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579 articles for “Learning Techniques”
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Sign Talk for Blind and Deaf Translating Hand Gestures into Audible and Textual Communication
Abstract: Sign language serves as a vital communication method for individuals who are deaf or have hearing impairments. It relies on hand gestures, facial cues, and body language to convey messages and express meaning. However, many people who do not know sign language find it hard to communicate with sign language users. The latest progress in artificial intelligence and computing has enabled the creation of systems that can automatically recognize sign …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 20–26 Read article
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AI and IoT in Sustainable Agriculture: A Review
Abstract: Artificial Intelligence (AI) and Internet of Things (IoT) integration have transformed the world of sustainable agriculture, presenting new ways of resource optimization, increasing crop yields, and making environmental sustainability more accessible. The current literature review analyzes the applications of AI and IoT in three significant agricultural systems: aquaponics, hydroponics, and poultry farming. By critically analyzing recent studies, this paper emphasizes how deep learning- enabled computer vision techniques allow for the …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 2, 2025 · pp. 32–45 Read article
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Artificial Intelligence in Drug Repurposing: A Short Impact Assessment
Abstract: Artificial intelligence (AI) in pharmaceutical repurposing has become a game-changing tool that opens new avenues for the application of new drugs that have already been approved. Traditional drug discovery is a lengthy and expensive process, whereas AI can rapidly analyze vast datasets of biological, chemical, and clinical information to predict drug-disease interactions. AI-driven techniques, such as machine learning, natural language processing, and deep learning, enable the identification of potential repurposing …
Published in Trends in Drug Delivery · Vol. 11, Issue 3, 2024 · pp. 42–45 Read article
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OBD-II Big Data–Driven ML and AI-Based Virtual Sensing for Fuel Economy, Component Health, and Carbon Intelligence
Abstract: The rapid growth of connected vehicles has led to the large-scale availability of high-frequency On-Board Diagnostics II (OBD-II) data; however, much of this data remains underutilised, as existing studies and commercial systems typically address fuel economy, maintenance, or emissions in isolation or rely on additional physical sensors. Such fragmented and sensor-dependent approaches limit scalability and increase system cost, particularly in high-volume and resource-constrained vehicle markets. To address this gap, this …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 39–50 Read article
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Enhancing Road Safety: A Robotic System for Automatic Pothole Identification and Filling
Abstract: Innovative technologies called automatic pothole detection systems are made to automatically locate and identify potholes on road surfaces. These systems incorporate many components, including sensors such as cameras, LiDAR, and accelerometers, to acquire data on the road conditions. Potholes on roads provide serious risks to passing cars and pedestrians, which can result in collisions, damage to cars, and deterioration of the infrastructure. Automatic pothole detection systems, which make use of …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 3, 2024 · pp. 1–6 Read article
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Survey on Retinal OCT Image Preprocessing, Segmentation, and Deep Learning Based Classification
Abstract: Optical coherence tomography (OCT) is a non-invasive technique that generates high-resolution, detailed cross-sectional images of biological tissues. By utilizing low-coherence interferometry, OCT enables visualization of tissue microstructure with micron-scale resolution, making it useful in various medical fields such as ophthalmology, cardiology, and dermatology. In ophthalmology, OCT is extensively used for diagnosing and monitoring retinal diseases like macular degeneration and diabetic retinopathy, allowing doctors to assess changes in tissue morphology over …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Solar Panel Defect Detection Using Geospatially-Aware Deep Learning framework
Abstract: Large-scale photovoltaic (PV) systems demand reliable inspection techniques to maintain efficiency, as manual methods remain labor-intensive and inconsistent. This study introduces a geospatially informed deep learning framework for defect detection and localization in PV panels from drone and satellite imagery. The framework incorporates an adaptive tiling mechanism that adjusts tile boundaries according to object size, reducing information loss and enhancing detection performance. In addition, coordinate transformation between image pixels and …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 2, 2026 Read article
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Machine Learning Approaches for Phishing Detection: A Comparative Study
Abstract: These days, everyone has an internet addiction. All of us have used the internet for banking, booking, recharging, and buying. Phishing is a type of website threat that exists online. On the original website, phishing is an attempt to illegally obtain information such as login ID, password and credit card information. In this research, we proposed an efficient phishing detection system based on machine learning. Overall, the experimental findings demonstrated …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 1, 2024 · pp. 35–45 Read article
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Developing a Comprehensive Framework for User and Entity Behavior Analytics (UEBA): Integrating Advanced Machine Learning and Contextual Insights
Abstract: User and Entity Behavior Analytics (UEBA) has emerged as a crucial approach in modern cybersecurity for detecting and mitigating insider threats, compromised accounts, and other malicious activities within organizational networks. However, existing UEBA frameworks often face challenges in scalability, detection accuracy, and response effectiveness. This research work proposes a novel framework for UEBA that aims to address these limitations and enhance threat detection and response capabilities. The framework integrates advanced …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 2, 2024 · pp. 20–32 Read article
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Optimization of Automatic Energy Management System Using Renewable Resources
