Deep Learning
183 articles · search the full text for this term
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Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation
Abstract: Artificial intelligence (AI) refers to the capacity of a machine or a computer to ‘think’ or reason in the way a human would, utilizing experience, learned facts, and flexible rules to solve problems that may not fit the standard outlines for a normal algorithm. From this follows the utilization of AI in various sectors, such as the information technology (IT) industry, media, healthcare and medicine, logistics, environmental sustainability, finance, business, …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 34–39 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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DC Motor Control using Deep Reinforcement Learning for Enhanced Robustness and Precision
Abstract: DC motors remain the workhorse of industrial automation and mobile robotics, but achieving simultaneous high-speed transient response and negligible steady-state error under variable load conditions continues to challenge classical Proportional-Integral-Derivative (PID) controllers. These model-dependent systems often require extensive tuning and struggle to maintain optimal performance when confronted with parametric uncertainties, non-linear friction, or sudden voltage fluctuations. This study presents a novel, model-free control paradigm utilizing Deep Reinforcement Learning (DRL)—specifically, a …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 22–29 Read article
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AI-Driven Home Security System
Abstract: The fast-paced growth in Artificial Intelligence (AI) and computer vision technologies has created new opportunities in the realm of home security. This paper outlines a developed model of an AI-powered home security system that encompasses face recognition technology for accessing and conducting surveillance at a home in real time. The real time system is designed with advanced algorithms for facial recognition, allowing it to target authorized individuals and intruders from …
Published in Journal of Communication Engineering & Systems · Vol. 15, Issue 3, 2025 Read article
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Evaluating the Efficiency of LLMs-SA (Sentiment Analysis) via Social Media Texts
Abstract: Sentiment analysis (SA) is becoming popular in business and scientific communities as the processing of natural language (NLP), computational linguistics, text analytics, image-based processing or video- based processing is used in extracting and mining subjective information in the web, social network, etc. It is able to detect positive, negative or neutral information and can be selected to absorb polarity, sentiments, urgency and goals of mount importance. The majority of the …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Depression Detection Using AI with Chatbot Support
Abstract: Depression is a major global health concern and a significant contributor to suicide rates worldwide. India reports a high number of suicide cases, making the early detection of mental distress and depression essential for timely intervention. This research presents an AI-based system for depression detection that integrates deep learning, natural language processing (NLP), and a chatbot for user support. The system analyzes facial expressions using convolutional neural networks (CNNs) and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 14, Issue 1, 2025 · pp. 01–08 Read article
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Risk After Pediatric MRI Scanning: A Nation-Wide, Population Based Case-Control Study
Abstract: This paper investigates the potential association between pediatric MRI (Magnetic resonance imaging) exposure and the risk of developing childhood brain tumors, using action-wide, population-based and case-control methodology. The increasing use of MRI in pediatric healthcare has raised concerns about potential long-term health risks, including the risk of developing brain tumors. Detecting the presence or absence of brain tumor through traditional methods might require a lot of time as well as …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 3, Issue 2, 2025 Read article
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Exploring AI-Driven Student Performance Analysis as a Dimension of an AI-Powered Assessment and Feedback System: A Comprehensive Review
Abstract: The rapid proliferation of artificial intelligence (AI) in educational technology has heralded a paradigmatic transformation in assessment methodologies, transitioning from static, summative evaluations to dynamic, data-driven systems that emphasize continuous formative feedback. This comprehensive review interrogates AI-driven student performance analysis as a cardinal dimension of AI-powered assessment and feedback systems (AI-PAFS), synthesizing findings from forty-five rigorously curated open-access empirical studies published between 2015 and 2024. Employing a methodological lens, the …
Published in Current Trends in Information Technology · Vol. 16, Issue 1, 2025 · pp. 24–31 Read article
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A Review on Lung Cancer Prediction Using Machine Learning
Abstract: Lung cancer continues to be a major contributor to cancer-related mortality across the globe. Timely diagnosis and reliable prediction models play a crucial role in enhancing treatment outcomes and survival rates for patients. The present study focuses on the utilization of machine learning (ML) methods for the prediction of lung cancer. Using datasets that incorporate clinical records, imaging modalities, and genetic profiles, the research assesses the predictive capabilities of multiple …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–11 Read article
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Deep Learning-Based Alzheimer’s Disease Detection: A CNN Approach
Abstract: Alzheimer’s disease (AD) is a neurological condition that worsens with time and impairs a patient’s quality of life by causing cognitive loss. For prompt intervention and management of AD, early identification is essential. In this work, we propose a deep learning-based method for automatically classifying Alzheimer’s disease from medical imaging data using convolutional neural networks (CNNs). Our algorithm is intended to evaluate brain MRI images and detect anatomical variations suggestive …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 Read article
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An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids
