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218 articles for “convolutional”
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Plants Disease Detection Using TensorFlow and OpenCV
Abstract: Growing healthy and productive crops is crucial in the global battle for food security. To minimize crop losses and apply timely control measures, early and precise diagnosis of plant diseases is essential. Conventional illness detection techniques are subjective, labor-intensive, and complicated; they frequently rely on eye inspection. The TensorFlow and OpenCV libraries are used in this study to explore the use of Convolutional Neural Networks (CNNs) for plant disease discovery. …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 1, 2024 · pp. 31–38 Read article
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Innovative CNN Strategies for Superior Handwritten Digit Recognition
Abstract: Handwritten digit recognition is a fundamental problem in the field of computer vision and machine learning with numerous applications, such as postal code recognition, bank check processing, and digitizing historical documents. Convolutional Neural Networks have demonstrated remarkable success in various image recognition tasks, making them a popular choice for digit recognition. In this study, we present an enhanced approach to handwritten digit recognition using CNNs. Handwritten digit recognition plays a …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 2, Issue 1, 2024 · pp. 27–34 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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Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network
Abstract: The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 3, 2025 Read article
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An Efficient CNN Model for Automated Cotton Leaf
Abstract: Timely and accurate identification of cotton leaf diseases are essential for maintaining healthy crop production and minimizing agricultural losses. Early detection allows farmers to take preventive or corrective measures, reducing the risk of disease spread and improving overall yield. In this study, we propose a Convolutional Neural Network (CNN) based model for the automated classification of cotton leaf diseases using image-based detection techniques. The model is trained on a diverse …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 14, Issue 3, 2025 · pp. 01–10 Read article
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Animal Species Prediction Using Deep Learning
Abstract: In the face of escalating biodiversity loss, effective monitoring of animal species is critical for conservation efforts. This study presents a deep learning approach for species detection and a multimodal feature identification technique for animals vulnerable to poaching. The suggested prediction system recognizes objects automatically by the application of deep learning techniques to detect objects and then recognize them by using computer vision techniques, and it is triggered when an …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 14–22 Read article
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ASL Mobile Translator with CNN Algorithm
Abstract: There are around 63 million people in India with speech and hearing disabilities, and the number goes all the way up to 300 million across the world. All of them face issues in their day-to-day life as they can only have conversations with gestures. American Sign Language (ASL) mobile translator with convolutional neural network (CNN) algorithm is an easy-to-use mobile application, which uses complex images and video recognizing models built …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 2, 2023 · pp. 31–38 Read article
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Hand Gesture Recognition Systems: A Review of Vision-based and Sensor-based Approaches
Abstract: With many real-world uses, such as sign language translation and human-computer interaction, hand gesture detection is a crucial area of study in the science of computer vision. In this study, we propose a Convolutional Neural Network (CNN) model that uses real-time camera images to recognise hand gestures. A collection of hand motion photographs spanning the English alphabet (A-Z) was gathered, and the images were pre-processed to exclude any backdrop and …
Published in International Journal of Optical Innovations & Research · Vol. 1, Issue 1, 2023 · pp. 15–20 Read article
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An Automation Detection for Sign Language Using AI
Abstract: Sign language recognition has attracted considerable interest because of its ability to facilitate communication between the deaf community and the public, thereby bridging communication divides. Traditional approaches to sign language recognition often face challenges in accurately interpreting the complex and nuanced gestures inherent in sign languages. However, recent advancements in deep learning techniques have shown promising results in improving the accuracy and robustness of sign language recognition systems. This study …
Published in Recent Trends in Programming languages · Vol. 11, Issue 1, 2024 · pp. 1–14 Read article
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Classifying Abnormalities in Heartbeat Sound
Abstract: Heartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure in their early stages. In the proposed method, various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (mel-frequency cepstral coefficients). Then, a deep learning approach is applied in which the MFCC audio signals are sent to the CNN (convolutional neural …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 1, 2024 · pp. 24–31 Read article
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Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models
Abstract: Artificial intelligence markup language (AIML) project represents a pioneering endeavor in the realm of media player control through hand gesture recognition, merging advanced technologies like convolutional neural networks (CNN) and recurrent neural networks (RNN). By harnessing the image analysis capabilities of CNN, our system ensures accurate, real-time detection, and interpretation of intricate hand gestures, enabling users to interact with their media content naturally and seamlessly. What sets our project apart …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 29–34 Read article
