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662 articles for “machine learning method”
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A Review of Automated Pomegranate Disease Detection and Classification Using Machine Learning
Abstract: The abstract outlines a research study focused on developing an automated system for detecting and classifying diseases that affect pomegranate fruits. Pomegranates, like many other crops, are vulnerable to several types of diseases that appear as visible colored spots on the fruit’s surface. These visible symptoms, such as lesions or discoloration, can significantly impact the fruit’s quality, market value, and yield. Therefore, timely and accurate identification of such diseases is …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 01–13 Read article
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A Machine Learning Based Artificial Intelligence Model for Detecting Heart Illness
Abstract: This study centers around the improvement of an artificial intelligence- and computerized reasoning-based heart sickness determination framework. We exhibit how AI can help with foreseeing whether an individual will get cardiovascular infection. In this review, a Python-based application for medical care research is created since it is more reliable and helps track and lay out many kinds of well-being observing applications. We show information handling, which incorporates working with all …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 50–58 Read article
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Classification of Mammogram Images by Machine Learning Algorithm Attacker Nodes
Abstract: Abstract— Breast cancer is basic in ladies' these days. In beginning when cells in the breast start to develop out of control. These cells generally structure a tumor that will frequently be seen on a x-beam or felt as a lump. Cells in almost any piece of the body can move toward becoming malignant growth and can spread to different regions of the body. There are very nearly 6 phases …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 6, Issue 2, 2019 · pp. 1–10 Read article
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DRIVE-DEFENDER: A Driver Safety-oriented Alcohol and Drowsiness Detection System
Abstract: DRIVE-DEFENDER presents a pioneering approach in driver safety through the development of a real-time machine learning system for alcohol detection. The detrimental impact of alcohol-impaired driving on road safety necessitates efficient detection mechanisms. Current methodologies are often hindered by their cost, invasiveness, and reliance on specialized sensors. DRIVE-DEFENDER utilizes a camera for recording the face of the driver in real time, employing image processing techniques to identify facial landmarks. These …
Published in Journal of Open Source Developments · Vol. 11, Issue 1, 2024 · pp. 15–26 Read article
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Discernment of Parkinson’s disease using Machine learning
Abstract: Parkinson’s disease is one of the most common neurological disorders in the world that is caused when the cells in the brain that are responsible for the creation of dopamine breakdown and as a result less dopamine is produced .The cells in the brain that create dopamine are in charge of movement regulation, adaptability, and fluency. Since researchers believe that the disease starts developing many years before the motor (movement-related) …
Published in Journal of Microcontroller Engineering and Applications · Vol. 9, Issue 2, 2022 · pp. 14–19 Read article
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Improving Dataset Integrity Through Automated Data Cleaning Techniques
Abstract: High-quality data is a fundamental requirement in data science for producing trustworthy analytical insights and effective machine learning models. Problems, including incomplete records, inconsistent entries, duplicate observations, and anomalous values, can severely reduce the accuracy and robustness of predictive systems. As modern datasets continue to expand in both volume and structural complexity, relying on manual data cleaning methods become time-consuming and error-prone, highlighting the growing importance of automated data preprocessing …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 40–45 Read article
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Investigative Study of Adaptive Fault Tolerance in Optical Networks
Abstract: Optical networks have become the backbone of modern telecommunications infrastructure, enabling high-speed data transmission across global networks. However, these networks face significant reliability challenges due to component failures, signal degradation, and environmental factors. This investigative study examines adaptive fault tolerance mechanisms in optical networks, focusing on emerging technologies and methodologies that enhance network resilience. The research analyzes various fault detection techniques, including machine learning-based approaches, self-healing protocols, and dynamic reconfiguration …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 2, 2025 · pp. 24–30 Read article
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A Comprehensive Study of Risk-Adaptive Access Control in Advanced Database Management Systems
Abstract: The current control methods for the accession of a crucial resource often struggle to provide adequate security in dynamic and complex advanced database management systems (DBMS). These static models lack the flexibility to adapt to evolving threats and contextual changes, leaving potential vulnerabilities. Risk-adaptive access control (RadAC) emerges as a sophisticated solution, integrating real-time risk assessment into authorization decisions to dynamically adjust access permissions. This review article provides a comprehensive …
Published in Journal of Advanced Database Management & Systems · Vol. 13, Issue 1, 2026 · pp. 19–28 Read article
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The Role of Artificial Intelligence in Enhancing Athlete Performance and Training Strategies
Abstract: Artificial Intelligence (AI) is transforming modern sports by improving athlete performance, training methods, and decision-making processes. The integration of AI technologies such as machine learning, data analytics, wearable sensors, and computer vision has enabled coaches and sports scientists to analyze large amounts of performance data with greater accuracy and efficiency. Athletes' physiological indicators, movement patterns, injury risks, and recuperation processes are all monitored by these technology. Training regimens can therefore …
Published in Recent Trends in Sports · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
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A Technical Review on Intrusion Detection System with Various Machine Learning Algorithms in Data Mining
Abstract: With the progression in information & communication technology (ICT), it has become a vital element of human’s life. But this technology has brought a lot of threats in cyber world. These threats increase the chances of network vulnerabilities to attack the system in the network. To evade these attacks there are distinct ways in which one is Intrusion-Detection System (IDS). IDSs are software or hardware products that automate this monitoring …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 5, Issue 1, 2018 · pp. 1–8 Read article
