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374 articles for “Deep Networks”
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Salient Region Guided Deep Network for Violence Detection in Surveillance Systems
Abstract: Abstract: It is significant to detect violent actions in video surveillance systems automatically, for example, bus stands, malls and railway stations. Though, the earlier detection techniques generally extract statistic features around the spatiotemporal interest points or extract descriptor in the regions where movement takes place, leading to limited abilities to successfully detect violence activities in video surveillance systems. To solve this problem, a new approach for the automatic detection of …
Published in Journal of Computer Technology & Applications · Vol. 10, Issue 3, 2019 · pp. 19–28 Read article
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Sign Language and Face Expression Recognition Using Neural Networks: Deep Learning Approach to Break Communication Barriers
Abstract: Our study proposes a multimodal gesture recognition system specifically designed to aid communication for the deaf community. By employing neural network concepts, we utilize 3D convolutional neural networks (3D CNNs) to extract features from both hand and face images, focusing on relevant regions. Preprocessing techniques are applied to isolate these areas of interest prior to feature extraction. Unique 3D CNN architectures are then trained for each modality to capture the …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article
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Development of Intensity-based Segmentation Technique for Meningioma Tumor Detection in MRI Images
Abstract: There are many types of brain tumors. Some brain tumors detection system using segmentation and classification of MRI images. Brain tumors can have any shape or cut. This encourages us to use high-capacity deep neural networks. Segmentation task and 8000 images for classification task of our neural network and found the best architecture to use. convolutional neural network. In recent years, the three most common forms of brain tumours—glioma, meningioma, …
Published in Research and Reviews : Journal of Computational Biology · Vol. 11, Issue 03, 2022 · pp. 50–54 Read article
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Traffic Sign Detection and Recognition Using Deep learning based- Convolutional Neural Network Algorithm
Abstract: The concept of Deep Convolutional Neural Organizations (CNNs) is a quickly arising new zone for Automatic traffic sign detection and recognition among the few master frameworks, such as independent driving and driver assistance. Here, in this paper, for traffic sign detection, we have utilized another methodology that uses a newly developed identification calculation and an RGB-based tone thresholding procedure. Results of the proposed identification and acknowledgement approaches are assessed on …
Published in Recent Trends in Electronics Communication Systems · Vol. 8, Issue 1, 2021 · pp. 24–29 Read article
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A Comprehensive Review on Brain Tumour Classification through Deep Learning Utilizing Convolutional Neural Networks
Abstract: Abstract- Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. Brain tumours are classified using a biopsy, which is not normally done before conclusive brain surgery. The enhancement of this technology by machine learning could aid radiologists in tumour detection without the use of …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 12, Issue 3, 2023 · pp. 24–29 Read article
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Enhancing Image Classification Performance with Deep Neural Networks
Abstract: Classifying images is useful in many domains, including the study of plant diseases and the analysis of human expressions. Image categorization employing the idea of a “deep neural network” helps to compact otherwise cumbersome photos. It is possible to classify images by using the idea of a “deep neural network”. Self-driving cars, medical diagnosis, automatic translation, etc., all make use of Deep Neural Networks. Recently, excellent results have been achieved …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 1, 2024 · pp. 13–23 Read article
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A Survey on Neural Network based Classifier for Arrhythmia Detection
Abstract: Electrocardiogram (ECG) is one of the important diagnostic tool for the detection of the heart problem. Increasing number of cardiac patients need automatic detection techniques for various abnormalities or arrhythmias of the heart to reduce pressure on physicians and share their load. Coronary Care Units (CCUs) emphasizes on the task of accurate analysis of ECG signal at an early stage that can prevent disease, like tachycardia, to escalate there by …
Published in Journal of Communication Engineering & Systems · Vol. 8, Issue 2, 2018 · pp. 88–97 Read article
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Predictive Modeling System for Automated Skin Lesion Classification Using Deep Neural Networks and Voting Ensembles
Abstract: Skin cancer is one of the most prevalent cancers globally. Early and accurate diagnosis is critical for timely treatment and improved prognosis. This study presents a predictive modeling system for automated classification of skin lesions from dermoscopic images using deep neural networks and voting ensemble techniques. A customized 16-layer convolutional neural network architecture is developed for feature learning from lesion images. The concept of horizontal voting ensemble is implemented by …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 2, 2023 · pp. 29–35 Read article
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AI-Driven Prediction of Square-Hole Laser Trepanning Performance in AA7075/15%SiC/15% Glass Fiber Hybrid Composites Using Taguchi–ANOVA and Deep Neural Networks
Abstract: Hybrid AA7075 composites reinforced with 15% silicon carbide (SiC) and 15% glass fiber were fabricated via the stir casting technique to improve machining and structural performance. The addition of dual reinforcements into the aluminum matrix was aimed at enhancing hardness, thermal stability, and surface quality during non-traditional drilling operations. Square-hole drilling was performed using a laser trepanning process, and the key responses—hole size accuracy, surface roughness, and taper angle—were systematically …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1932–1943 Read article
