriptive Answers
Abstract: This paper offers a smarter system of automatic evaluation of descriptive responses in educational tests. The time-consuming aspect of testing with traditional manual marking, the subjectivity of that process, and the impossibility of scaling it makes it inapplicable, particularly to large academic environments. To resolve the issues, the proposed solution combines the methods of Natural Language Processing (NLP) and hybrid image-text processing on the responses of handwriting and typed answers. …
Published in Emerging Trends in Languages · Vol. 3, Issue 2, 2026 · pp. 1–9 Read article
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Intelligent Big Data Processing: Optimizing Analytics Pipelines Through Ensemble Machine Learning, Distributed Computing, and Adaptive AI Architectures
Abstract: Traditional data analytics methods are facing significant challenges due to the explosive rise of digital data. Global data generation is expected to reach nearly 175 zettabytes by 2025. This puts significant pressure on existing computing infrastructure. Conventional analytics methods often struggle with scalability, processing speed, and smart decision-making. Rule-based systems and standalone machine learning models frequently cannot handle the complexity and scale of modern big data environments effectively. To tackle …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 1–11 Read article
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The Influence of Data Analytics on Sports Performance
Abstract: Data analytics has drastically changed how we evaluate, improve, and maintain athletic performance. Coaches used to use subjective observations as well as only limited numbers of statistics to consider player performance; however, tracking technology is now advancing at a fast pace. There are now very large amounts of real-time data available on athletes in regards to speed, movement patterns, fatigue, efficiency, etc. This enables all teams to more accurately make …
Published in Recent Trends in Sports · Vol. 3, Issue 1, 2026 · pp. 15–21 Read article
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Artificial Intelligence and the Future of Education
Abstract: Artificial Intelligence (AI) refers to the capability of a computer system to mimic human intelligence by performing tasks such as learning, reasoning, problem-solving, and decision-making. As a rapidly evolving and emerging technology, AI holds the potential to transform a wide range of sectors, including healthcare, transportation, business, and notably, education. Its application in the educational domain is gaining momentum, aiming to create more efficient, engaging, and personalized learning environments. Incorporating …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 39–44 Read article
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Machine Learning Approach to Predict the Mental Health Issues of Employees in Tech Companies
Abstract: Mental disorders are conditions that have an impact on your emotions, thoughts, and behaviour. It may happen infrequently or have a persistent effect. The topic of mental health has been major and challenging, especially for working professionals. Over time, the urbanized living and workload put a strain on people, making them more vulnerable to mental disorders like anxiety and mental disorders. Working professionals therefore seem to be at an elevated …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 3, 2022 · pp. 1–10 Read article
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Machine Learning-Based Task Scheduling and Resource Optimization in Edge-IoT Systems
Abstract: The rapid expansion of Internet of Things (IoT) applications has introduced significant challenges in managing computational workloads across distributed edge environments. Edge- IoT systems are characterized by limited computational capacity, dynamic task arrivals, and strict latency constraints. Traditional heuristic-based scheduling techniques often fail to adapt to fluctuating workloads and heterogeneous resource availability. This study proposes a machine learning-based task scheduling and resource optimization framework for Edge-IoT systems. The proposed model …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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V-Belt Fatigue Life Prediction
Abstract: The system of the V-belt drives was studied by means of the similarity theory in this research, and the major factors and the basic dimensions which would affect the fatigue life of V-belt were ascertained. Then the prediction hypothesis of the V-belt’s fatigue life was deduced. The theory and the method proposed in this paper would optimize testing of the fatigue lives of V-belts, and the test workload would be …
Published in Trends in Machine design · Vol. 2, Issue 1, 2015 · pp. 66–72 Read article
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Real-Time System Monitoring and Resource Optimization Using Shell Scripts in UNIX/Linux Environments
Abstract: Real-time system monitoring is a fundamental aspect of system administration in UNIX/Linux environments, as it ensures optimal system performance, reliability, and continuous availability of services. In modern computing infrastructures, systems are expected to operate efficiently under varying workloads, and any degradation in performance, such as CPU overload, memory exhaustion, disk bottlenecks, or network congestion, can significantly impact user experience and system stability. Regular observation and prompt action are crucial for …
Published in Journal of Advances in Shell Programming · Vol. 13, Issue 1, 2026 · pp. 08–15 Read article
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Smart Patient Monitoring and Motion Tracking System
Abstract: The integration of smart technologies in healthcare has revolutionized patient monitoring and diagnostics. This paper presents a Smart Patient Monitoring and Motion Tracking System designed for hospitals, leveraging EEG (Electroencephalogram) signals to track patient movements and monitor neurological health. The proposed system combines motion tracking with real time EEG signal analysis to enhance patient safety, especially for individuals prone to seizures, neurological disorders, or other mobility-related risks. The system employs …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 2, 2025 · pp. 9–23 Read article
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Designing Self-Optimizing Operating Systems: Information-Theoretic Approaches to Thread Scheduler Implementation
Abstract: Thread Level Scheduling (TLS) in multi-core and many-core processor environments represents a critical frontier in next-generation operating system design. As computing systems grow increasingly heterogeneous and concurrent, traditional scheduling strategies often rely on heuristics or localized resource metrics, frequently overlooking the deeper, quantifiable relationships and uncertainties inherent in complex concurrent workloads. This study explores the application of information-theoretic approaches, specifically entropy-based task allocation, mutual information-driven dependency analysis, and channel capacity-inspired …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 31–39 Read article
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Machine Learning Revolutionizing Server Management and Performance
Abstract: The modern data center is a complex and dynamic environment, grappling with ever-increasing workloads, stringent performance demands, and the constant pressure for cost optimization. As such, applying machine learning (ML) directly to the server infrastructure offers a powerful avenue for achieving advanced automation, resource optimization, and proactive problem resolution. This article explores the transformative potential of integrating machine learning into server systems, leveraging insights gleaned from the abstract and conclusion …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 36–44 Read article
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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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Approximation-Aware Computation for Graceful QoS Degradation in Modern Multiprocessor Operating Systems
Abstract: Modern multiprocessor operating systems face unprecedented challenges in maintaining Quality of Service (QoS) guarantees under dynamic workload conditions and resource constraints. Traditional approaches to resource management often result in abrupt service degradation or complete task failure when system resources become scarce. This study presents a comprehensive framework for approximation-aware computation that enables graceful QoS degradation in multiprocessor environments. We explore the integration of approximate computing paradigms with operating system schedulers, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 08–15 Read article
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Advanced Collaboration and Project Management Platform
Abstract: The growing dependence of employees on remote and hybrid working modes worldwide has driven a demand for modern collaborative platforms to optimize project management. All organizations are keen on solutions that enhance workflow efficiency, facilitate seamless communications, and boost overall productivity. The study presents an advancement in project management collaboration and aims at finding solutions to meet those demands, using API integration. The study explores the extent to which API …
Published in Journal of Web Engineering & Technology · Vol. 12, Issue 2, 2025 · pp. 11–18 Read article