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160 articles for “State machine”
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Transformer Health Monitoring System
Abstract: Rising demands for reliable and efficient power distribution in modern electric control grid increasingly call up for robust monitoring systems for critical substructure. Being a vital part of the power conduction system, transformer are subjected to mechanical, electrical, and environmental stresses, which, if not properly controlled, can cause failures. In this project, we propose a Transformer Health Monitoring System (THMS) using machine learning (ML) models and real-time monitoring method to …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 3, 2025 · pp. 1–9 Read article
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Renewable Solar Energy Prediction in India : 2025-2030
Abstract: As India endeavors to realize its objective of achieving 500 GW of renewable energy capacity by the year 2030, the precise forecasting of solar power generation is rendered increasingly essential. This scholarly article conducts a comprehensive review of the utilization of machine learning (ML) methodologies in the prediction of solar energy across diverse Indian states, underscoring their potential to address the complexities associated with the variability of solar irradiance. Conventional …
Published in Journal of Power Electronics and Power Systems · Vol. 14, Issue 3, 2024 · pp. 41–46 Read article
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Autonomous Drones for Search and Rescue Opera-tions: State of the Art and Future Prospects
Abstract: Autonomous drones have significantly transformed search and rescue (SAR) operations by improving the speed, precision, and overall effectiveness with which rescuers are able to locate and provide assistance to individuals in need. By leveraging state-of-the-art technologies such as computer vision, artificial intelligence (AI), machine learning, and advanced navigation systems, drones have proven invaluable in carrying out complex rescue missions. These technologies enable drones to navigate hazardous environments autonomously, even in …
Published in International Journal on Drones · Vol. 1, Issue 2, 2025 · pp. 9–13 Read article
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Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer
Abstract: Breast cancer continues to be the most commonly diagnosed cancer among women, with more than 2.3 million new cases diagnosed yearly worldwide. It is stated as the leading cause of cancer-related deaths. Therefore, this emphasizes the dire necessity for early diagnosis with a view to improving survival. Early diagnosis elevates the effectiveness of prediction and treatment. This research carries out a structured and analytical evaluation of various machine learning algorithms, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 113–129 Read article
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Harnessing Artificial Intelligence for Precision Physics: A Machine Learning Framework for Data Reconstruction in Support of India's Deep-Tech Missions
Abstract: India's emergence as a global leader in deep-tech innovation is driven by ambitious scientific megaprojects, including the Laser Interferometer Gravitational-Wave Observatory (LIGO)-India, the X-ray Polarimeter Satellite (XPoSat), the Aditya-L1 solar observatory, and the National Quantum Mission (NQM). However, the unprecedented scale and complexity of the observational data generated by these missions present severe computational bottlenecks. Traditional analytical frameworks struggle with non-stationary noise transients, diffusion blurring, and the exponential scaling limits …
Published in Research & Reviews : Journal of Physics · Vol. 15, Issue 2, 2026 · pp. 48–55 Read article
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AI-Enhanced Interpretation of Cardiac Troponins: Toward Predictive Precision in Myocardial Injury
Abstract: Background: Cardiac troponins (cTn) represent the gold standard biomarkers for myocardial injury detection, yet their interpretation remains challenging due to various confounding factors and clinical contexts. Artificial intelligence (AI) technologies provide remarkable possibilities to improve the interpretation of troponin levels by utilizing pattern recognition, predictive modeling, and clinical decision-making support. Objective: This review examines the current state and future potential of AI-enhanced cardiac troponin interpretation, focusing on machine learning applications, …
Published in Research and Reviews: A Journal of Medicine · Vol. 15, Issue 3, 2025 · pp. 1–9 Read article
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Smart Monitoring and Controlling of the Battery and Motor
Abstract: The demand for effective battery and motor monitoring and management to guarantee dependability, safety, and peak performance has increased due to the quick development of electric vehicles and smart industrial systems. The smart monitoring and control methods used in battery management systems and motor control systems are thoroughly reviewed in this paper. In addition to speed, torque, efficiency, and fault situations in motors, it emphasizes important characteristics like temperature, voltage, …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 11–15 Read article
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Implementation of Anticipating Rainfall Using Machine Learning
Abstract: Rainfall forecasting is crucial for many aspects of our national economy and should help prevent major seasonal droughts. Since agriculture is a beloved profession in many states, some Asian countries are economically hooked to decline. Previous precipitation info is beneficial. Farmers are cancerous in managing their crops, resulting in economic progress for the country. downfall prediction is hard for earth science scientists because of unordering time and unordered quantity of …
Published in International Journal of Satellite Remote Sensing · Vol. 1, Issue 1, 2023 · pp. 1–8 Read article
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Identification of Brain Stroke Using Artificial Intelligence
Abstract: Globally, strokes are the primary cause of disability and mortality. Recently, machine learning (ML) and deep learning (DL) have been employed by artificial intelligence algorithms as effective stroke diagnosing techniques. These days, machine learning and data mining technologies are used in the construction of the main models. We have used five machine learning algorithms to determine if a stroke has occurred or is likely to occur based on a patient’s …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 15–22 Read article
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Experimental and Numerical Assessment of Sustainable Machine Tool Design: A Life Cycle Perspective on Structural Optimization and Energy Performance
