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258 articles for “computational frameworks”
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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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Intelligent Design Approaches in Microwave Engineering Using Machine Learning Techniques
Abstract: In microwave engineering, machine learning (ML) has become a potent technology allowing quicker design cycles, improved modelling accuracy, and automatic optimisation of complicated systems. Recent developments in the use of ML methods to microwave components and systems, including antennas, filters, and high-frequency circuits, are summarised in this study. In the framework of electromagnetic simulation, surrogate modelling, and parameter extraction, supervised and unsupervised learning algorithms are addressed. Moreover, the study looked …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 2, 2025 · pp. 31–38 Read article
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Detectiverse: Advancing Supply Chain Efficiency with AI-Enhanced Screw Counting
Abstract: Accurate screw counting is essential in the manufacturing sector to ensure efficient inventory management and maintain quality control standards. The current manual counting method is prone to errors and lacks the ability to identify the source of missing screws. To address this challenge, we propose implementing an automated screw counting system at Indo Metal Tech in Ambattur, Chennai. This system would utilize advanced image processing and machine learning algorithms to …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 1, 2024 · pp. 21–26 Read article
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Photonic-Assisted Spintronic Solid-State Switching Model for High-Speed Memory Devices
Abstract: The rapid advancement of high-speed computing and data-centric applications has intensified the demand for energy-efficient and ultra-fast memory technologies. This paper proposes a Photonic-Assisted Spintronic Solid-State Switching Model for next-generation high-speed memory devices. The proposed framework integrates photonic excitation mechanisms with spintronic switching dynamics to enhance data transfer speed, minimize switching delay, and reduce power dissipation in solid-state memory architectures. By combining optical pulse-assisted spin polarization with magnetic tunnel junction-based …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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Secure Framework for Government Tender Allocation
Abstract: Governments and public sector entities worldwide are actively seeking innovative strategies to adapt to rapid technological progress, aiming to enhance governance effectiveness, streamline work processes, and optimize expenditure. Blockchain technology stands out as a prime example, captivating the interest of governments globally in recent years. Its ability to offer heightened security, enhanced traceability, and cost-efficient infrastructure positions blockchain as a versatile solution applicable across diverse sectors. Typically, governments engage third-party …
Published in Current Trends in Information Technology · Vol. 14, Issue 2, 2024 · pp. 23–27 Read article
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Prediction of Mechanical Properties for Advanced Engineering Applications utilizing Polymer Composite Materials by Machine Learning
Abstract: Polymer composites show great promise as engineering materials because of their mechanical performance, resistance to corrosion, lightweight nature, and adaptability in design. Aerospace, automotive, biomedical, maritime, and civil engineers all rely on mechanical property prediction to cut down on trial expenses, expedite product development, and optimize material selection. Speedy design optimization is not possible using traditional numerical and experimental methods due to the high costs associated with material characterisation, computational …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
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Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach
Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article
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Impact of Partially Observable Markov Decision Process in Next Generation Satellite for Remote Sensing
Abstract: The integration of Partially Observable Markov Decision Processes (POMDPs) in next- generation satellite systems represents a transformative advancement in remote sensing technology. This article explores how POMDP frameworks address the inherent uncertainties and incomplete observability challenges in satellite operations, including dynamic task scheduling, resource allocation, and adaptive sensing strategies. By modeling satellite decision-making under uncertainty, POMDPs enable autonomous systems to optimize mission objectives while managing constraints such as limited power, …
Published in International Journal of Satellite Remote Sensing · Vol. 3, Issue 2, 2025 · pp. 20–28 Read article
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Passive Digital Phenotyping for Longitudinal Burnout and Occupational Mental Health Surveillance: A Transformer-Based Explainable Deep Learning Approach Using Smartphone Behavioral Streams
Abstract: Occupational burnout constitutes a pervasive yet chronically under-surveilled public health threat, its insidious temporal evolution rendering episodic self-report instruments structurally inadequate for early detection. This paper introduces BurnoutSense, a passive digital phenotyping framework that continuously harvests eight heterogeneous smartphone behavioral data streams encompassing application usage ecology, communication metadata, geospatial mobility, screen interaction dynamics, inferred sleep rhythmicity, keystroke kinematics, ambient noise exposure, and battery/charging cadence to construct individualized multivariate behavioral signatures …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 2, 2026 · pp. 44–53 Read article
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Trends in Computer Programming and Language
