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1070 articles for “LOG”
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Machine Learning-Based Disease Prediction: A Comparative Analysis for Diabetes, Brain Tumor, and Parkinson's Disease
Abstract: This paper presents a web-based disease prediction system that integrates machine learning and deep learning techniques to assist in the early detection of Parkinson’s Disease, Diabetes, and Brain Tumors. By utilizing clinical data and MRI images, the platform provides rapid and interpretable predictions to support proactive health management. Logistic Regression models are applied to classify structured datasets for predicting Parkinson’s disease and Diabetes, making use of their effectiveness in binary …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 44–54 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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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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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube
Abstract: In the digital age, social media platforms play a vital role in facilitating user engagement, encompassing both positive interactions and avenues for negative, often harmful behaviors. Recognizing and addressing toxic exchanges is paramount to nurturing healthy online communities and preserving users’ well-being. This study introduces a novel method for identifying toxic interactions by utilizing Gradient Boosting Regression Trees (GBRT) algorithm, a machine learning approach renowned for its exceptional accuracy and …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 3, 2025 · pp. 7–14 Read article
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Breast Cancer Detection Using Machine Learning: A Comparative Analysis of Supervised Learning Algorithms
Abstract: Globally, breast cancer remains a predominant cause of mortality among women, highlighting the urgent need for timely and precise diagnostic approaches. This research explores the application of machine learning algorithms—including Logistic Regression, SVM, Naïve Bayes, KNN, and Random Forest—on the Wisconsin Breast Cancer Dataset for effective tumor classification. Key pre-processing steps such as missing value handling, feature scaling, and dimensionality reduction were employed to improve model performance. The study evaluated …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 46–52 Read article
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NutriHeart with Chatbot
Abstract: Heart disease stands as one of the world's principal reasons for human deaths since it causes major preventable fatalities each year. Healthcare institutions currently explore machine learning (ML) integration for establishing new approaches toward predicting, and acting ahead of healthcare developments. NutriHeart presents an AI-based platform that accomplishes cardiovascular risk detection early and extends heart wellness by delivering customized nutritional and lifestyle recommendations. Using Support Vector Machines (SVM) along with …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 3, 2025 · pp. 22–34 Read article
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Residential Sanctioned Load Monitoring and Controlling Using PLC & SCADA
Abstract: Efficient load monitoring and control are crucial for managing energy resources in various industrial and commercial applications. Sanctioned load, which refers to the maximum permissible electrical load imposed by authorities or utilities, needs to be closely monitored and controlled to ensure compliance and optimize energy usage. Programmable Logic Controllers (PLC) and Supervisory Control and Data Acquisition (SCADA) systems offer reliable and efficient solutions for monitoring and control in industrial and …
Published in Trends in Electrical Engineering · Vol. 15, Issue 3, 2025 · pp. 7–11 Read article
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A Comprehensive Analysis of Machine Learning Models for Credit Card Fraud Detection
Abstract: This paper presents an indepth comparison of various machine learning models—Logistic Regression, Support Vector Classification (SVC), and Neural Networks (NN)—in the context of credit card fraud detection. The analysis spans multiple performance metrics, including accuracy, F1 score, precision, recall, and computational efficiency. Logistic Regression demonstrates competitive performance in terms of accuracy, but its poor precision renders it unsuitable for fraud detection tasks. Conversely, the Neural Network exhibits balanced precision and …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Fallacious Reasoning (Emphasizes the flawed or misleading nature of the reasoning)
Abstract: It is essential to recognize that faulty reasoning and & faulty logic denote the same principle, both indicating a deficiency in reasoning. The term faulty denotes the presence of errors or imperfections, while logic pertains to sound reasoning or judgment. Faulty logic can also be seen as a rhetorical technique. This paper demonstrates how incorrect or misleading answers can ultimately guide us toward accurate conclusions. By honing the ability to …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 3, 2025 Read article
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A Quantitative Fuzzy MCDM Framework for Decision Support in Uncertain Environments
Abstract: Fuzzy mathematics play an increasingly generalized role in decision-making, and thus, this paper details different types of fuzzy mathematics and it signs other possible solutions in addition to fuzzy mathematics. Fuzzy models offer a versatile and precise approach to assessing complex situations through the use of fuzzy sets, membership functions, and aggregation methods. Through time, cost and quality, the project management case study illustrates how fuzzy logic works for them. …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 1, 2026 · pp. 1–8 Read article
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Comparative Analysis of Supervised Learning Algorithms
