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33 articles for “random forest”
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Speech Emotion Recognition using Multi- Kernel CNN for Features extraction and Random Forest Classifier
Abstract: Identifying human emotional states from speech signals is the main goal of Speech Emotion Recognition (SER), which has grown to be a significant area of study in affective computing. However, class imbalance and efficient feature extraction from speech data pose problems for many of the current methods. This study suggests a hybrid framework that combines a Random Forest (RF) classifier with a multi-kernel Convolutional Neural Network (CNN) to address these …
Published in International Journal of Digital Communication and Analog Signals · Vol. 12, Issue 2, 2026 Read article
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COVID-19 Detection Using Decision Tree Support Vector Machine Random Forest.
Abstract: The rapidly spreading novel coronavirus (SARS-CoV-2) presents a substantial global health risk, necessitating swift detection through efficient diagnostic methods. Reports highlight lung-related issues in many COVID-19 patients, and both chest CT scans and X-rays are effective in identification. Chest X-ray (CXR) imaging, due to its convenience, faster imaging, simplicity, and lower cost, stands out. In our study, we applied Decision Tree, Random Forest, and Support Vector Machine to identify COVID-19 …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 2, 2023 · pp. 7–21 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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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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Securing Smart Electrical Systems through Comparative Analysis of Machine Learning Models for Classification
Abstract: In the realm of cybersecurity, the intersection of technology and security measures has spurred significant advancements, particularly in safeguarding electrical systems against cyber threats. This research delves into the evolving landscape of cybersecurity within electrical systems, leveraging machine learning techniques to mitigate challenges. By incorporating cybersecurity protocols with machine learning algorithms, this study demonstrates enhanced security measures tailored for electrical infrastructures, pivotal in ensuring efficiency and resilience. Through meticulous examination, …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 2, 2026 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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Brain Tumor Detection using Machine Learning Methods-KNN, SVM, DT, RF
Abstract: Brain tumor is a major contributor to illness and death worldwide, and timely diagnosis is essential for successful treatment and improved outcomes Recent research has looked into the possibility of machine learning algorithms for detecting and classifying brain tumours in medical pictures. In this study, four commonly used machine learning algorithms (K-Nearest Neighbours, Decision Trees, Support Vector Machines, and Random Forests) were evaluated for their effectiveness in brain tumor detection. …
Published in International Journal of Chemical Engineering and Processing · Vol. 9, Issue 2, 2023 · pp. 48–59 Read article
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Discovery of Materials through Applied Machine Learning and General Machine Learning Architecture
Abstract: Advances in artificial intelligence technologies, especially machine learning, have generated possibilities in the material sciences to facilitate material exploration and to achieve a profound understanding of the relationship between the constituent elements of the material and the properties represented by the material. The application of machine learning to the exploration of experimental materials is sluggish due to the monetary and temporal costs of experimental data, however parallel methods, such as …
Published in International Journal of Analog Integrated Circuits · Vol. 6, Issue 2, 2020 · pp. 48–57 Read article
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CyberSentinel: A Proposed Hybrid AI Framework for Real-Time Cyber Threat Intelligence
Abstract: Modern cyberattacks have grown far more sophisticated, frequent, and fast-moving than the signature-based intrusion detection systems many organizations still rely on. This paper proposes CyberSentinel, a hybrid artificial intelligence framework for real-time cyber threat intelligence (CTI) that combines three complementary techniques into a single detection pipeline: a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) module for network traffic anomaly detection, a Random Forest ensemble for structured log classification, and a transformer-based …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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Use of Natural Language Processing for Hate Speech Detection on SocialMedia:
Abstract: The exponential growth of social media platforms has led to an increase in user- generated content, enhancing global connectivity but also facilitating the spread of harmful language, including hate speech. Addressing this issue requires robust, Naïve Bayes, Decision Tree, K-Nearest Neighbours (KNN), Linear Regression, and Random Forest—were tested, with Linear Regression achieving the highest accuracy automated systems for detecting and mitigating offensive content. This paper presents a comprehensive analysis of …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 2, 2025 · pp. 9–16 Read article
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Soft Computing Based Prediction of Ultimate Bearing Capacity of Footings Resting on Rock Masses
