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Journal articles on the topic 'Predictive Machine Learning'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Mahida, Ankur. "Predictive Incident Management Using Machine Learning." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 1977–80. http://dx.doi.org/10.21275/sr24401231847.

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Tong, Tingting, and Zhen Li. "Predicting learning achievement using ensemble learning with result explanation." PLOS ONE 20, no. 1 (2025): e0312124. https://doi.org/10.1371/journal.pone.0312124.

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Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning

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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Implementing machine learning techniques for customer retention and churn prediction in telecommunications." Computer Science & IT Research Journal 5, no. 8 (2024): 2011–25. http://dx.doi.org/10.51594/csitrj.v5i8.1489.

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This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data qua

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Jogalekar, Tanmay. "Predictive Modelling for Stock Market Analysis (June 2025)." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51062.

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The stock market is a complex and dynamic system, and predicting its behaviour is a challenging task. This research paper presents a comparative study of machine learning algorithms for predictive modelling of stock market analysis. We evaluate the performance of six machine learning algorithms, including Linear Regression, Decision Trees, Random Forest, Support Vector Machines, Artificial Neural Networks, and Gradient Boosting, on a dataset of historical stock prices. Our results show that the Gradient Boosting algorithm outperforms the other algorithms in terms of accuracy, precision, and re

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Husam Kadhim Gharkan, Mustafa Jawad Radif. "Predicting Student Performance Using a Hybrid Model Based on Machine Learning and Feature Selection Techniques." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 192–99. https://doi.org/10.52783/jisem.v10i4.8921.

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Accurately predicting student performance plays a critical role in modern educational institutions. It enables targeted interventions and enhances educational outcomes. This paper proposes a hybrid predictive model for predicting student performance employing feature selection based on standard deviation filtering, coupled with machine learning techniques. In the machine learning phase used Decision Tree (DT), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machines (SVM) were used. The proposed model is tested and evaluated over the Student Performance Prediction—Multiclass

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Nagarjuna, N., and Dr Lakshmi HN. "Predictive Modeling of Diabetes Mellitus Utilizing Machine Learning Techniques." CVR Journal of Science and Technology 26, no. 1 (2024): 112–17. http://dx.doi.org/10.32377/cvrjst2618.

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Diabetes mellitus represents a persistent metabolic condition distinguished by elevated levels of blood sugar, which results from the inadequacy of the body to secrete and respond to insulin, leading to health risks and frequent hospitalizations. Accurate predictive models are vital for targeted interventions to reduce readmissions and improve healthcare quality and cost. Early prediction can mitigate its impact, aid in control, and potentially save lives. Machine learning algorithms show promise in medical applications, including diabetes prediction and diagnosis. Limited data quality hinders

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Natanael, David, and Hadi Sutanto. "Machine Learning Application Using Cost-Effective Components for Predictive Maintenance in Industry: A Tube Filling Machine Case Study." Journal of Manufacturing and Materials Processing 6, no. 5 (2022): 108. http://dx.doi.org/10.3390/jmmp6050108.

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Maintenance is an activity that cannot be separated from the context of product manufacturing. It is carried out to maintain the components’ or machines’ function so that no failure can reduce the machine’s productivity. One type of maintenance that can mitigate total machine failure is predictive maintenance. Predictive maintenance, along with the times, no longer relies on visuals or other senses but can be combined into automated observations using machine learning methods. It can be applied to a toothpaste factory with a tube filling machine by combining the results of sensor observations

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MISHRA,, SAURABH. "HEALTH PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34438.

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Machine learning techniques have transformed healthcare by enabling precise and timely disease prediction. The capacity to forecast multiple diseases simultaneously can greatly enhance early diagnosis and treatment, leading to improved patient outcomes and lower healthcare expenses. This research paper delves into the use of machine learning algorithms for predicting various diseases, highlighting their advantages, challenges, and prospects. It provides a comprehensive overview of different machine learning models and the data sources frequently employed in disease prediction. Furthermore, it

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Sreshta, Maradapu Ananya. "Analyzing Cancer Prognosis with Advanced Machine Learning Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30443.

