Kishore
@voorheescollege.edu.in
Professor
Voorhees College
is known for his Excellence in Conducting Workshops on Plagiarism-free Report Writing, Publication Ethics, E-Content Development and Data Analysis Using SPSS. He did his B.Com. at Sacred Heart College, Tirupattur, and M.Com. at Muthurangam Government Arts College, Vellore, followed by his M.Phil at Pondicherry Central University in 2001. He did his Ph.D. at Arignar Anna Government Arts College, Cheyyar from Thiruvalluvar University in 2016, and hold PGDCA and Diploma in Virtual Course for Teachers. He has also cleared NET in 2005.
started his career in the year 2002 as Assistant Professor and is specialized in teaching Accounts and Research Methodology. He is passion towards organizing workshops and seminars. He has conducted and acted as resource persons for more than 30 Online Hands-on Workshops, Seventy (70) Offline Workshops in India and Seven Workshops at Dubai. Chaired for More than Thirty Seminars, Conferences and Panel Discussions in India.
As a res
RESEARCH INTERESTS
HR, SPSS, Data Analysis
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Tanvi Jindal, Neelam Sheoliha, K. Kishore, Dipesh Uike, Shopita Khurana, and Devvret Verma
IEEE
The growth of digital technology presents organisations with both huge potential and significant obstacles. Given the growing awareness of “loT and Big Data”, the goal of this study was to lay out the present status of business digitizing and to add to existing theory. The “Internet of Things” (IoT), Big Data, and data analytics along with cloud platforms, all provide opportunities for industrial enterprises to use technology to transform their approaches, particularly in the implementation of “new service-oriented marketing strategies”. In other words, marketing managers can, in general, incorporate new gadgets into old processes or enhance the technical content of products or services. Businesses may gather and evaluate review information and data, allowing them to develop relevant marketing strategies. A collection of innovations constitutes the foundations of today's technology landscape, as well as the catalysts of the resulting business change. Among these technologies, the “Internet of Things” (IoT), particularly the “Industrial Internet of Things” (1IoT), is crucial in assisting businesses in increasing the utilization of their machines and in the creation of service-based products in manufacturing organisations. This research paper has considered survey or primary quantitative method for data collection based on impact of loT towards creating digital transformation. In this context, 55 random people are selected and collect their responses.
Ayan Das Gupta, K. Kishore, Arun B Prasad, B Kannadasan, M Srinivasa Narayana, and Prabhdeep Singh
IEEE
The Software Defined Network (SDN) structure is a novel approach to network management. Switches in SDN do not process incoming packets in the same way that they do in a traditional network computing environment. They look in the linking tables for incoming packets, and if none are found, they are sent to the control system for processing, which is the SDN's operating system. The most serious threat to cyber security in an SDN network is a Spread Denial of Service (Dodos) attack. The attack will take place at the network or application layers of the infected devices that are linked to the network. In this paper, an intelligent machine learning-based method for detecting whether incoming packets are infected or not is proposed Smart city activities seek to go beyond the limitations of traditional urban planning, which regulates critical infrastructures in silos, and to profit on the pervasiveness of data and services given by electronic technologies such as machine learning, the internet of things, affordable and big data.
G. Ramachandran, CHANDRA KUMAR DIXIT, K. Kishore, and A. Arunraja
IEEE
Approximate computing provides a new approach to design high level of performance by using their technique of low-level power arithmetic circuits. Approximate computing is a technique which provides a slightly inaccurate results rather than accurate results for a scenario where an inexact result is sufficient for a purpose. Deterministic algorithm is used to view the appropriate computing techniques to overcome and attainment of efficiency. It is required to monitor different parameters from different number of systems and calculate their stability. Approximate floating-point arithmetic is used in variety of error tolerant applications such as image processing, digital processing such as filtering and Fast Fourier transform (FFT) and machine learning. The main objective of this paper is to minimize the power consumption and to rise the speed of execution by implementing an algorithm for multiplying two floating point numbers. In this paper, the existing and proposed algorithm are designed and compared in terms of area, power and delay. In order to design this, VHDL is the hardware description language used. It is simulated using Modelsim 6.3f and synthesized using Xilinx 8.1i and are further applied in Fast Fourier Transform (FFT).
T. Saimounika and K. Kishore
IEEE
In the present, attendance techniques are usually supplemented manually, because the number of university students is increasing within training institutes, the problem with getting hold of a hand requires human effort to report and maintain student attendance. Consequently, human errors are common in this process. In recent years, there has been an increase in the number of applications based on RFID (Radio Frequency Identification) systems. RFID technology facilitates automatic wave identification using passive and active passive electronic labels with convenient readers. In this paper, an attempt has been made to address the problem of continuous attendance of lectures in developing countries and to find the location of special students using RFID technology. The implementation of RFID for attending student attendance as developed and deployed in this study is capable of eliminating lost time during manual attendance gathering and an opportunity for education administrators to capture classroom statistics for sharing appropriate outcomes Attendance and for further managerial decisions.