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136 articles for “Analytical Methodology”
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Evaluating Advancements and Identifying Research Gaps in Automotive Spare Parts Demand Forecasting
Abstract: The automotive industry, a key driver of global economic activity, relies heavily on the effective management of spare parts to ensure vehicle longevity and reliability. Accurate prediction of demand for these components is imperative to uphold ideal stock levels, minimize expenditures, and elevate customer contentment. This review of literature assesses recent progressions in demand prediction methodologies for automotive spare parts, with a specific emphasis on conventional statistical methods and contemporary …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 47–58 Read article
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Design and Development of OLAP Tool for Data warehousing
Abstract: The complex information requirements and the ever-growing demand for increasingly sophisticated data analysis sparked the creation of a new type of data repository. This repository contains data in formats that facilitate data extraction, data analysis, and decision making. This data repository is known as decision support systems. The need for more intensive decision support On Line Analytical Processing (OLAP), create an advanced data analysis environment that supports decision making, business …
Published in Journal of Advanced Database Management & Systems · Vol. 5, Issue 3, 2018 · pp. 8–13 Read article
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Integrating Advanced Technologies for the Scientific Revalidation of Ayurvedic Principles
Abstract: Background: Ayurveda, a 5,000-year-old holistic healthcare system, emphasizes the dynamic balance of body, mind, and spirit. Recent advancements in science and technology have accelerated its integration into global healthcare systems. Objective: This paper explores the contribution of modern tools and methodologies to the scientific validation and enhancement of Ayurvedic principles across education, clinical practice, and research. Methods: A comprehensive literature review was conducted using databases like PubMed, Google Scholar, and …
Published in Research and Reviews: A Journal of Medicine · Vol. 16, Issue 1, 2026 · pp. 1–6 Read article
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Deploying Fuzzy Logic for Self-Tuning Regulator Design for Motion Control in Modern Electrical Machines
Abstract: Modern electrical machines require sophisticated motion control systems capable of adapting to varying operating conditions, load disturbances, and parameter uncertainties. Traditional self-tuning regulators (STR) based on classical control theory often struggle with nonlinearities, time-varying dynamics, and complex operational environments characteristic of contemporary electric drives. This article presents a comprehensive framework for deploying fuzzy logic in self-tuning regulator design to address these challenges in motion control applications. Fuzzy logic controllers leverage …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 11–21 Read article
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Selection of R14-tyres using Analytic Hierarchical Process on the Basis of Uniformity and Strength
Abstract: Tyres play a commanding role in an automobile industry. Balancing and safety of the vehicle are the primary issues for automobile industry. These two parameters are highly dependent on the uniformity and strength of the tyres. There are various criteria that affect tyres uniformity and strength. Consequently, selection of tyres is a Multi-criteria Decision Making (MCDM) problem. In that respect are many methods available for selection of tyres; however, this …
Published in Journal of Automobile Engineering and Applications · Vol. 1, Issue 3, 2014 · pp. 7–17 Read article
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Data Driven Controller Design For Distributed Power Sources In Microgrid
Abstract: AbstractUsing renewable power sources requires controlled procedures to accomplish satisfactory stabile levels of power transmission and keep up with grid power deviation. Additionally, these methodologies in a perfect world require no pre-information of the grid structure, and as meagre correspondence with neighbouring power sources as would be prudent. As a next stride, we concentrate on how the power can be dispatched from different power hotspots for enhanced grid productivity. Microgrids …
Published in Journal of Electronic Design Technology · Vol. 8, Issue 2, 2017 · pp. 1–8 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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Alzheimer’s Disease Detection Using ML Algorithm
Abstract: A degenerative neurological state of affairs, Alzheimer's disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We've implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 3, 2024 · pp. 53–57 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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Study of Social Trends Prediction Using AI
Abstract: AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlations in a variety of available datasets, from social media to economic indicators to public records, and fundamentally changes decision-making based on …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 19–29 Read article
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Quantitative Approaches to Legal Decision-Making: The Mathematics of Justice
Abstract: The research paper examines the application of quantitative approaches to decision-making in legal systems, presenting the concept of "mathematical justice." As legal systems grow increasingly complex, the adoption of statistical and data-driven methods can provide valuable insights, offering clarity and consistency to judicial processes. By leveraging these methods, courts and policymakers can make more informed, evidence-based decisions, enhancing both fairness and accountability within the justice system. The paper is divided …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 3, 2024 · pp. 27–31 Read article
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Analysis aspects of pressure tank: A review
Abstract: This review paper presents a detailed analysis of pressure vessels, focusing on finite element analysis (FEA) to evaluate structural integrity. Pressure vessels are widely used in industries such as chemical processing, power plants, and oil refineries, where they are subjected to extreme temperature and pressure conditions. Ensuring their safe and efficient operation requires a thorough understanding of stress distribution, deformation characteristics, and failure mechanisms. This study reviews existing research on …
Published in Journal of Materials & Metallurgical Engineering · Vol. 15, Issue 3, 2025 · pp. 49–55 Read article
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A Web Application for Predicting Diabetes Using Machine Learning Methods
Abstract: Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 92–102 Read article
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Evolution of Kinematic and Dynamic Design in Robotic Mechanisms: A Systematic Overview
Abstract: The field of robotics has experienced significant advancements in both kinematic and dynamic design, driven by the growing need for precision, adaptability, and autonomy in mechanical systems. Early robotic mechanisms were predominantly rigid and operated based on simple serial architectures, offering limited degrees of freedom and relying heavily on analytical formulations for motion planning and control. Over time, the demand for greater dexterity and operational versatility led to the development …
Published in Trends in Machine design · Vol. 12, Issue 2, 2025 · pp. 38–43 Read article
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Smart Education through Machine Learning: A Review of Trends, Benefits, and Risks
Abstract: Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern. The article emphasises …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 24–28 Read article
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Modulation of Receptor Proteins: A Recent Paradigm in Drug Discovery and Target
Abstract: The main aim in drug discovery is to design different ligands those work on specific drug targets. However, various effective drugs work through the modulation of many proteins than single target. Advancement in system biology are establishing a phenotypic strength and a network structure, which suggests that, the excellent selective compounds may be performed less than desired clinical efficacy in comparison to multi-target drugs. In this new appreciation of the …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 5, Issue 1, 2018 · pp. 14–24 Read article