No abstract

No abstract

<p>Apple is one of the most popular plants in the Indian-origin Kashmir valley, where it is grown on about half of the horticultural land. Every year, the apples from Kashmir are exported to other areas of the globe, creating a substantial amount of revenue. However, apple trees are prone to diseases like apple scab, alternaria leaf blotch, and apple rot, which devastate apple yields and cause major losses for apple growers. Disease in apple plants mostly originates in the leaves and causes significant losses to apple farmers. Consequently, the prompt detection or prediction of such diseases is essential in a country like India, where half of the population does farming. The early detection of apple plant diseases may enable apple producers to take the necessary precautions immediately to save the fruits from illness. The conventional methods of apple plant disease prediction are time-consuming and laborious, involving lab assistance to diagnose the apple leaves for possible diseases. With the advent of machine learning and deep learning, it is now possible to quickly determine if a plant is infected or not with reliable accuracy. In this article, we introduce D-KAP, a deep learning-based Kashmiri apple plant disease prediction framework capable of detecting the above-described diseases. For feature extraction and prediction, our model employs the advanced deep learning capabilities of Convolutional Neural Networks (CNN). In conclusion, our framework produces state-of-the-art results in identifying apple plant diseases with an accuracy of 92 percent over testing samples. In addition, we also introduce a novel Kashmiri apple plant leaf dataset containing samples of three distinct diseases along with healthy leaves.</p>

Heart disease detection and early prediction is one of the most difficult tasks in the medical field. Almost two people die every minute due to cardio vesicular diseases. According to World Health Organization (WHO), 17.9 million people depart their life every year out of which 4.77 million people are from India alone. About 13% of the world's total population is involved in cardiac disease. Early detection of the disease is crucial for effective treatment that can save millions of lives in the world. Traditional methods of heart disease detection typically involve a combination of medical history, physical examination, and diagnostic tests which are less accurate. With the advancements in machine learning and deep learning techniques, the development of accurate prediction models for heart disease has become possible. Nowadays a large volume of data is being generated in the healthcare sector, which can be leveraged to empower the development of accurate prediction models for heart diseases. Various techniques such as logistic regression, decision trees, random forest, support vector machine, artificial neural networks, and convolutional neural networks have been applied to predict heart diseases. Over the years, advancements in medical technology have led to the development of new diagnostic tools and techniques for detecting heart disease. In this study, a comparative analysis of these techniques is carried out to understand the architectures, parametric characteristics, and datasets involved in heart disease prediction. Our analysis indicates that most heart disease prediction system that have been designed using deep learning algorithms show promising performance.

<p>Apple is one of the most popular plants in the Indian-origin Kashmir valley, where it is grown on about half of the horticultural land. Every year, the apples from Kashmir are exported to other areas of the globe, creating a substantial amount of revenue. However, apple trees are prone to diseases like apple scab, alternaria leaf blotch, and apple rot, which devastate apple yields and cause major losses for apple growers. Disease in apple plants mostly originates in the leaves and causes significant losses to apple farmers. Consequently, the prompt detection or prediction of such diseases is essential in a country like India, where half of the population does farming. The early detection of apple plant diseases may enable apple producers to take the necessary precautions immediately to save the fruits from illness. The conventional methods of apple plant disease prediction are time-consuming and laborious, involving lab assistance to diagnose the apple leaves for possible diseases. With the advent of machine learning and deep learning, it is now possible to quickly determine if a plant is infected or not with reliable accuracy. In this article, we introduce D-KAP, a deep learning-based Kashmiri apple plant disease prediction framework capable of detecting the above-described diseases. For feature extraction and prediction, our model employs the advanced deep learning capabilities of Convolutional Neural Networks (CNN). In conclusion, our framework produces state-of-the-art results in identifying apple plant diseases with an accuracy of 92 percent over testing samples. In addition, we also introduce a novel Kashmiri apple plant leaf dataset containing samples of three distinct diseases along with healthy leaves.</p>