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38 articles for “logistic regression analysis”
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Fault Detection in Solar PV Systems Integrated with the Power Grid: Evaluating Logistic Regression through Confusion Matrix Analysis
Abstract: This paper proposes a method for failure detection in grid-integrated solar photovoltaic (PV) systems using logistic regression and real-time sensor data. The approach effectively classifies and identifies seven distinct fault types. The developed model demonstrates a high fault identification accuracy, ranging from 93% to 96.5% across various fault types and operational conditions. By leveraging logistic regression, the system utilizes key independent variables that significantly influence the classification process. Additionally, the …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 2, 2025 · pp. 45–52 Read article
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Evaluation of Machine Learning Classifiers for Sentiment Analysis
Abstract: Sentiment in social media refers to users’ emotions and opinions through their posts and interactions. Sentiment analysis (SA) refers to relating and classifying the sentiments expressed as engagement and interactions between users. When analyzed, tweets frequently produce a large source of clustered data. These data help determine people’s opinions about a variety of motifs. Thus, this study presents an Automated Machine Learning (ML) Sentiment Analysis Model to detect media sentiment. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 141–154 Read article
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A Study on Clinical Characteristics and The Impact Of Covid-19 In Hemodialysis Patients
Abstract: Background: The Corona Viruses Disease-19 pandemic has severely impacted Hemodialysis patients, who are vulnerable due to compromised immune systems and underlying comorbidities. Frequent Dialysis center visits increase their exposure risk to severe acute respiratory syndrome- Coronavirus Disease, and chronic inflammation, uremia, and immunosenescence may impair their immune response. Methods: This retrospective study included 60 Hemodialysis patients diagnosed with COVID-19 between January -July 2024. Data on demographics, clinical characteristics, laboratory results …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 2, 2025 Read article
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A Study to Assess the Risk Factors Associated with Sudden Death in Population on Hemodialysis
Abstract: Background: Patients with chronic kidney disease (CKD) receiving maintenance hemodialysis (HD) experience disproportionately high mortality, with sudden death remaining a leading cause. Multiple clinical, biochemical, and care-related factors influence outcomes, yet comprehensive risk stratification models and the role of dialysis timing and early nephrology care remain inadequately explored in resource-limited settings. Objectives: This study aimed to (i) identify clinical and biochemical risk factors associated with mortality in HD patients, (ii) …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 16, Issue 1, 2026 · pp. 14–19 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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Twitter Emoticon Interpretation Using Machine Learning Algorithms in Sentiment Analysis
Abstract: In the current era, thousands of people share their opinions every day on the well-known microblogging platform Twitter in the form of tweets. A tweet must be brief and straightforward in order to be effective, though sentiment analysis of Twitter data will be the main emphasis of this study. Sentiment analysis study encompasses NLP and text data mining. We will conduct sentiment analysis on Twitter data using several logistic machine …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 1, 2024 · pp. 1–6 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Determinants of the Adoption of chemical Fertilizer in Kaffa, Bench Sheko and Sheka zones of Southwest Ethiopia
Abstract: Agriculture is the backbone of the Ethiopian economy, but the production system was backward, and the adoption of agricultural technology was low. This study aims to identify and determine factors affecting smallholder farmers' adoption of chemical fertilizer. Bita, Chena, Andiracha, and Sheyi Bench district of the southwest Ethiopia region was selected for this study. Household individual survey interview, key informant interview, and focus group discussion were the primary data collection …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 1, 2026 · pp. 1–12 Read article
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Assessing the Increasing Incidence of Prostate Cancer in Urban Delhi: Risk Factors, Early Detection, and Treatment Challenges
Abstract: Background: Prostate cancer has emerged as the second most prevalent cancer among men in India, with rapidly increasing incidence rates in urban areas like Delhi. This rise is attributed to urbanization, lifestyle changes (high-fat diets, tobacco use, sedentary behavior), increased life expectancy, and improved diagnostics. Despite its growing burden, low awareness, limited screening programs, and treatment barriers hinder early detection and effective management. This study examines the epidemiological trends, risk …
