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42 articles for “CNN models”
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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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Towards Inclusive Communication: Reviewing CNN Models for Hindi Varnamala Sign Language Recognition
Abstract: Dealing with an individual who has hearing impairments is never easy. This research represents an effort (extension) to investigate the challenges associated with character categorization in Indian Sign Language (ISL).It is necessary for two people to know and comprehend the same language to create communication. However, sign language is necessary for communication for those who have hearing impairments. The gestures used by the deaf and dumb people are easy for …
Published in International Journal of Analog Integrated Circuits · Vol. 9, Issue 2, 2023 · pp. 16–21 Read article
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General Health Check-up, Health Care Model using CNN and Internet-of-Medical Things
Abstract: Many people may believe that undergoing a comprehensive medical check-up is unnecessary when they feel perfectly healthy. However, this perception is mistaken. Regular health check-ups are essential to maintain overall well-being and to detect potential health issues early, enabling proactive steps to prevent them from worsening. Such check-ups are valuable for both individuals in good health and those with existing medical conditions, as they provide insight into the progress of …
Published in International Journal of Distributed Computing and Technology · Vol. 9, Issue 2, 2023 · pp. 20–25 Read article
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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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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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Performance Comparison of Handwritten Digit Recognition Using Machine Learning Techniques
Abstract: Handwritten pattern, character and digit recognition has always been the most demanding and interesting fields of image processing. The objective of this work is representing and demonstrating the algorithms which are used to recognize handwritten digits. Recognition of handwritten digits has always a bit complex, but it is mostly used in various day-to-day applications. In the task of recognition, the digits are not correctly or accurately scripted or written as …
Published in International Journal of Digital Communication and Analog Signals · Vol. 7, Issue 1, 2021 · pp. 9–18 Read article
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Evaluation of Deep Learning Optimizers for Gujarati Handwritten Numerals
Abstract: Handwritten digit recognition is a fundamental task in the field of pattern recognition and computer vision with numerous applications. The development of deep learning algorithms has greatly increased the efficiency and accuracy of recognition of handwritten digits systems. However, the choice of optimizer plays a crucial role in determining the convergence rate and final performance of deep learning models. In this study, we provide a thorough analysis of many deep …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 1, 2025 · pp. 34–41 Read article
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Performance Analysis of SVM and CNN Networks for Defect Detection in Solar Panel
Abstract: Use of solar panel in generating electricity is increasing globally day by day to meet the increase in demand of power. Many developments have been made in order to achieve higher efficiency of the system. But efficiency of the system may still decrease due to harsh environmental conditions or poor handling of the. These factors highly contribute in the performance degradation of the system. For detection of these irregularities, human …
Published in International Journal of Analog Integrated Circuits · Vol. 7, Issue 1, 2021 · pp. 15–24 Read article
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Deep Learning Approach Based Brain Tumor Detection
Abstract: The brain tumor is one of the most deadly disease which is of great concern and hence to detect and extract the effected tumor area in the brain from the MRI has lot of research potential. It is also a very difficult task to perform by the experts. The perfection of the vivisection depends on how much experienced the radiologist or the clinical expert is. So to overcome the wastage …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 8, Issue 1, 2022 · pp. 57–63 Read article
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CyberSentinel: A Proposed Hybrid AI Framework for Real-Time Cyber Threat Intelligence
Abstract: Modern cyberattacks have grown far more sophisticated, frequent, and fast-moving than the signature-based intrusion detection systems many organizations still rely on. This paper proposes CyberSentinel, a hybrid artificial intelligence framework for real-time cyber threat intelligence (CTI) that combines three complementary techniques into a single detection pipeline: a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) module for network traffic anomaly detection, a Random Forest ensemble for structured log classification, and a transformer-based …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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Facial Expression Recognition by Using Multifeatured Fusion And Deep Learning Techniques
Abstract: In our daily lives, sentiment analysis is a vital part of communication. A vital component is information regarding how the user feels when someone communicates. You will need a solution that can handle everything from recognizing a user's emotional state to personalizing the user experience. This study's objective is to look into emotions. Today, deep learning techniques are quickly advancing in a variety of fields, including computer vision. Without a …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 8, Issue 2, 2022 · pp. 16–22 Read article
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Facial Emotion Based Stress Detection Using CNN and Haar Cascade Algorithms
Abstract: Understanding human conduct requires the ability to recognize facial emotions, which has applications in everything from human-computer interaction to psychological wellness monitoring. This research provides a new approach to stress detection using Convolutional Neural Networks (or CNNs) and Haar Cascade classifiers. The suggested method uses CNN to recognize facial expressions and Haar Cascade algorithm for face detection. The methodology begins with preliminary processing the input photos, followed by face detection …
Published in International Journal of Microwave Engineering and Technology · Vol. 10, Issue 2, 2024 · pp. 41–50 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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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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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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Using LapSRN (Image Resolution Deep Learning Model) with Transfer Learning
Abstract: In order to create high-resolution photographs, Super Resolution (SR) tries to transform low-resolution photos. SR methods can be categorized into two categories: Single Image Super Resolution (SISR) and Video Super Resolution (VSR). SISR initially needs to upscale low-resolution photos to high-definition images. VSR, which stands for "image super resolution," is used to transform low-quality videos into ones with higher resolution. Deep learning techniques use Convolutional Neural Networks (CNN), a special …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 8, Issue 1, 2022 · pp. 18–24 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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Deep Learning to Detect Pneumonia from Chest X-Ray Image
Abstract: Pneumonia is an acute pulmonary infection that is caused by viruses or by bacteria and it causes inflammation in the lungs. In 2019, a total of 2.5 million people died because of pneumonia, out of which, one third were younger than the age of 5. This infection was diagnosed by taking a chest Xray. The prediction of diseases is mainly based on human knowledge. In this, the probability of human …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 8, Issue 1, 2022 · pp. 8–14 Read article
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Analyzing Crowds with Deep Learning: A Review of Methods and Challenges
Abstract: With the growing population, the need for an effective crowd management tool emerged. It is important to monitor and analyze unusual crowd movements and behavior for many applications such as peaceful events, protests, gatherings, or traffic management. Current systems, such as video surveillance, are a useful technique for crowd monitoring, but they require human visual capabilities to monitor the crowd. This work is a paper on Crowd Analysis using convolution …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 2, 2023 · pp. 30–42 Read article