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250 articles for “Regression”
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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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Machine Learning Approaches Towards Resume Classification
Abstract: Finding the right person for an open position can be an unnerving task, especially when there are many applicants, and if the recruiter or the Human Resources department must sort and further categorize all those resumes then it will be a labor-intensive, time-consuming, and tiresome task. Additionally, human assessment of resumes may be biased and prone to mistakes. Manually screening the proper candidate's resume from the pool is not practicable; …
Published in International Journal of Electronics Automation · Vol. 1, Issue 2, 2023 · pp. 1–7 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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Developing an AI-Based Novel Forecasting Framework for Surface Irregularity in Metal Matrix Materials
Abstract: Surface irregularity in metal matrix materials (MMM) signifies the deviations from smoothness, influencing structural integrity and performance frequently arising from the manufacturing process along with intrinsic material characteristics that influence effectiveness. Limitations in data, model interpretability and complexity are the difficulties that impede artificial intelligence (AI) based surface irregularity in MMM. In this study, we suggested a novel framework of Gaussian regression fused multi-strategy adaptive boosting classifier (GR-MABC) for the …
Published in Journal of Polymer & Composites · Vol. 12, Issue 5, 2024 · pp. 48–56 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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Comparative Analysis of Heart Disease Prediction System
Abstract: In the present world, where heart illnesses are on the rise, it is crucial to forecast these diseases. Performing the task on heart disease is a bit difficult and it must be finished precisely and successfully. Heart disease identification relies heavily on Machine Learning (ML) and data mining approaches. The primary focus of the review paper is that patients are easily prone to cardiac diseases depending on medical traits. Using …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 1, 2023 · pp. 1–6 Read article
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The Role and Influence of Emotional Intelligence in Shaping Job Crafting: An Empirical Study
Abstract: The project titled "The Role and Influence of Emotional Intelligence in Shaping Job Crafting: An Empirical Study" explores the significant influence of Emotional Intelligence (EI) on job crafting while also examining how demographic factors impact this relationship. In today’s evolving workplace, employees are increasingly reshaping their roles to align better with their skills, values, and motivational process known as job crafting. Emotional intelligence, which includes the capacity to identify, comprehend, …
Published in International Journal of Manufacturing and Production Engineering · Vol. 2, Issue 2, 2024 · pp. 15–23 Read article
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Risk Management in Civil Engineering by FTA, FMEA and Risk Matrix
Abstract: Infrastructure projects, particularly in civil engineering, are fraught with uncertainties that manifest as both physical and financial risks. The project report, titled Quantifying and Managing Financial and Physical Risks in Infrastructure Projects, aims to bridge the existing gap between managing these two critical categories of risks. By integrating advanced statistical methodologies such as Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Risk Matrix, and Regression Analysis, this study …
Published in Journal of Structural Engineering and Management · Vol. 12, Issue 1, 2025 · pp. 34–46 Read article
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Optimization of Turning Process Parameters by Genetic Algorithm Approach
Abstract: In this research, turning parameters were optimized through a genetic algorithm for the purpose to minimize surface roughness and to maximize the material removal rate. High finish quality is guaranteed through minimum surface roughness, and efficient process planning is facilitated through maximum material removal rate optimization. For predicting surface roughness and material removal rate with respect to spindle speed, feed rate, and depth of cut, the empirical models were developed …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 33–41 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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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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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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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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SpecForesight: A Predictive Analytics Pipeline for Laptop Price Forecasting
Abstract: This paper frames laptop pricing as a supervised predictive analytics problem, transforming product specifications into feature-rich signals to forecast price with calibrated regression models and operational guardrails against drift. A structured pipeline ingests tabular listings, performs data cleaning, and engineers domain-informed features (e.g., central processing unit (CPU) family and clocks, graphics processing unit (GPU) tiering, memory/storage density, display, and touch capabilities), followed by encoding and normalization to optimize model learnability. …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 61–71 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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Socioeconomic Inequalities in Utilisation of AYUSH Systems of Medicine in North-East India: Evidence from NSS 79th (2022–2023) Round on AYUSH
Abstract: Background: Socioeconomic inequality in healthcare Utilisation continues despite policy focus on universal health coverage and Sustainable Development Goal 3. India's traditional, complementary, and alternative medicine systems are generally termed as AYUSH. AYUSH has been consolidated into the national health policy; however, empirical findings on its distributive equity persist as limited. This study estimates and decomposes socioeconomic inequality in Utilisation of AYUSH systems of medicine among the adult age group 15 …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 29–42 Read article
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Process Optimization of Spot Welding for Galvanized Automotive Steel Sheets
Abstract: Resistance Spot Welding (RSW) is a pillar of the modern automotive industry with the usage of lightweight and high-strength products at the highest point of demand. The optimum weld quality of galvanized steel sheets which is a material of choice because of its additional corrosion protective property is however not achieved easily. This study is a well-developed data-based solution to designing the RSW process in the most efficient way, providing …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 32–42 Read article
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Lightweight Models for Per-PC Energy Consumption Forecasting: Comparative Study with ML and DL Approaches
Abstract: We have collected primary data from automated logging of parameters like CPU utilization, estimated power, active or idle state, user logging activity, and the type of day. Additionally, survey data showed user awareness, energy-saving behaviour, and PC usage patterns. The data is pre-processed and merged by applying processes such as data cleaning, normalization, and feature extraction, i.e., determining the peak active timings and downtime. Developed lightweight prediction models based on …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 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