Dnyandeo Khemnar – författare

Master's Thesis from the year 2017 in the subject Computer Science - Applied, grade: 10, Rajiv Gandhi University (PATEL COLLEGE OF SCIENCE AND TECHNOLOGY), course: COMPUTER SCIENCE, language: English, abstract: In this paper I collect healthcare data, which consists of all the details of the patients' symptoms, disease etc. After the collection of data, there will be pre-processing on all the details of the patients' data, as we need only filtered data for our analysis. The data will be stored in Hadoop. A user can retrieve the data by symptoms, disease etc. Big Data is a collection of large and complex data. It consists of structured, semi-structured, and unstructured types of data. Data gets generated from various sources and from different fields. In today's era, data is being generated in huge amounts. The whole world is moving towards the digitalization. Social media sites, digital pictures and videos, and many others. All this type of data is known as big data. Data mining is a useful technique for extracting a pattern. This is helpful from large scale data sets. Useful and meaningful data can be extracted from this big data with the help of data mining by processing on that data.

Academic Paper from the year 2023 in the subject Biology - Botany, grade: A, Savitribai Phule Pune University, formerly University of Pune, language: English, abstract: Cotton plant diseases location is a tremendous issue and frequently needs proficient support to identify the infection. This exploration center's on making a profound learning model that distinguishes the sort of sickness that impacted the plant from the pictures of the leaves of the plants. The profound learning is finished with the assistance of Convolutional Brain Organization by performing move learning. This technique accomplished condition of workmanship results for the dataset utilized. The primary objective is to bring down the expert assistance to identify the cotton plant illnesses and make this model open to however many individuals as could reasonably be expected. Fast upgrades in profound learning (DL) procedures have made it conceivable to identify and perceive objects from pictures. DL approaches have as of late entered different horticultural and cultivating applications, subsequent to being effectively utilized in different fields.