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Publication Type : Conference Paper
Publisher : 2020 6th IEEE Congress on Information Science and Technology (CiSt)
Authors : Sandhya Harikumar, Mannam, V., Mahanta, C., Smitha, M., Zaman, S.
Source : Colloquium in Information Science and Technology, CISTthis link is disabled, 2020, 2020-June, pp. 104–109, 9357237
Keywords : Machine learning algorithms,Image color analysis,Data visualization,Clustering algorithms,Machine learning,Prediction algorithms,Task analysis,data analysis,data reduction,data visualisation,gender issues,geography,learning (artificial intelligence),pattern clustering,statistical analysis.Mapbox,Haryana,Punjab,Rajasthan,Violence Against Women Map,column pivoting algorithm,projected clustering algorithm,VAWMap,data reduction,map-making platform,ML algorithms,North India,hot spot prime areas,QR decomposition,smart map,machine learning,data visualization,interactive map
Campus : Amritapuri
School : Department of Computer Science and Engineering
Department : Computer Science
Year : 2020
Abstract : This project aims to build a smart map, named VAWMap (Violence Against Women Map), that displays the intensity of various crimes against women across the states and districts of India in efforts to inspire action to combat this prevalent violence. The data set to be depicted on the map consists of the number of victims of rape, dowry deaths, kidnapping, cruelty by husbands, insult to modesty, and assault for each district of every state in India through the years between 2001-2013. Depicted states and their respective districts include Rajasthan, Punjab, and Haryana. Two algorithms, QR Decomposition with Column Pivoting and the Projected Clustering Algorithm, are leveraged to reveal prominent crimes and their locations-effectively yielding certain hot spot crime areas of North India. Two novel strategies to depict the intricate statistics obtained from these ML algorithms are proposed using a map-making platform, Mapbox. The result is two maps of India that display the intensity of the most prevalent crimes through a color gradient. The effectiveness, in terms of data reduction and visualization, between the two algorithms are compared and discussed.
Cite this Research Publication : S B Sonu, A. Suyampulingam, "Linear Regression Based Air Quality Data Analysis and Prediction using Python", Madras Section Conference (MASCON) 2021 IEEE, pp. 1-7, 2021.