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This study investigates the use of Support Vector Machines (SVM) and other machine learning algorithms for predicting the prognosis of lung, breast, and cervical cancer patients. The research evaluates the predictive accuracy, influential features, algorithm performance, and model generalization across different cancer types. Using publicly available datasets, the study highlights SVM's exceptional accuracy in prognosis prediction, with findings indicating its superiority over alternative algorithms. Notably, it identifies the key clinical, molecular, and pathological features that significant

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Fokkema, Marjolein, Dragos Iliescu, Samuel Greiff, and Matthias Ziegler. "Machine Learning and Prediction in Psychological Assessment." European Journal of Psychological Assessment 38, no. 3 (2022): 165–75. http://dx.doi.org/10.1027/1015-5759/a000714.

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Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based o

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Patil, Ketan. "Customer Churn Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29907.

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Customer churn, the phenomenon where customers cease their relationship with a business, is a critical concern for e-commerce platforms striving for sustained growth and profitability. Predicting churn in advance can empower businesses to implement proactive retention strategies, thereby mitigating revenue loss and enhancing customer satisfaction. In this study, we propose a machine learning-based approach to predict customer churn in e-commerce settings. We begin by collecting extensive data encompassing various customer attributes, transactional history, browsing behavior, and engagement met

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Yadav, Shivani. "Heart Disease Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem36858.

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Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the ac

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Journal, IJSREM. "PARKINSON’S DISEASE PREDICTION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–13. http://dx.doi.org/10.55041/ijsrem27762.

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Parkinson's disease (PD) is a condition that affects a substantial number of individuals worldwide, leading to impairments in motor functions and a decline in overall quality of life. The timely identification of PD is vital for effective intervention and better patient outcomes. This study investigates the Utilization of machine learning methods in predicting and diagnosing Parkinson's disease using clinical and biomedical data.The dataset is pre-processed to address missing values and standardize features. Subsequently, various ML algorithms, including Support Vector Machines (SVM) are emplo

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Ali, Ahmed, Ahmed Fathalla, Ahmad Salah, Mahmoud Bekhit, and Esraa Eldesouky. "Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models." Computational Intelligence and Neuroscience 2021 (November 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/8551167.

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Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: mach

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Alam, Gazi Touhidul, Mohammed Majid Bakhsh, Nusrat Yasmin Nadia, and S. A. Mohaiminul Islam. "Predictive Analytics in QA Automation:." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 4, no. 2 (2025): 55–66. https://doi.org/10.60087/jklst.v4.n2.005.

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An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve sof

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Sakalle, Sonal, and Sakshi Rai. "Effectiveness of a machine learning classifier model." Journal of Advances and Scholarly Researches in Allied Education 22, no. 01 (2025): 34–43. https://doi.org/10.29070/4sfq6836.

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As a statistical tool, predictive modelling may foretell how something will act in the future. In order to foretell how people will act in the future, machine learning has become a popular tool. Determining which of the many accessible algorithms is best suited to the data at hand is an intriguing challenge. The field of study that finds the greatest value in predictive modelling is educational data mining. Accurately predicting undergraduate students' grades has several benefits for both students and teachers. Students might be more motivated to choose their future endeavours with the support

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Jhajharia, Kavita, Neha V. Sharma, and Pratistha Mathur. "A Machine Learning Model for Crop Yield Prediction Using Remote Sensing Data." International Research Journal of Multidisciplinary Scope 06, no. 02 (2025): 577–90. https://doi.org/10.47857/irjms.2025.v06i02.03182.

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Precisely estimating crop yields is a critical aspect of agricultural planning, resource allocation, and food security. Satellite data integrated with machine learning algorithms have recently become a potential solution for predicting crop yield at local and global levels. The present study provides detailed investigation of satellite-based crop yield prediction using machine-learning algorithms. The proposed methodology integrates satellite imagery data with precipitation data. We use machine learning algorithms for predictive modelling, random forests, support vector machines, decision tree

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M, Mrs Adithi, ,. Pavan Kumar R, Priya Y. S, Sneha B. S, and Vaishnavi O. "Smart Healthcare Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39440.

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In this paper, the utilization of machine learning techniques in the healthcare system is introduced. As the healthcare industry generates increasingly vast amounts of data daily, manual processing by humans becomes impractical for prompt disease diagnosis and treatment decisions. To address this challenge, data management techniques and machine learning algorithms are explored in healthcare applications to facilitate more accurate decision-making processes. Detailed descriptions of medical data are provided, enhancing various facets of healthcare applications through the adoption of this cutt

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Basha, Md Chan, A. Veena sindhu, CH .Vaishnavi, E. Sujatha, and K. Praveen kumar. "Lung Cancer Prediction using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40415.

