This paper describes a natural language-based tool which aims at supporting the Analysis stage of software development in an Object-Oriented framework. This Natural Language Processing technique is to analyze software requirement texts written in English and build an integrated discourse model of the processed text, represented in a Semantic Network. This Semantic Network is then used to automatically construct an UML diagrams such as Class Model representing the object classes mentioned in the text and the relationships anions them & sequence diagram of the dynamic model. Requirement analysis determines the user expectation for the application. We propose a method to extract the diagrams from requirement analysis with strong semantic support. The tool can also convert the user modelling information into the blocks of programming source code; Code generation is made available in Java. The aim is to demonstrate the use of NLP (Natural Language Processing) techniques for the extraction of UML diagrams with code template generation in JAVA by implementing a prototype tool that uses the NLP techniques.
Electronic Medical Records (EMR) carry important information about a patient’s journey. The past decade shows substantial use of Natural Language Processing (NLP)-based Information Retrieval (IR) techniques to extract insights such as symptoms, diseases, and tests from these unstructured records. The state-of-the-art shows that convolutional neural networks (CNN) make a significant contribution to the disease classification task.A significant improvement in precise knowledge mining is possible with precise feature extraction. Feature selection addresses undesirable, unneeded, or irrelevant features. This article proposes a Modified Rider Optimization Algorithm (MROA) to choose important features by selecting optimal weights from a pool of randomly generated weights based on high accuracy and less training time in the CNN algorithm. A modified approach is trained on 114 N2C2 patients’ records to extract symptoms, disease, and tests are performed on them to perform disease classification tasks. The proposed approach is found to be accurate, with 97.77% accuracy in the disease classification and treatment prediction task from EMR.
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