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32 articles for “convolutional neural networks (CNN)”
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A Comprehensive Review of Convolutional Neural Network Architectures and Evolution
Abstract: Convolutional Neural Networks (CNNs) have become a foundational deep learning framework in computer vision because they can automatically extract layered, increasingly complex features directly from raw image data. CNNs use convolutional, pooling, and activation layers to extract spatial patterns from basic edges to intricate object components, drawing inspiration from the human visual cortex. An overview of CNNs’ basic architecture is given in this study, along with an explanation of important …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 32–40 Read article
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Advances in Gender Classification from Facial Images Using CNNs: A Comprehensive Review
Abstract: Predicting age and gender from facial images is important for various uses, like social interactions, security, and customizing user experiences. Despite progress in facial recognition technology, getting accurate and reliable predictions is still a challenge. This paper reviews recent advancements in Convolutional Neural Networks (CNNs) for age and gender classification, comparing their effectiveness to traditional methods. CNNs excel in extracting features from images and have shown notable improvements in accuracy …
Published in International Journal of Digital Communication and Analog Signals · Vol. 10, Issue 2, 2024 · pp. 1–5 Read article
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Enhancing Image Quality with Deep Learning Techniques for Restoration and Denoising
Abstract: Abstract: Clear and high-quality images have become more and more necessary in recent years due to the rapid development of computer vision-based autonomous systems, including autonomous driving, underwater robots, video surveillance, and medical imaging. However, real-world circumstances, such as inclement weather, underwater scenes, and moving cameras, can lead to poorer-quality photographs. These degradations, which include color distortions, noise, and blurring, hurt how well visual systems accomplish tasks like target tracking, …
Published in International Journal of Broadband Cellular Communication · Vol. 10, Issue 2, 2024 · pp. 1–6 Read article
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Intelligence Video Surveillance Using Deep Learning
Abstract: Intelligent video surveillance powered by deep learning represents a transformative paradigm in security and surveillance. This new approach uses the power of complex neural networks to revolutionize the analysis and management of video data. Through the seamless incorporation of deep learning technologies like convolutional neural networks (CNN) and recurrent neural networks (RNN) into observation systems, automated identification, tracking, and classification of objects can be accomplished, alleviating storage constraints on the …
Published in International Journal of Software Computing and Testing · Vol. 10, Issue 1, 2024 · pp. 9–20 Read article
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The Implementation of Machine Learning in the Search to Fight Plant Disease
Abstract: The agriculture business loses money and time due to plant diseases. Diagnosing disease accurately takes skill and devotion. Infected plants may have spots or streaks of a different color on their leaves. Several fungal, bacterial, and viral species can also infect plants. Individual plant disease signs and indications are assessed. Neural network applications are growing. Recent research studies have determined how well machine learning reviews traditional plant disease diagnosis methods. …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 1, 2024 · pp. 25–34 Read article
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Facial Expressions Detection Using Machine Learning
Abstract: Facial expressions are necessary in human communication and understanding emotions. First and foremost, identifying emotions is a critical factor for many businesses to comprehend how their customers are responding to the things they have introduced. It can also be utilized to find out if the amenities provided to their staff are meeting their needs. Additionally, it has a plethora of other applications, such as utilizing a camera to identify someone's …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 1, 2024 · pp. 11–16 Read article
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Deep Learning with CNNs: A Survey of Techniques and Real-World Applications
Abstract: Convolutional Neural Networks (CNNs) have emerged one of the most powerfully used tools in deep learning, particularly in image and pattern recognition tasks. CNNs are being revolutionized through deep learning resulting in a dominant architecture in computer vision as well as other various domains. This paper provides a comprehensive survey of CNN-based deep learning, its methodologies, regularizations, and computational efficiencies. We also address the challenges and future directions of CNN. …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 2, 2025 · pp. 1–5 Read article
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Investigation of FSO Communication System Using Various Neural Networks
Abstract: A prospective method for high-speed data transfer via light propagation in free space, such as between buildings, satellites, or metropolitan areas, is Free Space Optics (FSO) communication. FSO systems have great promises, but their performance can be severely hampered by atmospheric disturbances. Problems like fog, rain, and turbulence cause the optical signal to be absorbed and scattered, increasing BER. To address these challenges, various Neural Network (NN) techniques such as …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 · pp. 12–20 Read article
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Hybrid Machine Learning Model for EfficientBotnet Attack Detection in IoT Environment
Abstract: The rapid advancement and widespread adoption of internet technologies have led to a surge in cyber-attacks, with botnet attacks emerging as particularly detrimental. Identifying botnet activities is increasingly challenging due to the diverse attack vectors and the evolving nature of malware. As the Internet of Things (IoT) continues to expand, network devices become more vulnerable to these sophisticated attacks, resulting in significant security breaches and financial losses. In order to …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 · pp. 31–38 Read article
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Advanced Classification of Diabetic Retinopathy Using GAN and Deep Residual CNN Model
Abstract: Diabetic retinopathy (DR) is a leading cause of blindness worldwide, impacting the largest number of individuals who have diabetes. Early identification of diabetic retinopathy may help to prevent severe effects; however, the very limited availability of labeled datasets, and the possibility of overfitting of the model, presents obstacles to obtaining an accurate diagnosis. In this research, a hybrid deep learning strategy is introduced, where Generative Adversarial Networks (GANs) are used …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 8–16 Read article