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Past years have experienced increasing mortality rate due to lung cancer and thus it become crucial to predict whether the tumor has transformed to cancer or not, if the prediction is made at an early stage then many lives can be saved. This investigates the potential of machine learning (ML) algorithms for predicting lung cancer risk based on clinical data and Medical Imaging, patient demographics and several ML models, including Decision Trees, Support Vector Machines (SVM), random forests and K- Nearest neighbors(KNN),CNN(convolutional neural network).Evaluation metrics like accuracy ,preci

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Alassafi, Madini O., Wajdi Alghamdi, S. Sathiya Naveena, Ahmed Alkhayyat, Absalomov Tolib, and Ibrokhimov Sarvar Muydinjon Ugli. "Machine Learning for Predictive Analytics in Social Media Data." E3S Web of Conferences 399 (2023): 04046. http://dx.doi.org/10.1051/e3sconf/202339904046.

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Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is

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J Nadhiya R and P. Rajendran. "Prediction of Machine Failure Status Using Machine Learning Techniques." International Journal of Scientific Research in Science and Technology 12, no. 4 (2025): 122–26. https://doi.org/10.32628/ijsrst251262.

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This abstract presents a study on predicting machine failure status using machine learning techniques. With the increasing complexity of industrial systems, early detection of machinery failures is crucial for maintaining operational efficiency and minimizing downtime. In this research, various machine learning algorithms are employed to analyse historical sensor data and identify patterns indicative of impending failures. The proposed approach demonstrates significant potential in accurately predicting machine failures, thus enabling proactive maintenance strategies. Experimental results show

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Yeo, Michelle, Tristan Fletcher, and John Shawe-Taylor. "Machine Learning in Fine Wine Price Prediction." Journal of Wine Economics 10, no. 2 (2015): 151–72. http://dx.doi.org/10.1017/jwe.2015.17.

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AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the tr

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Gangthade, Ritesh Ananda. "Stock Price Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3472–77. http://dx.doi.org/10.22214/ijraset.2024.60725.

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Abstract: The "Stock Price Prediction Using Machine Learning" project aims to develop an advanced predictive model for forecasting stock prices in financial markets. The volatility and complexity of stock markets make accurate predictions challenging, and the utilization of machine learning techniques offers a promising approach to address this challenge. This project leverages historical stock data, technical indicators, and sentiment analysis to create a robust predictive model. The methodology involves collecting and preprocessing a vast dataset of historical stock prices and relevant finan

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Vijayakumar, Ranjith, and Mike W. L. Cheung. "Replicability of Machine Learning Models in the Social Sciences." Zeitschrift für Psychologie 226, no. 4 (2018): 259–73. http://dx.doi.org/10.1027/2151-2604/a000344.

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Abstract. Machine learning tools are increasingly used in social sciences and policy fields due to their increase in predictive accuracy. However, little research has been done on how well the models of machine learning methods replicate across samples. We compare machine learning methods with regression on the replicability of variable selection, along with predictive accuracy, using an empirical dataset as well as simulated data with additive, interaction, and non-linear squared terms added as predictors. Methods analyzed include support vector machines (SVM), random forests (RF), multivaria

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Patel, Ananya (Ph D. Candidate). "ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 1–7. https://doi.org/10.55640/ijidml-v02i02-01.

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The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application

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Xu, Hai, Jian Zhou, Panagiotis G. Asteris, Danial Jahed Armaghani, and Mahmood Md Tahir. "Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate." Applied Sciences 9, no. 18 (2019): 3715. http://dx.doi.org/10.3390/app9183715.

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Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine

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NANDHINI K, Ms. "SALES FORECASTING USING MACHINE LEARNING." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03473.

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Abstract - Sales forecasting remains a critical component in the strategic planning and operational efficiency of retail enterprises. This study presents a data-driven forecasting model tailored for retail sales prediction, using a real-world dataset comprising historical sales records, outlet characteristics, and product features. The methodology emphasizes robust data preprocessing, including handling of missing values, outlier treatment, and categorical encoding, followed by the application of ensemble-based regression. Among various algorithms evaluated, the Extreme Gradient Boosting Rando

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Talele, Amit. "Customer Churn Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41260.

