In this day and age of occupied time plans, individuals are confronting numerous issues in following the employees and specialists in colleges, schools, universities, doctor's facilities and so forth, hence it ends up vital that each assignment is expert intelligently, productively and in an auspicious way. To achieve this, easy accessibility of people of interest is a must. To alleviate this problem, we propose a solution called “Personnel Presence Locator System (PPLS)” that is an internetworked amalgam of small systems working independently. Moreover, in this article, we give an overview of the naturally multidisciplinary writing of human-detection, concentrating essentially on the extraction of five regularly required properties: in particular Location, Count, Presence, Track, and Identity. The objective of this work is to uncover the abilities and constraints of existing solutions from different disciplines, to manage the production of new frameworks like PPLS and point toward future research bearings.

Today computers are continuously betrothed in almost all domains and organizations. Thus, databases act as the heart for storing and retrieving information that contain huge digital data. However, in order to interact with such databases, it is necessary to have knowledge about the Structured Query Language (SQL), which is difficult for non-expert users to understand and manipulate. So, there is an emergent need to develop a smart and a user friendly computational technique to interact with databases. The current work proposed a smart technique that can handle such context. The proposed “Smart Data Retrieval Engine for Databases (SDRED)” provided an environment that allows a non-expert user to write and to execute the database queries easily. Furthermore, it retrieved the data stored in databases without a prior knowledge of the SQL. SDRED, which enables the non-expert user to write database queries in natural language (such as English) and to convert them to their SQL query equivalents. The current work presented a detailed design and evaluation for the proposed system by executing different database queries in English. The results established that SDRED successfully converted the non-expert user’s natural language queries into their equivalent SQL queries, thereby providing an easy and user-friendly environment to interact with databases.