EFFECTIVE CROSS-PLATFORM MOBILE APP DEVELOPMENT USING PROGRESSIVE WEB APPS, DEEP LEARNING AND NATURAL LANGUAGE PROCESSING

Abstract

The proposed system uses Natural Language Processing (NLP) and Deep Learning (DL) techniques to extract voice data and translate it to text during medical consultations. Iterative model was adopted in the design of the system and the user interfaces was implemented by using NLP techniques, especially speech recognition and natural language understanding. Deep learning algorithm shows a great ability to build clinical decision support systems by extracting various information for medical diagnosis and produce result is few seconds. The result form the system testing shows that the installation size of the Progressive Web App (104 KB) is 42 times smaller than the native Android app (4.37 MB). In terms of render-speeds, the PWA rendered different results. The native app will launch the Android activity after 1408 ms after app icon tap (launch), while the progressive web app launches the application in 230 ms. The advent of cross-platform application development frame-works have made it much easier to create applications for multiple platforms for mobile devices. In spite of reduced learning effort, usually lower costs, and a faster time-to-market cross-platform methods always do not prevail in most cases. Although there are normal exclusions – like graphic-intensive games, which should to be programmed with the native software development kits (SDKS), choice between native apps, cross-platform generated ones, and Web apps can remain delicate. Whereas many diverse efforts have been made with respect to how cross-platform development frameworks ought to work, no technology is deemed unequivocally superior than the others. But a cross-platform mobile app has got an edge over native app development. It also recommends that developers adopt this technology of mobile app development due to its huge gains.