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Customer attrition poses a major issue for the telecommunications sector, resulting in revenue decline and higher expenses for gaining new customers. Predicting churn accurately enables telecom companies to take proactive measures to retain customers. This study leverages specifically Random Forest, Decision Tree, and XGBoost, to develop a robust churn prediction model. These algorithms analyze customer behavioral data, including call duration, internet usage, billing history, and complaints, to identify potential churners. Decision Tree provides an interpretable model, Random Forest improves

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Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.

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The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total

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B.S, Mr Akshay Nayak. "Predictive Medicine Recommendation System Powered by Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50712.

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Abstract - The Predictive Medicine Recommendation System Powered by Machine learning (ML), a system where intelligently suggests suitable medications based on user-provided inputs, using machine learning techniques to enhance accuracy and relevance symptoms or medical conditions. By integrating advanced classification algorithms such as Support Vector Machines (SVM), Random Forest, and Naive Bayes, the system aims to reduce prescription errors, enhance diagnostic efficiency, and support personalized healthcare delivery. The platform supports early disease prediction, medication advice, and ris

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Shin, Seung-Jun, Young-Min Kim, and Prita Meilanitasari. "A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes." Processes 7, no. 10 (2019): 739. http://dx.doi.org/10.3390/pr7100739.

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The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and

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Rachakonda, Shriya Aishani, Srinidhi Pudipedi, and T. S. Shiny Angel. "PREDICTIVE MODELLING FOR DIABETES USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–16. http://dx.doi.org/10.55041/ijsrem37149.

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Diabetes is a prevalent and long-lasting medical disorder that has significant consequences for health. It is important to diagnose diabetes promptly and accurately in order to effectively manage it. This study use machine learning algorithms to forecast the occurrence of diabetes by analyzing a dataset obtained from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Important diagnostic characteristics such as the count of pregnancies, insulin levels, age, body mass index (BMI), and other health measurements are used. We utilize various supervised learning classific

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Khaja, Abdullah Mazharuddin, Michidmaa Arikhad, Yawar Hayat, and Saad Rasool. "Predictive Modeling for Chemotherapy Response Using Machine Learning." International Journal of Innovative Research in Computer Science and Technology 13, no. 3 (2025): 62–66. https://doi.org/10.55524/ijircst.2025.13.3.10.

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One of the most difficult and urgent problems in oncology is still predicting how a patient will react to chemotherapy. Interpatient variability still restricts therapeutic success and increases the likelihood of side effects, even with major improvements in treatment regimens. Machine learning (ML) has been a game-changing technique in biomedical research in recent years, allowing high-dimensional information to be integrated and interpreted to inform clinical judgment. With an emphasis on both historical advancements and contemporary advances, this thesis offers a thorough analysis of the fu

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Vora, Deepali, and Kamatchi Iyer. "Evaluating the Effectiveness of Machine Learning Algorithms in Predictive Modelling." International Journal of Engineering & Technology 7, no. 3.4 (2018): 197. http://dx.doi.org/10.14419/ijet.v7i3.4.16773.

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Predictive modelling is a statistical technique to predict future behaviour. Machine learning is one of the most popular methods for predicting the future behaviour. From the plethora of algorithms available it is always interesting to find out which algorithm or technique is most suitable for data under consideration. Educational Data Mining is the area of research where predictive modelling is most useful. Predicting the grades of the undergraduate students accurately can help students as well as educators in many ways. Early prediction can help motivating students in better ways to select t

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Swamy, Mr K. K., Mrs SRILATHA PULI, S. SWETHA, B. SHARANYA, A. ANUHYA, and J. SHREYAS. "CRIME ANALYSIS USING MACHINE LEARNING." YMER Digital 21, no. 05 (2022): 412–16. http://dx.doi.org/10.37896/ymer21.05/44.

