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68 articles for “deep convolutional neural networks (CNNs)”
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Elderly Healthcare Using Federated Learning Approach
Abstract: The healthcare system for elderly people faces several challenges, which can be addressed using advanced machine learning models. These models can help monitor chronic diseases, detect falls, and provide personalized health recommendations. The study uses comprehensive datasets like MIMIC-III/IV, WESAD, and UCIHAR to explore human movements, device limitations, and the differences in fall occurrences. A detailed review of existing literature discusses current technologies for activity monitoring and fall detection, focusing …
Published in Current Trends in Information Technology · Vol. 16, Issue 1, 2025 · pp. 13–23 Read article
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Empowering Vehicle: The Impact of Deep and Reinforcement Learning in IoV
Abstract: Deep learning and reinforcement learning represent two pivotal pillars within the realm of artificial intelligence and machine learning, bearing transformative potential in the domain of the Internet of Vehicles (IoV). This abstract explores the multifaceted applications of these cutting-edge techniques within the IoV framework. Deep learning, exemplified by convolution neural networks (CNNs) and recurrent neural networks (RNNs), empowers IoV systems with the prowess to discern complex patterns in sensory data. …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 2, 2025 · pp. 1–12 Read article
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Sustainable Cotton Crop Productivity through Precision Weed Detection: A Deep Learning-Based Approach with UAV Integration
Abstract: Weeds present a major challenge to crop productivity by competing with crops for vital resources, including water, sunlight, and nutrients, often resulting in significant yield reductions. On a global scale, weeds are responsible for approximately 13.2% of annual crop losses, a quantity sufficient to feed nearly one billion people. These invasive plants disrupt agricultural systems and adversely impact crop yields. Given their uneven distribution in fields, ground or aerial robots …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 1, 2025 · pp. 19–26 Read article
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Optimizing Mango Harvest Timing in the Nasik Region (Maharashtra, India) by CNNs (Residual Network 101)
Abstract: The determination of optimal harvest timing is one of the most critical decisions in mango production, directly affecting postharvest quality, market value, transportation resilience, and export readiness. In regions such as Nashik, Maharashtra—one of India’s major fruit- producing belts—the climatic variability, cultivar differences, monsoon patterns, and market- driven pressures make accurate harvest timing essential. Traditional maturity assessment relies on subjective visual inspection, specific gravity, or destructive testing, each of which …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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Pneumonia Detection and Classification Using Deep Learning
Abstract: Pneumonia, an infectious lung disease primarily caused by bacteria, often exacerbated by environmental factors, leads to the accumulation of pus in the lung’s alveoli. Accurate diagnosis through chest X-rays, ultrasounds, or lung biopsies is crucial to avoid misdiagnosis and ensure proper treatment, crucial for patients’ quality of life. Diagnostic capacities have been greatly improved by deep learning advances, especially with convolutional neural networks (CNNs). This research presents a robust CNN-based …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 9–19 Read article
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Automated Plant Disease Detection and Treatment Advisor Using Artificial Intelligence
Abstract: Automated plant disease detection and treatment advisors using artificial intelligence represent a significant advancement in modern agriculture. The identification of plant leaf diseases is essential to maintaining food security and agricultural output. Machine learning models, particularly deep learning algorithms like convolutional neural networks (CNNs), are trained on labeled datasets containing images of healthy and diseased plants. These models learn to classify images into different disease categories with high accuracy. Convolutional …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 1–7 Read article
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Self-Driving Cars and Computer Vision: Enhancing Computer Vision for Autonomous Vehicle Navigation
Abstract: Autonomous vehicles, commonly known as self-driving cars, are transforming the transportation sector by aiming to enhance road safety, ease traffic congestion, and boost overall efficiency. Central to the operation of these vehicles is computer vision, which enables them to perceive and understand their environment. This paper examines how computer vision contributes to the navigation of autonomous vehicles and highlights its continuous developments. Specifically, it examines key challenges such as real-time …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 · pp. 1–10 Read article
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Role of Machine Vision in Autonomous Vehicles: A Review
