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159 articles for “regression analysis”
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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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The Importance of Self-Care for Healthcare Professionals in North India: A Regional Analysis of Burnout, Resilience and Institutional Support
Abstract: Background: Burnout in healthcare professionals is described using the framework of Occupational Health and Resilience Theory, where self-care is an individual-level coping mechanism and institutional support is an organizational-level moderator. However, there is a lack of regional data from North India on the structural and contextual factors influencing burnout. Healthcare workers (HCWs) in North India face mounting pressures due to high patient loads, limited mental health resources, and overlapping professional …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 1, 2026 · pp. 24–29 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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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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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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ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article
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Study on Single-Slope Solar Still for Experimental and Data-Driven Analysis for Improving Productivity with Different Basin Materials.
Abstract: This study investigates the single-slope solar still under the diurnal variation of water temperature and distillate yield under identical operating conditions. Experimental analysis was conducted to evaluate the performance enhancement through the incorporation of natural basin materials, namely hemp and sand. The water distillation process is focused on improving potable water productivity and thermal behaviour. The inclusion of hemp and sand in the basin leads to noticeable differences in productivity …
Published in Emerging Trends in Chemical Engineering · Vol. 13, Issue 2, 2026 · pp. 31–46 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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ML-Driven Optimization Framework for the Analysis, Design, and Development of Efficient Wireless Power Transfer Systems for EV Charging
Abstract: The fast uptake of electric vehicles (EVs) has heightened the necessity of effective, dependable and convenient charging systems. The Wireless Power Transfer (WPT) systems can be taken as a potential solution as they allow charging cells without contact, without any risks, and without any overcrowding; the efficiency of the system is strongly influenced by the alignment of coils, the fluctuations of air-gaps, the conditions of the loads, and geometrical arrangements …
Published in International Journal of Manufacturing and Production Engineering · Vol. 4, Issue 1, 2026 · pp. 1–9 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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Experimental Assessment and Statistical Argument of Al-Si/CSA/MoS2 Hybrid Composites for Mechanical and Tribological Characteristics
Abstract: To augment the mechanical and tribological properties of Al-Si matrix composites complement with molybdenum disulphide (MoS₂) and coconut shell ash (CSA), a mixed experimental and Face-Centered Composite (FCC)strategy was employed. A liquid metallurgical method called stir casting was used to create hybrid composites with 5–15 wt.% CSA and 1–3 wt.% MoS₂. A FCC experimental design with thirty runs was used to thoroughly evaluate the materials. This design allowed for the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 792–802 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Agronomic Performance, Harvest Dynamics, and Oil Yield of Lemongrass Varieties in a Semi‐Arid Ecosystem: Effects of NPK Regime and Harvest Timing
Abstract: This study investigated the agronomic performance, biomass production, and essential oil yield of three lemongrass (Cymbopogon spp.) varieties (CKP-25, Krishna & Pragati) under varying NPK fertilization regimes and harvest timings in a semi-arid ecosystem. A split-plot factorial design with four nutrient levels (Control, 50%, 100%, and 150% of the recommended NPK dose) and two harvest stages (90 and 180 days after planting, DAP) was implemented. Results revealed that both NPK …
Published in Research & Reviews : Journal of Botany · Vol. 15, Issue 1, 2026 · pp. 8–21 Read article
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A Retrospective Analysis of Resin-Based Polymer Composites and Bioactive Glass-Polymer Hybrids Regarding Secondary Caries and Durability
Abstract: Aim- To compare the 2-year clinical survival and failure modes of a Resin-Based Polymer Composites and BioactiveGlass-Polymer Hybrids in Class I and II posterior restorations. Methods-A total of 550 restorations were placed in adult patients across various private dental practices in India to ensure a diverse clinical demographic. Teeth were randomly assigned and restored with either a hybrid composite (Te-Econom, IvoclarVivadent; n=275) or a Resin-Modified Glass Ionomer Cement (GC Gold …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1089–1095 Read article
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Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions
Abstract: Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems. In fifth- generation (5G) New Radio (NR) and emerging sixth-generation (6G) networks, precise knowledge of the wireless channel is essential for achieving reliable data transmission, high spectral efficiency, and low Bit Error Rate (BER). Conventional estimation techniques such as Least Squares (LS) and Minimum Mean Square Error (MMSE) rely on mathematical channel models and predefined …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 2, 2026 Read article
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CIPHER Intelligence: AI-Powered Global Military Expenditure Analysis and Predictive Modeling
Abstract: Military expenditure analysis has emerged as a critical component of economic and geopolitical intelligence in the modern era. This paper presents CIPHER Intelligence, a comprehensive AI-powered platform for analyzing and predicting global military spending patterns across 211 countries spanning54 years (1970-2024). We employ advanced machine learning techniques, particularly Random Forest regression models, to achieve 99.5% prediction accuracy for military expenditure forecasting based on economic indicators. The platform integrates data from …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 Read article
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Effect of Malaria Endemic on Socio-Economic Activities in South-East Nigeria: A Study from 2012 to 2023
Abstract: The research investigated the effects of malaria on socio-economic activities in South-East Nigeria between 2012 and 2023, emphasizing the effectiveness of different malaria management strategies. The analysis utilized both primary and secondary data, employing frequency tables and a five-point Likert scale to gauge respondents' levels of agreement. Furthermore, multiple regression models were utilized to explore the relationships among the variables.. Findings indicated that while diverse malaria interventions had a positive …
Published in Research and Reviews: A Journal of Health Professions · Vol. 14, Issue 3, 2024 Read article
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Advanced Lithium-Ion Battery Prognostics: A Comprehensive Review of Machine Learning Approaches for Remaining Useful Life Prediction
Abstract: The lithium-ion battery (LIB), as one of the main sources for portable power systems, has been increasingly popular owing to its widespread applications in electric vehicles, consumer electronics, aerospace and renewable energy. Despite their advantages in high energy density and long cycle life, LIBs suffer from degradation over time of aging and cycling, resulting in loss of performance, safety issues, and economic bottlenecks. Predicting their Remaining Useful Life (RUL) is …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 2, 2025 · pp. 12–27 Read article
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Evaluating Epidemiological and Physical Risk Factors in the Development of Chemotherapy-induced Peripheral Neuropathy (CIPN): An In-depth Systematic Review and Meta-analysis
Abstract: Background of study: Chemotherapy-induced peripheral neuropathy (CIPN) encompasses a range of adverse effects caused by various cytotoxic medications and stands as a primary source of pain among individuals who have survived cancer. In instances of pronounced and sudden CIPN, it might become necessary to diminish chemotherapy dosages or discontinue their application. Currently, no efficacious strategy exists for proactively preventing CIPN, managing established chronic CIPN is constrained, and the risk factors …
Published in International Journal of Oncological Nursing and Practices · Vol. 1, Issue 2, 2023 · pp. 10–21 Read article
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Predicting and Prohibiting the Risk of Heart Failure Using Machine Learning
Abstract: It is challenging to estimate the likelihood of complex chronic disease while treating conditions like heart failure. The application of machine learning, an area of artificial intelligence, in cardiovascular care is growing quickly. In essence, it defines how computers classify and understand data, or choose a task with or without human intervention. The theoretical underpinnings of machine learning are models that accept input data (such as images or text) and …
Published in International Journal of Computer Science Languages · Vol. 1, Issue 1, 2023 · pp. 15–20 Read article