John E P
@srmistvdp.edu.in
Assistant Professor, Faculty of Management
SRM Institute of Science & Technology, Chennai
EDUCATION
BE (Mechanical Engineering)
MBA (HR & Operations)
Ph.D. (Management)
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
M Shunmugasundaram, K.Sankar Ganesh, T. Joel Gnanapragash, John E P, John Paul Raj, and V. Smitha
IEEE
This study clarifies and determines how service quality affects customer loyalty and reliability. The support of quality in the open and private financial sphere and understanding of its connection to customer loyalty and conduct goal Utilizing an upgraded SERVQUAL (BANQUAL) tool with 26 items, the review was conducted among 802 bank customers. The social goal battery was used to estimate the clients’ expected conduct. The expert used a seven-point Likert scale to assess the standard and saw service quality (implementation), as well as the social expectations of the clients. The most reliable tool to quantify the conceptualization of the differentiation score is the BANQUAL instrument. It is used to evaluate gaps in service between assumptions and perceptions of service quality. The SERVQUAL instrument is modified to make it suitable in the banking industry. Questions on parking at the bank, the variety of things and programmes available, and the banks’ genuine efforts to address customer grievances are added to the instrument (Responsiveness). The writing audit was sufficiently compiled from many sources, reflecting both an Indian and foreign environment. The postulation included several hypotheses then examined using structural equation modelling. To meet the exploration goals, the views were tested using the products AMOS and SISS. The data were analysed using corroborative and explorative element research to confirm the BANQUAL instrument’s dependability and legitimacy of the financial business execution and service quality aspects. The resulting CFA model value exhibits excellent psychometric qualities. Professional businesses and clients increasingly use artificial intelligence support specialists (AISA) for management. However, no measure measuring the support quality can fully capture the essential factors affecting AISA service quality. By developing a scale for evaluating the quality of AISA service, this study seeks to solve this deficiency(AISAQUAL).
Cuddapah Anitha, E.P John., K. Kathiresan, N.J Krishnakumar., V. Karthik, and J. Sasidevi
IEEE
Modified steady inclination affirmation considering multi-station EEG signals is shaping into a basic PC upheld technique for feeling issue finding in sensory system science and psychiatry as a problematic model affirmation task. In light of expansive space data, standard machine learning approaches call for arranging and eliminating various features from a lone or various channels. Along these lines, these strategies could acquaint a test with individuals who need point dominance. Then again, huge learning frameworks have been truly applied in various persistent created endeavors to see consolidates and sort different kinds of information. In this overview, standard signals are considered, and a direct yet strong pre-managing methodology is proposed to expand confirmation accuracy. In the interim, by truly learning compositional spatial-standard depiction of unpalatable EEG streams, a cross mix brain network that joins "Convolution Brain Organization (CNN)" and "Long Brain Organization (RNN)" has been used to portray human tendency states. The CNN module changes the chain-like EEG gathering into a 2D packaging improvement to mine the between channel relationship among genuinely lining EEG signals.Fulfillment, harshness, and wrath are several the various sentiments that people experience reliably. Electroencephalography (EEG) data should in this way have a convincing inclination recognizing confirmation structure to unequivocally reflect feeling. in the present. In spite of the way that new assessment on this issue can offer great execution estimations, they are at this point lacking for the execution of a full inclination affirmation system.