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203 articles for “Convolutional Neural Network”
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Lung Cancer Detection and Classification Using Deep Learning
Abstract: Lung cancer is a disease that can be effectively treated if detected early. Various technologies, such as magnetic resonance imaging, isotopes, X-rays, and computed tomography scans, are employed for diagnosis. One of the most crucial strategies in combating cancer is early detection, which greatly enhances a patient’s likelihood of survival; this is where artificial intelligence plays a significant role. The approach proposed in this study leverages historical medical data to …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 13, Issue 3, 2024 · pp. 11–17 Read article
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Cardiovascular Illness Detection and Categorization with Innovative Neural Networks
Abstract: Health-related problems are increasingly prevalent in modern-day societies and are significantly shaped by a multitude of factors encountered in everyday life. Among these, cardiovascular diseases have emerged as one of the primary causes of death on a global scale, posing serious challenges to public health systems. In response to this growing concern, the present study proposes a machine learning-based framework that is not only highly effective but also reliable and …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 21–30 Read article
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Advancements in AI-Driven Diagnostics for Dental Health: A Comprehensive Review
Abstract: Dental diseases, also known as oral diseases or dental conditions, encompass a range of health problems affecting the teeth, gums, mouth, and associated structures. These conditions can lead to pain, discomfort, and severe complications if left untreated. Early detection and accurate diagnosis are crucial for effective treatment and prevention of further complications. This comprehensive literature review aims to identify common dental problems such as Tooth Decay (Cavities), Gingivitis, Periodontitis, and …
Published in Current Trends in Signal Processing · Vol. 14, Issue 2, 2024 · pp. 1–7 Read article
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Hybrid Approach for Community Detection Using Deep Learning Techniques
Abstract: Community detection in complex networks is a fundamental problem with applications across diverse domains, ranging from social networks to biological systems and beyond. Traditional methods based on graph theory have been widely used for identifying communities within networks. However, the intricate and evolving nature of modern networks demands more sophisticated approaches. This research work proposes a hybrid approach that combines the strengths of deep learning techniques with traditional community detection …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 18–26 Read article
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Implementation of Human Gesture Recognition Using CNN
Abstract: A gesture popularity system based entirely on convolutional neural networks (CNNs). Preprocessing techniques include segmentation, polygonal approximation, contour construction, morphological filters, and resource characteristic extraction. Various convolutional neural networks are employed for training and testing, with results compared to existing architectures and protocols. All generated measurements and convergence graphs produced at any point during education are examined and contested in order to verify the reliability of the approach offered. Our …
Published in Journal of Control & Instrumentation · Vol. 15, Issue 2, 2024 · pp. 24–37 Read article
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Innovative Eyewear for the Visually Impaired
Abstract: Object detection systems are essential tools for identifying and locating objects within images or videos. When integrated into spectacles or wearable devices, these systems provide users with real-time information about objects present in their surroundings. This functionality serves diverse purposes, such as assisting visually impaired individuals in navigating their environment or offering augmented reality data to workers during tasks. Region-based Convolutional Neural Networks (RCNN) represent a prominent machine learning model …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 1, 2024 · pp. 27–34 Read article
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Automated Plant Disease Detection and Treatment Advisor Using Artificial Intelligence
Abstract: Automated plant disease detection and treatment advisors using artificial intelligence represent a significant advancement in modern agriculture. The identification of plant leaf diseases is essential to maintaining food security and agricultural output. Machine learning models, particularly deep learning algorithms like convolutional neural networks (CNNs), are trained on labeled datasets containing images of healthy and diseased plants. These models learn to classify images into different disease categories with high accuracy. Convolutional …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 1–7 Read article
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Mood Mate: A Solid-State Edge-AI System for Real-Time Facial Emotion Recognition
Abstract: Recent progress in solid-state electronics and embedded vision systems has enabled real-time emotion-aware applications at the edge. This paper presents MoodMate, a solid-state edge-AI framework for real-time facial emotion recognition using camera-based sensing and embedded processing. The proposed system integrates a solid-state image sensor with an AI- driven emotion classification pipeline optimized for low-latency and resource-constrained environments. Intelligent, emotion-aware apps can now be deployed right at the network edge thanks …
Published in International Journal of Solid State Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 24–30 Read article
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Detection of Pneumonia in COVID-19 Patients Using X-ray Images
