Search
61 articles for “CNN”
-
Performance Comparison of Handwritten Digit Recognition Using Machine Learning Techniques
Abstract: Handwritten pattern, character and digit recognition has always been the most demanding and interesting fields of image processing. The objective of this work is representing and demonstrating the algorithms which are used to recognize handwritten digits. Recognition of handwritten digits has always a bit complex, but it is mostly used in various day-to-day applications. In the task of recognition, the digits are not correctly or accurately scripted or written as …
Published in International Journal of Digital Communication and Analog Signals · Vol. 7, Issue 1, 2021 · pp. 9–18 Read article
-
Deep Learning Approach Based Brain Tumor Detection
Abstract: The brain tumor is one of the most deadly disease which is of great concern and hence to detect and extract the effected tumor area in the brain from the MRI has lot of research potential. It is also a very difficult task to perform by the experts. The perfection of the vivisection depends on how much experienced the radiologist or the clinical expert is. So to overcome the wastage …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 8, Issue 1, 2022 · pp. 57–63 Read article
-
Knowing Captchas Using Deep Learning
Abstract: Websites can use CAPTCHA (Completely Automated Public Turing Test to Differentiate Computers from Humans) to distinguish between humans and machines and prevent automated attacks by programmers and malicious programmers. Studies on CAPTCHA recognition have the potential to uncover security flaws, advance CAPTCHA technology, and even advance handwriting and license plate recognition. However, the whole objective of the CAPTCHAs can be bypassed by utilizing the principles of deep learning and computer …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 8, Issue 2, 2022 · pp. 30–40 Read article
-
Diabetic Retinopathy Detection from Retinal Images Using Convolutional Neural Network
Abstract: Diabetic retinopathy (DR) is a popular problem of diabetes and the major reasons of loss of sight in the active inhabitants. Many of the problems of DR can be avoided by blood glucose manage and regular remedy. Since the kinds and the difficulties of DR, it is actually hard for DR detection in the time-consuming regular analysis. This document is to effort towards discovering an automated method to sort out …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 1, 2023 · pp. 8–17 Read article
-
AI-Based Noise Reduction Techniques for Digital and Analog Communication Systems
Abstract: Noise is one of the major challenges in modern communication systems because it significantly affects signal quality, transmission efficiency, and overall system reliability. Both digital and analog communication systems are influenced by different types of noise such as thermal noise, impulse noise, quantization noise, atmospheric noise, and channel interference. Conventional noise reduction techniques including filtering, adaptive equalization, and statistical signal processing have been extensively used to improve communication performance. However, …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 1, 2026 · pp. 40–48 Read article
-
An Overview on Harmonic Intelligence: BridgingMicrowave Sensing and AI-Driven IoT for Autonomous Decision-Making
Abstract: As the Internet of Things (IoT) ecosystem expands into complex industrial and urban environments, the demand for high-fidelity data acquisition and near-instantaneous edge processing has reached a critical juncture. Traditional optical and infrared sensors often falter under adverse conditions, such as dense smoke, occlusions, or low-light environments. This paper explores the transformative potential of microwave signals—leveraging their unique ability to penetrate non-metallic materials and operate independently of ambient lighting—as the …
Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 1, 2026 · pp. 1–8 Read article
-
A Review on Design and Analysis Model for Detectionand Classification of Lung Diseases Using Medical Images
Abstract: A key component of the human respiratory system, the lung regulates blood oxygen levels through gasexchange. Diseases linked to the lungs are becoming more common as pollution levels rise. In order topreserve human life, it is crucial to identify lung disorders early. In order to forecast lung disease, clinicalprofessionals analyze medical imaging, such as CT or X-rays. Clinicians identify lung disease by applyingtheir medical expertise and knowledge. Because expert knowledge …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 2, 2025 Read article
-
Enhancing Glaucoma Detection with Convolutional Neural Networks: A Data-Driven Approach
Abstract: One of the main causes of irreversible blindness is glaucoma, which is characterized by certain alterations in the optic nerve that over time can impair vision. Manual evaluations, while invaluable, are challenged by inconsistencies, subjectivity, and the extensive time required. The study employed a dataset comprising retinal images annotated by experts. By using Convolutional Neural Networks (CNNs) to study and recognize important patterns in eye images, it was possible to …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 11, Issue 1, 2025 · pp. 1–6 Read article
-
Deep Learning for Smarter Farming: Innovations in Plant Disease Identification
Abstract: Agriculture is the backbone of global food security, yet plant diseases significantly impact crop yields and farmers' livelihoods. Traditional disease detection methods are often time-consuming and require expert intervention, making them less feasible for large-scale farming. Recent advancements in deep learning have revolutionized plant disease identification by offering automated, accurate, and efficient solutions. This article explores innovative deep learning techniques, including convolutional neural networks (CNNs) and transformer-based models, that enhance …
Published in International Journal of Microwave Engineering and Technology · Vol. 11, Issue 1, 2025 · pp. 27–32 Read article
-
Advanced Diabetic Retinopathy Detection Using Deep Learning Classifiers
Abstract: Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 1, 2025 · pp. 26–33 Read article
-
Pregcare Record Analysis Based on BERT and Anticipatory Computing: A Review