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Advanced Diabetic Retinopathy Detection Using Deep Learning Classifiers
Abstract: Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 1, 2025 · pp. 26–33 Read article
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Speech Emotion Recognition using Multi- Kernel CNN for Features extraction and Random Forest Classifier
Abstract: Identifying human emotional states from speech signals is the main goal of Speech Emotion Recognition (SER), which has grown to be a significant area of study in affective computing. However, class imbalance and efficient feature extraction from speech data pose problems for many of the current methods. This study suggests a hybrid framework that combines a Random Forest (RF) classifier with a multi-kernel Convolutional Neural Network (CNN) to address these …
Published in International Journal of Digital Communication and Analog Signals · Vol. 12, Issue 2, 2026 Read article
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Machine Learning-Based Channel Estimation for 5G NR Systems: A Comprehensive Review of TDL/CDL Channel Models and CNN Architectures
Abstract: Accurate channel estimation is a fundamental requirement for reliable communications in fifth-generation New Radio (5G NR) systems, where highly dynamic propagation environments impose severe challenges on conventional pilot-based methods. This study provides a thorough analysis of channel estimate methods for 5G NR, including everything from contemporary deep learning (DL) techniques based on Convolutional Neural Networks (CNNs) to traditional Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators. Our analysis …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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Enhancing Facial Expression Analysis Using CNNs: Insights from ML and Deep Learning Comparisons
Abstract: Emotion recognition from facial manifestations increases the interaction between humans and computers by enabling the interaction between humans and computers to effectively explain human feelings. This study presents a comparative analysis of three approaches to the emotional recognition of faces: a custom Convolutional Neural Network (CNN) model, Traditional Machine Learning (ML) methods, and other deep learning architectures such as VGG, ResNet, and Hybrid CNN–RNN models. The proposed CNN-based system, developed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 23–31 Read article
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Comparison Analysis of Lumpy Skin Disease Detection System Using X-Ray Images of Animals with the Help of Deep Learning
Abstract: Lumpy Skin Disease (LSD), caused by the Capripoxvirus, is a highly contagious viral infection impacting cattle and other livestock, resulting in significant economic losses due to decreased productivity and mortality. Early detection is critical to mitigate its spread and reduce its impact on agriculture and dairy sectors. This study investigates the efficacy of deep learning methodologies for detecting LSD through X-ray images, offering a non-invasive diagnostic approach to identify internal …
Published in International Journal of Animal Biotechnology and Applications · Vol. 11, Issue 2, 2025 · pp. 1–5 Read article
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Enhancing Glaucoma Detection with Convolutional Neural Networks: A Data-Driven Approach
Abstract: One of the main causes of irreversible blindness is glaucoma, which is characterized by certain alterations in the optic nerve that over time can impair vision. Manual evaluations, while invaluable, are challenged by inconsistencies, subjectivity, and the extensive time required. The study employed a dataset comprising retinal images annotated by experts. By using Convolutional Neural Networks (CNNs) to study and recognize important patterns in eye images, it was possible to …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 11, Issue 1, 2025 · pp. 1–6 Read article
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AI-Integrated Precision Agriculture System for Fertilizer Optimization, Multimodal Crop Yield Prediction
Abstract: Agriculture is rapidly transforming into a data-driven ecosystem supported by advances in artificial intelligence (AI), Internet of Things (IoT) sensor networks, and satellite remote sensing technologies. These innovations enable precision-driven decision-making aimed at improving productivity while minimizing environmental impact. This paper presents an AI-integrated precision agriculture system designed to optimize fertilizer usage and enhance crop yield prediction accuracy through a robust multimodal deep learning framework. The proposed approach integrates heterogeneous …
Published in International Journal of Plant Biotechnology · Vol. 12, Issue 1, 2026 · pp. 11–17 Read article
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A Study on CNN-Based Image Mosaicing
Abstract: The intricate tapestry of image mosaicing, while a cornerstone of computer vision, has long been constrained by the rigidity of classical, geometry-driven pipelines. Hand-crafted feature detectors and homography estimators, while effective in controlled environments, prove brittle when confronted with the challenges of real-world scenes: significant parallax, dynamic moving objects, repetitive textures, and illumination variance. This work introduces a fundamental paradigm shift, proposing a deep convolutional neural network (CNN) architecture designed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 1–7 Read article
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Classification of power quality disturbances using advanced deep learning algorithms for Accurate and efficient electrical signal analysis
Abstract: Power systems today have to deal with more and more power quality issues (PQDs) like voltage drops, rises, harmonics, and transients because more people are using renewable energy sources and electronic devices. These problems can hurt equipment, stop services, and lower the overall reliability of power. In real-time settings, traditional detection methods often don't work as quickly or accurately as they should. This paper looks at how deep learning models, …
Published in International Journal of Power Electronics Controllers and Converters · Vol. 12, Issue 2, 2026 Read article
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AI-Based Image Detection of Rice Crop Diseases Using Deep Learning and Grad-CAM
Abstract: The Indian economy relies heavily on agriculture, and rice is one of the most significant staple crops grown in the country. Nevertheless, rice cultivation faces numerous challenges due to several leaf diseases, such as bacterial leaf blight, brown spot, and leaf smut. These diseases can lead to substantial yield losses and financial burdens for farmers. Therefore, the early detection and classification of these diseases are essential to minimize crop damage …
Published in International Journal of Plant Biotechnology · Vol. 12, Issue 2, 2026 Read article