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225 articles for “Random Forest”
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Optimizing Sentiment Analysis with Naïve Bayes and Random Forest Techniques: A Result-based Approach
Abstract: In the increased digitalization, the sentiment analysis and classification have evolved as an eminent area to determine the polarity of positive, negative, and neutral reviews of the customers and users on products. It is an integral application field that employs supervised learning, Machine Learning, and Natural Language Processing concepts. The proposed Semantic Analysis and Classification using Naive Bayes and Random Forest system accomplishes the sentiment polarity by classifying the user …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 46–57 Read article
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Detection of Phishing Website URLs and Email/SMS Using Random Forest and Multinomial Naive Bayes
Abstract: Currently, phishing attacks via SMS/email and URL have become significant threat to cybersecurity, posing risks to both individuals and organizations alike. Phishing attacks typically involve the creation of fraudulent websites or the dissemination of deceptive emails and SMS messages to trick users into disclosing sensitive information such as passwords, credit card numbers or personal details. To respond to these attacks, we develop a robust system for the detection of phishing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 22–30 Read article
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Optimizing Marketing Campaigns Using Random Forest and A/B Testing
Abstract: Marketing initiatives play a vital role in driving business growth by reaching targeted consumer segments through tailored strategies across multiple channels. The success of these initiatives is influenced by various factors, including the type and duration of the campaign, the characteristics of the target audience, the communication channels employed, and the overall efficiency of each strategy. These factors collectively impact key performance metrics such as conversion rates, customer acquisition costs, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 01–09 Read article
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Loan Prediction Using Random Forest Algorithm
Abstract: With the growth of the banking sector, more and more people are applying for bank loans. All of these loans are not allowed. The principal income of bank assets arises from the interest earned on the loan. Bank profit or loss depends largely on the amount of the loan, i.e., whether customers pay off the loan or fail. The main purpose of banks is to invest their assets in secure …
Published in Journal of Advanced Database Management & Systems Read article
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A Random Forest Approach to Navigating Cryptocurrency Market Fluctuations
Abstract: This study looks at the main elements influencing daily price variations to improve our analysis and prediction of Bitcoin values. Our forecasting algorithm is based on comprehensive data that we have collected and analyzed over the last few years. Because the Random Forest algorithm provides more accurate forecasts than previous techniques, that is why we chose it. Predicting the price swings of cryptocurrencies, like Bitcoin, can be challenging due to …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 2, 2024 · pp. 7–11 Read article
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Comparative Analysis of a Lie Detector using Support Vector Machine, Naive Bayes, and Random Forest Algorithm with Speech to Text Conversion
Abstract: Lie detection, additionally known as deception detection, uses questioning techniques to determine truth and falsehood in response. Physiological responses like vital sign, blood pressure, heartbeat, and respiratory rate are used to discriminate between truth and lie. Once we lie, our blood pressure goes up, our heart beats quicker, we have a tendency to breathe faster (and our breathing slows once the lie has been told), and changes occur in our …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 9, Issue 1, 2021 · pp. 13–18 Read article
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Forecasting of Crushing Strength of Sustainable Concrete by Employing Deep and Random Forest Machine Learning
Abstract: Sustainable concrete is one of the milestone of the concrete industry. This concrete fulfills the requirements of concrete manufacturing industry such as strengthen, Durability, environment friendly and many of other. With this properties of concrete, sustainable concrete is an ideal substitute for ordinary concrete in the concrete industry. In the 21th century Machine learning is a tool which is use to employ the characteristics of sustainable concrete by using deep …
Published in Journal of Polymer & Composites · Vol. 12, Issue 7, 2024 · pp. 41–46 Read article
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SmartCloud: Extending an AI-Powered Cloud Storage System with Automated S3 Glacier Archiving, SimHash Near-Duplicate Detection, and a Custom-Trained Random Forest Recommendation Engine
Abstract: Cloud storage services have become fundamental infrastructure for both individuals and organizations, yet they continue to charge users for every byte retained without offering any mechanism to automatically reduce that footprint over time. Files accumulate progressively, near-identical document revisions go undetected, and cold data occupies expensive active storage tiers indefinitely. The present work introduces SmartCloud, a fully deployed multi-cloud storage optimization platform that addresses these inefficiencies through four integrated engineering …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 13, Issue 2, 2026 · pp. 36–44 Read article
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Loan Prediction Using Random Forest Algorithm
Abstract: With the growth of the banking sector, more and more people are applying for bank loans. All of these loans are not allowed. The principal income of bank assets arises from the interest earned on the loan. Bank profit or loss depends largely on the amount of the loan, i.e., whether customers pay off the loan or fail. The main purpose of banks is to invest their assets in secure …
Published in Journal of Advanced Database Management & Systems · Vol. 9, Issue 1, 2022 · pp. 33–39 Read article
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Cloud-driven Fraud Detection: Evaluating Decision Tree and Random Forest Classifiers for Credit Card Transaction Security
Abstract: With the alarming rise in global financial fraud, necessitating substantial annual losses, modern techniques for fraud detection are continuously evolving across various business domains. Fraud detection involves constant monitoring of user activities to estimate, perceive, or prevent undesirable behaviour. Cloud Computing emerges as a promising solution, accelerating application deployment, fostering creativity and innovation, reducing costs, and enhancing overall business acumen. This study introduces a cloud-driven approach to fraud detection, specifically …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 13–27 Read article