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Polypyrrole-Based Conductive Polymer-Gated Single Electron Transistor with Deep Neural Network Assistance for Biomedical Energy Harvesting and Charge Detection
Abstract: The increasing demand for intelligent biomedical monitoring systems has accelerated research into ultra-low-power sensing technologies capable of operating with high sensitivity and minimal energy consumption. Conductive polymers have attracted considerable attention for biomedical and nanoelectronic applications due to their tunable electrical properties, biocompatibility, and environmental stability. Among them, Polypyrrole (PPy) is a promising functional polymer that can enhance charge transport and electrostatic coupling in nanoscale devices. In this work, a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing
Abstract: The exponential growth of internet of things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 46–62 Read article
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Comparison and Analysis of Facial Emotion Detection Using Various Deep Learning Neural Networks
Abstract: Facial emotion recognition employs Convolutional Neural Networks (CNNs), Residual Networks (ResNet), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNNs) to automatically identify various emotions, including disgust, anger, fear, happiness, sadness, surprise, and neutrality. This study utilizes transfer learning along with data preprocessing techniques such as rotation, flipping, brightness adjustment, and enhancement methods. Traditional machine learning models achieve an accuracy range of 45 to 50%. In contrast, our proposed …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 37–42 Read article
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An Abnormal Expression Detection System (AEDS) Using Deep Learning Algorithms
Abstract: In the last decade, many deep learning algorithms have achieved remarkable success and gained popularity in various computer vision tasks, including object detection, image recognition, and segmentation. This AEDS (Abnormal Expression Detection System)leverages the power of deep learning algorithms to detect abnormal facial expressions in real-time automatically. AEDS proposed two important models; those are Deep CNN and RNN. CNN is responsible for learning discriminative features from facial images and capturing …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 2, 2023 · pp. 72–79 Read article
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Convolutional Neural Network and its Architectures
Abstract: Convolutional neural network (CNN) is a type of artificial neural network (ANN) with multiple layers. From the past decades, it has been considered as a powerful classification technique as it can handle a huge amount of imagery data. It can be applied in the field of image recognition. The name CNN has been derived from the mathematical linear operation known as convolution which is performed between two matrices. CNN has …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 2, 2021 · pp. 6–14 Read article
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A Deep Survey on Techniques Used to Recognize Locust Based on CNN
Abstract: The major threat in agriculture is insect pests and crop disease. Outbreaks and upsurges of insects can cause huge loss to crop production. Locusts are crop devouring pests found in many parts of the world. Recognition of locusts in early stage helps to prevent the spread of locusts by taking appropriate counter-measures and biological control methods. Initially, locusts were classified and recognized manually which is time consuming and requires taxonomic …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 1, 2021 · pp. 1–8 Read article
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Pneumonia Detection Using Deep Learning–Convolutional Neural Network
Abstract: Pneumonia disease is associate in nursing infectious and deadly illness in metabolic process that is caused by microorganism, fungi, or a deadly disease that infects the human respiratory organ air sacs with the load choked with fluid or pus. Chest X-rays area unit the common methodology accustomed diagnose respiratory disorder and it wants a health worker to gauge the results of X-ray. The hard methodology of detection of the respiratory …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 1, 2021 · pp. 9–16 Read article
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Evaluation of Ensemble and Deep Learning Classifiers on CSE-CIC-IDS2018 Dataset for Intelligent NIDS
Abstract: Network Intrusion Detection System (NIDS) plays an active role in preventing cyberattacks by early detection of threats before it really starts affecting targeted information services. Over the years, many intrusion detection system (IDS) have been developed applying signature or rule-based approach to prevent unauthorised access of network or computer devices. However, ever growing landscape of cyberattacks in recent years has motivated present day researchers to design and develop more accurate …
Published in Current Trends in Information Technology · Vol. 13, Issue 1, 2023 · pp. 1–11 Read article
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Numerical Simulation of Deep Convolutional Neural Network Based Flower Classification System
Abstract: There are more than 250,000 recognized floral plant forms in 350 families. Further more the order, the plant checks of structures, the gardening industry, live plantations and scientific flower classification instructions depend on fruitful flower classification, including a content-based image recuperation. A wide range of applications also includes flower portrayals. The manual classification is however tedious and tiresome, particularly when the picture foundation is perplexing, with a huge number of …
Published in Journal of Computer Technology & Applications · Vol. 12, Issue 3, 2021 · pp. 23–31 Read article
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Real-world Pothole Detection Using Image Processing and Deep Learning Convolutional Neural Network Model
Abstract: Potholes are a major problem of concern in many parts of the cities across the country. Road accidents are one of the causes that significantly affect humanity and result in damage to vehicles and road surface. Potholes are dangerous for pedestrians who walk along the road and vehicular traffic on busy roads. Road accidents are caused due to improper maintenance of roads, and it is imperative to attend to such …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 3, 2023 · pp. 95–103 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