Deep Learning
274 articles · search the full text for this term
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Maximum Power Point Tracking of PV, Wind System Using Deep Learning Approach
Abstract: The mixing of renewable liveliness fonts, especially photovoltaic (PV) and gale schemes, has faster due to the rising call for fresh and supportable liveliness on a total rule. In the extended lane, these bases are cheap, copious, and naturally kind. Yet, they are basically irregular and nonlinear unpaid to them from top to bottom trust on ecofriendly variable star like fever, gale rapidity, and astral irradiance. Extreme Power Point Tracking …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 3, 2026 Read article
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Harmonic Minimization and Voltage Boosting In PV-based Multilevel Inverter Using Advanced Deep Learning Approach
Abstract: The integration of photovoltaic (PV) systems with multilevel inverters has gained significant attention due to their capability of producing smooth output waveforms with fewer distortions. However, conventional modulation and optimization techniques often face limitations in minimizing total harmonic distortion (THD) while simultaneously improving voltage levels under varying solar irradiance conditions. To address these issues, this work proposes an advanced deep learning-based framework for harmonic minimization and voltage boosting in PV-driven …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 3, 2026 Read article
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Smart Electric Fence: A Deep Learning-Based Intrusion Detection System
Abstract: Intrusion of humans and wild animals into protected areas such as forest boundaries and agricultural lands poses significant challenges in terms of safety and resource management. Conventional electric fencing systems operate continuously without distinguishing the type of intrusion, leading to inefficient energy usage and potential harm to wildlife. The design and partial implementation of an intrusion detection system for smart electric fence based on deep learning are presented in this …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 3, 2026 Read article
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Enhanced Deep Learning Techniques for Defect Detection in Steel-Fiber-Reinforced Composites: A Comparative Study of KerasTuner, CNN–PSO, and CNN–GWO
Abstract: The identification of microstructural defects in steel-fibre-reinforced composites is useful for the material integrity, mechanical reliability and longevity. Classic material inspection techniques face difficulties in recognizing the complex and visually similar defects because of heterogeneous material morphology, changing imaging conditions, and limits of manual feature extraction. To tackle those problems, this study presents a comparative deep-learning framework that integrates Convolutional Neural Networks (CNNs) with three hyperparameter optimization techniques namely KerasTuner, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 · pp. 377–403 Read article
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Deep Learning-Based Prediction of Elasticity and Mechanical Strength for Structure–Property Characterization of Functional Polymer Materials
Abstract: The prediction of the elasticity and mechanical strength of functional polymer materials is one of the key requirements for characterizing the structure–property relationship of such materials, which can greatly speed up polymer design, reduce the need for experimental characterization and enable the development of materials with mechanical properties appropriate to their functions. The existing works are based on the traditional machine learning model or deep learning model, which cannot be …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Predictive Modeling of Polymer Elasticity and Mechanical Strength Using Deep Learning for Functional Polymer Material Characterization and Performance Assessment
Abstract: Knowing the polymers elasticity and mechanical strength is essential in the characterization of functional polymer materials, for rapid characterization and to avoid extensive experiments. Traditional polymer characterization methods are often time consuming and resource intensive and the mechanical response of polymers can be nonlinear, thus complicating the prediction of the properties. These limitations can be overcome by introducing a Variational Temporal Fusion Transformer (VaTFT) for the deep learning prediction of …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Artificial Intelligence for Sustainable Agriculture, Forestry and Rural Development: Recent Advances, Applications and Research Opportunities
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize agriculture, forestry, and rural development by enabling data-driven decision-making, resource optimization, and sustainable management practices. Rapid developments in computer vision (CV), machine learning (ML), deep learning (DL), natural language processing (NLP), and predictive analytics have increased the use of AI in a variety of rural industries. In agriculture, AI-driven technologies support precision farming, crop yield prediction, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Intelligent Paradigms in Subsea Connectivity: A Comprehensive Review of Artificial Intelligence in Underwater Communications
Abstract: Underwater wireless communication (UWC) plays a critical role in ocean exploration, environmental monitoring, offshore energy operations, disaster management, and naval defense. However, the underwater environment presents significant communication challenges, including severe signal attenuation, multipath propagation, Doppler effects, limited bandwidth, high latency, and energy constraints. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising solutions to address these limitations and enhance the efficiency, reliability, and adaptability …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Deep Learning for Earth Observation Using Satellite Imagery: A Comprehensive Review
Abstract: Earth observation (EO) satellites provide continuous, large-scale information about the Earth's land, oceans, atmosphere, vegetation, infrastructure, and environmental conditions. The rapid growth of multispectral, hyperspectral, synthetic aperture radar (SAR), thermal, and high- resolution satellite missions has generated large volumes of heterogeneous spatial and temporal data. Conventional image-processing and machine-learning techniques often require manually designed features and may have difficulty representing the complex spatial, spectral, temporal, and multimodal characteristics of satellite …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 2, 2026 Read article
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Artificial Intelligence Techniques for Air-Interface Optimizations and Future Research Directions in 6G Networks
Abstract: This paper is the second instalment of a two-part review on artificial intelligence (AI) for sixth-generation (6G) wireless networks. Where the companion paper traced the generational evolution of mobile networks and the Digital Twin architecture that underpins 6G, this paper narrows the focus to the specific AI techniques expected to drive 6G's autonomous operation and to the air-interface problems those techniques are being asked to solve. We review machine learning, …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 2, 2026 · pp. 50–57 Read article
