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662 articles for “machine learning method”
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An Evaluation of Sentiment Analysis in Online Reviews using FRN Algorithm
Abstract: The internet is rich in directional text (i.e., text containing opinions and emotions). World Wide Web provides volumes of text-based data about consumer preferences, stored in online review websites, web forums, blogs, etc. Sentiment analysis is a technique to classify people’s opinions in product reviews, blogs or social networks and has emerged as a method for mining opinions from such text archives. It uses machine learning methods combined with linguistic …
Published in Journal of Operating Systems Development & Trends · Vol. 1, Issue 1, 2014 · pp. 14–20 Read article
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Accessavior: AI-Driven Real-Time Adaptive Web Interfaces for Intelligent User Experiences
Abstract: As the web has become more diverse, many traditional, one-size-fits-all web accessibility solutions that must be configured manually have proven inadequate in meeting the needs of each user. This paper introduces “Accessavior”, which is an intelligent, behavioural pattern-driven accessibility framework that can automatically customize the personalization of web interfaces based on the interaction patterns of users. The proposed system will continuously track cursor behaviours such as cursor movements, keystroke characteristics, …
Published in Current Trends in Information Technology · Vol. 16, Issue 2, 2025 · pp. 14–22 Read article
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Topology and Geometry in Data Science: Persistent Homology and Beyond
Abstract: In recent years, the interplay between topology, geometry, and data science has gained substantial momentum, offering powerful frameworks to analyze and interpret complex datasets. Traditional statistical and machine learning methods often rely on linear or metric- based assumptions, which may fail to capture the intrinsic structure of high-dimensional or nonlinear data. In contrast, topological and geometric methods provide shape-oriented, scale- invariant tools that focus on the continuity, connectivity, and global …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 21–27 Read article
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Graph based Word Sense Disambiguation for Gujarati
Abstract: Due to high Semantic Ambiguity that is associated with language, Word Sense Disambiguation is an open problem of Natural Language Processing and Ontology in Computer Linguistics. For instance, the Gujarati word પુવૅ (Purva) can mean East and Past. The task of automatically assigning the most suitable meaning to a polysemous word can be defined as Word Sense Disambiguation. Several approaches have been done, from Dictionary-and Knowledge-based method that uses Lexical …
Published in Journal of Advancements in Robotics · Vol. 5, Issue 2, 2018 · pp. 35–43 Read article
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An Analytical Review on Word Sense Disambiguation
Abstract: Due to high Semantic Ambiguity that is associated with language, Word Sense Disambiguation is an open problem of Natural Language Processing and Ontology in Computer Linguistics. For instance, the word Head can mean the part of body and a person in charge of organization. The task of automatically assigning the most suitable meaning to a polysemous word can be defined as Word Sense Disambiguation. Several approaches have been done, from …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 5, Issue 1, 2018 · pp. 32–37 Read article
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Block Chain Based Supply Chain for Grading Milk Quality Using Machine Learning Techniques
Abstract: Milk is a primary source of nutrition in practically every country on the globe. Milk is widely packaged in a variety of containers such as packages, cans, glass bottles, and polyethylene terephthalate (PET) bottles. Milk, on the other hand, has a limited period for the customer to use it because it has an expiration date. Despite the clearance of the expiration date, it is possible that the milk could have …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 2, 2023 · pp. 80–92 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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Review of Machine Technique for Prediction of Depression Using EEG Signal
Abstract: Depression is a prevalent mental health disorder that affects millions worldwide, often leading to significant personal and societal burdens. Traditional diagnostic methods for depression rely heavily on subjective assessments, which can be prone to bias and inconsistency. In recent years, there has been a growing interest in utilizing machine learning techniques to predict depression based onelectroencephalogram (EEG) signals, offering a promising avenue for more objective and reliable diagnosis. This review …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 3, 2023 · pp. 23–29 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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Credit Card Fraud Detection Using Machine Learning Techniques
Abstract: Credit card fraud has become a significant concern in today's digital economy, with billions of dollars being lost annually to fraudulent transactions. Conventional rule-based approaches frequently prove inadequate in addressing the constantly changing strategies employed by fraudsters. Machine learning methods have emerged as robust solutions for detecting credit card fraud, presenting the capability to accurately identify fraudulent transactions promptly. In this study, we investigate the efficiency of three widely used …
Published in Journal of Open Source Developments · Vol. 11, Issue 1, 2024 · pp. 1–7 Read article
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Impact of ML in Evolution of Recommender Systems: A Review
Abstract: Recommender systems have become indispensable tools in today's digital era, catering to the overwhelming demand for personalized recommendations in various domains such as e-commerce, content streaming, and social media. The continuous growth of data and user interactions has necessitated the integration of advanced machine learning (ML) techniques to enhance the accuracy and efficiency of these systems. This study provides a thorough analysis of current developments in machine learning methods used …
