RETRACTED ARTICLE: Machine learning technique to evaluate on adversity and effects caused by daily travellers in urban and metro cities

Abstract

In today’s competitive world of technology and commerce. Travelling around the cities and across the Globe is mandatory requirement. In Indian metropolitan cities, an individual spends an average of 4.5 h a day travelling towards workspace. Thus time to travel (ToT) reaches 6–8 h on various reasons such as traffic, poor infrastructure and unorganized cities. In this article, a systematic analysis is computed and designed to study various adversities effects caused by regular travelling by an individual for a given bandwidth of time in order to plan for the medical care. Typically, a multi-board survey is conducted over Indian metropolitan cities. These studies are future collected and calibrated over realignment of datasets to generate a standard dataset for processing and henceforth a platform over cloud-serve is established for processing. Using trivial user data, attributes are extracted from query and independent linkages for regular database is established. The proposed system uses K Nearest Neighbor (KNN) approach to expand the queries dependencies and relationship ratio. Future, a two tire neural network is designed to establish a decision making with KNN resultant inputs from the experimental setup. The validated results can be expanded on user-input time bandwidth, visualization of corresponding impact of travel and adversity effects with respect to diseases and symptom causes. The technique also discusses the availability if medical care in nearest location and the various facilities/services provided by such identified hospitals using resource optimization and time minimization approach to provide maximum services on the regular travel route.