Ram Singh
@mmumullana.org
Professor
Maharishi Markandeshwar University Mullana Ambala Haryana
Professor at MM Institute of Management, Maharishi Markandeshwar University Mullana Ambala and Life member of Indian Commerce Association, Life Member in the International Association of Academic plus Corporate Society, Life Member in the Indian Academic Researchers Association, Life Member in the International Institute of Organized Research.
RESEARCH INTERESTS
Finance/Accounts General Management & International Business
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Simerjeet Singh Bawa, Rajit Verma, Sunayna Khurana, Ram Singh, Vinod Kumar, Meenu Gupta, Mandeep Kaur, and Makarand Upadhyaya
IGI Global
There are several challenges with ICT use in education, notably moral and legal ones. Both educators and learners ought to have a basic awareness of the challenges and issues related to using ICT in the classroom. In respective capacities as teachers, students, or potential teachers, they must be beyond criticism. Incorporating modern technology in education is essential in the digital world, according to an increasing number of studies. Teachers and students have a lot more opportunities to collaborate online since educational programmes incorporate information and communication technology (ICT). However, various obstacles could make teachers hesitant to use ICT in the classroom and hinder them from introducing supplementary materials. Examining the challenges associated with implementing ICT in education can help educators get over them and incorporate the technology into routine instruction. The objective of this chapter is to learn more about how teachers view the obstacles and difficulties that impede them from integrating ICT in the classroom.
Ram Singh and Vyomkesh Bhatt
De Gruyter
Vandana Madaan, Ram Singh, and Anil Dhawan
IGI Global
Organizations from different sectors are using blockchain technology, but still it is only 0.5% of the world population that is using blockchain technology in 2019, but there is a steady increase in its demand, and it is anticipated that this demand will increase to 80% of the population using it. HR is becoming an inevitable strategic function of the organizations, and blockchain is helping the organizations to overhaul the HR functioning. HR managers are using blockchain in various processes such as recruitment and selection, validation, mapping skills, processing payroll, security of data, and prevention of frauds. This chapter attempts to identify the characteristics and uses of blockchain technology and scope of its application in human resource management. The study would be conducted by reviewing the extant literature from various secondary sources of data collection. The study will assist in providing an insight about implementation of blockchain in HRM and also facilitate in organizational decision making about the implementation of technology.
Ram Singh, Rohit Bansal, and M. Niranjanamurthy
Wiley
Rajyalaxmi M, Reeta, Ram Singh, Nishant Kumar Gupta, Sunil Adhav, and Sampathirao Suneetha
IEEE
Due to developments in machine learning (ML), banking and additional financial sectors will be capable to implement novel items and assistance and, more crucially, withstand disturbances to their client experiences. Without the assistance of fintech sectors, which employ cutting-edge technology to supplement or attempt to replace human professionals with complicated computations, the banking sector would battle to prosper in modern automated market. Banking and financial sectors must embrace ML and incorporate it into their business processes and duties in order to maintain a decisive advantage. By concentrating on some of the most fundamental and confusing business concerns in this area, this research project will examine the components of computerized smart settings in the banking and financial sector and the way they are swiftly emerging as an essential disruptor. The study additionally looked into the various ML-powered technologies and how they can affect financial transactions and functions. For the correct conclusion of the study's task, the research employed the instruments and strategies in this study to collect the evidence for the study. There are a variety of perspectives on ML's possibilities in this area, with the bulk focusing on the implications and importance it will possess on work in the financial and banking assistance sector.
Vinay Pal Singh, Rohit Bansal, and Ram Singh
Wiley
Rohit Bansal, Nishita Pruthi, and Ram Singh
IGI Global
Technological advancements improve the knowledge potential of business and help in building interactions with the customers. Artificial intelligence is drastically changing the way businesses used to engage with the customers by extracting and analyzing tremendous data generated through customer interaction. However, this area is not much explored in academic research. Hence, this study aims at understanding the role of artificial intelligence in enhancing customer engagement. It also deals with artificial intelligence tools used for engaging customers, challenges in using artificial intelligence for customer engagement, and the future of artificial intelligence in customer engagement. This study depends on secondary data that have been gathered from various sites, journals, books, and other available e-content. This study has implications for marketers in enhancing customer engagement and for academicians as it contributes to the literature on the role of artificial intelligence in developing customer engagement.
Ram Singh and Vyomkesh Bhatt
IEEE
This study explores how Industrial Revolution 4.0 will probably affect banking operations. The emergence of digital technology in Industry 4.0 has altered business operations across virtually all sectors. The fourth industrial revolution will significantly disrupt traditional banking due to the growth of digital technologies. Conventional banking may be threatened by competition from financial technology (fintech), which offers comparable or even dominant financial services. To thrive in the financial business in the future, innovative, sophisticated, adaptable, real-time strategies and technologies are required.
Mehak, Ram Singh, and Vyomkesh Bhatt
IEEE
In the context of this study, an attempt was made to evaluate the long-term implications that this trading pattern will have on the Indian commodity market. Gold, silver, copper, zinc, lead, and nickel, together with their respective spot and futures market values over the course of three years (April 2019-March 2022), are factored into a variety of economic models for this purpose. This study used several distinct economic models in order to conduct an analysis of the pertinent purpose. This study looks at the efficiency and volatility of prices in India's commodity market using the Johansen Cointegration, VECM, and Granger Causality tests, as well as the EGARCH model for a subset of MCX commodities. According to the evidence, future commodity prices are expected to outperform spot market prices in terms of price competency and information availability. When it comes to metal commodities, the research findings reveal that the impacts of unpredictability spillover have been discovered to be particularly robust throughout futures and spot markets. Because fundraisers and market participants want to minimise the pricing risks associated with price volatility, the findings of the research contain some constructive suggestions for mitigating such risks. The most recent information is made available to investors through the use of futures contracts, which may frequently be executed considerably more swiftly than spot prices.
