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224 articles for “Random Forest”
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Comparing Algorithm Performance in Machine Learning for Landslide Susceptibility Studies: An Overview
Abstract: Machine learning algorithms have gained popularity over the past years, and with this comes the rise in their efficiencies; this study demonstrated that machine learning algorithms could successfully be used to identify landslide susceptibility areas. Machine learning algorithms like decision trees, support vector machines, K-Means, Hierarchical Clustering, Self-Organizing Maps (SOMs), artificial neural networks, and Bayesian networks have been used in recent studies. This study thus reviews several studies and results …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 1, 2023 · pp. 71–89 Read article
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Machine Learning Based Approaches for the Detection and Classification of Crop Leaf Diseases
Abstract: Crop leaf diseases pose a significant threat to agricultural productivity worldwide, leading to substantial crop losses and economic consequences. Timely and precise identification and categorization of these diseases are vital for effective disease management and safeguarding crops. Over the past years, machine learning methods have gained prominence due to their potential to automate disease diagnosis and classification procedures. This review paper presents an overview of the various machine learning based …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 2, 2023 · pp. 31–38 Read article
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Artificial Intelligence Based Plant Disease Detection
Abstract: This paper analyses the advanced Neural Network (NN) techniques for hyperspectral data processing, with a particular focus on detecting plant disease. To initiate, we will discuss the NN mechanism, types, models, and classifiers that are used to process the hyperspectral data using different algorithms. Following that, this project discusses the current state of imaging and non-imaging hyperspectral data for early screening and diagnosis of Disease. The NN-hyperspectral hybridization method has …
Published in Recent Trends in Programming languages · Vol. 8, Issue 3, 2021 · pp. 11–24 Read article
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Flood Inundation Mapping for using Sentinel-1 SAR Data for Assam during 2018
Abstract: This paper is focusing on the flood inundation mapping of Hailakandi, Assam flood during 2018 using Sentinel 1 Single Look Complex (SLC) and Ground Range Detected (GRD) data. For the analysis of water pixels, pre and post event images of SLC and GRD data were downloaded with similar specification such as same date, pat and row. Supervised classification is performed with the pre and post event of the study area …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 8, Issue 2, 2019 · pp. 16–25 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Semantic Similarity Framework for Automatic Hallucination Detection in Large Language Models
Abstract: Large Language Models can generate fluent, contextually appropriate text across a range of NLP tasks, but they frequently produce outputs that are factually wrong while sounding confident and plausible. This problem, referred to as hallucination, poses serious risks in domains where accuracy matters. We propose a post-processing framework that detects hallucinated responses by comparing them against verified reference text using sentence embeddings. The system computes cosine similarity between the response …
Published in Emerging Trends in Languages · Vol. 3, Issue 2, 2026 · pp. 16–22 Read article
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Machine Learning Approach to Predict the Mental Health Issues of Employees in Tech Companies
Abstract: Mental disorders are conditions that have an impact on your emotions, thoughts, and behaviour. It may happen infrequently or have a persistent effect. The topic of mental health has been major and challenging, especially for working professionals. Over time, the urbanized living and workload put a strain on people, making them more vulnerable to mental disorders like anxiety and mental disorders. Working professionals therefore seem to be at an elevated …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 3, 2022 · pp. 1–10 Read article
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Tweets Classification using Different Classifiers for Sentiment Analysis
Abstract: Twitter is a famous platform for social networking where people share and engage with "tweets" comments. It is a way of expressing people's opinions or emotions on various topics. Different parties, like producers and consumers, have carried out a sentiment analysis on tweets to gain insights into goods or to analyze the sector. Also, the accuracy of our sentiment analysis forecasts will increase with the recent advances in machine learning …
Published in Research & Reviews : Journal of Statistics · Vol. 10, Issue 3, 2021 · pp. 34–43 Read article
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A Comprehensive Method for Sentiment Analysis on Twitter Posts
Abstract: AbstractIn recent years, with ascent of users of social media, researchers get drawn to sentiment analysis. The challenge is that sentiment-dependent info from multiple sources does not seem to be thought of usually in existing sentiment classification techniques. Sentiment analysis is incredibly vital as a result of it helps folks and organizations in their decision-making process. There is vast explosion of “sentiments” on the market from social media like Twitter, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 4, Issue 2, 2017 · pp. 1–5 Read article
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Smart Education through Machine Learning: A Review of Trends, Benefits, and Risks
Abstract: Machine learning (ML) is transforming the contemporary education by transforming it into smarter, data-driven and personalised learning. This review examines the key tendencies, advantages, and possible threats of applying ML in intelligent education. ML promotes adaptive learning, automatization of assessments, and student engagement, which is highly beneficial both to learners and educators. Nonetheless, issues like data privacy, algorithmic bias or unequal access are also a significant concern. The article emphasises …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 24–28 Read article
