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239 articles for “methodological frameworks”
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Application of MCDM Techniques for Selection of Battery for Electric Vehicle
Abstract: An electric vehicle (EV) operates using an electric motor, which differs from traditional vehicles powered by internal combustion enginesElectric motors are used to power EVs rather than gasoline and gasses. These motors are powered by fuel cells, solar panels, or battery packs that recharge. As a result, EVs are increasingly considered as potential replacements for conventional automobiles, aiming to combat issues such as pollution, global warming, and resource depletion. Electric …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 3, 2024 · pp. 33–40 Read article
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Adaptive Drift Correction in Polymer-Based Wearable Biosensors via Data-Driven Signal Modeling
Abstract: Polymer-based wearable biosensors have emerged as a promising technology for continuous health monitoring due to their mechanical flexibility, biocompatibility, and suitability for long-term physiological interfacing. However, prolonged exposure to biofluids, environmental variability, and mechanical deformation introduces signal drift, which significantly degrades measurement accuracy and limits clinical reliability. This paper presents a data-driven methodology for compensating signal drift in polymer-based wearable biosensors using adaptive signal processing and machine learning techniques. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 131–139 Read article
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Risk Assessment in Hybrid Cloud: A Systematic Research Methodology
Abstract: AbstractNow a days, Cloud computing has drawn a great attention towards the users of cloud as a tremendous result of its advantages. The latest prototype of computing infrastructure is cloud computing. It provides its services to different sectors of society like education, banking, research, governance, risk identification etc. This research focuses on overview of risk identification and governance withmultiple frameworks and standards available in hybrid cloud computing. This research work …
Published in Journal of Advanced Database Management & Systems · Vol. 5, Issue 3, 2018 · pp. 19–22 Read article
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Revitalizing São Cristóvão: Integrating Industry, Commerce, and Community for a Sustainable Urban Future
Abstract: This paper introduces a comprehensive framework for the sustainable regeneration of São Cristóvão, a historically industrial district in Rio de Janeiro, South America, which presently demonstrates significant urban deterioration, socio-economic instability, and increasing environmental hazards. Through an interdisciplinary methodology integrating spatial analysis, ethnographic profiling, environmental assessment, and stakeholder engagement, the study develops a phased master plan aimed at transforming the neighborhood into a resilient, inclusive, and economically diverse urban environment. …
Published in International Journal of Urban Design and Development · Vol. 4, Issue 1, 2026 · pp. 58–68 Read article
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TensorFlow: Architecture, Applications, and Future Challenges
Abstract: TensorFlow, an open-source machine learning platform created by Google, has revolutionized how artificial intelligence (AI) systems are built and implemented. Designed to support scalable and flexible model training across CPUs, GPUs, and TPUs, TensorFlow enables researchers and developers to construct advanced deep learning models with efficiency and precision. This study provides an in-depth examination of TensorFlow's architecture, including its use of dataflow graphs and tensor-based computation. We explore its adaptability …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 41–50 Read article
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Analysis of Friction Stir Welding Utilizing ANSYS: A Study Based on Simulation
Abstract: Friction Stir Welding (FSW) represents a sophisticated solid-state welding methodology that has attracted considerable scholarly attention due to its efficacy in amalgamating high-strength, lightweight substrates including aluminium, magnesium, and titanium alloys. FSW enables the fabrication of defect free products with a substantial amount of mechanical and wear properties. The process is highly sensitive to various parameters that significantly influence the quality and strength of the final product. Studies have shown …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 2, 2025 · pp. 62–77 Read article
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An Overview of the Design and Optimization of Antennas Employing Machine Learning Algorithms and Methodologies
Abstract: This paper offers a comprehensive exploration of the integration of machine learning (ML) techniques in antenna design and optimization, reflecting the burgeoning interest and potential impact of ML in emerging technologies. The first segment conducts a detailed literature survey, elucidating conventional computational electromagnetic and numerical methods preceding an in-depth discussion on the core facets of ML, encompassing diverse learning categories and frameworks. Additionally, it provides a mathematical exposition on regression …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 10, Issue 3, 2023 · pp. 1–12 Read article
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A Security Investigation Survey of Ransomware Detection and Avoidance Strategies for IoT Networks
Abstract: The internet of things (IoT) refers to the interconnection of a large number of distinct physical objects, which in turn makes possible a variety of services and applications. Because the IoT sector is developing at such a rapid rate, ensuring its safety ought to be a high concern. At this time, ransomware attacks constitute the biggest danger to IoT posed by cyberattacks. Ransomware is software that blocks access to or …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 2, 2023 · pp. 14–23 Read article
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An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids
Abstract: The increasing adoption of next-generation smart grids has introduced significant cybersecurity challenges due to their reliance on interconnected digital infrastructures and IoT-based control mechanisms. This study aims to analyze cybersecurity threats in smart grids and explore AI-driven mitigation strategies to enhance grid security and resilience. The research examines common cyber threats such as malware attacks, denial-of-service (DoS), data breaches, and insider threats while evaluating the effectiveness of AI-based solutions, including …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 16–25 Read article
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 · pp. 10–22 Read article
