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454 articles for “Prediction”
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Machine Learning Approaches for Customer Churn Prediction in Telecommunications
Abstract: Customer churn prediction is a critical challenge for companies in the telecommunications industry. The competition is intense, and acquiring new customers can be costly. Predicting when existing customers might leave (churn) helps companies take proactive measures to retain them, reducing churn rates and costs. By analyzing historical customer data, including demographics, service usage, billing, and customer service interactions, this study aims to identify customers at risk of leaving their current …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 10, Issue 2, 2024 · pp. 1–7 Read article
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The Role of Artificial Intelligence in ICU Patient Monitoring and Predictive Analytics: A Prospective Cohort Study
Abstract: Background: Intensive Care Units (ICUs) face challenges in effectively monitoring critically ill patients. Traditional monitoring methods may not detect subtle signs of deterioration in time, leading to worsened outcomes. Artificial Intelligence (AI)-driven monitoring systems have emerged as a potential solution, promising improved predictive capabilities and patient outcomes. Objective: This study aimed to evaluate the impact of AI-based monitoring systems on ICU patient outcomes, focusing on mortality rates, length of ICU …
Published in International Journal of Nursing Critical Care · Vol. 11, Issue 1, 2025 Read article
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Comparison of predictive ability of stroke-associated pneumonia risk scores in acute hemorrhagic stroke
Abstract: Objective: To determine the best scoring system among 5 scores (Pneumonia score, PANTHERIS, ISAN score, A2DS2, ACDD4) for prediction of pneumonia after acute haemorrhagic stroke in Stroke Centre. Subjects and Methods: We carried out retrospective study of 508 patients with acute haemorrhagic stroke admitted to Department of Neurology of Pyongyang medical university from January 1, 2018 to August 1, 2022. Demographic and clinical traits of patients were collected and analysed. …
Published in International Journal of Nursing Critical Care · Vol. 10, Issue 2, 2024 · pp. 12–19 Read article
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Mathematical models in Predictive microbiology for food safety
Abstract: The study of predictive microbiology is predicated on the notion that populations of microorganisms react in a predictable manner to the conditions of their environment and that historical observations can be used to predict how populations will react in the future by looking at environments in terms of the dominant limitations they present. Predictive microbiology is a relatively new branch of the field of microbiology that has recently gained significant …
Published in International Journal of Green Chemistry · Vol. 9, Issue 1, 2023 · pp. 23–29 Read article
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Prediction of Perineal Trauma During Childbirth By Assessment of Striae Gravidarum Score
Abstract: Perineal trauma is a common complication during vaginal childbirth, often resulting in short- and long-term maternal morbidity. Early prediction of women at risk can aid in preventive strategies and improve maternal outcomes. Striae gravidarum, commonly known as stretch marks, are skin changes that occur during pregnancy due to hormonal, mechanical, and genetic factors. Recent studies have suggested that the presence and severity of striae gravidarum may be associated with the …
Published in International Journal of Obstetrics, Perinatal and Neonatal Nursing · Vol. 11, Issue 1, 2025 · pp. 11–23 Read article
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Predictive Models for Drinking Water Quality: Leveraging Machine Learning for Improved Safety and Efficiency
Abstract: Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 1, 2025 Read article
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AI-Driven Predictive Maintenance in Smart Homes: Enhancing Efficiency and Reliability
Abstract: The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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AI-Integrated Precision Agriculture System for Fertilizer Optimization, Multimodal Crop Yield Prediction
Abstract: Agriculture is rapidly transforming into a data-driven ecosystem supported by advances in artificial intelligence (AI), Internet of Things (IoT) sensor networks, and satellite remote sensing technologies. These innovations enable precision-driven decision-making aimed at improving productivity while minimizing environmental impact. This paper presents an AI-integrated precision agriculture system designed to optimize fertilizer usage and enhance crop yield prediction accuracy through a robust multimodal deep learning framework. The proposed approach integrates heterogeneous …
Published in International Journal of Plant Biotechnology · Vol. 12, Issue 1, 2026 · pp. 11–17 Read article
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Unveiling Employee Attrition: Harnessing Predictive Modeling for Proactive Workforce Management
Abstract: This study aims to predict employee turnover and identify organizational factors contributing to attrition by employing predictive analytics techniques. Employee attrition, which refers to the loss of employees without immediate replacements, leads to reduced overall workforce productivity. The study evaluates different machine learning techniques, such as gradient boosting, random forest, and logistic regression, to identify employees who are most likely to leave, and uses SPSS software to ensure the reliability …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 2, 2024 · pp. 1–10 Read article
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Estimation of Crop Yield Prediction Using Machine Learning Technique.
Abstract: Agriculture serves as the primary source of livelihood for a significant portion of India's population, but it faces a myriad of challenges, including climate variability, soil fertility issues, and reliance on traditional farming practices. To address these challenges, researchers are increasingly turning to machine learning (ML) methodologies to predict crop yields more accurately. Studies have delved into the effectiveness of various ML algorithms such as Random Forest, Gaussian Process Regression, …
Published in International Journal of Agrochemistry · Vol. 10, Issue 1, 2024 · pp. 7–12 Read article
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Crime Rate Prediction System Using Machine Learning.
