END2END UNSTRUCTURED DATA PROCESSING, CONFIDENTIAL DATA STRUCTURING & STORAGE USING IMAGE PROCESSING, NLP, MACHINE LEARNING, AND BLOCKCHAIN

The expediting magnification of automating the manual jobs into automated is incrementing day by day, as there are approximately 2.5 quintillion bytes of data exchanged over the cyber world per day. With the incrementing need for process automation and immensely colossal unstructured data, there is increasing demand for incorporating automated objectives-specific classifiers for businesses. To make better and improvised automated end-to-end solutions, data structuring utilizing advanced technologies such as ML, Big data processing, data science, etc. will avail in abbreviating the resource consumption extracting better data semantics, handle multiple parallel requests which result in high-end organized automated solutions with efficient data processing. This paper demonstrates an objective-specific classifier will accommodate as a commencement point in automating any process. In this paper confidential data processing is demonstrated on confidential data of students, dataset contains unstructured data from the university library which will then be structured into confidential data and non-confidential data automatically. Image processing is utilized to extract features and ML algorithms are acclimated to train the classifier. This intelligent classifier can further be used along with encryption methodology to protect and store confidential data. The article is organized as an introduction section, literature review, methodology section, result-implementation details, and last section conclusion. The introduction section introduces the importance of artificial intelligence in the field of the education system, literature review section covers the background work carried out on artificial intelligence in the field of education, methodology section covers the proposed method of applying machine learning algorithm to perform the automatic classification of documents in the education system, result-implementation section shows the result analysis from different machine learning algorithms and end the conclusion section provides the summary of overall work.