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57 articles for “categorical features”
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Real-time DDoS Attack Prediction in SDN Environments Using Machine Learning
Abstract: The ever-growing reliance on sdn-based services necessitates robust security measures against Distributed Denial-of-Service (DDoS) attacks that threaten service availability. This project investigates the development of a real-time prediction system for DDoS attacks in sdn environments, leveraging the power of machine learning. The proposed system employs a Decision Tree classification algorithm implemented in Python. To ensure accurate attack identification, the system meticulously addresses data preprocessing challenges inherent in network traffic datasets. …
Published in Journal Of Network security · Vol. 13, Issue 1, 2025 · pp. 16–27 Read article
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Alzheimer disorders diagnosis system design using machine learning for EEG signal
Abstract: The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered …
Published in Journal of Control & Instrumentation Read article
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Automation of Waste Segregation System by Using IoT
Abstract: The rapid urbanization and the increasing volume of waste generation have made waste management a critical issue globally. Efficient waste segregation at the source is one of the most effective ways to reduce the adverse environmental impact of waste disposal. In this study, we propose an automated waste segregation system based on Internet of Things (IoT) technology, aimed at improving the efficiency of waste management. The system integrates a range …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 2, 2025 · pp. 1–10 Read article
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Prediction of Customer Churn Using Machine Learning Classification Models
Abstract: Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 86–92 Read article
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Skin Disease prediction and classification from dermoscopy images using Neural Network
Abstract: Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article
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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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A Comparative Study Between GSM and CNN to Develop Gesture Detection Based Alert System for Women Safety
Abstract: Women’s safety is a pressing issue in today’s world, and technology can play a crucial role in addressing it. This project introduces a facial expression recognition device that uses Convolution Neural Network (CNN) technology and develop it’s comparison with an expression system with use of GSM is done. Unlike traditional methods relying on manual activation or dedicated devices, this system reacts instantly to threatening situations by recognizing predefined gestures, ensuring …
Published in International Journal of Electrical Power and Machine Systems · Vol. 2, Issue 1, 2024 · pp. 24–30 Read article
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Fake Product Detection Using Convolutional Neural Networks
Abstract: The widespread circulation of counterfeit products in global markets presents a significant threat to both consumer trust and the integrity of established brands. With the advancement of artificial intelligence, particularly deep learning, there is growing potential to develop more sophisticated systems to combat this issue. This study introduces a novel counterfeit detection framework using the VGG16 Convolutional Neural Network (CNN) to distinguish between authentic and counterfeit products through image analysis. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 2, 2025 · pp. 08–15 Read article
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A Comparative Study of Routing Protocols and Artificial Intelligence in Manets
Abstract: Decentralized networks called Mobile Ad hoc Networks (MANETs) allow mobile nodes to dynamically connect to one another without the need for fixed infrastructure. An overview of MANETs is given in this work, along with a discussion of their features, uses, and difficulties. It explores how routing protocols, such as proactive, reactive, and hybrid protocols, are categorized in MANETs and provides examples and features for each. It also examines the performance …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 1, Issue 2, 2023 · pp. 31–38 Read article
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A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction
Abstract: Heart disease ranks among the top causes of death globally. Accurately predicting cardiovascular conditions has become a key challenge in the realm of clinical data analysis. It has been shown that machine learning is an effective means of assisting with predicting and decision-making based on the large volume of data produced by the medical industry. In this study, we describe a unique approach that increases the prediction accuracy of heart-related …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 69–75 Read article
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FoodieHUB: Food Recipe Suggestion Using AI-ML On Web
Abstract: Finding a delicious recipe to cook with limited ingredients at home can be a challenging task. Many individuals struggle to prepare meals using only the ingredients they have on hand, creating uncertainty and limiting options. This project aims to develop a recipe recommendation system that utilizes machine learning algorithms to suggest recipes based on available ingredients, dietary preferences, cuisine types, cooking time, and user ratings. The project utilizes a Gradient …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 01–07 Read article
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Feature Extraction and Analysis of Bearing Faults: A Review
Abstract: One of the most important steps in identifying bearing problems is feature extraction. In order to provide a more meaningful dataset, it entails locating and extracting pertinent features from raw bearing vibration signals. Tasks involving categorization and prediction can then make use of these attributes. In many practical applications, such as monitoring rotating machinery or electronic components, the raw signals collected (e.g., vibration, current, temperature) are often complex, high-dimensional, and …
Published in Trends in Mechanical Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 20–28 Read article
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Exploring Lactic Acid Bacteria in Nasiriyah’s Locally Manufactured Cheese
Abstract: This investigation aimed to identify and diagnose lactic acid bacteria in locally produced cheese. Forty-eight isolates were obtained by categorizing them according to their phenotypic and microscopic features. After conducting biochemical tests, we acquired ten bacterial isolates from the Lactobacillus species. The VITEK 2 instrument was utilized to distinguish the samples to the species level with a threshold for the Lactobacillus genus. In the city of Nasiriyah, visiting the various …
Published in International Journal of Pathogens · Vol. 1, Issue 2, 2024 · pp. 15–23 Read article
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Enhancing Image Classification Performance with Deep Neural Networks
Abstract: Classifying images is useful in many domains, including the study of plant diseases and the analysis of human expressions. Image categorization employing the idea of a “deep neural network” helps to compact otherwise cumbersome photos. It is possible to classify images by using the idea of a “deep neural network”. Self-driving cars, medical diagnosis, automatic translation, etc., all make use of Deep Neural Networks. Recently, excellent results have been achieved …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 1, 2024 · pp. 13–23 Read article
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Developing a Chatbot System Utilizing Artificial Intelligence and Natural Language Processing
Abstract: Software applications commonly feature a user interface that falls into broad categories, namely graphical user interface (GUI), text-based UI, or a blend of both. This interface is predominantly employed in web-based and desktop applications. A chatbot, designed to engage with users, operates through the storage and retrieval of session data. It proves particularly beneficial in situations where obtaining information about individuals who are not affiliated as students or employees of …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 1, 2024 · pp. 24–29 Read article
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The Silent Mineral: Structural Complexity and Industrial Utility of Natural Kaolin
Abstract: Kaolin, a naturally occurring aluminosilicate clay predominantly comprising the mineral kaolinite (Al₂Si₂O₅(OH)₄), exhibits a 1:1 layered silicate structure that imparts distinctive physicochemical properties suitable for extensive industrial utilization. Deposits of kaolin are broadly classified into primary (residual) and secondary (sedimentary) categories, each defined by their geological origin and mineralogical features. The industrial processing of kaolin encompasses stages such as raw material extraction, beneficiation, and purification, aiming to optimize parameters including …
Published in International Journal of Crystalline Materials · Vol. 2, Issue 1, 2025 · pp. 52–61 Read article
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An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 Read article
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Evaluating Web Content and Design Trends: A Comprehensive Study of Forest Institute Library Websites of ICFRE
Abstract: This study evaluates the website content and design features of forest institute libraries under the Indian Council of Forestry Research and Education (ICFRE). A comprehensive checklist comprising eight categories and 60 parameters was developed to assess nine regional forest institute websites systematically. The findings reveal that all websites (100%) feature visible and contrasted colour schemes with clear and easily readable text. Moreover, every institute’s website includes webmail functionality, copyright information, …
Published in Journal of Advancements in Library Sciences · Vol. 12, Issue 2, 2025 · pp. 1–11 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article