Selecting a business site is a significant decision in managing a commercial organization. It becomes more complex in countries like India, where population density is high, and it is tough to distribute services fairly. With this difficulty, investing in a location without a thorough survey is challenging. This study aims to provide an information map that can help choose the optimal neighborhood for a particular business. The research aims to use data science powers to provide an unsupervised machine-learning approach to offer a recommendation-based solution that suggests the best location for a given use case. The objective of this paper is the in-depth analysis for site-finding that includes clustering regions based on various factors such as geographical locations (like schools, bus and railway stations, and hospitals), sanitation, distance, footfall, and other entities. The idea is to obtain the user's use case query and then create hotspots on the map that show the most suitable locations where the specific use case will benefit the stakeholders the most. We need to identify neighborhoods where there may be an unsatisfied need for Indian restaurants. Additionally, we require places that are uncrowded and have little competition. If the first two requirements are satisfied, we would also like a location close to a prominent city neighborhood. The advantages of each area will then be clearly expressed so that stakeholders can choose the best possible final location.

Computer Science and Engineering

(SoE) School of Engineering

2024 IEEE International Conference on Contemporary Computing and Communications

Location-Based Ideal Site Selection using Clustering

This research explores the intricate relationship between weather-related variables and the transmission dynamics of two pivotal diseases: COVID-19 and dengue. Our primary goal is to advance our understanding of how climate factors influence the occurrence and spread of these diseases through a comparative analysis. In the context of the pressing need for accurate prediction systems, our research aims to contribute to the refinement of public health planning and decision-making strategies. In this work, our findings underscore the superiority of certain models in predicting epidemic outbreaks. Notably, Random Forest consistently demonstrated exceptional performance across all metrics for both COVID-19 and Dengue predictions. The model exhibited superior accuracy, precision, and correlation with actual values.

Asian Conference on Intelligent Information and Database Systems

Predicting Epidemic Outbreak Using Climatic Factors

World University root node

Mechanical Engineering

Tuijin Jishu/Journal of Propulsion Technology

Application of Artificial Intelligence to Measure the Productivity of an Assembly - Line Worker

International Relations and Governance Studies

Electrical Engineering

English

Shiv Nadar University

(SoNS) School of Natural Sciences

Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.