Published in International Journal of Oncological Nursing and Practices · Vol. 3, Issue 2, 2025 · pp. 21–28 Read article
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Breast Cancer Screening Awareness and Participation Among Women in North India
Abstract: Breast cancer is the most prevalent malignancy among Indian women, accounting for 25% of all female cancers (ICMR, 2020). Significant urban-rural disparities in screening persist across North India due to socioeconomic, cultural, and healthcare access barriers. This study evaluates these disparities to inform targeted interventions. A cross-sectional study was conducted among 1,500 women (750 urban, 750 rural) in Delhi, Uttar Pradesh, Punjab, and Haryana, using stratified random sampling. Data were …
Published in International Journal of Women's Health Nursing And Practices · Vol. 3, Issue 2, 2025 · pp. 43–50 Read article
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HPV Screening as an Alternative to Pap Smear in Cervical Cancer Prevention: A Comparative Study in North India
Abstract: Cervical cancer remains a leading cause of cancer-related morbidity and mortality among Indian women, especially in low-resource settings. While Pap smear testing has been the standard screening method, HPV DNA testing offers potential advantages in sensitivity, cost-effectiveness, and feasibility for large-scale implementation. This study compares the efficacy, cost, and acceptability of HPV screening versus Pap smears in urban and rural populations of North India. A comparative cohort study was conducted …
Published in International Journal of Women's Health Nursing And Practices · Vol. 3, Issue 2, 2025 · pp. 31–37 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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Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection
Abstract: The expanding realm of data analysis holds considerable importance in healthcare, particularly in the medical sector where forecasting heart disease is considered a complex endeavor. Early prediction of serious health conditions can be the determining factor between survival and fatality, with heart disease being one such critical health issue. Over the past decade, the main reason for death has been heart disease. Heart disorders come in many different forms, and …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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Synthetic Highs in Urban India: A Quantitative Study of Synthetic Drug Use Among Youth in Maharashtra’s Metropolitan Cities
Abstract: Background: The increasing prevalence of synthetic drug use among urban youth is emerging as a serious public health issue in cities like Mumbai and Pune. The transition from traditional drugs to more modern ones, such as MDMA, LSD, and meth, is indicative of deeper global shifts fueled by urbanization, increased access to the internet, and peer pressure. There is an abundant increase in reports about the availability of synthetic drugs, …
Published in Research and Reviews: A Journal of Health Professions · Vol. 16, Issue 1, 2026 · pp. 6–12 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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Fault Diagnosis of Air Compressor (AC) System using Local Mean Decomposition (LMD) and Logistic Regression (LR) Machine Learning Classifier
Abstract: This article presents a detailed and systematic procedure for performing fault diagnosis in an air compressor (AC) system by analyzing the audio signals generated during its operation. The analysis covers both normal (healthy) conditions and seven distinct types of faults, including bearing failure, flywheel malfunction, inlet valve leakage, outlet valve leakage, non-return valve failure, piston ring defect, and rider belt issues. To acquire the acoustic signals, the researchers utilized a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 416–427 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article
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Factors Affecting Farmers Participation in Soil and Water Conservation Practices at Qenshiben Watershed Central Ethiopia
Abstract: Soil erosion is among the foremost causes of declining soil resources in Ethiopia, which in turn affect agricultural productivity. To limit this problem, for the last two decades’ soil and water conservation measures have been practiced via free labor community mass-mobilization program. Following the launch of the program, farmers massively and voluntarily implemented soil and water conservation measures on the farm lands at different parts of the country. Participation of …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 13, Issue 3, 2024 · pp. 10–22 Read article
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Sentiment Analysis of X (Formerly Twitter) Using Machine Learning
Abstract: Sentiment analysis is a methodology to determine the nature and behavior of each and every user for the content posted on the social media platform in the form of post and feed. Consumers of the online platform are encouraged to post reviews of the product that they purchase. Little attempt is created by Amazon to confine or limit the content of these reviews. The number of reviews for various merchandise …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 2, 2024 · pp. 28–37 Read article
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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