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139 articles for “machine learning classifiers”
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Optical Image Sensing and Analysis of Iron Ore Pellets: A Machine Learning Approach
Abstract: The present work is aimed to improve quality control in steel production using SEM imaging and machine learning. High-resolution SEM images of iron ore pellets, primarily composed of hematite and magnetite, are analyzed to understand their microstructural features, which significantly impact pellet performance during reduction processes. Traditional microstructure analysis is manual, time- consuming, and prone to inconsistencies. This study proposes an automated approach using K-Means Clustering, Canny Edge Detection, DBSCAN, …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 7–18 Read article
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Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
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Smart Patient Monitoring and Motion Tracking System
Abstract: The integration of smart technologies in healthcare has revolutionized patient monitoring and diagnostics. This paper presents a Smart Patient Monitoring and Motion Tracking System designed for hospitals, leveraging EEG (Electroencephalogram) signals to track patient movements and monitor neurological health. The proposed system combines motion tracking with real time EEG signal analysis to enhance patient safety, especially for individuals prone to seizures, neurological disorders, or other mobility-related risks. The system employs …
Published in International Journal of Radio Frequency Innovations · Vol. 3, Issue 2, 2025 · pp. 9–23 Read article
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A Survey On Leveraging Machine Learning for Phishing Attack Prediction and Detection
Abstract: Phishing is one of the biggest cybersecurity threats that exploits user trust by masquerading as a legitimate site or email to steal personal and sensitive information. A state- of-the-art-phishing detection systems survey, this review showcases the evolution from traditional list-based techniques, including blacklisting and whitelisting to machine learning and deep learning models. While list-based systems cannot evolve to detect new and zero-day attacks, the ML algorithms of Decision Tree, Random …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 3, 2025 · pp. 1–10 Read article
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Identifying and Blocking of Non-productive Calls in Emergency Call System Using Machine Learning and IVRS Integration
Abstract: The Dial 100 emergency reaction gadget handles over 1,000,000 calls every day, with over 95% being unproductive, such as blank, machine-generated, and spoofed calls. These futile calls waste resources and put off responses to actual emergencies. This look presents a comprehensive solution integrating advanced name evaluation, system learning, and an interactive voice response system (IVRS) to filter out and prevent these calls. Our technique starts with studying incoming calls to …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 3, 2024 · pp. 20–28 Read article
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Pothole Detection utilising Machine Learning: A Review
Abstract: Potholes must be found and fixed quickly in order to maintain infrastructure, maximize transportation systems, and guarantee road safety. Using the Sequential API and the Keras library, this study presents a neural network model for pothole detection. Convolutional layers with ReLU activation, global average pooling, dense layers with dropout, and softmax activation for binary classification make up the model architecture. Image loading, resizing, array conversion, labeling, shuffling, normalization, and one-hot …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 1, 2025 · pp. 35–43 Read article
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Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication
Abstract: This paper presents a hierarchical adaptive RF communication system that integrates signal quality-based pre- processing with machine learning-driven signal classification to achieve robust and resource-efficient operation in dynamic, interference-prone environments. Unlike prior art that addresses adaptive RF, ML classification, or anti-jamming individually, this work uniquely combines real-time SNR/RSSI-based signal strength estimation with dynamic FFT size selection (64-, 256- , or 512-point) and arithmetic-level multiplier reconfiguration (CORDIC, Distributed Arithmetic, and hybrid …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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Artificial Intelligence in Image Recognition: Context of Machine Vision
Abstract: The machine learning discipline is as old as decades, but some problems such as image recognition, location detection, image classification, image generation, speech recognition, and natural language processing cannot be solved. Image classification studies are another basic, most classic and essential line of research in deep learning. Computer intelligent recognition of the images technology has enabled a gradual reaction (updating) to foreign measurement trends, which promotes advancement of different areas …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 01–06 Read article
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Advances in Polymer-Modified Concrete using XAI
Abstract: Industry 4.0 technologies are being quickly adopted by the construction sector, opening new avenues for enduring operational and environmental issues. This sector looks at how explainable AI can forecast air and enhance the quality of building materials. XAI, AI, ML, and big data drive a new paradigm in polymeric material development. The effective XAI and ML-assisted design creates innovative, high-performance polymeric materials. It covers building a database and representing structures, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 133–144 Read article
