Amit Wadhwa
@glbitm.org
Associate Professor
G L Bajaj Institute of Technology and Management, Greater Noida
EDUCATION
PhD in Computer Science and Engineering
M.Tech in Computer Science and Engineeirng
B.Tech in Computer Engineering
C-DAC in Advanced Computing (DAC Course)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Engineering, Computer Science Applications, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Amit Wadhwa and Neerja Arora
IEEE
With the advancement of technologies like cloud computing and Internet of things, there are enormous areas in which these can be utilized. One such domain is Smart City concept utilizing or based on the architecture of Internet of everything. In a smart city the distinguish concept is the utilization of internet of everything architecture to effectively manage the services provided to users. Moreover, integration of internet of everything with cloud computing architecture would provide a better ecosystem in the area of smart cities. Providing services to smart city users could pose certain challenges and security issues with the integration of cloud computing. As per previous studied literature there is scope for improving access and authentication-based security of services imparted to smart city users. In this work, initially the consolidation of internet of everything (IoE) and cloud computing in smart cities is put forward. Further, this work focusses upon issues and challenges encountered in leveraging IoE in smart city environments with analysis of existing techniques for authentication and access control of cloud-based services and later provides a proposed system focusing upon imparting access control and authentication-oriented security for services delivered over a cloud and IoE followed by its analysis.
Timothy Mayer, Ate Poortinga, Biplov Bhandari, Andrea P. Nicolau, Kel Markert, Nyein Soe Thwal, Amanda Markert, Arjen Haag, John Kilbride, Farrukh Chishtie,et al.
Elsevier BV
Manu Panwar, Amit Wadhwa, and Sanjeev Pippal
IEEE
Digital data is generated on a vast scale nowadays and has been for the past two decades. This phenomenon implies shift in how data is managed, and conclusions drawn from it. Furthermore, artificial intelligence approaches and procedures for new ideas of analyzing Big Data, Sentiment Analysis (SA), also known as Opinion Mining (OM), has been a hot topic in recent, because of its ability to extract value from data, it has been around for a long time. It is, however, a topic that has gotten more attention in the fields of engineering and linguistics. As a result, the goal of this research is to provide insights into the field of exploratory data analysis, as well as an orientation toward the study of a recommendation system employing big data in the context of an e-commerce system, through the use of machine learning. probable machine learning based model. Initially the research contribution briefly summarizes discussion of the strategies and processes currently used in Sentiment Analysis and further we work upon the analysis of a recommendation system with e- commerce data using LSTM Model.
Manu Panwar, Amit Wadhwa, and Sanjeev Pippal
IEEE
Its not news that people have been increasingly relying on digital technologies in recent years. As a result of this consumption, humans and machines are producing more data. On the other hand, these tools imply that organizations are complex. Furthermore, as the volume of data generated by users grows as a result of improved internet connections around the world, the issues of handling that data grow as well. As a result, techniques like machine learning (ML) can assist firms in managing and utilizing data supplied by users. Further, the focus of this work is to give insights in the field of exploratory data analysis to e-commerce system by applying probable machine learning based model.
Lisa Hjelm, Astrid Mathiassen, and Amit Wadhwa
SAGE Publications
Background: Poverty and food insecurity are intrinsically linked as poor households often lack the resources required to access sufficient nutritious food to live an active and healthy life. Consumption and expenditure surveys are typically used to identify poor versus nonpoor households but are detailed and costly. Measures of wealth based on asset ownership and housing characteristics can be generated from lighter, less costly surveys. Objective: To examine whether indices based on asset ownership and housing characteristics (stock) complement household consumption (flow) when used to analyze inequalities in food security outcomes. Methods: Comprehensive data from Nepal, Malawi, Tanzania, Uganda, and Madagascar are used to examine correlations and overlaps in classification between indices of household wealth and consumption per capita. Inequality in food security indicators representing quantity, quality, and vulnerability is examined across wealth and consumption per capita quintiles. Results: Wealth indices are correlated with consumption per capita, with coefficients between 0.5 and 0.6. The prevalence of food insecurity decreases from poorer to wealthier quintiles for all variables and for all food security measures in all countries. Energy deficiency varies much more across consumption quintiles than wealth index quintiles. Interestingly, inequalities in the share of consumption of food are more pronounced across the wealth index quintiles than per capita consumption. Conclusion: Although wealth indices and consumption per capita are related and both are drivers of food security, they cannot be used interchangeably for food security analysis. Each inequality measure is important for describing different aspects of food security.