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A Comparative Machine Learning Framework for Early Prediction of Liver Cancer Using Clinical Attributes
Abstract: One of the main causes of cancer-related death globally is liver cancer, and improving patient outcomes depends heavily on early detection. However, low contrast, noise, organ similarity, and tumor shape and size variability make it difficult to accurately identify and segment liver tumors from medical imaging. Automated liver cancer diagnosis, segmentation, and prognosis have been greatly improved by recent developments in artificial intelligence (AI), especially deep learning. This work presents …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 · pp. 39–47 Read article
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Artificial Intelligence for Tracking Cognitive Deviation in Aging Populations: A Comprehensive Review of Techniques, Challenges, and Ethical Concerns
Abstract: Population aging is accelerating worldwide, and with it the burden of cognitive health conditions such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), and dementia. Detecting and monitoring cognitive change early is central to timely intervention, yet conventional diagnostic tools often miss the subtle signals that appear before overt symptoms. Artificial intelligence (AI) has emerged as a promising complement to clinical assessment because it can work through high-dimensional data and …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 2, 2026 · pp. 27–37 Read article
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Machine Learning Approach to Detect and Analyze Attention-Deficit/Hyperactivity Disorder
Abstract: Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by difficulties with attention, impulse control, behavioral regulation, and daily functioning that persist across childhood and adulthood. Clinical diagnosis is predominantly based on behavioral assessments and expert interpretation, which may result in subjectivity and delayed clinical decisions. To reduce reliance on subjective evaluation, this study introduces an automated ADHD identification framework that integrates resting-state functional Magnetic Resonance Imaging (rs-fMRI) with advanced machine …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 2, 2026 · pp. 22–26 Read article
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Brain Stroke Detection Using Deep Learning and Grad-CAM Explainability Framework
Abstract: Seconds matter when a brain stroke occurs; it is a race against time where rapid, precise intervention is the only way to preserve a patient’s quality of life. This research introduces a deep learning framework designed to act as a vital ally for clinicians, providing automated, high-speed stroke detection through brain MRI analysis. At the heart of our approach is EfficientNetB0, a sophisticated neural network chosen for its ability to …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 2, 2026 · pp. 8–14 Read article
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AI-Driven Malware Detection in Linux: A Survey of Techniques, Challenges, and Future Directions
Abstract: With the rapid growth of Linux-based systems across cloud computing, Internet of Things (IoT) devices, servers, and embedded platforms, Linux environments have increasingly become attractive targets for sophisticated malware attacks. This survey provides a comprehensive review of Artificial Intelligence (AI)-based approaches for detecting Linux malware, focusing on research published between 2022 and 2026. More than 40 different detection methodologies are examined, covering both traditional Machine Learning (ML) techniques, such as …
Published in Journal of Operating Systems Development & Trends · Vol. 13, Issue 2, 2026 · pp. 7–14 Read article
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An Analysis of Machine Learning Models for Early Cardiac Risk Stratification
Abstract: The paper shows an in-depth study of machine learning and artificial intelligence solutions to early cardiac risk stratification which has a crucial necessity because cardiovascular disease (CVD) prediction remains a significant issue that needs to be improved beyond the conventional risk score. Since CVD is the most serious disease killer in the world, claiming 17.9 million deaths every year, there is a strong need to get the most sophisticated predictive …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 2, 2026 Read article
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Motors Using a Deep Learning-Based Torque Control with Torque Ripple Reduction under Nonlinear Magnetic Conditions
Abstract: This research discusses a deep learning strategy for torque management to minimize the effect of torque ripple in a nonlinear electric motor. Nonlinear electric motor losses may include: magnetic saturation, harmonic flux losses and inverter losses. In many cases when the system parameters deviate and/or instability issues occur, the traditional method with a model-based approach or PI control may encounter challenges. In this case, the authors proposed a hybrid approach …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article
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Intelligent Failure Detection in Biomedical Composite Materials Using Machine Vision
Abstract: The biomedical composite materials are intelligent failure-detecting, which is necessary to ensure the reliability, safety, and durability of the current healthcare equipment. This paper describes a machine vision design, which incorporates convolutional neural networks, transformer models, and ensemble learning to correctly detect and localize material defects. The proposed system takes advantage of the capabilities of high-resolution imaging, advanced preprocessing software, and deep feature learning in the identification of the intricate …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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JATAYU: A Stereo Vision–Based UAV for Autonomous Navigation and Human Following in GPS-Denied Environments
Abstract: Traditional unmanned aerial vehicle (UAV) systems primarily rely on Global Positioning System (GPS)-based navigation and conventional computer vision techniques. However, these approaches face significant challenges in GPS-denied environments, such as dense urban areas, indoor spaces, and disaster-affected regions where GPS signals may be weak, unavailable, or unreliable. In addition, limitations in real-time processing, object detection accuracy, and environmental perception can reduce UAV effectiveness in dynamic and unpredictable situations. This paper …
Published in International Journal on Drones · Vol. 2, Issue 2, 2026 · pp. 30–38 Read article
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Attention-Enhanced YOLO Framework for Robust Real-Time Object Detection in Autonomous Driving Environments
Abstract: Accurate object detection is essential for autonomous driving systems, where vehicles must continuously perceive surrounding road users and infrastructure in real time. Although the You Only Look Once (YOLO) family of deep learning detectors has achieved significant success due to its high speed and efficiency, detecting small or partially occluded objects in complex traffic environments remains challenging. This paper presents an attention-enhanced YOLO framework designed to improve feature representation and …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 2, 2026 · pp. 41–52 Read article