Abstract: Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 25–30 Read article
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Sustainable Waste Management through Polymer Recycling: A Review of Business Model and Managerial Innovation
Abstract: Plastic and other polymeric materials have transformed modern life by providing durability, versatility, and cost-effective solutions across industries such as packaging, healthcare, construction, and transportation. However, their extensive use and improper disposal have created significant environmental concerns, including plastic pollution, landfill accumulation, and marine ecosystem degradation. This review synthesizes existing literature on polymer recycling with a focus on business-model innovation and managerial practices that can support sustainable waste management at …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 155–166 Read article
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Explainable Machine Learning Integrated with Polymer-Based Diagnostic Technologies for Liver Health Classification
Abstract: Early and reliable assessment of liver health is essential for timely treatment, yet most machine-learning approaches face limitations such as class imbalance and low clinical interpretability. This study proposes a polymer-integrated, explainable machine-learning framework that combines SMOTE-based data balancing, Logistic Regression, and XAI techniques (SHAP and LIME) for transparent liver-health classification. In addition to ML modelling, the study emphasizes the emerging role of polymer-based biosensors, microfluidic polymer chips, polymer nanomaterials, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 631–643 Read article
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Lyapunov-Stable Adaptive Fractional-Order Interval Type-2 Fuzzy Control for Robust Anti-Lock Braking Under Uncertain Road Adhesion Conditions
Abstract: This paper proposes a Lyapunov-stable Adaptive Fractional-Order Interval Type-2 Fuzzy Logic Controller (FO-IT2FLC) for robust anti-lock braking system (ABS) control under nonlinear vehicle dynamics and uncertain road adhesion conditions. The proposed framework integrates fractional-order error dynamics to capture memory-dependent tire–road interaction, interval Type-2 fuzzy inference to model uncertainty via footprint-of-uncertainty representation, and a Lyapunov-based adaptive learning mechanism for real-time parameter tuning. A rigorous stability proof guarantees boundedness of all closed-loop …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 2, 2026 · pp. 31–41 Read article
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Machine Learning Classification of Normal Versus Age-Related Macular Degeneration OCT Images
Abstract: The macula is a small area of the retina which is especially very important for good eyesight. The age related macular degeneration (AMD) is a type of visual impairment that can cause central vision blur or even loss of visionblindness or even loss of eyesight. AMD was a dangerous and progressing chronic disease which affects people over the age of 60. One of the most common symptoms of this condition …
Published in Journal of Control & Instrumentation · Vol. 13, Issue 3, 2022 · pp. 1–8 Read article
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Adaptive Neuro-Fuzzy Inference System (ANFIS) Control for Total Harmonic Distortions Reduction of Multi-Pulse STATCOM
Abstract: In this paper proposed ANFIS System is adapted for controllable mitigation of Total harmonic distortions of STATCOM with Multiple pulse technology. Integrated power electronics device, Gate Turn Off (GTO) is used in switching mode with Voltage source convertor during application of distinct load conditions. The proportional and integrated Controller is replaced by a smart fuzzy logic controller that offers great support for harmonic mitigation by redefining the electrical magnitudes of …
Published in Journal of Mechatronics and Automation · Vol. 9, Issue 2, 2022 · pp. 17–31` Read article
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Machine Learning Techniques for Early Detection of Heart Disease
Abstract: Cases of heart disease are increasing rapidly, thus it's important and concerning to be aware of any potential ailment beforehand. This diagnosis is a difficult task that must be completed fast and precisely. The primary goal of this study is to determine which patient, based on different medical features, has a higher chance of having heart disease. We created a heart disease prediction algorithm based on the patient's medical history …
Published in Journal of Microelectronics and Solid State Devices · Vol. 10, Issue 3, 2023 · pp. 16–21 Read article
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Efficient Encoding And Decoding Block Turbo Codes
Abstract: A channel coding approach is performed to lower the noise and interference that erupted in the communication channel. There are indeed a myriad of alternative channel coding schemes that have been developed. Block and convolutional codes are the most prevalent. Turbo code is a new near-channel capacity error-correcting code that has been introduced recently. The information can be transmitted across the channel with a very low bit error rate using …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 9, Issue 1, 2022 · pp. 11–23 Read article
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Automation of Rack Milling Machine
Abstract: Our project is about automation of old milling machine by replacing its control circuitry withPLC logic. There is one old Germen rack milling machine in the power tools manufacturingindustry named Eibenstock Positron Ltd. Tardal for making the racks of metal bar. Thismachine was working on old hard wired control logic. This control logic had difficulties introubleshooting problems, modification of system could not be done easily and control boxwas large, also …
Published in Trends in Electrical Engineering · Vol. 7, Issue 1, 2017 · pp. 48–50 Read article