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116 articles for “tree fin”
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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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A Comprehensive Review of Green Hybrid Polymer–Inorganic Composite Systems Incorporating Palm Tree Ash and Titanium Dioxide
Abstract: The increasing requests on sustainable and high performance systems have motivated extensive investigations on polymer-based hybrid systems with bio-derived and/or functional inorganic fillers. In this review, the importance of palm tree ash (PTA) and titanium dioxide (TiO₂) as synergistic fillers for polymer–inorganic hybrid composites, particularly about structure–property relations, interfacial adhesion and durability performance, is discussed. Agricultural waste-derived palm tree ash (PTA) plays as a silica-rich filler which not only aids …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1811–1820 Read article
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Data Extraction Using NLP for Unstructured Text Categorization
Abstract: Abstract In this research project, I tried to crawl the web and files to create a dataset so that it can be fetched to FRL (Fuzzy rough set-based semi-supervised learning algorithm). The approach used in the project is with the help of semi-supervised learning that made use of unlabeled data for training typically a small amount of labeled data with a large amount of unlabeled data. We de ne and …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 4, Issue 3, 2017 · pp. 30–39 Read article
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Molecular Docking of Arjuna Tree Phytocompounds for Ischemic Heart Disease
Abstract: Heart disease is a serious condition that can be caused by several lifestyle changes and sometimes genetic problems. The primary protein component of apolipoprotein is the LDL receptor, which indicates that a reduction in LDL levels can reduce the risk of ischemic heart disease. Apolipoprotein levels are associated with increased levels of ischemic heart disease. Using ADMET analysis, drug likelihood prediction, and molecular docking, this work attempted to find natural …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 3, Issue 1, 2025 · pp. 42–52 Read article
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Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network
Abstract: The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 3, 2025 Read article
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Vehicle Insurance Fraud Detection Using Supervised Neural Network Model
Abstract: Vehicle insurance fraud is a serious problem that causes huge financial losses to insurance companies. In recent years, developing fraud detection models using machine-learning techniques has been of great interest. This study proposes a novel approach for vehicle insurance fraud detection using Supervised Neural network model on the Kaggle Vehicle Insurance Fraud Detection dataset. The initial step in this study involves performing data pre-processing and feature extraction on the dataset, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 10, Issue 1, 2023 · pp. 32–39 Read article
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Analyzing Potential Leads Data to Improve Marketing Strategies
Abstract: To develop a lead scoring model using multiple machine learning models and create a user-friendly web application, the first step is to clearly define the problem and collect relevant data from various sources such as CRM, website, social media, email campaigns, etc. After cleaning and pre-processing the data, identify the essential features that can affect the lead score and split the data into training and testing sets. Next, select a …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 18–25 Read article
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Measuring Tree Diversity of Sitaram Peth Village of Bhadravati Taluka of Chandrapur District by Applying Whittaker’s Additive Model
Abstract: Forest plays a major role for sustaining biodiversity and various ecological services. There are 12 tribal districts in the Maharashtra state, which holds about 60 percent of total forest cover of the state. And among these tribal districts, Chandrapur and Gadchiroli together shares about 46 percent of forest cover. For the better conservation and management practices, the tree diversity of these forest should be measured periodically. In the present study, …
Published in Research & Reviews : Journal of Ecology · Vol. 14, Issue 2, 2025 · pp. 49–54 Read article
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Fuzzy Minimum Spanning Tree Problem: A Greedy Algorithm using Quasi-Gaussian Fuzzy Weights
Abstract: The classical Minimum Spanning Tree Problem (MST) deals with determining the spanning tree in a given undirected graph G(V, E) for which the sum of the weights assigned to the edges is minimum. The weights assigned to the edges are real numbers representing parameters like demand, cost, link capacity and distance which are not naturally precise and the uncertainty involved can be modelled using fuzzy numbers. There are several types …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 1, Issue 3, 2014 · pp. 15–22 Read article
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Machine Learning Approaches for Phishing Detection: A Comparative Study
Abstract: These days, everyone has an internet addiction. All of us have used the internet for banking, booking, recharging, and buying. Phishing is a type of website threat that exists online. On the original website, phishing is an attempt to illegally obtain information such as login ID, password and credit card information. In this research, we proposed an efficient phishing detection system based on machine learning. Overall, the experimental findings demonstrated …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 35–45 Read article
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A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket
Abstract: This study explores the application of machine learning techniques to forecasting outcomes in limited overs cricket matches, with a particular focus on One Day Internationals (ODIs). The research investigates how classification algorithms can be effectively utilized to analyze both contextual and dynamic factors that influence match results, including venue details, toss decisions, team strength, and historical performance records. By employing a structured methodology encompassing feature selection, data preprocessing, model training, …
Published in Recent Trends in Sports · Vol. 2, Issue 2, 2025 · pp. 09–19 Read article
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Money Laundering Transaction with Machine Learning
Abstract: This study discusses the use of machine learning algorithms to discover firms that are prone to money laundering. The purpose of this research is to develop, describe, and test a machine learning model for determining which bank transactions should be physically scrutinized for money laundering activities. To train a supervised machine learning model, three categories of historical data are required: legitimate "normal" transactions, transactions flagged as suspicious by the bank's …
Published in Current Trends in Information Technology · Vol. 14, Issue 2, 2024 · pp. 1–15 Read article
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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 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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A Systematic Review for Heart Disease Prediction Using AIML Algorithms
Abstract: Cardiovascular diseases (CVDs), commonly known as heart diseases, have consistently held the position of being the primary global cause of mortality for many years. They are also the most serious illness in India and the rest of the world. So, a method that is reliable, accurate, and easy to use is needed to find these diseases early and start the right treatment. Using a variety of medical datasets, machine learning …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 53–61 Read article
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Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection
Abstract: The expanding realm of data analysis holds considerable importance in healthcare, particularly in the medical sector where forecasting heart disease is considered a complex endeavor. Early prediction of serious health conditions can be the determining factor between survival and fatality, with heart disease being one such critical health issue. Over the past decade, the main reason for death has been heart disease. Heart disorders come in many different forms, and …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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Medicinal Plants Used by Traditional Healers of West Jaintia Hills District of Meghalaya, India
Abstract: A survey was conducted to record the medicinal plants used by traditional medicine practitioners in the West Jaintia Hills district of Meghalaya. A total of 44 species were recorded, out of which 2 were pteridophytes. Zingiberaceae was the most dominant family with 5 species. 48% of the medicinal plants used were herbs, followed by trees (20%) and shrubs and climbers (16% each). Underground parts and leaves of plants are mostly …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 9, Issue 1, 2019 · pp. 156–164 Read article
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Role of Shringyadi Churna on Tamak Shwasa with special reference to Bronchial Asthma
Abstract: In Ayurvedic texts, the signs and symptoms of disease Tamak Shwasa caused due to vitiation of Vata (Pran vayu) and Kapha dosha shows the most parity with bronchial asthma. We used the Shringyadi Churna mentioned in chakradatta by Chakrapani, to find its role in the management of Tamak Shwasa. It showed statistically highly significant results (p value <0.0001). The study showed that for the best results the patients of Tamaka …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 5, Issue 2, 2016 · pp. 1–6 Read article