Abstract: With the increasing demand for energy efficiency and sustainability, smart home energy management systems (SHEMS) have emerged as promising solutions to optimize residential energy consumption. This paper presents a comprehensive review of SHEMS technologies, focusing on their design, implementation, and impact. SHEMS integrates advanced sensors, real-time data analytics, and intelligent algorithms to monitor, control, and optimize energy usage within the home environment. By leveraging machine learning and predictive modelling techniques, …
Published in Journal of Thermal Engineering and Applications · Vol. 11, Issue 2, 2024 · pp. 15–22 Read article
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Advanced Computational Models for Predicting Molecular Interactions
Abstract: Understanding molecular interactions is essential for a number of disciplines, including biochemistry, materials science, and medication development. Traditional experimental methods, while accurate, are often time-consuming and expensive. Advanced computational models have emerged as powerful tools to predict molecular interactions efficiently. In order to predict the behavior and interactions of molecules at the atomic and subatomic levels, this paper reviews the most recent developments in computational techniques, such as machine learning …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 1, 2024 · pp. 8–13 Read article
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Counter Terrorism Prediction and Risk Evaluation (C-TRIP)
Abstract: The global landscape in the 21st century is marked by complex and evolving security challenges, none more pressing than the threat of terrorism. Acts of terror have left a profound impact on societies, economies, and governments worldwide, underscoring the critical importance of effective counter terrorism strategies. The “Counter Terrorism Prediction and Risk Evaluation (C-TRIP)” represents a significant stride in addressing this ever-pressing challenge. In a time marked by global security …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 14–24 Read article
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Parkinson’s Disease Detection on Spiral Images Using CNN with Meta-Classifiers
Abstract: In this work, we provide a detailed method for identifying Parkinson’s Disease (PD) by integrating Convolutional Neural Network (CNN) and meta-classifiers. Through the utilization of a varied dataset consisting of handwritten spiral images, our methodology demonstrates commendable accuracy across a range of models. Specifically, our CNN model with meta-classifiers surpasses alternative approaches, achieving an impressive accuracy rate of 95.07%. By utilizing pre-established VGG16 and ResNet50 architectures as bases, the region-based …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 55–66 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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Design, Development, and Optimization of Autonomous Robots for Enhanced Performance
Abstract: Autonomous robots are transforming industries by executing complex tasks with minimal human intervention, improving efficiency, precision, and adaptability across various domains such as manufacturing, healthcare, logistics, and exploration. Their performance relies on a synergy of robust hardware design, intelligent control mechanisms, and advanced optimization techniques. This paper explores the key components of autonomous robots, including sensor integration, locomotion systems, control architectures, and decision-making frameworks that enable autonomous operation in dynamic …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 2, 2025 · pp. 22–30 Read article
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Bio-Inspired FGPCs for Biomedical and Structural Applications
Abstract: Bio-inspired functionally graded polymer composites (FGPCs) represent a new class of smart materials that use gradient material distributions to enhance mechanical and biological properties, mimicking natural systems like bones, shells, and plant stems. FGPCs exhibit smooth gradient distributions across interfaces, which improves biocompatibility and reduces the risk of failure under complex loading and environmental conditions. In this study, bio-inspired FGPCs were designed, fabricated, and validated using a combined experimental and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 456–481 Read article
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A Detailed Review on Intelligent and Robust Control Strategies for Autonomous Underwater Vehicles with Emphasis on Navigation, Path Tracking, and Stability Enhancement
Abstract: Autonomous Underwater Vehicles (AUVs) have gained significant attention due to their applications in ocean exploration, underwater surveillance, environmental monitoring, and offshore industries. The control of AUVs presents various challenges due to the highly dynamic and uncertain underwater environment, nonlinear hydrodynamics, and external disturbances. This review paper explores various control strategies employed for AUVs, including classical control methods such as Proportional-Integral-Derivative (PID) controllers, modern techniques like Model Predictive Control (MPC), and …
Published in International Journal of Electronics Automation · Vol. 3, Issue 2, 2025 · pp. 28–52 Read article
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QSAR Modeling Techniques: A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
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Depression Detection Using Machine Learning: A Comprehensive Review
Abstract: Depression remains one of the most prevalent mental health conditions globally, yet it frequently goes undiagnosed due to the reliance on subjective evaluation methods. With the growing availability of digital behavioral data and significant progress in machine learning (ML), new possibilities have emerged for the automated detection of depression. This review offers a detailed examination of recent advancements in ML-driven approaches to identifying depressive symptoms. It covers a range of …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 27–32 Read article
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Identification of Papaya Fruit Ripening Process Using AI
Abstract: Identifying the ripening process of papaya fruit using artificial intelligence involves employing machine learning algorithms to analyze various features such as color changes, texture alterations and chemical compositions. This model is capable of analyzing visual cues to determine the stage of ripeness. The dataset compares images of papaya at various ripening stages, and our AI model demonstrated high accuracy in classifying these stages. Employing machine learning algorithms and image processing …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 13, Issue 2, 2024 · pp. 23–30 Read article