Abstract: Machine tools are high-mass, energy-intensive systems with long service lives, contributing significantly to the carbon footprint of manufacturing. Enhancing their sustainability requires integrated structural and energy optimization without compromising functionality, accuracy, or productivity. While sustainable design has been studied broadly, systematic reviews from an experimental and applied mechanics perspective remain limited. This paper reviews sustainable machine tool design through a life cycle lens, emphasizing mechanics-based strategies and assessment methods. Key …
Published in Journal of Experimental & Applied Mechanics · Vol. 17, Issue 2, 2026 · pp. 13–39 Read article
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Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
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Speed Control of An Inverter Fed Three-Phase Induction Motor Using IP Controller Considering Core Loss and Stray Load Loss
Abstract: Consideration of core losses and stray load losses to design a controller of an inverter fed induction motor are affected to the flux and torque decoupling strategy adversely. As a consequence, the detuning of field-oriented control (FOC) is required. The detuning strategies have been developed in some literatures. But the implementation of detuning becomes difficult because of higher order expression. In general, the controlling and estimation of AC drives are …
Published in Trends in Electrical Engineering · Vol. 5, Issue 1, 2015 · pp. 28–35 Read article
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Advancements in Phishing Detection: Automated Systems in Real-World Scenarios
Abstract: The goal of the abstract is to offer an automated method that uses login URLs to identify real-world scenarios. Phishing is a type of cyberattack that involves social engineering, when malefactors trick victims into providing their login credentials via a login form that sends the information to a hostile site. In this research, we offer a system that uses URL analysis to detect phishing websites by comparing machine learning and …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 2, 2024 · pp. 12–17 Read article
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State Predominant Concept of PSS Design
Abstract: This paper presents the design of Power System Stabilizer (PSS) based on state predominant concept. An approach based on state predominant PSS with LQR for low frequency oscillation damping, Pole Placement and fuzzy control has been presented. The concept has been tested for both SMIF and multi machine system. The mathematical model of multimachine has been derived and the system has been simulated for various operating point changes, the results …
Published in Trends in Electrical Engineering · Vol. 2, Issue 1-2-3, 2012 · pp. 45–56 Read article
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Advanced Computational Models for Predicting Molecular Interactions
Abstract: Understanding molecular interactions is essential for a number of disciplines, including biochemistry, materials science, and medication development. Traditional experimental methods, while accurate, are often time-consuming and expensive. Advanced computational models have emerged as powerful tools to predict molecular interactions efficiently. In order to predict the behavior and interactions of molecules at the atomic and subatomic levels, this paper reviews the most recent developments in computational techniques, such as machine learning …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 1, 2024 · pp. 8–13 Read article
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Early Autism Diagnosis: Machine Learning Models and Their Effectiveness
Abstract: Diagnosis is of utmost importance for timely intervention and support. However, traditional diagnosis methods, which are based on subjective assessment, are delayed. This project explores the role that machine learning techniques might play in enhancing the accuracy and effectiveness of ASD detection. Several state-of-the-art classification algorithms were benchmarked using a dataset from Kaggle. Logistic Regression, XG Boost, Random Forest, Decision Tree, and Gradient Boosting were taken into consideration. Other performance …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
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Implementing Machine Learning in Data Classification
Abstract: Data classification forms an essential aspect of artificial intelligence (AI) and soft computing, helping a great deal in the transformation of raw data into knowledge that forms the basis of numerous applications, such as fraud detection, medical diagnostics, and natural language processing. This study discusses the challenges and the state of the art in data classification, as far as scalability, noise handling, and feature selection optimization are concerned. It gives …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 15–22 Read article
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Steady State Visual Evoked Potential Based Brain Computer Interface System
Abstract: The steady-state visual evoked potential (SSVEP) is a brain response that can be measured by an electroencephalogram (EEG) when a subject looks at periodic luminance- or contrast-modulated stimuli. In this study, flickering Light Emitting Diode (LED) is used as visual stimulation. Mostly, brain response is recorded using an electroencephalograph (EEG) and recorded in the brain's occipital lobe which is associated with human vision. The study encompasses key stages, beginning with …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 35–43 Read article
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Performance Analysis And Battery Management System Optimization In Electric Vehicles
Abstract: The rapid electrification of the automotive industry has led to an increased need for battery management systems that are not only efficient and safe but also intelligent. BMS is the device that guarantees the best use of the battery, prolongs its life, and allows its safe operation even under different environmental and load conditions. Through the synthesis of literature and industry practices, this paper acts as a tribute to the …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 Read article
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Remediation of Condemn Oil Polluted Loamy Soil Using Azadirachta Indica (Neem)
Abstract: The remediation of condemn oil polluted loamy soil using neem (azadirachta indica) was investigated. The neem leaves came from Akpugo in the southeast Nigerian state of Enugu State's Nkanu West local government area. The leaves were dried at room temperature and subjected to grinding machine to obtain the powdered form of the dried leaves that serve as treatment to remediate condemn oil polluted loamy soil. Three masses 5g, 10g and …
Published in International Journal of Climate Conditions · Vol. 1, Issue 1, 2024 · pp. 21–27 Read article