Savita Kumari Sheoran
@igu.ac.in
Professor, Department of Computer Science & Engineering, Faculty of Engineering and Technology
Indira Gandhi University Meerpur, Reewari
EDUCATION
MCA, Ph.D.
RESEARCH INTERESTS
Computer Science & Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Romika Yadav and Savita Kumari Sheoran
IEEE
Crimes are serious threat to society. The recent economic developments and globalizations have bloated it as serious global concern associated with demographic factors due to which crime site selection and pattern identification have became a central concern in crime prediction. During spatio-temporal crime prediction Linear Model and Generalized Linear Model are used for quantitative improvement in prediction. Both of these models lack certainty in crime prediction. In this paper we have enhanced the Generalized Linear Model for Crime Site Selection and analyze it for crime events using Modified ARIMA (Auto Regressive Integrated Moving Average) with big data technologies. Such enhancement is support similar crime trends among various crime locations for criminal site selection. The simulation results show that out model presents a more significant insight into the scope and complexion of crime prediction and improves certainty in crime prediction.
Romika Yadav and Savita Kumari Sheoran
IEEE
Crime is undesired anti-social behavior and poses serious threat to society. The civilized societies make everything possible to reduce crime within its regime of influence. Alarming the crime prone areas in advance is one of the best strategies for crime to be ceased to happen. The recent socio-economic developments and proliferation of internet technologies have turned the crime into a global phenomenon. In such scenario the crime data to be dealt is huge in volume, diverse in variety and highly location dependent. Hence the contemporary crime data set is highly spatio-temporal in nature where the traditional system of criminal records has failed to maintain the desired level of intelligence and make a substantial prediction. A blend of ‘Big data’ tools for data management and Generalized Linear Regression for statistical analysis is used to draw a useable inference from such time series data set. Such enhancement is supportive to detect similar crime trends among various crime locations for criminal site selection. Consequently ARIMA (Auto Regressive Integrated Moving Average) model affords to minimize the error generated in the predictive model. This research paper aims to locate the offender site in advance with more accuracy. We have explored the Auto Regression Techniques to accurately predict the crime with minimum error for such time series data by identifying the relationship among crimes attributes. The experimental result obtained using "R" tool show that our formulation work well for all parameters and improves certainty in prediction.
Jeebananda Panda, Indu Kumari, Nitish Goel, and Savita Kumari
IEEE
Digital watermarking is a technique to employ copyright protection and ensure the authenticity of the owner using a proof of ownership embedded in a multimedia file. The objective of this paper is to present a novel digital video watermarking scheme using dual watermark. The binary watermark image is distributed over audio samples in which first four samples of each frame are watermarked with 4 bits of the image using multiple bit plane scheme. For the gray scale watermark, the FFT is taken and the samples are embedded in the FFT samples of video frames using Energy Efficient scheme. The watermarked video is subjected to different attacks and the efficiency of the technique is measured using Correlation Factor and PSNR. The algorithm presented is robust, secure and is energy efficient with decreased payload on the host signal.
Seema Verma, Rakhee Kulshrestha, and Savita Kumari
IGI Global
The data broadcast policies have been developed for single channel and multi channel with various scheduling and indexing techniques. For the data management policies which consider the different broadcast cycles for different broadcast operators, it can be said that traditional types of data management policies are known previously, and the policies of Central Server (CS) and Unified Index Hub (UIH), which consider single broadcast cycle for all operators, are recent. This chapter presents both strategies very simply for better understanding, discusses the work done in the past and present on data broadcast management, along with suggestions for the future possibilities to explore the field.