Archive

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES > Journal > Page 31

The Effect of Message Source Credibility on Consumer Purchase Intention: An Empirical Examination of Appreal Industry

Authors:

S Sudheer, M Siva Koti Reddy, A Sai Manideep

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00069

October 30, 2019

Abstract:

It is must to a marketer to indulge his self to identify that what consumers percept about the companies advertisement message credibility and as well as what factor in that message would influences them most. By intensive literature review the three higher order factors as part of message source credibility are considered in the present study they are message trustworthiness, message expertness and message attractiveness. An empirical examination was performed through a survey by considering the sample size of 139 respondents who purchase apparels. Descriptive and inferential statistical techniques are performed such as factor analysis and multiple regressions and also consumer’s educational qualification was determined as control variable. Observations are presented and discussions are made as per the results.

Keywords:

Constructs source credibility,Endorser credibility,Message appeal,Grocery items purchase intention,

Refference:

I. CARE Ratings Limited. (2019). Indian Readymade Garments ( Apparel ) Industry Overview.
Retrieved from http://www.careratings.com/upload/NewsFiles/Studies/Indian
Ready Made Garments (Apparel) Industry.pdf
II. Ferle, C. La, & Choi, S. M. (2001). The Importance of Perceived Message
Expertness in South Korean Advertising. Journal of Current Issues and
Research in Advertising, 27, 29–46.

III. Flanagin, A. J., Metzger, M. J., Pure, R., Markov, A., & Hartsell, E. (2014).
Mitigating risk in ecommerce transactions: Perceptions of information
credibility and the role of user-generated ratings in product quality and
purchase intention. Electronic Commerce Research, 1, 1–23.
https://doi.org/10.1007/s10660-014-9139-2
IV. Goldsmith, R. E., Lafferty, B. A., Newell, S. J., Taylor, P., Goldsmith, R. E.,
Lafferty, B. A., & Newell, S. J. (2019). Advertisements and Brands The
Impact of Corporate Credibility and Celebrity Credibility on Consumer
Reaction to Advertisements and Brands. Journal of Advertising, 29(3), 43–
54.
V. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi
region of Andhra Pradesh. International Journal of Innovative Technology
and Exploring Engineering.8(7), pp. 1708-1714.
VI. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards
commodities market with reference to GUNTUR city, Andhra
Pradesh.International Journal of Engineering and Technology(UAE). 7(2),
pp. 1104-1106.
VII. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology.8(4), pp.
1913-1920.
VIII. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite
wellness interventions towards occupational stress. International Journal
of Recent Technology and Engineering.8(1), pp. 2874-2879.
IX. Lafferty, B. A., & Goldsmith, R. E. (1999). How Influential are Corporate
Credibility and Endorser Attractiveness When Innovators React to
Advertisements for a New High- Technology Product ? Corporate Reputation
Review Volume, 7(1), 24–36.
X. Manukonda et al. (2019).What Motivates Students To Attend Guest
Lectures?.The International Journal of Learning in Higher Education.Volume
26, Issue 1. 23-34.
XI. Neelima, J., Koneru, K.(2019). Assessing the role of organizational culture in
determining the employee performance – empirical evidence from Indian
pharmaceutical sector.International Journal of Innovative Technology and
Exploring Engineering. 8(7), pp. 1701-1707.
XII. Sivakoti Reddy, M. (2019).Impact of RSERVQUAL on customer
satisfaction: A comparative analysis between traditional and multi-channel
retailing. International Journal of Recent Technology and Engineering. 8(1),
pp. 2917-2920.

XIII. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.
XIV. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail
service quality in food and grocery retailing: A comparative study between
traditional and multi-channel retailing. International Journal of Management
and Business Research. 9(2), pp. 68-73.
XV. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016).Saga of
silicon plate: An empirical analysis on the impact of socio economic factors
of farmers on inception of solar plants. International Journal of Control
Theory and Applications. 9(29), pp. 257-266.
XVI. Suhasini, T., Koneru, K. (2019).Employee engagement through HRD
practices on employee satisfaction and employee loyalty: An empirical
evidence from Indian IT industry. International Journal of Engineering and
Advanced Technology. 8(4), pp. 1788-1794.
XVII. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving
factors and their impact over employee satisfaction – An empirical evidence
from Indian it industry.International Journal of Mechanical Engineering and
Technology. 9(4), pp. 725-732.
XVIII. Wu, P. C. S., & Wang, Y. (2011). The influences of electronic word-ofmouth
Message attractiveness and message Message Trustworthiness on
brand attitude. Asia Pacific Journal of Marketing and Logistics, 23(4), 448–
472. https://doi.org/10.1108/13555851111165020