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Calorie Measurement and Food Recognition Using Machine Learning
Abstract: Nowadays, all over the world most people are suffering from different types of diseases or obesity. This is because of bad food habits or eating food without knowing the calorie and other sources from the foods. Precise techniques for gauging food and energy consumption play a vital role in addressing obesity. Offering users or patients accessible and smart solutions to assess their food intake and gather dietary information constitute valuable …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 1–9 Read article
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Face Emotion Recognition to Detect Depression
Abstract: In the current competitive world, one of the most familiar and grave mental illness we encounter in humans is Depression also called as major depression or major depressive disorder. It makes you feel depressed and disinterested all the time, which has a bad impact on your thoughts and behaviour. Thus affecting not only the victim but also people associated with them, such as family, friends and society. If not treated …
Published in Current Trends in Signal Processing · Vol. 14, Issue 1, 2024 · pp. 1–14 Read article
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Machine Learning Approaches in Breast Cancer Diagnosis: Current Trends and Future Perspectives
Abstract: Since cancer is still one of the world's top causes of death, precise and effective detection techniques must be developed. Machine learning (ML) approaches have shown promise in recent years for enhancing cancer prognosis and detection. This paper presents a comprehensive review of the application of ML in cancer detection, focusing on various modalities including medical imaging, genomic data, and clinical records. We highlight the challenges associated with traditional cancer …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 1, 2024 · pp. 14–20 Read article
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Innovations in Forensic Imaging: Leveraging Deep Learning for Authenticity Verification
Abstract: The advent of digital media has necessitated advancements in forensic imaging, especially for the detection and verification of image authenticity. In this context, digital image forensics plays a critical role in identifying manipulated or counterfeit images. This paper presents a new method that uses deep learning techniques to enhance image forgery detection. The approach utilizes a convolutional neural network (CNN) to automatically learn and recognize the intricate features present in …
Published in Journal of Advances in Shell Programming · Vol. 11, Issue 2, 2024 · pp. 28–33 Read article
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Skin Cancer Detection System Based on Machine Learning for Recognition of Cancerous Images
Abstract: Skin cancer ranks among the most prevalent types of cancer globally and poses significant risks when left untreated. Skin cancer arises when abnormal cells proliferate uncontrollably in the skin. This uncontrolled growth can be triggered by genetic mutations, exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds, or various other factors. In this, the early detection of cancer plays a crucial role in treatment and …
Published in Research and Reviews: A Journal of Medicine · Vol. 14, Issue 2, 2024 · pp. 1–8 Read article
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Detection and Classification of Brain Tumor from MRI And CT Images using Harmony Search Optimization and Deep Learning
Abstract: Primary brain tumor detection and classification are critical factors in ensuring effective treatment and, ultimately, improving patient well-being. This paper describes a novel method for detecting and classifying brain tumors with the help of magnetic resonance imaging (MRI) and computed tomography (CT) images. The suggested method combines harmony search optimization (HSO) and Convolution Neural Networks (CNN) based on deep learning techniques, yielding an impressive accuracy rate of 99.13% for both …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 31–49 Read article
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Glaucoma Detection Using CNN
Abstract: The word “glaucoma” refers to both the progressive loss of retinal cells within optic nerve, and the gradual loss of vision caused by optic neuropathy. A condition that affects eye vision is called glaucoma. This condition is thought to be permanent and causes visual impairment. There are no early warning signs of this glaucoma in them. The effect is so subtle that we could not even observe that your vision …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 2, Issue 1, 2024 · pp. 7–15 Read article
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Integrating Deep Learning and Computer Vision for Recognizing American Sign Language
Abstract: The only way the hearing-impaired community can exchange ideas is by utilizing non-verbal communication. The main challenge, however, is that the non-impaired community, which may not comprehend non-verbal communication, would struggle to communicate effectively with this group, and vice versa. The project is purposely devised to admit unwilling and dumb societies to transport ideas and connect with the organization. It aims to bridge the gap between the hearing- and speech-impaired …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 10–17 Read article
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Recipe-Fusion: Multimodal Food Recipe Recommendation System
Abstract: The food recipe recommendation system using data science is a software solution designed to help users discover new and delicious food options based on their food history and other relevant data. This system recommends various recipes based on the input given by the user and it helps to filter out the recipes on course type, diet type, and nature of the food (including non-veg, and veg) using a recommendation technique. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 82–91 Read article