Abstract: Development of a sophisticated Medical Pregnancy Care chatbot leveraging state- of-the-art Natural Language Processing (NLP) techniques, specifically the BERT framework. The chatbot offers a seamless user experience with two key functionalities: patient registration and comprehensive medical report generation. New users are prompted to register, providing essential personal details, and subsequently prompted to fill detailed medical reports, ranging from the 1st to the 9th month of pregnancy. The system employs anticipatory …
Published in International Journal of Analog Integrated Circuits · Vol. 10, Issue 1, 2024 · pp. 27–35 Read article
-
Blind Assistance System using Tensorflow
Abstract: Because of item recognition's cozy relationship with video investigation and picture understanding, it has drawn in much exploration consideration lately. Customary item discovery techniques are based on carefully assembled highlights and shallow teachable designs. Their exhibition effectively deteriorates by building complex outfits which join numerous low-level picture highlights with undeniable level setting from object finders and scene classifiers. With the fast improvement in profound learning, all the more amazing assets, …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 7, Issue 1, 2021 · pp. 6–12 Read article
-
Deep Learning to Detect Pneumonia from Chest X-Ray Image
Abstract: Pneumonia is an acute pulmonary infection that is caused by viruses or by bacteria and it causes inflammation in the lungs. In 2019, a total of 2.5 million people died because of pneumonia, out of which, one third were younger than the age of 5. This infection was diagnosed by taking a chest Xray. The prediction of diseases is mainly based on human knowledge. In this, the probability of human …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 8, Issue 1, 2022 · pp. 8–14 Read article
-
Criminal Face Identification Using Deep Learning and Machine Learning
Abstract: Criminal face identification is a crucial task for law enforcement agencies to prevent and solve crimes. The traditional methods for identifying criminals using human expertise and eyewitness testimonies have several limitations, such as subjectivity, inaccuracy, and unreliability. Machine learning and deep learning techniques have yielded promising gains in facial recognition and identification applications in recent years. In this study, we propose a criminal face identification system that combines machine learning …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 9, Issue 1, 2023 · pp. 28–34 Read article
-
Anomaly Detection Network for Surveillance Video
Abstract: Anomaly Activity is the prediction of a suspicious activity from a picture or video. This project would include using neural networks to detect suspicious human activity from real-time CCTV video. Human Anomaly Activity is a central issue in computer vision that has been researched for over 15 years. It is significant due to the large number of applications that can benefit from Activity detection. Human pose estimation, for example, is …
Published in International Journal of Software Computing and Testing · Vol. 7, Issue 2, 2021 · pp. 13–20 Read article
-
Fake News and Hate Speech Detection Using Deep Learning
Abstract: Due to the potential for such content to hurt both people and society as a whole, the issue of identifying fake news and hate speech on internet platforms has grown in importance in recent years. This problem has shown great potential for resolution through deep learning, a specific area of machine learning. In this research work, we examine recent studies on the application of deep learning methods for the identification …
Published in International Journal of Software Computing and Testing · Vol. 9, Issue 1, 2023 · pp. 15–23 Read article
-
Semantic Segmentation for Object Detection in Videos using Mask RCNN
Abstract: The segmentation of object instances is an essential step in real-time video detection. Object detection is the problem of detecting the information of all types of items in an image by designating their location with a rectangular box. Using its remarkable feature-learning capabilities, deep learning promotes object detection research. Numerous researchers have utilised various machine learning algorithms for object detection and have focused on enhancing the accuracy of feature extraction. …
Published in International Journal of Digital Communication and Analog Signals · Vol. 8, Issue 2, 2022 · pp. 7–15 Read article
-
Object Detection using Machine Learning: A Survey
Abstract: ABSTRACTComputer vision techniques have become increasingly popular in the modern era. One of the applications of computer vision is in the detection of object in an image or video. In object detection a target object is detected, localized and labelled based on the type of object. Real time detection of objects in harsh environments, accuracy and speed are some of the major challenges in computer vision technology. A variety of …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 5, Issue 2, 2019 · pp. 7–11 Read article
-
Blood Cell Subtype Classification Using Deep Learning
Abstract: A vital component of human bodies is blood. Despite making up only 1% of the human body, blood cells have a greater impact on disease and infection detection. These blood cell subtype classifications also aid in ensuring that patients receive the quickest-acting medical treatment and recover swiftly. To get the findings, many machine learning techniques are used, including the Feature Extraction methods (K* classifier and Decision table) and the Image …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 1, 2023 · pp. 37–45 Read article
-
Analyzing Crowds with Deep Learning: A Review of Methods and Challenges
Abstract: With the growing population, the need for an effective crowd management tool emerged. It is important to monitor and analyze unusual crowd movements and behavior for many applications such as peaceful events, protests, gatherings, or traffic management. Current systems, such as video surveillance, are a useful technique for crowd monitoring, but they require human visual capabilities to monitor the crowd. This work is a paper on Crowd Analysis using convolution …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 2, 2023 · pp. 30–42 Read article