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761 articles for “deep learning”
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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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An Expected Cardiovascular Disease Detection Using Deep Learning Techniques
Abstract: Many avoidable deaths globally are caused by CVD, often due to individuals remaining unaware of their risk factors until severe symptoms, such as heart attacks or strokes, appear. This study utilizes retinal images as the dataset to explore the potential of retinal imaging as a non-invasive diagnostic tool for early detection of cardiovascular diseases (CVD). The delay in diagnosis and treatment highlights the need for sophisticated diagnostic instruments that can …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 2, 2024 Read article
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Fingerprint Recognition for Crime Scenes Using Deep Learning
Abstract: Crime-scene fingerprint photos are crucial hints for resolving ongoing cases. Using deep machine learning and convolutional neural networks, we provide a comprehensive crime scene fingerprint identification method in this research (CNN). Precision photography and sophisticated physical and chemical processing techniques are used to collect images from crime scenes, which are then kept as databases. It can be challenging to categorize the photographs taken from the crime scene because they are …
Published in Trends in Opto-electro & Optical Communication · Vol. 12, Issue 2, 2022 · pp. 13–18 Read article
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Robustness of Deepfake Detection Systems Against Adversarial Attacks
Abstract: This paper explores a deep learning system to detect deepfake videos, a common type of fake media. With the use of sophisticated methods such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), our system can reliably discern between authentic and altered videos. It analyzes both the images and the audio in videos to find signs of deepfake manipulation. We process video frames and audio, extract features with CNNs …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
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Handwritten Sanskrit Word Recognition: A Deep Learning Approach Using AlexNet
Abstract: Handwritten Sanskrit word recognition poses significant challenges due to the intricate structure of the script and the considerable variations in handwriting across individuals. To address these challenges, this research introduces a novel methodology employing transfer learning with the AlexNet convolutional neural network. The study utilized two distinct datasets: a specifically curated Sanskrit word image dataset containing 2616 samples, alongside a broader Devanagari character dataset used for validation purposes. The established …
Published in Current Trends in Signal Processing · Vol. 15, Issue 2, 2025 · pp. 33–43 Read article
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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article
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Advancements in Pneumonia X-Ray Image Detection: A Review
Abstract: Pneumonia remains a primary cause of morbidness and mortality worldwide, necessitating the continuous advancement of diagnostic techniques for timely and accurate detection. Pneumonia is common, it is potentially a life-threatening infection for respiration, poses significant challenges to healthcare systems worldwide. Recently, the arrival of deep learning techniques has stirred up the field of medical imaging, offering promising avenues for enhanced pneumonia detection. In this paper, the advancements in pneumonia detection …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 15, Issue 1, 2025 · pp. 1–11 Read article
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Diagnosing Pneumonia from Chest X-rays Using Deep Learning Algorithms Through Convolutional Neural Network, Transfer Learning and Fine Tuning
Abstract: Pneumonia is an inflammatory condition of the lungs that induces air sacs which leads to a contagious infection of lungs. Patients who are afflicted with the virus can be saved from death and the virus can be eradicated from spreading further through effective diagnosis. X-rays of the chest are frequently used to diagnose pneumonia. Detecting pneumonia from a Chest X-ray is typically slow and inaccurate. It is essential to identify …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 8–16 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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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 1–9 Read article
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Fusion of deep learning autoencoders with random forest for wetland classification using Sentinel-2A data: A case study on Sirpur wetland
Abstract: Present study analyses the performance of deep leaning algorithm-autoencoder to reduce data dimension as compared to conventional models. Classification accuracies of Sirpur wetland using Sentinel 2A dataset with different inputs have also been studied. These inputs sets comprise the reconstructed data through compression of original 13 bands into 4 bands using decoder algorithm, first four Principal Components, all spectral bands, and spectral indices. Random Forest classifier (RF) is used to …
Published in Journal of Remote Sensing & GIS · Vol. 17, Issue 1, 2026 · pp. 25–35 Read article
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Deep Learning for Grape Cluster Segmentation
Abstract: Grape cluster identification and its segmentation for the purpose of vineyard total weight prediction tasks indicate the need for more accurate segmentation atomization. The Grape Cluster Segmentation challenge is supplied as an answer the usage of deep neural community fashions including YOLO v3, Mask RCNN, and U-net. In the sense of a modified U-net model for segmenting grapes using training and testing strategies, this contribution contributes to the validation of …