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Crimes are the significant threat to the humankind. as crimes are increasing at a rapid rate approach for identifying trends in crime. Our project can predict regions which have high probability for crime occurrence and can visualize crime prone areas by visual representation of data by bar graphs, pie charts etc. It speed up the classification of criminal activities by calculating the average accuracy rate .It uses crime data set and predicts the types of crimes in a particular area which help in Accurate results based on the predictive analysis of logistic regression. The objective would be

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Bussa, Santhosh. "Machine Learning in Predictive Quality Assurance." Stallion Journal for Multidisciplinary Associated Research Studies 1, no. 6 (2022): 54–66. https://doi.org/10.55544/sjmars.1.6.8.

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Predictive quality assurance (PQA) uses machine learning (ML) for enhancing the quality assurance process from traditional reactive systems to proactive predictive systems: predicting and preventing defects toward quality across the board. This paper is an exploration of the ML techniques of PQA with a focus on supervised, unsupervised, and reinforcement models, along with their interaction with real-time quality control systems. Techniques of data preprocessing, dealing with imbalanced datasets, and validation of the model in detail are discussed. Major applications in manufacturing, automoti

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Li, Xiaochang, Xiaoman Chen, Qiulian Wang, Ning Yang, and Congjiao Sun. "Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens." Genes 15, no. 6 (2024): 690. http://dx.doi.org/10.3390/genes15060690.

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Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens

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Li, Yanrong, Shibiao Xu, Li Guo, Qing Zhang, and Hailong Jin. "PREDICTION OF KIDNEY TRANSPLANTATION INFECTION BASED ON TRADITIONAL MACHINE LEARNING AND DEEP LEARNING." International Journal on Bioinformatics & Biosciences 13, no. 1 (2023): 1–15. http://dx.doi.org/10.5121/ijbb.2023.13102.

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Infection prediction after kidney transplantation is significant. Most existing models for predicting kidney transplant infection are statistical, unintelligent, and straightforward. The foremost task of this paper is to analyze kidney transplantation data, introduce existing traditional machine learning and deep learning methods from non-temporal and temporal scenarios, respectively, and comprehensively evaluate the predictive power of the methods for kidney transplantation infection. Specifically, in the non-temporal scenario, we use Naïve Bayes, K-Nearest Neighbor, Support Vector Machines,

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Gupta, Tarun, and Supriya Bansal. "Machine Learning Algorithms for Predictive Analytics in E-Commerce." International Journal of Science and Research (IJSR) 9, no. 8 (2020): 1550–57. http://dx.doi.org/10.21275/sr24302212009.

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Reena Dhan, Archana, and Binod Kumar. "Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine." International Journal of Science and Research (IJSR) 13, no. 6 (2024): 1307–13. http://dx.doi.org/10.21275/mr24608013906.

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Nordin, Noratikah, Zurinahni Zainol, Mohd Halim Mohd Noor, and Chan Lai Fong. "A comparative study of machine learning techniques for suicide attempts predictive model." Health Informatics Journal 27, no. 1 (2021): 146045822198939. http://dx.doi.org/10.1177/1460458221989395.

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Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depress

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Yeole, Vaishali, Rushikesh Yeole, and Pradheep Manisekaran. "Analysis and prediction of stomach cancer using machine learning." Scientific Temper 16, Spl-1 (2025): 131–35. https://doi.org/10.58414/scientifictemper.2025.16.spl-1.16.

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Cancer prediction and analysis systems offer aid in the management of patients and have been found to provide accurate forecasts for stage and survival prediction. This study presents a cancer prediction system developed using machine learning models and implemented with Streamlit. This system is capable of accurately predicting cancer stage onset along with chances of the patient’s onset of survival based on prior patient information. For predictive purposes, categories such as random forest and XGBoost were employed. The model achieved an effective accuracy of 85% for stage prediction and 97

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Dimitrovska, Ivana, and Toni Malinovski. "Creating a Business Value while Transforming Data Assets using Machine Learning." Computer Engineering and Applications Journal 6, no. 2 (2017): 59–70. http://dx.doi.org/10.18495/comengapp.v6i2.205.

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Machine learning enables computers to learn from large amounts of data without specific programming. Besides its commercial application, companies are starting to recognize machine learning importance and possibilities in order to transform their data assets into business value. This study explores integration of machine learning into business core processes, while enabling predictive analytics that can increase business values and provide competitive advantage. It proposes machine learning algorithm based on regression analysis for a business solution in large enterprise company in Macedonia,

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P. Karthik, P. Jayanth, K. Tharun Nayak, and K. Anil Kumar. "Crime Prediction Using Machine Learning and Deep Learning." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 08–15. http://dx.doi.org/10.32628/ijsrset241134.

