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47 articles for “convolutional neural network (CNN)”
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Region-based Object Detection and Classification using Faster R-CNN
Abstract: With the advent of Deep Learning the machine learning systems are able to recognize and classify objects of interest in an image. Various advancement has been done in the field of object recognition and classification. Our research work focusses on improving the R-CNN, Fast R-CNN, YOLO architecture. The work focused on using Region Proposals Network (RPN) to extract region of interest in an image. RPN outputs an image based on …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 6, Issue 2, 2020 · pp. 22–30 Read article
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Classification of Fetal ECG Arrhythmia by Using Advance Hybrid Deep Learning Technique CNN-LSTM
Abstract: Fetal heart abnormalities, particularly fetal heart arrhythmias, are becoming an increasing concern in maternal healthcare. Early and accurate detection remains challenging due to limitations in conventional diagnostic techniques and inadequate signal processing methods. Existing studies have applied various machine learning and deep learning approaches; however, capturing the complex characteristics of fetal electrocardiogram (ECG) signals remains a significant gap. This study proposes an advanced deep learning-based approach for detecting fetal heart …
Published in International Journal of Cardiovascular Nursing · Vol. 12, Issue 1, 2026 · pp. 27–36 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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CNN-Based Traffic Sign Recognition with Voice Alerts
Abstract: Traffic sign recognition is a crucial component of intelligent transportation systems, significantly enhancing road safety by aiding both autonomous and human drivers. This paper presents a convolutional neural network-based traffic sign recognition system integrated with voice alerts. Our approach utilizes a deep learning model trained on a comprehensive dataset of traffic signs to accurately identify various road signs in realtime. Upon recognition, the system issues corresponding voice alerts to inform …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 10, Issue 2, 2024 · pp. 1–9 Read article
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AI-Driven Hybrid Neural Network Architecture for Medical Image Segmentation: A Novel Approach for Early Disease Detection
Abstract: One of the key factors in detecting early diseases is medical image segmentation. Medical image segmentation can be very complex due to the differences in image quality, noise, and specific anatomical structures of individual patients. A novel approach for a hybrid neural network structure that combines the power of convolutional neural networks, and the transformer-based models is described here for improved accuracy and robustness of medical image segmentation. Unlike traditional …
Published in International Journal of Microwave Engineering and Technology · Vol. 11, Issue 1, 2025 · pp. 1–7 Read article
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Automated Image Captioning Using Xception–CNN and LSTM for Descriptive Sentence Generation
Abstract: Generating descriptive texts for images is a complex and challenging task that requires advanced deep learning techniques. This paper presents a descriptive sentence generator for images using a combination of convolutional neural networks and recurrent neural networks. The proposed model utilizes a pretrained convolutional neural network, Xception, to extract image features, which are then processed by a long short-term memory network to generate meaningful textual descriptions. The model is trained …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 2, 2025 · pp. 20–28 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 and Haar Cascade classifiers. The suggested method uses convolutional neural network to recognize facial expressions and Haar Cascade algorithm for face detection. The methodology begins with preliminary processing of the input photos, followed by face …
Published in International Journal of Microwave Engineering and Technology · Vol. 10, Issue 2, 2024 · pp. 1–11 Read article