Abstract: The field of computer programming has undergone significant transformations driven by evolving technologies, increasing demand for more efficient software solutions, and the continuous need for enhanced system performance. This paper examines the latest trends in programming languages and methodologies that are influencing the future of software development. Key trends include the rise of functional programming paradigms, the growing importance of concurrent and parallel programming to handle multi-core processors, and the …
Published in Recent Trends in Programming languages · Vol. 11, Issue 3, 2024 · pp. 28–35 Read article
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A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article
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The Heart of Charani Poetry: An AI Interpretation of Emotional Resonance
Abstract: Poetry has long been a means of expressing emotions and ideas, yet understanding the emotional depth within poems can be challenging using traditional computer-based tools. This study explores the emotions embedded in Charani poems, an important genre in Indian literature, through the lens of their distinctive poetic style. The primary objective is to develop a novel method of analyzing emotions in Charani poetry, contributing to the broader field of emotion …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 34–39 Read article
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Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 Read article
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A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments
Abstract: The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial …
Published in Emerging Trends in Metabolites · Vol. 3, Issue 2, 2026 Read article
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Navigating the Dual Edge: A Comprehensive Technical Survey of Security, Privacy, and Countermeasures in the Era of Artificial Intelligence
Abstract: Artificial Intelligence (AI) is seamlessly weaved into vital sectors such as self-driving cars, high-speed trading systems, and defense strategies, it has triggered a counterintuitive development in advanced cyber-attacks. This survey paper attempts to perform an in-depth technical analysis on “AI Attack Surface.” There are threats across three main vectors. Data Integrity Attacks focuses specifically examining ‘Clean Label’ poisoning and backdoor injection. Model Confidentiality Breaches is discussing the mathematics behind Model …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 1, 2026 Read article
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Idempotent Semiring Path Algebras and Fuzzy Dominance Automata for Multi-State Communication Reliability
Abstract: A semiring-based theory is proposed for multi-state communication reliability with fuzzy dominance constraints. Links are weighted in the max-product semiring, while nodes carry dominance coefficients and operating states that modulate admissible transitions in a weighted automaton. The paper derives semiring matrix products, Kleene closures, fixed point equations, congestion-regularized path scores, and reliability inequalities. A fuzzy dominance automaton is introduced so that route selection depends not only on link reliability but …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 2, 2026 · pp. 07–14 Read article
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Machine Learning Assisted Optimization of Nanoscale MOSFET Parameters Using TCAD Simulation
Abstract: This paper presents a machine learning (ML) assisted framework for the multi-objective optimization of nanoscale bulk n-channel metal-oxide-semiconductor field-effect transistors (nMOSFETs) with a 10 nm physical gate length, high-k HfO₂ gate dielectric, and TiN metal gate. Technology computer-aided design (TCAD) simulations employing drift-diffusion transport, Shockley-Read-Hall recombination, Lombardi mobility degradation, and density- gradient quantum correction models are used to generate a parametric dataset of 2,400 device configurations spanning gate length (L), …
Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 10–19 Read article
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A Novel Secure Cloud Storage Solution: Combining AES-OTP, RSA, and Time-Limited Access Control with Adaptive Key Management
Abstract: The reliance of cloud computing on the data processing and storage structure creates serious security risks. Ensuring availability, security, and integrity of data in cloud settings becomes a challenge for both the individual and the enterprise. This paper will, therefore, introduce a novel Hybrid Cryptographic Framework that combines RSA, One-Time Pad (OTP), and AES as a means of enhancing data security in cloud storage. It employs RSA for secure key …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 11–20 Read article
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Remote Sensing and Atmospheric Modelling: Data, Processes, Integration and Future Directions
Abstract: Atmospheric modelling plays a central role in weather forecasting, climate projection, and air quality assessment; however, the availability, accuracy, and representativeness of atmospheric observations fundamentally constrain its reliability. Over the past two decades, rapid advances in remote sensing (RS) have transformed atmospheric observation by providing spatially continuous, multiscale measurements of key atmospheric variables, including aerosols, trace gases, clouds, precipitation, and atmospheric thermodynamic profiles. This review synthesises recent progress in integrating …
Published in International Journal of Atmosphere · Vol. 3, Issue 1, 2026 · pp. 54–67 Read article
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Digital Modules of the Green Buildings to Study Protection of Air Quality
Abstract: The aims of this study are to simulate air flows inside and around multi-storey green building spaces together with the distribution of concentrations of harmful substances and microorganisms to increase the sustainability of design solutions for green modules. Among the objectives of the study are assessment of bioclimatic comfort, risk analysis of bioclimatic eco-safety in the framework of aeration and pollution modeling, as well as eco-optimization of building design, which …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 838–846 Read article