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238 articles for “Deep Convolutional Neural Network”
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Depression Detection Using AI with Chatbot Support
Abstract: Depression is a major global health concern and a significant contributor to suicide rates worldwide. India reports a high number of suicide cases, making the early detection of mental distress and depression essential for timely intervention. This research presents an AI-based system for depression detection that integrates deep learning, natural language processing (NLP), and a chatbot for user support. The system analyzes facial expressions using convolutional neural networks (CNNs) and …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 14, Issue 1, 2025 · pp. 01–08 Read article
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Vegetation Classification Based on Leaf Pattern
Abstract: In this current world, vital species of plants are going to be lost day by day. To command, this phenomenon, steps are first discovering then restoring and retain them. Among them, spotting of plant is giving up challenging. Usually, plants are recognizing from their leaves. In this paper, a deep neural network model is implemented which will extract the hidden pattern form leaf images. A classifier named as Convolutional Neural …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 8, Issue 3, 2021 · pp. 9–16 Read article
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Keras Based Real-time Hand Gesture Recognition
Abstract: Everyone can communicate effectively with hand gestures. Hand gesture recognition, as opposed to the traditional text-based or GUI-based method, can be viewed as a way for computers to better grasp the language of the human body and develop richer human-computer connections. Hand gestures can help to simplify tasks by allowing you to give orders to the computer using simple hand movements. The camera records gestures, the computer interprets them, and …
Published in Journal of Operating Systems Development & Trends · Vol. 9, Issue 1, 2022 · pp. 10–16 Read article
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AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study
Abstract: The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Next-Gen Agriculture: Deep Learning Algorithms for Real-Time Plant Disease Detection via IoT
Abstract: In addition to providing high-quality food, the agriculture industry plays a critical role in supporting expanding people and economies. Plant diseases can have a detrimental effect on biodiversity and result in significant losses in food production. Automated methods for early and precise identification of plant diseases can reduce financial losses and enhance the quality of food produced. Deep learning has significantly improved object detection and picture classification accuracy in recent …
Published in Recent Trends in Sensor Research & Technology · Vol. 11, Issue 1, 2024 · pp. 18–23 Read article
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License Plate Detection using CNN
Abstract: Tag recognition is a picture preparing innovation used to distinguish vehicles by their tags. This advancement is used in various security and traffic applications. Deep learning employs complex mechanisms to extract features from samples This paper proposes a system trained using the MobileNetV2 convolutional neural network (CNN) model to detect the characters and digits from vehicle license plates. Our approach is highly influenced by the recent advancements made in the …
Published in Journal Of Network security · Vol. 9, Issue 2, 2021 · pp. 21–27 Read article
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Disaster Impact Assessment Using Multi-Sensor Satellite Data: An AI-Based Remote Sensing Approach
Abstract: Natural disasters such as floods, earthquakes, and wildfires cause significant damage to human life and infrastructure every year. Rapid and accurate assessment of the affected areas is essential for effective disaster response and recovery planning. Traditional image-based analysis using single-sensor data often fails under adverse conditions such as cloud cover, smoke, or poor lighting. To overcome these limitations, this study proposes a novel framework for disaster impact assessment using multi-sensor …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 10–22 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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AI, Robotics, and the Future of Waste Management: A Systematic Review of Advanced Collection and Sorting Systems
Abstract: The rapid growth of cities and rise in population have made waste management a major concern that calls for innovative and efficient solutions. Conventional waste collecting techniques are dangerous, time-consuming, and frequently ineffective. The development of automated waste management systems powered by cutting-edge technology like robotics, deep learning, artificial intelligence (AI), and the Internet of Things (IoT) is examined in this study. Vision-based systems, convolutional neural networks (CNN) for garbage …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 1, 2025 · pp. 1–6 Read article
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A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery
Abstract: Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
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Multivariant Disease Detection from Different Plant Leaves and Classification
Abstract: Agricultural growth is significant in Indian GDP which is based on yield of crops, quality of the plants and procedure of the plants taken. To maintain good quality of plant, the plant diseases should be identified and then given proper suggestions to farmers for specific fertilizers and pesticides to be used. The use of specific fertilizers or pesticides makes plant more health with good quality so that farmers can get …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 27–35 Read article