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Monitoring and Controlling System of COVID-19 Symptoms Using Random Forest
Abstract: Corona virus (COVID-19) has already claimed hundreds of lives and infected millions of people around the world. The early detection of COVID-19 is shown in this paper. Machine learning random forest techniques were used to implement the detection procedure on cloud computing. Objectives of the study is to provide health assurance directly from home using some smart tools with cloud computing. Our research contribute the categorization of patients into different …
Published in Research and Reviews : Journal of Computational Biology · Vol. 11, Issue 1, 2022 · pp. 22–29 Read article
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Fraudulent Data Detection of Credit Card by Using Random Forest Method to Calculate ROC and Confusion Matrix
Abstract: Credit card fraud defaults in trillions of dollars online merchants. Researchers have more and more complex methods of detecting fraud using algorithms for machine learning, but there are rare reports of hands-on implementations. We describe the implementation of an extortion discovery framework in a huge e-tail retailer. The paper looks at the combination of manual and robotized classification and offers experiences into the whole handle of advancement. The paper can …
Published in Journal of Advanced Database Management & Systems · Vol. 7, Issue 1, 2020 · pp. 15–22 Read article
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Fusion of deep learning autoencoders with random forest for wetland classification using Sentinel-2A data: A case study on Sirpur wetland
Abstract: Present study analyses the performance of deep leaning algorithm-autoencoder to reduce data dimension as compared to conventional models. Classification accuracies of Sirpur wetland using Sentinel 2A dataset with different inputs have also been studied. These inputs sets comprise the reconstructed data through compression of original 13 bands into 4 bands using decoder algorithm, first four Principal Components, all spectral bands, and spectral indices. Random Forest classifier (RF) is used to …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 1, 2026 · pp. 25–35 Read article
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A Review on Various Approaches of Intrusion Detection System and Random Forest in Data Mining
Abstract: With the development in Information and Communication Technology (ICT), it have become a fundamental factor of human’s life. But this technology has brought lots of threats in cyber world. These threats increase the chances of network vulnerabilities to attack the structure in the network. DM based intrusion area techniques consistently fall into any of the two classes; anomaly detection and mistreat finding. In general course of DM alludes to extricating …
Published in Recent Trends in Programming languages · Vol. 5, Issue 1, 2018 · pp. 6–13 Read article
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DDoS Detection Using Cascade Correlation for Improving Network Resources in Cloud Environment
Abstract: Intrusion detection is critical for protecting network security from emerging cyber threats. This study describes a unique intrusion detection system (IDS) based on the Random Forest algorithm. Random Forests are used as an effective classifier to identify patterns linked with malevolent behaviour. This technique uses Random Forests to improve the accuracy and efficiency of intrusion detection systems. The suggested methodology's value is shown by its performance on the benchmark KDD …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 17–22 Read article
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Climate Change Including Forest Fire Prediction using Machine Learning and Deep Learning
Abstract: Climate change alludes to long haul shifts in temperatures and atmospheric conditions. These movements might be regular, for example, through varieties in the sun-oriented cycle. In any case, since the 1800s, human exercises have been the fundamental driver of climate change, basically because of consuming fossil fuels like coal, oil and gas. Many individuals think climate change mostly implies hotter temperatures. Be that as it may, the temperature climb is …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 Read article
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Hypertuning in Random Forest Model
Abstract: Regarding the part of the monetary climate and the entirety of data made each second, the conclusionmaking handle is switching and obtaining to be data-driven, particularly controlling the trade procedures set up in organizing to keep the competitive benefit. Be that because it may, without innovation, data examination would not be feasible, the cause why machine learning is seen as a trouble for advancement trades, particularly due to its ability …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 3, 2022 · pp. 1–11 Read article
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Genomic Selection for Grain Yield in Wheat Using Machine Learning on DArT Molecular Markers: A Comparative Evaluation Across Multi-Environment Trials
Abstract: Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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Customer Churn Prediction Using ML Algorithms
Abstract: Comprehending customer churn is essential for businesses aiming to enhance and sustain customer relationships. This study introduces a machine learning approach aimed at forecasting customer churn by leveraging demographic and behavioral data. Our research involved developing predictive models using support vector machines (SVM), random forests, and decision trees, evaluating their efficacy using real-world data from the telecom industry. Our findings underscore that random forests consistently outperform SVM and decision trees …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 70–75 Read article
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AI/ML-Based Approach to Solar Irradiance Prediction and Energy Suitability
Abstract: In this paper, due to challenges in precisely predicting solar irradiance, which is essential for solar power system optimization, we employed six diverse machine learning (ML) techniques: Linear Regression, Decision Tree, Random Forest, Gradient Boosting methods (including XGBoost), and Neural Networks—to analyze and predict outcomes using a dataset containing meteorological and temporal features. Key variables include wind speed, humidity, and temperature, which significantly influence the model’s predictive capability. Each method …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 16, Issue 3, 2025 · pp. 36–48 Read article