Jeidy Panduro-Ramirez, Shaik Vaseem Akram, Ch.Srinivasa Reddy, Jenny Maria Ruiz-Salazar, Budesh Kanwer, and Ram Singh
IEEE
When a consumer switches from one service provider to another, they are considered a churner. With an expanding number of fierce competitors inside the industry, important banks place a premium on client relationship management. A detailed and real-time credit card holder churn review is critical and helpful for bankers looking to retain credit cards. According to extensive research, maintaining an existing client is more than five times simpler than acquiring a new one. As a result, this research provides a strategy for predicting churns using a bank dataset. The "Synthetic Minority Oversampling Technique" (SMOTE) was employed in this study to handle the unbalanced dataset. Randome forest, K closest neighbour, and two boosting algorithms, XgBoost and CatBoost, are used to forecast credit card user turnover. To improve accuracy, hyperparameter tweaking using grid search was performed. The testing results demonstrate that Catboost has an accuracy of 97.85 percent and outperforms the other models.
Rohit Bansal, Ahmed J. Obaid, Ankur Gupta, Ram Singh, and Sabyasachi Pramanik
IOP Publishing
Abstract One of the potential top-level goals for 5G heterogeneous networks may be intellectual and perfect network which modifies consumer preferences in a proactive manner in addition to needs of channel. Research provides an interdisciplinary approach to e-health, primarily concern BDA, and radio space management inside a various level fifth generation network in the company of massive data. The growing need for and usage of big data fuelled digital transformation. The research focuses on the effect of Big Data on digital technologies during the 5G era. To carry out digital transformation, three machine learning (ML) algorithms are identified. In addition to decision tree DT the other algorithms used for the classification are NB, LR. These algorithms run on the large data processing engine they work. These algorithms serve as an ensemble tool for examining old records of stroke outpatients (OPs) and body built IOT based sensors [19]. These readings are available as Big Data. In the model which has been proposed here, OP-Centric Network Optimization Framework was presented before evaluating the machine learning algorithm function and all appropriate steps to plan massive data. An ensemble method in the company of NB classification device, decision tree classification device, and logistic classification device was used in this analysis. These entire classification devices are highly controlled and managed classification device. This method is based on the OP data set and feeds the predicted stroke probabilities to an SV classifier.
Rohit Bansal, Ankur Gupta, Ram Singh, and Vinay Kumar Nassa
IEEE
Research has focused on the implementation of E-Learning amidst the COVID-19 pandemic. During COVID-19, the school and educational institutions were closed due to lockdown. During this period the classes of students are taken online. The digital technology used for e-learning during the COVID-19 pandemic has gained popularity in a very short period. Online classes are taken using Microsoft team, any desk, Zoom, WhatsApp applications. Educational contents are transferred frequently over the internet. Research is considering the impact of e-learning amidst COVID-19 and considering issues such as performance and security during transmission of digital content. The education is provided over cloud environment in a more secure manner with better performance. It has been observed that there have been several kinds of research in the area of cloud computing to provide online education. Issues in such research are performance and security of data. There is a need for a high-speed network to transfer educational content from one place to another. The educational contents needed to be secured and compressed at the time of data transfer. Cloud computing applications and the role of the cloud in e-learning are considered during research. The proposed work is supposed to integrate the proposed mechanism in the educational module. The proposed system is supposed to be secure and fast because data is compressed first then data is encrypted on the sender side. On receiving end the data is decrypted and decompressed. Delay in transmission issue is resolved because the size of data is less during transmission. Moreover, the packet dropping ratio gets reduced. The probability of cracking encrypted files also gets reduced as the data is encrypted after compression.
Arun Kant Painoli, Rohit Bansal, Ram Singh, and Ankur Kukreti
IGI Global
The buying behaviors of the consumers are changing very rapidly in the today's consumer-oriented market. New technologies are evolving in the market to attract the customers. Smart phones have become necessities to cope with the changing dynamics of the market and society as a whole. Due to competitive price offer by the various cellular operators, it has become easy for all to reach the internet. Due to ease of use, the young generations are using the application of internet for various uses especially for purchasing goods and services. Today, every company is applying the digital marketing tool to attract customers, especially the young generation. As per a report published in Economics, the internet users in India are expected to reach 627 million by the end 2020. Due to the digital marketing, a new concept of shopping has evolved in the market, which the authors call off-the-shop retailing.
Ankur Gupta, Ram Singh, Vinay Kumar Nassa, Rohit Bansal, Priyanka Sharma, and Kartikey Koti
IEEE
Very large volumes of data analytics study the uncovering of hidden patterns, interplay, and other discoveries. Today’s technology enables data analyzing and obtaining answers practically quickly- with more conventional solutions for business intelligence, an endeavor that is longer and less effective. The demand for big data analytics has been increasing on regular basis due to the increase in engagement of users. The role of clustering is to make the big data analytic system manageable. This paper has focused on several applications that are based on clustering and big data analytics. The uses of this technology have been increasing rapidly for distance learning, health care, and IoT environment. The issues in the area of clustering and big data are also considered in this research after considering some existing researches in the relevant field. The present research has made use of an advanced mechanism to make dynamic clusters by making use of the K-mean mechanism in order to perform big-data analytics.