NON-INVASIVE NEONATAL GOLDEN HUE DETECTOR
Species Classification and Disease Identification Using Image Processing and Convolutional Neural Networks
A novel meta-heuristic jellyfish Optimize for Detection and Recognition of Text from complex images
Rice Leaf Disease Detection Using Convolutional Neural Network
Comparative Analysis of usage of Machine learning in Image Recognition
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
Jaundice is a common newborn disease that causes yellowing of the skin and eyes due to high bilirubin levels. Although there are a number of underlying disorders that might cause it, it usually stems from the immature liver function of neonates. within the last five years, 60% of babies suffered from jaundice within their first week of life. Due to better screening and management procedures, the percentage of these individuals with severe jaundice that required treatment decreased by around 20% over the previous five years. It usually happens when the baby's liver develops and begins to metabolize bilirubin more effectively. The 'Golden Hue Detector,' a non-invasive device for detecting neonatal jaundice, is developed and validated in this research. Newborns frequently suffer from jaundice, a disease that needs to be diagnosed accurately and quickly to avoid problems like kernicterus. Blood tests are used in traditional techniques of identifying jaundice, but they can be stressful for infants and resource-intensive for medical facilities. These traditional methods frequently need many blood draws, which increases the danger of infection and puts the babies through a great deal of discomfort.
The 'Integrated Plant Disease Detection System (IPDDS)' presents a comprehensive approach to plant health assessment, combining Image Processing and Convolutional Neural Networks (CNN) for species classification and disease identification. In the initial phase, input images are subjected to preprocessing to remove noise and enhance clarity, followed by segmentation using k-means clustering for effective region identification. A CNN classifier then utilizes deep learning techniques to categorize the plants into distinct species such as apple, core, grape, pepper bell, potato, or tomato. Subsequently, the system employs an improved CNN architecture adapt for disease classification, distinguishing various diseases affecting each plant species. For instance, diseases like Black Rot, Scab, and Cedar Rust are identified in apples, while Common Rust, Northern Blight, and Cercospora are detected in corn. This methodology enhances accuracy and reliability in disease detection, enabling timely interventions to mitigate crop losses. Furthermore, the system suggests suitable fertilizers based on disease diagnosis, facilitating targeted disease management strategies. This integrated approach offers a promising solution for effective plant disease detection, contributing to sustainable agriculture and food security.
Recognizing text in images presents significant challenges, particularly in complex backgrounds. This technology is essential for aiding visually impaired individuals and interpreting semantic content. This survey examines various techniques developed in recent years for handling text recognition in complex images. The paper provides an analysis of related works and evaluates the performance of these recognition methods. Although image complexity is not easily defined, it can be described through parameters like background details, noise levels, lighting conditions, textures, and fonts. Additionally, this survey discusses several benchmark datasets used in the reviewed studies. By reviewing these works, we can identify and compare the challenges faced in this area. Background images being complex limits the accuracy achieved. For the accuracy increase, convolutional neural network is employed. In short, the proposed method comprises three parts. At first, the text is from a complex background detected. Next, the text is extracted from the image with Tesseract. Lastly, all the identified words are kept in a text file. Then the audio file is made from the text. The proposed system reads the text from the image with the aim to provide assistance to the visually impaired persons.
Rice is one of the most widely cultivated crops in India, playing a crucial role in the country's agricultural economy. However, rice production is frequently threatened by diseases, such as Bacterial Leaf Blight, Brown Spots, and Sheath Blight, which can significantly reduce yield. This project focuses on developing a deep learning based rice leaf disease detection system using Convolutional Neural Networks (CNN). A dataset containing images of rice leaves, categorized into different disease types and healthy leaves, was used to train the model. By applying advanced image processing and deep learning techniques, the system can accurately identify and classify diseases. A user-friendly web application allows farmers to upload images and receive real-time diagnostic feedback, empowering them to implement timely corrective measures. This system offers a scalable and cost-effective solution for enhancing crop management, supporting food security, and promoting sustainable agriculture in India.
This article provides an overview of the advancements and challenges in image recognition technology that utilizes machine learning. Image recognition involves identifying and categorizing objects in images, with the main goal of accurately classifying these objects. The paper outlines essential processes in image recognition, such as data acquisition, preprocessing, feature extraction , and classifier design, highlighting the importance of feature extraction and selection for improving accuracy . The Comparison of ML and DL techniques and Algorithms used in Image Recognition can be described in this article and Classification of Images by help of machine Learning can be depicted. The aim is to draw which process is most suitable for classifying detected objects. Furthermore, the research discusses the evolution of image recognition, particularly through the combination of ML and DL techniques. By examining the broad applications of ML in this field, the article aims to offer insights that can enhance the development of machine learning applications in image recognition, thereby improving the effectiveness of image processing technologies.
