In biomedical sciences, data mining skills are used to research and provide predictions to aid in the identification and classification of diseases. Controlling the spread of Corona Virus Disease requires screening a ...
详细信息
The deployment of machine literacy (ML) in the electric structure has been innovated by the arrival of the coming-generation power networks known as the smart grid. A detailed analysis related to smart grids has been ...
详细信息
Skin Cancer is one of the most common cancer forms in many countries, it is considered to be one of the dangerous types in the sense that it is lethal and its occurrence over time has been dramatically high. It is one...
详细信息
machinelearning has revolutionized research by extracting complicated patterns from complex data, particularly in healthcare and medical imaging, where accurate diagnosis is critical. The concept of federated learnin...
详细信息
In modern days Glaucoma is become a disease that make impact on matter of visions for human eyesight. This is an irreversible disease where vision difficulties will be outcomes. Now ML oriented learning models based f...
详细信息
ISBN:
(数字)9798331540142
ISBN:
(纸本)9798331540159
In modern days Glaucoma is become a disease that make impact on matter of visions for human eyesight. This is an irreversible disease where vision difficulties will be outcomes. Now ML oriented learning models based framework is created for the proper identifying of glaucoma. So purpose of particular work of making a Computer Aided Diagnostic System may perform faster detection and diagnosis as well as providing quicker treatment to patients. So the framework is created for detection of glaucoma using CNN and ML based approaches using SVM model on augmented datasets. The classes of diseases are classified as glaucoma and non-glaucoma found out for this jobs. Here CNN is required for image Classification of Glaucoma Positive or Glaucoma Negative, cases. So the use of manual imageprocessing methods or human intervention based methods are not needed here . Here medical image segmentation, AI and ML based approach is used for performing medical diagnosis more accurately. So CNN based Deep neural network are giving more than 90% of accuracy of efficiently classifying the diseases. Here data augmentation provides a significant roles of generation of several more new images that are very much useful for this research work for effective disease identification system. Also our created model gives more than 90% of train/validation/test accuracy score for classification of augmented datasets for AI based GAN network of creating higher resolution images.
Crop diseases pose a significant threat to agricultural productivity in Manipur, a state renowned for its rich agricultural heritage and diverse crop cultivation practices. Early detection of these diseases is crucial...
详细信息
ISBN:
(数字)9798331540142
ISBN:
(纸本)9798331540159
Crop diseases pose a significant threat to agricultural productivity in Manipur, a state renowned for its rich agricultural heritage and diverse crop cultivation practices. Early detection of these diseases is crucial for safeguarding the region's agricultural sustainability and ensuring food security. In this paper, we present a comprehensive machinelearning-based system utilizing Convolutional Neural Network (CNN) and the U-NET model for detecting crop diseases specifically tailored to the unique agricultural landscape of Manipur, India. Leveraging advanced techniques such as transfer learning with deep learning pre-trained models, including VGG16 and VGG19, we developed a robust system capable of accurately identifying diseased crops, including locally grown vegetables like "Kobiphun" (cabbage), "Kobilei" (cauliflower), "Aloo" (potato), "Khamen Asinba" (tomato), and "Chak-hao" (black rice). The datasets used in this project are purely self-collected primary data, reflecting extensive effort and dedication in gathering high-quality images for training and model development. Our methodology involves detailed imageprocessing, feature extraction demonstrating the effectiveness of our approach in addressing the specific challenges posed by crop diseases in Manipur's agricultural system. The findings highlight the model's high accuracy (over 92%), precision (exceeding 90%), and recall (approaching 100%), indicating its reliability in real-world applications. Additionally, the system's scalability allows for integration with real-time monitoring tools, enhancing its applicability for local farmers. This research contributes to improved disease management practices, enhances the resilience of Manipur's agriculture sector, and ensures the livelihoods of its farming communities.
Nowadays to detect and classify the objects from the sequence of image frames various machinelearning models are used. The performance of the object recognition model is purely depending on the number of trained imag...
详细信息
Fake news proliferation on social media platforms has become alarming because it poses threats to various aspects of society. Fake news encompasses intentionally falsified information designed to mislead readers and m...
详细信息
The recent growth of open source repositories and deep learning models brought big promises for the next generation of programming tools that can automate or significantly improve the software development process. Yet...
详细信息
For many developing countries like India the role of the agricultural sector is very significant. Chili has a very high economic value. India is ranked 5th in the production and 1st in exporting chili. Chili is one of...
For many developing countries like India the role of the agricultural sector is very significant. Chili has a very high economic value. India is ranked 5th in the production and 1st in exporting chili. Chili is one of the important cash crops of our country but they are vulnerable to various fungal, bacterial, and viral diseases. Among these diseases, the leaves of chili plants are more susceptible to harm, it's one main reason we have considered chili leaves for our study. This paper presents a detailed overview of various chili diseases and the work done in this field till now. Manual detection of diseases is a time consuming and strenuous task for the farmers. In this paper we are introducing various imageprocessing and machinelearning techniques for early and efficient detection of diseases in chili leaves. The diagnosis of chili disease by capturing leaf images is a very efficient and affordable system, especially for helping farmers to keep an eye on large plantations.
暂无评论