Crop and plant Diseases are the common problems in the food production fields. This is necessary for the improvement of the food production in agriculture and for fulfills the need of the society to solve these problems. In India most of the part of the country based on the production of food as a tradition. To solve these problems some advanced image processing, machine learning, computer vision etc. advancements included. This survey research on the identification of all that kind of technologies and the existing work also has done using them. How many kinds of models are proposed and what amount of success they have achieved by utilizing them. Image processing techniques provides the automatic disease detection technique to detect and identify the diseases in plants. Deep learning techniques are very good at prediction of the growth of plan and possibility of having disease within them. A comparison study also performed of several machine and deep learning techniques based on their accuracy.
Patients in hospitals have faced complications due to measurement errors, missing data, privacy issues etc. in electronic medical records. However, these medical records from heterogeneous sources have both structured and unstructured data. In particular, unstructured clinical data is valuable source of information including patient’s records of pathology data, radiology findings, medication order etc. However, to scrutinize, construe and presentation of this unstructured and high dimensional data is one of the significant modeling challenge that clinical support system has faced from many years before. Therefore, there is a need of some standard technique to locate both subjective and objective guesstimates of patient’s condition. Our endowments in this paper are twofold. First, present a multi-view learning technique, i.e. Collective Matrix Factorization to combine the extracted features from multiple views and gives a low dimensional representation of combined clinical data. Second, proposed a Genetic-K-means based clustering algorithm based on Collective Matrix Factorization for heterogeneous clinical records. It has been observed by the experiments that proposed method gives more accurate clustering results than existing method. Keywords: Clinical notes; Collective Matrix Factorization; Genetic; heterogeneous data; K-means; Multi-view learning.
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