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Advances in Deep Learning for Medical Image Analysis in the Era of Precision Medicine
Abstract: Medical imaging is fundamental to modern healthcare but analyzing the high-dimensional data requires advanced techniques. Manual image interpretation is time-consuming, subjective and limited in detecting complex patterns and minute details. Recent breakthroughs in Deep Learning offer transformative advances for unlocking clinically relevant information from medical images. This paper provides a comprehensive 6000+ word review of the current state-of-the-art Deep Learning techniques for medical image analysis including detailed coverage of key …
Published in Research and Reviews : Journal of Computational Biology · Vol. 12, Issue 2, 2023 · pp. 10–23 Read article
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Advancements in AI-Driven Sound Spectrogram Analysis: From Deep Learning to Quantum and Neuromorphic Processing
Abstract: The rapid advancement of artificial intelligence (AI) has significantly reshaped the field of audio signal processing, with sound spectrogram analysis emerging as a central research focus. Spectrograms provide a rich time–frequency representation of audio signals, making them particularly suitable for data-driven learning approaches. This paper presents an in-depth and original review of modern AI-based techniques applied to spectrogram analysis, highlighting their growing impact across critical application areas such as healthcare …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 13, Issue 1, 2026 · pp. 01–06 Read article
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Evolving Perspectives: Innovations in Object Detection and Identification
Abstract: One of the most important developments in computer vision has been the creation of object detection and identification systems, which have allowed robots to perceive and understand visual data similarly to humans. These systems locate each object by drawing a bounding box around it, in addition to detecting and classifying every object in an image or video. This study suggests a novel method for item identification and detection that makes …
Published in Trends in Opto-electro & Optical Communication · Vol. 13, Issue 3, 2023 · pp. 23–27 Read article
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Deep Learning Applications in Bone Fracture Detection for Improved Radiographic Diagnostics
Abstract: Bone fracture detection is a critical aspect of medical diagnostics, traditionally relying on manual interpretation of radiographic images by experienced radiologists. This discipline has undergone a revolution with the introduction of machine learning (ML), which can improve accuracy, shorten diagnosis times, and lessen human error. This study investigates the use of different machine learning methods to enhance and automate the identification of bone fractures in radiography pictures. We utilized a …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 2, 2024 · pp. 17–22 Read article
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Classification and Detection of Brain Tumor using Convolutional Neural Network
Abstract: Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient's standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
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The Convergence of AI and Composites - A Review Anchored in Patent Trends
Abstract: The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 182–198 Read article
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Harnessing Artificial Intelligence for Precision Physics: A Machine Learning Framework for Data Reconstruction in Support of India's Deep-Tech Missions
Abstract: India's emergence as a global leader in deep-tech innovation is driven by ambitious scientific megaprojects, including the Laser Interferometer Gravitational-Wave Observatory (LIGO)-India, the X-ray Polarimeter Satellite (XPoSat), the Aditya-L1 solar observatory, and the National Quantum Mission (NQM). However, the unprecedented scale and complexity of the observational data generated by these missions present severe computational bottlenecks. Traditional analytical frameworks struggle with non-stationary noise transients, diffusion blurring, and the exponential scaling limits …
Published in Research & Reviews : Journal of Physics · Vol. 15, Issue 2, 2026 · pp. 48–55 Read article
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Lip Reading: Transforming Speech to Text
Abstract: Lip reading, the ability to interpret spoken language by observing lip movements, is a valuable skill that can aid in various applications, particularly in enhancing speech recognition systems. This project explores the implementation of a deep learning-based lip-reading model to improve the accuracy and robustness of speech recognition in challenging environments, such as noisy or audio-limited settings. The proposed lip-reading system leverages Convolutional Neural Networks (CNNs) and Recurrent Neural Networks …
Published in Current Trends in Signal Processing · Vol. 14, Issue 1, 2024 · pp. 23–33 Read article
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Dual-Stream Deep Learning Framework for Brain CT Image Classification and Implications for Polymer Composite Neuro Implant Evaluation
Abstract: Early and accurate classification of brain CT images is critical for diagnosing conditions such as aneurysms, tumors, and related lesions. We present a dual-stream image-classification framework that fuses convolutional neural network (CNN) features with handcrafted Histogram of Oriented Gradients (HOG) descriptors to jointly capture global semantics and local textural cues. The pipeline begins with modality unification via pixel-wise averaging to form a fused input, which is then processed in parallel …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 172–179 Read article