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23 articles for “Decision Trees”
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 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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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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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article
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Decision tree method (Flexibility in adaptation - FIA) for evaluating the flexibility of flood risk adaptation options in Lad Krabang, Thailand
Abstract: Flexibility in planning and implementation of responses to prevent and adapt to flooding can be achieved using methods such as Real in Option (RIO), Adaptation Tipping Point (ATP) and Adaptation Pathway. However these methods lack the ability to evaluate the degrees of flexibility of the adaptation measures. Hence, this paper proposes a method for evaluating and making a comparison between a numbers of different flood prevention plans in terms of …
Published in International Journal of Water Resources Engineering · Vol. 5, Issue 1, 2019 · pp. 32–48 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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DESIGN AND ESTIMATION OF RESIDENTIAL BUILDING USING ETABS AND QUANTITY TAKE OFF SOFTWARE: A REVIEW
Abstract: The drafting, study, and design of a building are the most critical phases in its construction and planning. The project that will be completed is the construction of a multi-story building that will be used as a home. According to the requirements and available space, the building plan was created using AutoCAD software. The ETABS programme was used to evaluate and construct the superstructure, or the building frame. A quantity …
Published in International Journal of Architectural Heritage · Vol. 4, Issue 1, 2021 · pp. 1–3 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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Health Monitoring System for Fault Identification on Centrifugal Pump Using ML
Abstract: Centrifugal pumps are crucial equipment widely used in various industries, and their reliable operation is essential for maintaining production and preventing costly downtimes. This paper proposes a comprehensive health monitoring system for centrifugal pumps that combines model-driven and data- driven approaches, leveraging artificial intelligence (AI) and machine learning (ML) techniques. The system incorporates demand prediction using artificial neural networks and fault diagnosis employing different ML classifiers, such as decision trees …
Published in International Journal of Mechanics and Design · Vol. 10, Issue 2, 2024 · pp. 1–12 Read article
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Breakout Room Escape: Design and Development of an Immersive Unity Game
Abstract: Three-dimensional (3D) game development is a complex and challenging process that requires expertise in various fields, including programming, art, and design. Due to its adaptability, userfriendly interface, and thorough documentation, Unity is a well-known game engine that is frequently used in the game development industry. This research paper aims to explore the process of developing a 3D game using Unity, focusing on the technical aspects of game development and the …
Published in International Journal of Software Computing and Testing · Vol. 8, Issue 2, 2022 · pp. 20–30 Read article
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Predicting User Engagement on Social Media: A Comparative Study of Machine Learning-based Modeling Approaches
Abstract: Abstract Even while mobile social apps are becoming more and more important in people's daily lives, little is known about the elements that motivate users to interact with these apps. The goal of this work is to analyze and define the transition patterns of individual users' in-app actions as a temporally changing action graph. According to our investigation, action graphs are a useful tool for characterizing user behaviour patterns and …
Published in International Journal of Computer Aided Manufacturing · Vol. 11, Issue 2, 2025 · pp. 41–62 Read article
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Evaluation of Machine Learning and Deep Learning Models for Classification Tasks on Crime Data: A Comparative Study
Abstract: The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 1–15 Read article
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Advanced Diabetic Retinopathy Detection Using Deep Learning Classifiers
Abstract: Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 1, 2025 · pp. 26–33 Read article
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Predictive Analytics in E-Learning: Enhancing Student Performance Through Machine Learning Models
Abstract: With the explosion of electronic devices and internet technologies in today’s world, the amount of data we store has grown massively. Data is simply information that has been organized in a way that makes it easy to share or work with. A potent technology that enables computers to learn from data without direct programming is machine learning, which we employ in our research. The focus of this study is on …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 10, Issue 2, 2024 · pp. 1–5 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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Algorithmic Approaches for Data Mining and Machine Learning
Abstract: ABSTRACTTrends and technology always innovative and comes with some new. Sometimes technologies are introduces in more user friendly environment. In such version of technology advancement are also incorporated. Data mining is a best widespread knowledge attainment approach for knowledge discovery. The naval trends Machine learning also have a big interfere of data mining. Infect most of the data mining algorithm and methods are utilized for machine learning with minor amendment …
Published in International Journal of Distributed Computing and Technology · Vol. 6, Issue 2, 2020 · pp. 24–29 Read article
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A Realtime Coronavirus Notifier
Abstract: Fresh corona virus deadly disease has not clinically confirmed vaccine or medicines. Its tolerant are improving through the help of antibiotic drug, anti-viral treatments, over and above vitamin C supplementation. It is currently apparent that the world necessities an expedient and speedier answer for contain and tackle the further spread of COVID-19 across the world with the guide of non-clinical advance for instance data mining schemes, improved aptitude furthermore extra …
Published in International Journal of Distributed Computing and Technology · Vol. 7, Issue 1, 2021 · pp. 22–25 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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House Price Prediction using Machine Learning Algorithms
Abstract: House Prices depend on various factors such as location, features of the house, construction and so on. To factor all these aspects and predict an accurate price of the house can be very tricky. The following paper proposes a system that considers every aspect of a house that effects its pricing and gives an accurate prediction of the house. The system accomplishes this utilising Gradient Boosting, Random Forest, and Decision …
Published in International Journal of Digital Communication and Analog Signals · Vol. 8, Issue 1, 2022 · pp. 23–29 Read article
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Fault Diagnosis of Helical Gear Box Using Variational Mode Decomposition with Naïve Bayes and Bayes Net Classifiers through Vibration Signals
Abstract: Gear is a very important machine component which finds use in most of the machines which requires some sort of power transmission or reduction. Unattended faults in gears can be catastrophic for the machine leading to halts in production and consequent economic loss. The requirement for a gear fault detection and diagnosis system is thus emphasized. Vibration signals are often used in fault diagnosis applications along with Fast Fourier transform …
Published in International Journal of Manufacturing and Materials Processing · Vol. 1, Issue 1, 2014 · pp. 1–16 Read article