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Parametric Quantile Regression using Quantile-based Asymmetric Family of the Distributions
Abstract: Quantile regression is used for finding the effects of covariates on different quantiles of the response variable. The contribution of this article is to develop a theory of quantile regression using the Quantile-based asymmetric (QBA) family (similar to GAMLSS for mean regression) and then apply the proposed approach for analyzing COVID-19 data. More precisely, we estimate regression quantiles and investigate the asymptotic estimators under a generalized linear modeling framework, but …
Published in Research & Reviews : Journal of Statistics · Vol. 11, Issue 3, 2022 · pp. 54–66 Read article
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Implementation of Kalman Filter Using TDC and PLL for Object Detection and Tracking in Signal Processing
Abstract: The measurement uncertainty of GPS receivers is dependent on a wide range of external factors, including receiver clock precision, thermal noise, atmospheric influences, and minute variations in satellite positions. Estimating hidden states precisely and accurately in the face of uncertainty is one of the main problems facing tracking and control systems. Among the most significant and widely used estimate methods is the Kalman Filter. It uses imprecise and erratic measurements …
Published in Current Trends in Signal Processing · Vol. 13, Issue 2, 2023 · pp. 38–47 Read article
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Sacred Resilience: The Intersection of Religious Beliefs and Psychological Well-being in Crisis Situation
Abstract: Background: The relationship between religious beliefs and psychological well-being has been a focal point of psychological research, particularly in the context of crises. Understanding how religious beliefs contribute to psychological resilience may enhance support mechanisms for individuals facing crises. Objective: This study aims to examine the intersection of religious beliefs and psychological resilience during crises, focusing on how faith-based practices influence coping strategies and overall mental health outcomes. Importance: This …
Published in International Journal of Behavioral Sciences · Vol. 2, Issue 1, 2025 · pp. 16–21 Read article
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Exploring the Studies of Mathematics: Foundations, Evolution, Pedagogy and Prospects
Abstract: Mathematics, often regarded as the universal language of science and logic, has evolved through centuries as both a tool for understanding the natural world and a discipline of abstract reasoning. This study explores the multifaceted dimensions of mathematics by examining its foundations, historical evolution, pedagogical approaches, and future prospects. The foundational aspects trace back to ancient civilizations—Babylonian arithmetic, Greek geometry, and Indian and Arabic contributions to algebra— highlighting how early …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 25–31 Read article
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Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 55–66 Read article
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Predicting the Computer Access Needs of Employee Using Data Mining Algorithms
Abstract: Every business relies on data to better organize and access the information to improve their business operations and to identify opportunities for improvement. A better framework improves the ability to valuable products and better services to their customers. The wide swaths of data that every organization gathers requires a proper methodology to gain knowledge about the latest trends in the market. Data mining is the process that analyses the data …
Published in Journal of Advances in Shell Programming · Vol. 10, Issue 1, 2023 · pp. 12–16 Read article
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An Integrated Simulation Framework for Predicting Dielectric Breakdown and Electrical Aging in Epoxy-Silica Composite Insulation Systems
Abstract: This paper provides a combined computation approach in forecasting the dielectric breakdown and electrical aging within epoxy-silica composite of insulation system. The approach will consist of a three-complementary methodology (a combination of computing electric field using the finite element analysis, estimation of the probability of failures or breakdowns using Weibull statistics, and prediction of degradation tendencies using artificial neural networks). The epoxy-silica composites are of 10-40 volumes fillers. The simulations …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 339–376 Read article
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Exploring the Development of AI Models Using Open-Source Tools to Predict Patient Outcomes and Optimize Treatment Plans
Abstract: Integrating artificial intelligence (AI) into healthcare offers a transformative opportunity to enhance patient care and clinical decision-making. Through the use of predictive analytics, AI can significantly enhance the accuracy of outcome predictions and assist in developing personalized treatment plans that cater to each patient’s specific needs. This paper delves into the development of AI models using open-source tools, which are increasingly favored for their accessibility, collaborative nature, and capacity for …
Published in Journal of Open Source Developments · Vol. 11, Issue 3, 2024 · pp. 37–49 Read article
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Advanced Anomaly Detection in Cloud Infrastructures Using Deep Learning Algorithms
Abstract: It is critical to guarantee the stability and security of cloud environments as cloud computing is becoming the backbone of contemporary IT infrastructures. Neglecting to quickly identify and resolve anomalies, which might point to security breaches, performance problems, or system breakdowns, can lead to disastrous outcomes. The increasing size and complexity of cloud infrastructures are challenging the effectiveness of traditional anomaly detection methods. These approaches often depend on rule-based systems …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 1–11 Read article
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Understanding Food Spoilage: Mechanisms, Shelf-Life Determination, and Safety Standards
Abstract: Food spoilage poses significant challenges to the global food industry, impacting both food safety and economic sustainability. The main causes of food quality deterioration are microbial and non-microbial spoiling. Microbial spoilage primarily results from the activity of bacteria, yeasts, and molds, which thrive under favorable environmental conditions. These microorganisms can lead to food poisoning and spoilage through the production of off-flavors, discoloration, slime formation, and the accumulation of harmful toxins. …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 14, Issue 1, 2025 · pp. 6–9 Read article