Abstract: This study explores the use of chest X-ray image analysis and deep learning methods to identify pneumonia in COVID-19 patients. Due to the pandemic, Proper as well as immediate examination of COVID-19 is now essential for patient care and disease control. This study proposes a novel approach that uses convolutional neural networks (CNNs) to automatically predict pneumonia in COVID-19 patients using chest X-ray images. In this study, an X-ray of …
Published in International Journal of Radio Frequency Innovations · Vol. 1, Issue 1, 2023 · pp. 13–23 Read article
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Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges
Abstract: The demand for agricultural is increasing day by day as the population of the world is increasing. So, it becomes necessary for us to increase the production of agricultural products. Traditional ways of agriculture cannot meet such requirements. Nowadays, machine learning based technologies are being used to develop models for agriculture. Machine learning-based applications are very fast and produce high-quality results. It includes recurrent neural networks (RNN), convolution neural networks …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 88–96 Read article
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Identifying and Implementing a Machine Learning Model Suitable for Processing Visually Evoked Potential
Abstract: A Brain-Computer Interface (BCI) is a system that translates brain activity patterns into computer commands, bypassing physical movement. Electroencephalography (EEG) is commonly used to acquire signals in BCI research. Visual evoked potentials (VEPs) are brain responses in the visual cortex to visual stimuli. Recent studies show that exposing individuals to flickering at a consistent frequency generates EEG signals synchronized with the stimulation. Efficient extraction of VEP signals begins with preprocessing …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 1–8 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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Comprehensive Study of Least Squares Estimation in Fast Fading MIMO-OFDM Systems
Abstract: MIMO-OFDM technology is now the foundation for modern wireless communication systems, allowing dramatic improvements in spectral efficiency, power efficiency, and transmission rate. On the other hand, the least accurate channel estimation is still a critical task, especially when they are in fast-fading environments. This study gives a comprehensive survey of LSE-based approaches and their applications on different fast-fading channel models for MIMO-OFDM systems. Techniques like Pilot Assisted Channel Estimation (PACE), …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 1, 2025 · pp. 13–21 Read article
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An Intelligent Neural Networks Approach for Monitoring of Soilless Urban Farms
Abstract: Urban agriculture is increasingly recognized as a sustainable approach to addressing food security challenges in rapidly growing and densely populated cities. Conventional soil-based farming often faces limitations such as space scarcity, excessive water consumption, and environmental degradation. To overcome these challenges, soilless farming techniques such as hydroponics and aeroponics have gained significant attention due to their efficient utilization of space, reduced water requirements, and potential for year-round crop production. However, …
Published in Journal of Water Resource Engineering and Management · Vol. 12, Issue 3, 2025 · pp. 31–37 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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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
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AI-Assisted Defect Detection in Polymer Composite Insulators Using an Optimised Ensemble Deep Learning Framework for Structural Health Monitoring
Abstract: Polymer composite insulators, particularly those made from silicone rubber and epoxy resins, are increasingly adopted in high-voltage transmission systems due to their superior electrical insulation, lightweight design, hydrophobicity, and environmental durability. Despite their advantages, these materials are susceptible to surface degradation, mechanical cracking, and flashover under prolonged exposure to environmental pollutants, thermal stress, and electrical aging. Accurate, real-time condition assessment of these composite insulators is critical for ensuring operational safety, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 253–261 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article
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A Neuromorphic-Inspired, Low-Power VLSI Architecture for Edge AI in IoT Sensor Nodes
Abstract: As the proliferation of Internet of Things (IoT) devices continues to rise, there is an increasing demand for real-time, energy-efficient artificial intelligence (AI) processing directly at the network edge. Traditional edge AI accelerators, often based on deep learning models like convolutional neural networks (CNNs), struggle to meet the ultra-low-power requirements of battery-constrained IoT sensor nodes. In response to this challenge, this study introduces a neuromorphic-inspired, low-power very- large-scale integration (VLSI) …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 2, 2025 · pp. 41–47 Read article
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Deep Learning-Based Thermal Prediction Models for Solid-State Electronic Devices
Abstract: The rapid advancement of solid-state electronic devices in high-performance computing, communication systems, automotive electronics, and renewable energy applications has significantly increased concerns related to thermal management and device reliability. Excessive heat generation in semiconductor devices adversely affects operational efficiency, switching performance, lifespan, and overall system stability. Traditional thermal prediction methods often require complex numerical computations and extensive simulation time, making them less suitable for real-time monitoring and adaptive control applications. …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article