Published in E-Commerce for Future & Trends · Vol. 10, Issue 2, 2023 · pp. 26–32 Read article
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Computational Modeling of Polymer Semiconductors for Electronic Applications
Abstract: Polymer semiconductors have become important materials in modern electronic applications because they combine semiconducting behavior with mechanical flexibility, low-cost processing, and tunable molecular structure. Their growing use in organic field-effect transistors, organic photovoltaics, organic light-emitting diodes, and flexible sensing devices has increased the need for accurate computational approaches that can predict material properties and device performance before experimental fabrication. This paper reviews the major computational modeling techniques used for polymer …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 132–146 Read article
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Assessing Land use Dynamics and Policies in the Waghur Basin Using Geospatial Techniques
Abstract: Changes in land use and land cover (LULC) are key indicators of human–environment interactions, especially in river basins where anthropogenic pressure is increasing. This study evaluated land use change and policy implications in the Waghur Basin, India, through a geospatial analysis of 35 years (1990–2025). Remote sensing and GIS-based supervised classification with the help of machine learning methods were applied to multi-temporal Land satellite images to create LULC maps and …
Published in International Journal of Land · Vol. 3, Issue 1, 2026 · pp. 38–49 Read article
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Disease Prediction Using Machine Learning (ML)
Abstract: A technique called Machine Learning Disease Prediction uses symptoms reported by users or patients to forecast disease. The user-provided symptoms are entered into the system, and it outputs the disease probability. In disease forecasting, various popular supervised machine learning methods are known to be utilized. These algorithm estimates the likelihood of a disease occurrence. Precise analysis of medical information will support timely disease detection and management of patients based on …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 12, Issue 2, 2024 · pp. 40–48 Read article
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Sky Scanners: Using satellite remote sensing to figure out what Earth is like
Abstract: Satellite remote sensing has changed the way we look at, study, and learn about the Earth changing systems. Orbiting sensors take data from many different spectral bands, giving us constant, large-scale information about land, oceans, and the atmosphere. This study discusses the fundamental concepts of satellite remote sensing, including the various types of sensors, methods for data acquisition, and techniques for interpreting images. It talks about important uses like monitoring …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 22–33 Read article
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A Comparative Study of different Techniques to predict Maternal Morbidity and Mortality Model
Abstract: Artificial intelligence (AI) encompasses a range of techniques, including machine learning and deep learning, which are increasingly utilized in the healthcare sector for tasks such as disease diagnosis and drug discovery. To achieve accurate disease diagnosis through AI, it is essential to integrate data from multiple medical sources, including ultrasound imaging, magnetic resonance imaging (MRI), mammography, genomics, and computed tomography (CT) scans, among others. This article presents a comprehensive review …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 1, 2025 Read article
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Predicting Parkinson’s Disease: Python Machine Learning
Abstract: Parkinson's disease is a progressive neurological disease that affects a large number of people and has a significant impact on their quality of life. Human motor function is mostly affected by this condition. "Parkinsonism" or "Parkinsonian" syndrome refers to the primary motor symptoms. Parkinson's disease causes tremors, rigidity, slowness of movement, and trouble walking, as well as changes in thinking and behaviour. Anxiety and despair are frequent as well. It …
Published in Recent Trends in Programming languages · Vol. 8, Issue 3, 2021 · pp. 38–41 Read article
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Overview AI-Driven Antenna Technologies and Privacy- Preserving Methods for Next-Generation 6G Wireless Systems
Abstract: The next generation of wireless communications, 6G, will be built on the convergence of artificial intelligence (AI) and advanced antenna systems. AI-driven antennas are poised to address the unprecedented requirements for data rate, reliability, adaptability, and ubiquity in future networks. An overview of current advancements in AI-enabled antenna systems for 6G networks is provided in this study. From traditional base station deployments to distributed, cell-free, and user-centric frameworks, it examines …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 1, 2026 · pp. 28–34 Read article
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Genetic Variability and Statistical Methods: Key Insights for Computational Genetics Research
Abstract: Genetic variability, defined as the differences in DNA sequences among individuals, serves as the foundation of evolutionary biology and plays a pivotal role in species’ adaptability, resilience, and overall survival. Advances in genomic technologies, particularly high-throughput sequencing, have enabled unprecedented exploration of genetic diversity, fostering the growth of computational genetics. This interdisciplinary field combines statistical methods and computational tools to analyze genetic data, identify patterns, and link phenotypes to genotypes. …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 19–23 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