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Tribological Performance and Wear Coefficient Prediction of AA2024–TiC Composites via Python-Based Machine Learning
Abstract: Determining wear coefficient accurately serves as a critical factor to maximize engineering materials' tribological characteristics. The experiment examines the wear characteristics of TiC-reinforced AA2024 aluminum alloy subjected to different tribological operating conditions. A pin-on-disc tribometer performed wear tests under different conditions of load and TiC weight fraction and sliding speed and duration. ANOVA statistical results show that load intensity and TiC reinforcement density stand out as principal variables that affect …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 1099–1112 Read article
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Role of Quantum Chemistry in Catalysis: A Comprehensive Review
Abstract: Catalysis plays a crucial role in modern chemical manufacturing, energy conversion, and environmental protection by enabling chemical reactions to occur more rapidly, selectively, and with reduced energy consumption. A fundamental understanding of catalytic processes at the atomic and electronic levels is essential for the rational design and optimization of catalysts. Quantum chemistry has emerged as a powerful theoretical and computational framework that enables detailed investigation of electronic structure, reaction energetics, …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 1, 2026 · pp. 01–16 Read article
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Implement Artificial Intelligence and Machine Learning for Engineering Design, Predictive Modeling, and Optimizing Polymer Nanocomposites
Abstract: Polymer nanocomposites are high performance engineered materials obtained by inclusion of nano-sized fillers into the polymer matrix to enhance mechanical, thermal, electrical, barrier and functional properties. However, the complex and non-linear interactions among polymer chemistry, nanofiller characteristics, filler concentration, dispersion, interfacial bonding and processing conditions make it challenging to anticipate and maximize their properties. Artificial intelligence (AI) and machine learning (ML) offer powerful data-driven solutions to these difficulties by establishing …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Data-driven Approaches to Mineral Resource Management Using AI: A Brief Review
Abstract: The role of Artificial Intelligence (AI) in the mineral resource sector has become increasingly significant over the past few years, as industries seek to optimize and modernize their operations. AI encompasses a variety of technologies and techniques, such as machine learning, deep learning, and expert systems, that are now widely used in mineral exploration, resource estimation, and mine management. These AI-driven approaches have brought about a transformative shift, enhancing efficiency, …
Published in International Journal of Minerals · Vol. 2, Issue 1, 2025 · pp. 25–29 Read article
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Signal Feature Extraction and Machine Learning Techniques for Human Activity Recognition
Abstract: Human Activity Recognition (HAR) has emerged as a critical field of study with diverse applications in healthcare, fitness tracking, smart homes, and human-computer interaction. The aim of this research is to create an efficient HAR system through advanced techniques characterized by signal feature extraction and machine learning algorithms. The MEMS sensors are used appropriately during data mining to extract time-domain, frequency-domain, and statistical features, which are subsequently passed to the …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 24–41 Read article
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Comprehensive Survey on Intelligent Prognostication of Profitable Farming
Abstract: Nowadays, agriculture has become an important field of research. Particularly, when it comes to crop prediction, agriculture depends on soil, climate, and temperature. Formerly, farmers used to decide which crop to cultivate, what is needed for its growth, and when to harvest it at its best based on their expertise. But due to continuous changes in environmental conditions, it has become very difficult to take any decision related to farming …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 2, 2023 · pp. 63–69 Read article
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Facial Expression Recognition using Convolutional Neural Network
Abstract: Machine learning is a subset of Artificial intelligence. Machine learning enables self-learning from past data and predicts the future. Machine learning is used in many applications such as self-driving cars, Google translate etc. Deep learning is a subset of machine learning. CNN is a convolutional neural network. The CNN approach is very effective in image classification. The target of this exploration is to assist a look acknowledgment framework dependent on …
Published in Journal of Advancements in Robotics · Vol. 8, Issue 1, 2021 · pp. 30–35 Read article
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Computational Intelligence and Neuro-Fuzzy Modelling of Polymer Composites: A Critical Review of Performance Prediction and Optimization
Abstract: The increased variety in polymer matrices, reinforcements, fillers, and processing parameters has led to the need to better understand the structure-property, process-property relationships in order to accurately predict and optimize the performance of polymer composites. This paper reviews the applications of computational intelligence methods in polymer composites, with special focus on artificial neural networks, adaptive neuro-fuzzy inference systems, machine learning techniques, and hybrid optimization. The literature is analyzed based on …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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High-Definition Electroencephalography: A New Horizon in Neurological Pathology Research
Abstract: The advent of high-density electroencephalography (HD-EEG) has catalyzed a paradigm shift in the exploration of neurological pathologies. This editorial underscore its transformative potential in elucidating brain dynamics and refining diagnostic approaches for a spectrum of conditions, spanning from epilepsy and dementia to cognitive impairments in preterm infants. Our objective is to optimize the utility of HD-EEG by emphasizing the imperative for methodological homogenization and fostering collaborative endeavors. The remarkable spatial …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 15–21 Read article
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Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube
Abstract: In the digital age, social media platforms play a vital role in facilitating user engagement, encompassing both positive interactions and avenues for negative, often harmful behaviors. Recognizing and addressing toxic exchanges is paramount to nurturing healthy online communities and preserving users’ well-being. This study introduces a novel method for identifying toxic interactions by utilizing Gradient Boosting Regression Trees (GBRT) algorithm, a machine learning approach renowned for its exceptional accuracy and …
Published in Trends in Opto-electro & Optical Communication · Vol. 15, Issue 3, 2025 · pp. 7–14 Read article