Abstract: The present study attempts to predict the ultimate bearing capacity of shallow foundations on rock masses using five different soft computing techniques namely i) random forest regression approach, ii) artificial neural networks, iii) support vector machine with polykernel, iv) support vector machine with (radial basis function) RBF kernel and v) M5P model tree. The ultimate bearing capacity was assumed to be dependent rock mass rating, unconfined compressive strength of rock, …
Published in International Journal of Geological and Geotechnical Engineering · Vol. 5, Issue 2, 2019 · pp. 1–14 Read article
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Health Care System Analysis Using Different Datasets
Abstract: In this research work, we have discussed data science and various machine learning algorithms like KNN, Support Vector Machine, Artificial neural network, Random forest etc. and the results obtained from these algorithms which can be used for the prediction of heart disease, cancer and in many other fields of medical science. Requirement of data cleaning along with various methods of data cleaning are mentioned here, along with these various issues …
Published in International Journal of Distributed Computing and Technology · Vol. 8, Issue 2, 2022 · pp. 29–37 Read article
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Network Intrusion Detection Using Wireshark and Machine Learning
Abstract: The growth of networked devices has highlighted the desire for advanced intrusion detection (IDS) tools to protect digital systems from evolving cyber threats. Traditional IDS systems are often difficult to adapt to the threat environment because they rely on predefined signature lists. This study presents a new approach that combines Wireshark, a widely used network packet analysis tool, with advanced machine learning for intrusion detection. Our system leverages Wireshark's data …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 10, Issue 1, 2024 · pp. 23–31 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
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Predicting Traffic Congestion in Mumbai Using Artificial Intelligence
Abstract: Abstract — Mumbai's ongoing traffic problems are brought on by the city's fast urbanization, population expansion, and growing reliance on private automobiles. The majority of traffic management technologies in use today are reactive, frequently reacting only after congestion has already arisen. Using information from a variety of sources, such as commuter surveys, weather reports, and traffic incident logs, this study explores the application of artificial intelligence (AI) and machine learning …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 2, 2026 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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A Study on Machine-Learning-Driven Insight into theThermal Behavior of Polymers and Composite Materials
Abstract: The thermal performance of polymeric and composite systems underpins their suitability for aerospace, electronics, and energy‑storage applications, yet reliable prediction of temperature‑dependent properties remains a formidable challenge. In this work we present a data‑driven framework that harnesses modern machine‑learning (ML) techniques to forecast key thermal metrics – glass‑transition temperature (Tg), melting point (Tm), thermal conductivity (k), and decomposition onset (Td) – from a chemically informed descriptor space. A curated database …
Published in International Journal of Polymer Science and Engineering · Vol. 12, Issue 1, 2026 · pp. 1–11 Read article
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Transportable Water and Air Quality Monitoring Equipment Karishma
Abstract: The purpose of this article is to provide a broad summary of recent discoveries, identify important constraints, and suggest future lines of inquiry. In recent years, pollution has surpassed the environment's bearing capacity. There hasn't been much progress despite the installation of multiple pollution-control initiatives. Natural resources are occasionally exploited by unavoidable waste discharges, despite our government's best attempts to safeguard them. For instance, the water-intensive and polluting sectors of …
Published in International Journal of Environmental Chemistry · Vol. 12, Issue 1, 2026 Read article
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Data Science for Environmental Modelling
Abstract: Abstract: Soil moisture is a critical Earth system variable linking atmospheric, hydrological, and ecological processes. However, observations are sparse and fragmented: in situ gauges provide point data, satellite missions (SMAP, SMOS, Sentinel-1) offer broad coverage at coarse resolutionQ, and land‐surface models (GLDAS, ERA5, etc.) yield estimates with their own biase. Here we propose a conceptual framework for integrated environmental data modelling, combining rainfall, temperature, and soil moisture information with machine …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 · pp. 33–40 Read article
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Data Science for Environmental Modelling
Abstract: Abstract: Soil moisture is a critical Earth system variable linking atmospheric, hydrological, and ecological processes. However, observations are sparse and fragmented: in situ gauges provide point data, satellite missions (SMAP, SMOS, Sentinel-1) offer broad coverage at coarse resolutionQ, and land‐surface models (GLDAS, ERA5, etc.) yield estimates with their own biase. Here we propose a conceptual framework for integrated environmental data modelling, combining rainfall, temperature, and soil moisture information with machine …
Published in International Journal of Water Resources Engineering · Vol. 12, Issue 1, 2026 Read article