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81 articles for “Convolutional Neural Networks (CNNs)”
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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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Detection of Pneumonia in COVID-19 Patients Using X-ray Images
Abstract: This study explores the use of chest X-ray image analysis and deep learning methods to identify pneumonia in COVID-19 patients. Due to the pandemic, Proper as well as immediate examination of COVID-19 is now essential for patient care and disease control. This study proposes a novel approach that uses convolutional neural networks (CNNs) to automatically predict pneumonia in COVID-19 patients using chest X-ray images. In this study, an X-ray of …
Published in International Journal of Radio Frequency Innovations · Vol. 1, Issue 1, 2023 · pp. 13–23 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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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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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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CNN-Based Wound Segmentation: A Review of Models and Performance Evaluation
Abstract: Deep learning, particularly convolutional neural networks (CNNs), has altered medical image processing by automating and precisely segmenting complex medical pictures. Wound segmentation, a critical application in automated wound assessment, is essential for wound size estimation, classification, and healing progress monitoring. This study presents a comprehensive review of CNN-based wound segmentation models, focusing on their architectures, methodologies, and performance on diverse datasets. Four deep learning models, including two U-Net variants (5-layer …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 33–46 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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Advanced Deep Learning Techniques for Sickle Cell Anaemia Detection
Abstract: Sickle Cell Anemia (SCA) is a prevalent genetic blood disorder characterized by the presence of abnormal hemoglobin, resulting in the distinctive sickle shape of red blood cells. Timely and accurate identification of Sickle Cell Anemia (SCA) is essential for effective management and treatment. This study presents a new method that utilizes Convolutional Neural Networks (CNNs), a deep learning model particularly effective for image analysis. The process involves using microscopic images …
Published in Research and Reviews: A Journal of Medicine · Vol. 14, Issue 3, 2024 · pp. 9–15 Read article
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Avian Echoes: Convolutional Neural Network for Bird Vocalization Detection
Abstract: Bird species identification is a complex task within ornithology that demands advanced technological solutions. This research presents an approach leveraging Convolutional Neural Networks (CNNs) for bird species recognition based on identification of bird sound, each employing unique datasets and methodologies. The objective involves a two-stage identification process, beginning with the construction of an ideal dataset. The crucial step involves converting 1D audio waveforms to 2D spectrograms, enhancing CNNs' ability to …
Published in Journal of Aerospace Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 26–37 Read article
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Automated Suspicious Activity Detection in Video Surveillance Using Deep Learning: A Review
Abstract: In the current era of advanced security systems, video surveillance plays an essential role in ensuring safety by detecting suspicious activities. With the increase in real-time data, manual monitoring has become impractical, paving the way for automated surveillance systems utilizing machine learning (ML) and artificial intelligence (AI) technologies. This paper explores the integration of ML and AI models, specifically convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, for …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 20–27 Read article
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AGRISMART: Crop and Soil Management System
Abstract: Agriculture has played a crucial role in developing countries where the majority of the rural population relies on it for their livelihoods. A finer-grade crop classification has become crucial in the context of precision agriculture. In recent years, the volume of open image data has grown significantly. This can be used in combination with machine learning techniques to classify crop types in the agricultural industry. The proposed crop species recognition …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 3, 2025 · pp. 50–55 Read article
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Convolutional Neural Network Based Ripeness Detection of Fruits
Abstract: The accurate and efficient assessment of fruit ripeness plays a crucial role in ensuring the quality of fruits and optimizing supply chain management. This paper presents a novel approach for the automated detection of apple and banana ripeness using Convolutional Neural Networks (CNNs). The suggested method supports the capability of CNNs to learn hierarchical features from images, variations in color and shape associated with different ripeness stages. The online dataset …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 13, Issue 2, 2024 · pp. 30–36 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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Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation
Abstract: Deepfake technology, powered by highly advanced deep learning models, has raised significant concerns regarding media manipulation, identity theft, and the spread of online disinformation. Due to the increasing sophistication of deepfake content, traditional forensic methods often fail to detect such artificially generated images with high accuracy. Consequently, deep learning-based approaches have become essential in combating this challenge. This study compares six prominent deep learning architectures: VGG16, ResNet50, MobileNetV2, InceptionV3, EfficientNetB0, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 27–36 Read article
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A Study of Cloud-Enabled Deep Learning for Monitoring and Predicting Soil Health in Agriculture
Abstract: Soil health is a critical factor in ensuring sustainable agricultural practices and food security. Traditional methods for soil health assessment are often time-consuming, localized, and lack scalability. This study explores the integration of cloud-enabled deep learning techniques to monitor and predict soil health efficiently. Leveraging data from IoT sensors, satellite imagery, and lab-based analyses, a cloud-based framework is proposed to process and analyze soil health parameters such as pH, moisture …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 2, 2025 · pp. 8–16 Read article
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Machine Learning Approaches in Breast Cancer Diagnosis: Current Trends and Future Perspectives
Abstract: Since cancer is still one of the world's top causes of death, precise and effective detection techniques must be developed. Machine learning (ML) approaches have shown promise in recent years for enhancing cancer prognosis and detection. This paper presents a comprehensive review of the application of ML in cancer detection, focusing on various modalities including medical imaging, genomic data, and clinical records. We highlight the challenges associated with traditional cancer …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 1, 2024 · pp. 14–20 Read article
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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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An Automation Detection for Sign Language Using AI
Abstract: Sign language recognition has attracted considerable interest because of its ability to facilitate communication between the deaf community and the public, thereby bridging communication divides. Traditional approaches to sign language recognition often face challenges in accurately interpreting the complex and nuanced gestures inherent in sign languages. However, recent advancements in deep learning techniques have shown promising results in improving the accuracy and robustness of sign language recognition systems. This study …
Published in Recent Trends in Programming languages · Vol. 11, Issue 1, 2024 · pp. 1–14 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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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article