Published in Recent Trends in Programming languages · Vol. 8, Issue 2, 2021 · pp. 23–32 Read article
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Processing of Deepfake Images Using Deep Learning
Abstract: Face identification has been a significant exploration subject in the mid 2000s. Right around twenty years after the fact, this issue is essentially settled and face recognition is accessible as a library in most programming dialects. Indeed, even face-trade innovation is the same old thing and has been around for a couple of years. Such an advancement depends on neural organizations whole computational models that are inexactly roused by the …
Published in Journal of Computer Technology & Applications · Vol. 13, Issue 2, 2022 · pp. 24–29 Read article
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Remote Healthcare Diabetic Retinopathy Detection Using Deep Learning
Abstract: High blood glucose levels are a hallmark of diabetes mellitus (DM), a metabolic disease. This can give rise to a range of complications, with Diabetic Retinopathy (DR) being among them. DR can impair vision and, if not addressed, may lead to a loss of eyesight. Symptoms include aberrant blood vessels, fluid leaks, exudates, haemorrhages, and retinal microaneurysms. With the advancement of technology, medical imaging has become one of the most …
Published in Journal of Open Source Developments · Vol. 10, Issue 2, 2023 · pp. 42–47 Read article
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A Review Of Deep Learning Applications For Speech Processing Improvement
Abstract: Improve the quality of the spoken word is a common goal for many audio and speech signal processing applications. A noisy voice signal's quality and understandability may be improved via speech augmentation. Speech augmentation is critical in a wide range of fields, including hearing aids, ASR, and mobile communication. DNN-based architectures for speech recognition and augmentation have shown to be quite effective in recent years, according to a new study. …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 9, Issue 2, 2022 · pp. 14–19 Read article
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A Study of ROI Based Sign Language to Text Translation in Real Time Using Deep Learning
Abstract: Since sign language is their primary form of communication, it plays a significant role in the lives of hearing and speech disabled people. However, since not everyone is conversant in sign language, it is challenging for the disabled to interact with others daily. Sign language is made up of a variety of hand gestures that may stand in for a wide range of words and sentiments. The purpose of this …
Published in International Journal of Optical Innovations & Research · Vol. 1, Issue 1, 2023 · pp. 8–14 Read article
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Revolutionizing Agriculture: Botani Scan’s Deep Learning for Plant Disease Diagnosis
Abstract: Crop disease detection is of key importance because of its role in food safety but infrastructural issues still hamper diagnosis in most regions worldwide. Accurate plant disease identification is essential to secure food, predicting yield decline and managing epidemic outbursts. The advent of digital cameras along with the progress of computer vision technology brings to light the mounting demands for the development of automated disease detection methods in precision agriculture, …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 1, 2024 Read article
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Advanced Helmet Recognition System with Integrated Number Plate Detection for Enhanced Traffic Monitoring Using Deep Learning
Abstract: This study focuses on the crucial problem of non-adherence to traffic regulations, particularly with the compulsory use of helmets by motorcyclists. Motorcycle accidents have a greater mortality rate compared to other types of accidents, indicating a need for a more effective enforcement strategy. Current procedures depend on traditional techniques where traffic officers manually observe traffic rule infractions through patrols and monitoring CCTVs, requiring substantial labor and time resources. The inherent …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 1, 2024 · pp. 9–18 Read article
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Revolutionizing Motorcycle Safety: A Deep Learning Approach for Helmet and Triple Riding Detection using Computer Vision Technology and Machine Learning Model
Abstract: Introducing a revolutionary paradigm in road safety, our project unveils the Intelligent Traffic Surveillance System (ITSS), a groundbreaking initiative poised to transform urban traffic management. In an era where road safety is paramount, ITSS emerges as a beacon of innovation, harnessing the prowess of computer vision and machine learning to tackle two of the most pressing concerns plaguing our roads: helmet non-compliance and triple riding among motorcyclists. At its core, …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 2, Issue 1, 2024 · pp. 28–36 Read article
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Movie Popularity Content-based Database System Using Cloud Computing Based on Deep Learning and AI
Abstract: The digital age has brought rapid growth in both the production and consumption of movies within the entertainment industry. This surge in movie availability has created a need for effective methods to assist users in discovering films that align with their preferences. This paper introduces the concept of a “Movie Popularity Content-Based Database System,” designed to enhance the movie recommendation process by combining content-based filtering with popularity trends. In summary, …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 1, 2025 · pp. 7–12 Read article