Abstract: Crime rate prediction is an important research area that helps law enforcement agencies to take proactive measures to prevent crime. The proposed model uses a combination of historical crime data to predict crime rates.The proposed model uses a supervised learning approach where the crime rate is predicted based on a set of input features. The input features include historical factors such as area, year, crime type. This paper presents various …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 1, 2023 · pp. 18–27 Read article
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Advancements in Predictive Maintenance: A Review of Data-Driven Approaches, Machine Learning, and AI in Diverse Industrial Landscapes
Abstract: The Internet of Things (IoT), the use of machine learning (ML), and artificial intelligence (AI)-driven advances in predictive maintenance are the key topics of this study. With the goal of offering a thorough grasp of existing approaches and potential future developments, it analyzes applications in predictive maintenance in many sectors. Predictive maintenance approaches are the major topic of discussion, with an emphasis on their advantages, disadvantages, difficulties, and prospects. Additionally, …
Published in International Journal of Computer Aided Manufacturing · Vol. 10, Issue 2, 2024 · pp. 19–32 Read article
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Optimized Fault Current Prediction for DFIG-Based Wind Turbines Using Type-I Fuzzy Logic Controller
Abstract: The increasing integration of Doubly-Fed Induction Generator (DFIG)-based wind turbines into modern power grids requires robust fault detection and prediction mechanisms to ensure stable and reliable operation. However, accurately predicting fault currents in such systems is challenging due to the nonlinear dynamics involved. This study presents an optimized fault current prediction framework using a Type-I Fuzzy Logic Controller (FLC) to address these challenges. The proposed approach effectively models the uncertainties …
Published in International Journal of Analog Integrated Circuits · Vol. 10, Issue 2, 2024 · pp. 41–50 Read article
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Ethereum Price Prediction Using Extra Trees and Random Forest Regressors
Abstract: The Ethereum market is famous for its unpredictability and the possibility of significant profits, presenting both opportunities and obstacles for investors and stakeholders. Given the dynamic nature of this ecosystem, accurate prediction of Ethereum’s price movements is essential for informed decision-making. In this project, we propose an innovative approach that leverages ensemble learning techniques, specifically extra trees and random forest regressors, for Ethereum price prediction. By harnessing a diverse dataset …
Published in International Journal of Distributed Computing and Technology · Vol. 10, Issue 2, 2024 · pp. 9–25 Read article
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Predicting Mortality in Burn Patients in A Tertiary CareSetting Using the Revised Baux Score
Abstract: Burns are a significant risk factor contributing to mortality and morbidity, especially in underdevelopednations. While innovations in burn patient management have considerably reduced mortality rates indeveloped countries, the fatality rates remain unacceptably high in underdeveloped regions where theseadvancements have not been widely adopted or implemented. Accurate prediction of outcomes forsevere burn patients at the time of presentation is crucial for guiding clinical decisions and improvingpatient management. In developed countries, several …
Published in International Journal of Chemical Separation Technology · Vol. 10, Issue 1, 2024 · pp. 27–32 Read article
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Prediction of Compressive Strength of Concrete with Decision Trees
Abstract: In the past few researchers predicted the compressive strength of concrete from its ingredients employing data driven technique like artificial neural network (ANN) and Decision Tree (DT). For Indian concrete records, only one study was available which had employed ANN for prediction. Evaluation of different performance metrics indicated that there was scope of improvement in the prediction performance. In this paper experimental data from the literature was utilized for development …
Published in International Journal of Concrete Technology · Vol. 2, Issue 1, 2016 · pp. 19–29 Read article
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Prediction Models on Causing Factors for Delirium in Intensive Care Unit Patients
Abstract: This study aims to screen high-risk group for delirium in the intensive care unit by building optimal prediction models over multidimensional analysis of the factors causing delirium in intensive care unit patients, with providing the theoretical foundation for the development of protocols for delirium prevention and management in such patients. The data were collected over the electronic medical records by 3781 patients who were admitted to the medical/ surgical intensive …
Published in International Journal of Neurological Nursing · Vol. 3, Issue 1, 2017 · pp. 13–24 Read article
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Leveraging Prediction Strategies to Enhance Milk Quality and Promote Dairy Sustainability
Abstract: In the Dairy industry, ensuring high-quality milk production is vital. This research paper focuses on employing machine learning methods to predict milk quality based on diverse parameters including pH levels, temperature, fat content, and colour. By harnessing data encompassing these factors along with established quality markers, the study aims to construct a predictive model. This model seeks to anticipate variations in milk quality, providing insights into potential fluctuations in fat …
Published in International Journal of Green Chemistry · Vol. 9, Issue 2, 2023 · pp. 38–47 Read article
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System for Predicting and Recommending Placement on Campus
Abstract: The campus placement system uses classification algorithms like decision trees, random forests, support vector classifiers, and logistic regression to estimate the likelihood that an undergraduate student will be hired by a business. The placement prediction method assists in identifying students’ needs and the areas in which they fall short, allowing students to strengthen their profiles and increase their chances of being placed. The placement predictor uses a number of parameters …
Published in International Journal of Distributed Computing and Technology · Vol. 9, Issue 1, 2023 · pp. 18–25 Read article
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An Integrated Machine Learning and IoT-Based Framework for Predictive Urban Water Demand Management and Distribution Optimization
Abstract: Abstract Urban water resource management has become increasingly complex due to rapid urbanization, population growth, climate variability, and aging infrastructure. Conventional water management approaches are often reactive, inefficient, and incapable of handling real-time fluctuations in demand and supply. This study proposes an integrated framework that combines machine learning (ML) techniques with Internet of Things (IoT)-based real-time monitoring to enhance water demand prediction and optimize distribution systems in urban environments. The …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article