Abstract: The integration of machine vision in autonomous vehicles (AVs) is a critical advancement in the field of intelligent transportation systems. Machine vision systems enable AVs to perceive their environment, understand road conditions, detect obstacles, and make real-time decisions necessary for safe navigation. These systems rely heavily on image processing techniques, which have evolved significantly over the past decade, leading to improved performance in complex driving scenarios. These developments are largely …
Published in Trends in Machine design · Vol. 12, Issue 1, 2025 · pp. 38–43 Read article
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A Neuromorphic-Inspired, Low-Power VLSI Architecture for Edge AI in IoT Sensor Nodes
Abstract: As the proliferation of Internet of Things (IoT) devices continues to rise, there is an increasing demand for real-time, energy-efficient artificial intelligence (AI) processing directly at the network edge. Traditional edge AI accelerators, often based on deep learning models like convolutional neural networks (CNNs), struggle to meet the ultra-low-power requirements of battery-constrained IoT sensor nodes. In response to this challenge, this study introduces a neuromorphic-inspired, low-power very- large-scale integration (VLSI) …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 2, 2025 · pp. 41–47 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 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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Secure Forge: Deepfake Image Detection Using Vision Transformers
Abstract: Deepfake technologies have become a major risk to the credibility and trustworthiness of digital visual information. Using powerful generative models like GANs and autoencoders, deepfakes can generate highly realistic fake videos and images, resulting in misinformation, identity theft, and public loss of trust in digital media. Classic Convolutional Neural Networks (CNNs) while being highly effective in initial-stage, deepfake detection tend to be limited by their local receptive fields and dependency …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 32–45 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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Bias Detection and Accuracy Enhancement in Voice-based Banking Authentication Using Deep Learning
Abstract: Biometric systems have become an integral part of how many people access banking services today, and voice verification systems can be a secure and easy-to-use source of banking authentication that does not require any physical contact with the bank or any other person. From the security perspective, these systems would normally provide an effective means of identifying an individual but frequently exhibit bias with respect to demographics such as the …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 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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Automated Car License Plate Detection and Recognition Using Deep Learning
Abstract: The use of automated license plate detection and recognition (ALPR) systems to automate processes such as number plate detection is gaining popularity in traffic control, security, and law enforcement. This research focuses on achieving more accurate and efficient detection and recognition of number plates by leveraging deep learning techniques. The systems outlined in this study aim to improve the effectiveness of ALPR systems using advanced convolutional neural networks (CNNs) and …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 23–29 Read article
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Enhancing IoT Network Security with Hybrid Deep Learning Classifiers for DDoS Attack Detection
Abstract: The security and operational dependability of Internet of Things (IoT) networks are seriously threatened by the growing susceptibility to Distributed Denial of Service (DDoS) assaults brought about by their rapid expansion. The intricacy and dynamic character of these advanced attacks can provide a challenge to conventional intrusion detection systems. This study presents a novel method for strengthening IoT network security by combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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Real-Time Gesture Recognition with Convolutional Neural Networks
Abstract: Sign language detection plays a pivotal role in bridging communication barriers for the deaf and hard of hearing community. An extensive investigation on the use of convolutional neural networks (CNNs) for sign language recognition is presented in this article. Leveraging the power of deep learning, our research aims to develop an accurate and efficient system capable of recognizing and classifying sign language gestures in real-time. The report begins with an …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 2, 2024 · pp. 12–18 Read article
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Robustness of Deepfake Detection Systems Against Adversarial Attacks
Abstract: This paper explores a deep learning system to detect deepfake videos, a common type of fake media. With the use of sophisticated methods such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), our system can reliably discern between authentic and altered videos. It analyzes both the images and the audio in videos to find signs of deepfake manipulation. We process video frames and audio, extract features with CNNs …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article