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Analysis of Machine Learning Algorithms for the Detection of Mental Health Problems in Employees
Abstract: Mental health-related issues are more critical issues in the workplace. Mental health has been a major and challenging issue, especially for working professionals. A bad work environment can cause a variety of physical and physiological problems as well as decreased productivity. People are reluctant to ask for help from mental health specialists, according to studies. The fundamental reason for this is the stigma attached to mental illness. Working professionals therefore …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 9, Issue 3, 2022 · pp. 12–24 Read article
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Smart-Waste-Management-System
Abstract: The rapid urbanization and increasing waste generation pose significant challenges to traditional waste management systems, necessitating innovative solutions that integrate economic principles and management strategies. In order to enhance trash transportation and recycling procedures, this paper investigates the deployment of a Smart trash Management System that makes use of Internet of Things (IoT) components and machine learning algorithms. By applying economic principles such as cost-benefit analysis and resource allocation, and …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 2, 2025 · pp. 18–27 Read article
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Predicting Student Placement Readiness: A Machine Learning Approach Using Coding Activities and Multi-Dimensional Performance Indicators
Abstract: In the modern information-driven academic world, identifying student employability and placement preparedness has predicted. be made a part and parcel of academic planning and career. development. This study provides a machine learning-based. structure to evaluate and forecast student placement pre-paredness by combining various performance aspects-academic achieve- ment, coding activity, aptitude and behavioral engage-ment metrics. Multi-source was gathered and preprocessed in the study. student information, such as student records (CGPA, attendance), …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 Read article
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Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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Crop Disease Prediction by Machine Learning
Abstract: The classification of Crop can be classified into several methods. The data set of crop leaf illnesses, notably Bacterial Leaf Blight disease (BLB), a crop leaf disease with significant outbreaks throughout Thailand, and Brown Spot Crop disease (BSR), is classified employing image classification in this study. Additionally, image processing technology is used for identifying different types of crop leaf disease. These algorithms include the Random Forest, Decision Tree, Gradient Boost, …
Published in Trends in Machine design · Vol. 11, Issue 2, 2024 · pp. 21–25 Read article
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User Engagement in Mobile Applications: Modeling and Predicting
Abstract: The mobile ecosystem is expanding rapidly, making app developers highly competitive. To keep users engaged, apps can anticipate when they might disengage, allowing developers to implement intervention tactics. This work demonstrates that it is possible to accurately forecast user disengagement using numerical models. The framework includes Cox proportional hazards, negative binomial, random forest, boosted-tree, and optimized agglomerative hierarchical clustering models. A 12-month observation dataset from a trash recycling app development …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 10, Issue 3, 2023 · pp. 1–7 Read article
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A Complete Study of Different Models for Software Fault Prediction
Abstract: Context- Software faults prediction improves software quality, software reliability, and software efficiency by early identification of faults. Numerous classification methods have been suggested for this task.Objective- The main objectives of the studies are (i) find the number of defects in AEEEM defect datasets, (ii) Challenges faced with Imbalanced Datasets.Method- We use AEEEM defect datasets for the prediction of faulty classes using four deferent machine learning technique. This particular paper present …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 5, Issue 2, 2018 · pp. 1–10 Read article
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Finding Churn Trend Scenario Using ML Techniques
Abstract: Abstract Media transmission and in addition the network access suppliers assumes an essential part in the mobile communications and the data society. Telecom segment is consistently developing with its progression in highlights and innovation. Consumer loyalty is an imperative factor which chooses the execution of firms. Customer churn expectation is an essential technique in CRM (Customer Relationship Management), knowing the churn propensity appeared by the customers helps to take better …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 6, Issue 1, 2019 · pp. 1–8 Read article
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Human Activity Recognition Using For Smartphone Sensors To Predict The Best Accuracy Based On Machine Learning Algorithms
Abstract: Human activity recognition requires predicting the action of a person based on sensor-generated data. Due to the enormous number of applications possible by modern ubiquitous computing devices, it has sparked a lot of attention in recent years. It categorizes data into actions such as walking, sitting, standing, and lying. The accelerometer and gyroscope were used to generate the sensor data, and the sensor signals were pre-processed with noise filters. The …
Published in Recent Trends in Sensor Research & Technology · Vol. 9, Issue 1, 2022 · pp. 12–23 Read article
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Performance Analysis of Machine Learning Algorithms For Disease Prediction
Abstract: In this 21st century, where Digitization makes humans measure, record, analyze and to manipulate the huge amount of data as per the requirement, prediction of the decease based on Machine Learning models will be representing one of the good applications of the efficient data handling. An Automatic Decease Prediction system based on the symptoms would be the great boon for the medical practitioners. The Supervised Machine Learning models, such as …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 9–18 Read article