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The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and e

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Khalate, Vaishnavi, and Prof Borate Sukeshkumar. "Diabetes Prediction Using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1963–69. http://dx.doi.org/10.22214/ijraset.2024.59256.

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bstract: Diabetes, a chronic metabolic disorder affecting millions worldwide, requires early detection and management to mitigate its complications. Machine learning (ML) techniques have emerged as promising tools for predictive analytics in healthcare, offering the potential to improve diagnostic accuracy and patient outcomes. This paper presents a comprehensive review of ML algorithms applied to diabetes prediction, encompassing diverse methodologies and datasets. The study evaluates the performance of various ML algorithms, including but not limited to logistic regression, decision trees, s

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R. Patil, Chaitali, Sanika K. Jadhav, Asmeeta L. Bardiya, Ankita P. Davande, and Mahee P. Raverkar. "Machine Learning-Based Predictive Maintenance of Industrial Machines." International Journal of Computer Trends and Technology 71, no. 3 (2023): 50–56. http://dx.doi.org/10.14445/22312803/ijctt-v71i3p108.

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Ren, Zhongjun, Hu Zhang, and Tao Huang. "Application of Transfer Learning Models based on Multi-layer LSTM Deep Learning Framework in Cooling Prediction." Journal of Physics: Conference Series 3001, no. 1 (2025): 012020. https://doi.org/10.1088/1742-6596/3001/1/012020.

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Abstract Data-driven machine learning models have found extensive application in predicting building cooling loads, relying on substantial historical operational data to ensure prediction accuracy. Yet, challenges arise when specific buildings lack sufficient historical operational data, leading to subpar cooling loads prediction performance. Transfer learning emerges as a solution to overcome this limitation by leveraging data from alternative buildings. The hourly cooling load data of two hospital buildings were used as source domain and target domain to verify the transfer learning load pre

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Noori Mohammad Ali, Sara, and Nawzad Muhammed Ahmed. "Comparing some Machine Learning Models for Cardiovascular Disease." Journal of Pioneering Medical Sciences 14, no. 04 (2025): 60–66. https://doi.org/10.47310/jpms2025140408.

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Background and Aim: Cardiovascular disease remains a leading cause of morbidity and mortality worldwide, necessitating the development of accurate predictive models for early diagnosis. Therefore, this study aimed to evaluate and compare the performance of three machine learning models-Random Forest, Decision Tree, and K-Nearest Neighbors-in predicting cardiovascular disease based on key risk factors. Method: This retrospective study utilized patient data from Shar Hospital in Sulaimaniyah City. The dataset included demographic and clinical risk factors such as age, smoking status, diabetes, h

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Dixit, Ashish, Avadhesh Kumar Gupta, Neelam Chaplot, and Veena Bharti. "Emoji Support Predictive Mental Health Assessment Using Machine Learning: Unveiling Personalized Intervention Avenues." International Journal of Experimental Research and Review 42 (August 30, 2024): 228–40. http://dx.doi.org/10.52756/ijerr.2024.v42.020.

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Mental health disorders, including anxiety, depres-sion, and stress, profoundly impact individuals’ well-being and necessitate effective early detection for timely intervention. This research investigates the predictive capabilities of machine learning algorithms in assessing anxiety, depression, and stress levels based on questionnaire-derived scores. Utilizing a dataset comprising self-reported scores obtained through a tailored questionnaire designed for mental health assessment, we delve into the application of Decision Trees, Naive Bayes, Support Vector Machines (SVM), and Random Forests

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Amina H., Katu. "Integration of Machine Learning in Predictive Health Diagnostics." RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 4, no. 3 (2024): 1–7. https://doi.org/10.59298/rijses/2024/4317.

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The integration of machine learning (ML) into predictive health diagnostics is revolutionizing healthcare by enabling early detection, personalized treatment, and resource optimization. By leveraging large datasets, advanced algorithms, and interdisciplinary collaborations, predictive diagnostics empower healthcare providers to identify risks and manage diseases proactively. This paper investigates the fundamentals of predictive health diagnostics and ML, emphasizing their applications in disease prediction, risk stratification, and personalized medicine. It also addresses challenges such as d

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