Jagran Lakecity University

About: Jagran Lakecity University is a education organization based out in Bhopal, India. It is known for research contribution in the topics: Carbon nanotube & Blockchain. The organization has 39 authors who have published 78 publications receiving 461 citations.

The Impact of Sustainability Practices on Corporate Financial Performance: Literature Trends and Future Research Potential

Abstract: This paper presents an analysis of the literature concerning the impact of corporate sustainability on corporate financial performance. The relationship between corporate sustainable practices and financial performance has received growing attention in research, yet a consensus remains elusive. This paper identifies developing trends and the issues that hinder conclusive consensus on that relationship. We used content analysis to examine the literature and establish the current state of research. A total of 132 papers from top-tier journals are shortlisted. We find that 78% of publications report a positive relationship between corporate sustainability and financial performance. Variations in research methodology and measurement of variables lead to the divergent views on the relationship. Furthermore, literature is slowly replacing total sustainability with narrower corporate social responsibility (CSR), which is dominated by the social dimension of sustainability, while encompassing little to nothing of environmental and economic dimensions. Studies from developing countries remain scarce. More research is needed to facilitate convergence in the understanding of the relationship between corporate sustainable practices and financial performance.

Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

Abstract: In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.

Nanostructured nickel oxide and its electrochemical behaviour—A brief review

Abstract: This review article summarises few researches that took place on nickel oxide, which is an important transition metal oxide in various applications significantly as energy storage material. Nickel oxide is a promising metal oxide and is exhaustively investigated for its electrocapacitive property. Firstly, various easy and widely used method to prepare or synthesise nickel oxide has been discussed which includes wet chemical synthesis, sol–gel method, hydrothermal method, microwave assisted route and thermal decomposition. Then physical properties are reviewed briefly including its structural, magnetic, electrical and optical properties. Finally, the electrochemical behaviour of nickel oxide and nickel oxide based composite has been discussed.

Wavelet Based Digital Watermarking Scheme for Medical Images

Abstract: The transfer of medical images between hospitals is become a normal exercise for better decision making. Digital watermarking has played a very significant role in medical sciences for diagnosis and medication to protect reliability, availability, and confidentiality. Several methods are proposed for medical images using the spatial and transform domain. However, the data falsification ratio is high in the existing mechanisms while exchanging medical images. Hence, we suggest a wavelet based digital watermarking scheme for medical images using three level DWT and BCH coding, which can be used to the medical practitioners for accurate decisions. Security analysis of our proposed work has been done for security assessment, and performance results are discussed to confirm its implementation feasibility in medical sector.