Abstract: The increasing adoption of next-generation smart grids has introduced significant cybersecurity challenges due to their reliance on interconnected digital infrastructures and IoT-based control mechanisms. This study aims to analyze cybersecurity threats in smart grids and explore AI-driven mitigation strategies to enhance grid security and resilience. The research examines common cyber threats such as malware attacks, denial-of-service (DoS), data breaches, and insider threats while evaluating the effectiveness of AI-based solutions, including …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 16–25 Read article
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AI-Powered Defense: Advancing Cybersecurity Through Artificial Intelligence Innovations
Abstract: In the current landscape of escalating cyber threats and increasingly sophisticated attack vectors, integrating artificial intelligence (AI) within cybersecurity strategies has become a critical step forward. This study explores the role of AI in strengthening cybersecurity by utilizing its strengths in data analysis, pattern recognition, and predictive modeling. Through AI, organizations can greatly enhance their ability to detect and respond to threats. Machine learning algorithms enable ongoing adaptation to new …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 35–41 Read article
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A Machine Learning-Based Non-Invasive System for Blood Group Prediction Using Fingerprint Biometrics
Abstract: The research is targeted at the creation of innovative solution "Fingerprint Based Blood Group Prediction" for instant, non-invasive blood group determination from analysis of finger impressions, a breakthrough possibility in emergency health care. Sophisticated machine learning can be employed to map fingerprint patterns to corresponding blood group information and overcome the current lack of a direct connection between the two. Integration of various technologies: employed React for frontend development, Flask …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 9–18 Read article
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Leveraging Deep Learning for Accurate Weed Identification
Abstract: Weed control is very important for all types of agricultural businesses. The project here revolves around the application of computer vision techniques and, more concretely, deep learning techniques, for the effective recognition and classification of weeds. The EfficientNetB4 architecture is an appropriate backbone as its scalability and performance optimization is adequate. The modifier used is Adam optimization algorithm which will serve as a pre- processor for the model. Weeds at …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 2, 2025 · pp. 90–99 Read article
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Pneumonia Identification Using Explainable Artificial Intelligence
Abstract: Pneumonia, including tuberculosis (TB), remains one of the leading causes of death worldwide, especially in regions where access to healthcare is limited. Early and accurate diagnosis is critical for effective treatment and better patient outcomes, but traditional methods are time-consuming and require specialized expertise. This study explores the use of advanced deep learning models VGG16, VGG19, and ResNet50 to detect pneumonia and TB from chest X-ray images. By leveraging transfer …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 01–11 Read article
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An Analysis of Graph Database in Data Modelling and Analysis for a Recommendation System
Abstract: This research work focuses on graph databases, mainly Neo4j databases, in recommendation systems for e-commerce websites. The importance of research is that it explains how graph databases efficiently handle the complex relationship between user-items, which is difficult for traditional databases. Sparsity, limited diversity, and high setup costs are the challenges traditional databases face. This research work overcomes these problems using Ne04j with Cypher query language and graph algorithms (PageRank, Shortest …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 33–39 Read article
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The Convergence of AI and Composites - A Review Anchored in Patent Trends
Abstract: The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 182–198 Read article
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AI-Driven Micro-Expression Recognition for Early Mental Health Disorder
Abstract: Mental health conditions like anxiety and depression are often undiagnosed because the usual diagnostic methods based on basic regular instruments like questionnaires and clinical interviews have some limitations in them. They are not objective often and may not catch the initial signs of psychological distress. Micro-expressions have become valid measures of repressed or unconscious emotions and can provide greater insight into someone's mental condition. Also, identification and interpretation of these …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 3, 2025 · pp. 40–49 Read article
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AI Approaches in Gait and Posture Analysis: A Review
Abstract: This review synthesizes current research on the application of artificial intelligence (AI) in gait and posture analysis, focusing on methodologies, algorithms, and clinical applications. It examines the use of machine learning (ML) and deep learning (DL) techniques to extract relevant features from sensorderived data, offering objective, and automated assessments that surpass traditional methods. A systematic literature review was conducted, analyzing studies that utilized AI for gait and posture analysis with …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 1–3 Read article
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Personalized Virtual Interior Design Using Generative AI
Abstract: This study presents an AI-driven virtual interior design system that allows users to effectively redesign their home or workspace with an integrated shopping experience. The system tailors designs according to room type, style, and purpose, ensuring that the created layouts are aesthetically pleasing, well-organized, and functional. In contrast to conventional interior design, which involves professional skill, hand labor, and much time consumption, this AI-based method uses technology to automate fundamental …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 07–12 Read article