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Predictive Modeling of Polymer Composites for Medical Implants Using Artificial Intelligence Techniques
Abstract: The use of polymers in biomaterials was now key to designing the next generation of medical implants, which need to be strong and also compatible with living tissue. Tests for biocompatibility, such as those done in the laboratory and by doing experiments on animals, require much time and many resources, so the need for computer-based approaches becomes clear. An artificial intelligence approach was provided in this study to determine how …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 665–692 Read article
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Analysis of impact of Meditation on Cognitive Workload using EEG Signals
Abstract: AbstractMental activities can be indicated by the Cognitive workload which are useful in applications like Biomedical, Human Machine Interaction and Task analysis. The mental effort applied on the Working memory at a certain given time is commonly known as Cognitive load. The EEG Signals of Cognitive Workload can be studied and classified. The features such as Entropy, Energy, Power, etc. can be extracted from the EEG signals and processed using …
Published in Current Trends in Signal Processing · Vol. 10, Issue 1, 2020 · pp. 29–39 Read article
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Sentiment Analysis of X (Formerly Twitter) Using Machine Learning
Abstract: Sentiment analysis is a methodology to determine the nature and behavior of each and every user for the content posted on the social media platform in the form of post and feed. Consumers of the online platform are encouraged to post reviews of the product that they purchase. Little attempt is created by Amazon to confine or limit the content of these reviews. The number of reviews for various merchandise …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 2, 2024 · pp. 28–37 Read article
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Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report
Abstract: In recent years, diabetes has become the biggest disease in different countries around the world. This disease is caused by adulteration in food ingredients, unhealthy food habits, a lack of physical exercise, and changing the lifestyle every time without a routine chart. The main objective of this review paper is to provide a proper understanding of the machine learning algorithm used in the healthcare system to handle diabetic patients' data. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 1–10 Read article
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Emotion Recognition based on Human voice tones
Abstract: In this period of technology and automation, human interaction with machines has become an unavoidable occurrence. Machines are making our lives much easier, stretching their hands wherever it is possible for smoother operation of various tasks. Emotion recognition system is one of the system that can help humans for a much easier and meaningful interaction with machines. It can be used in various branches such as artificial intelligence, health care, …
Published in Current Trends in Information Technology · Vol. 10, Issue 2, 2020 · pp. 1–5 Read article
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Diabetes Prediction Using ML Techniques
Abstract: Diabetes mellitus, commonly referred to as diabetes, denotes a cluster of prevalent endocrine disorders characterized by persistent elevated levels of blood sugar. Diabetes is classified into two main types: type 1 and type 2. Type 1 diabetes arises when the body is unable to produce insulin, while type 2 diabetes involves either insulin resistance or insufficient insulin production. Early detection and intervention are essential to reduce its harmful impacts. The …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Deep Guard: A Comprehensive Deep Learning System for Unmasking Suspicious Activities in Surveillance Footage
Abstract: These days, video surveillance is quite vital. Technology has evolved considerably as machine learning, artificial intelligence, and deep learning become increasingly widespread. There are several algorithms that assist in identifying distinct kinds of suspicious behaviour from live footage by combining the techniques. A person's behaviour is the most unpredictable thing, and it can be quite challenging to determine whether it is normal or suspicious. Video surveillance is automated to address …
Published in International Journal of Satellite Remote Sensing · Vol. 1, Issue 1, 2023 · pp. 23–28 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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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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The Evolution and Impact of Numbers: From Ancient Tallies to Quantum Computing: Review Article on Numbers
Abstract: Numbers are among the most fundamental constructs in human civilization, serving as the backbone of mathematics, science, technology, and virtually every aspect of daily life. They represent not only quantities and measures but also relationships, structures, and patterns that underpin the fabric of human understanding. From the earliest tallies etched on bones by prehistoric humans to the sophisticated numerical systems embedded in today’s artificial intelligence and quantum computing, the evolution …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 3, 2025 · pp. 15–19 Read article