View Download

Journal Vol – 14 No -5, October 2019

Impact of Knowledge Sharing and Dissemination on Agriculture Supply Chain Management: A Case Study on Cotton and Chill Farmers in Guntur and Prakasam Districts

Authors:

Vidya Sagar. Mullapudi, Subba Raydu. Thunga, A. SrikanthBabu, Ch.Hymavathi

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00070

October 30, 2019

Abstract:

The Research is undertaken among chilli and cotton farmers in various villages of Guntur and Prakasam districts of Andhra Pradesh. This study is formulated to analyse perceptions of farmers on knowledge sharing and dissemination practices that are applying in chilli and cotton crop supply chain functions. By this research the researcher made a attempt to assess and evaluate impact of knowledge sharing practices on effectiveness of agriculture supply chain management. The results are elicited by conducting survey among chilli and cotton farmers in different villages in Guntur and Prakasam districts. The survey was executed by selecting farmers purposefully among various regions in Guntur and Prakasam districts. For critical investigation on variables associated with research problem three categories are undertaken i.e. Knowledge sharing practices, Expertise on suppliers and distributors and Knowledge on marketing quality standards. The study results are extracted by analysing and evaluating perceptions of farmers on knowledge sharing practices, knowledge on suppliers and distributors.

Keywords:

Agriculture Supplychain management,Knowledge sharing practices,distributors,marketing quality standards,

Refference:

I. Ch.Narahari (2017) “A Study on knowledge dissemination Among the
employees of ITES (BPO ) Employees”, Journal of Engineering
Development and Research, Volume 5 Issue 2, Pages 1871-1878.
II. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi
region of Andhra Pradesh. International Journal of Innovative Technology
and Exploring Engineering. 8(7), pp. 1708-1714.
III. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology. 8(4), pp.
1913-1920.
IV. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards
commodities market with reference to GUNTUR city, Andhra Pradesh.
International Journal of Engineering and Technology(UAE). 7(2), pp. 1104-
1106.
V. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite
wellness interventions towards occupational stress. International
VI. Manukonda et al. (2019). What Motivates Students To Attend Guest
Lectures?. The International Journal of Learning in Higher Education.
Volume 26, Issue 1. 23-34.
VII. Neelima, J., Koneru, K.(2019). Assessing the role of organizational culture in
determining the employee performance – empirical evidence from Indian
pharmaceutical sector. International Journal of Innovative Technology and
Exploring Engineering. 8(7), pp. 1701-1707.
VIII. N. Kumar and S. Arul Krishnan(2017) “An Empirical Study on knowledge
sharing Faced by the Employees Across the agriculture departments in
Chennai”, Indian Journal Of Economic Research, Volume 14, Pages 23-36
IX. Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer
satisfaction: A comparative analysis between traditional and multi-channel
retailing. International Journal of Recent Technology and Engineering. 8(1),
pp. 2917-2920.
X. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.

XI. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail
service quality in food and grocery retailing: A comparative study between
traditional and multi-channel retailing. International Journal of Management
and Business Research. 9(2), pp. 68-73.
XII. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016). Saga of
silicon plate: An empirical analysis on the impact of socio economic factors
of farmers on inception of solar plants. International Journal of Control
Theory and Applications. 9(29), pp. 257-266.
XIII. Sivaraman, A(2017) “ A Study on knowledge management practices among
Railway Employees with Special Reference to Thrissur Railway Station”,
International Journal ofSocial Sciences, Volume 8(1), Pages 53-61.
XIV. Suhasini, T., Koneru, K. (2019). Employee engagement through HRD
practices on employee satisfaction and employee loyalty: An empirical
evidence from Indian IT industry.
International Journal of Engineering and Advanced Technology. 8(4), pp.
1788-1794.
XV. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving
factors and their impact over employee satisfaction – An empirical evidence
from Indian it industry. International Journal of Mechanical Engineering and
Technology. 9(4), pp. 725-732.

View Download

Journal Vol – 14 No -5, October 2019

A Perceptual Study on Adoption of Technology in Farming: A Descriptive Analysis using Tam

Authors:

A Nagabhushna, M Siva Koti Reddy

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00071

October 30, 2019

Abstract:

In the present study we analyze the farmers’ perception towards adoption of technology such as ITC for better productivity in farming. The considered constructs are adopted from Technology adoption model (TAM). A total sample of 800 farmers from the Guntur district are collected through simple random technique and out of which survey respondents irregular responses are eliminated finally 756 samples are determined for statistical analysis. Chi-square test was performed to determine the association between perceptions and model constructs. Results are reported and discussions are made as per the results and in correlation between results and previous literature and finally, suggestions and future indication for extension of the study are proposed.

Keywords:

Technology,Farming,Ease of Use,Usefulness,Intention,

Refference:

I. ALI, S. (2005). Total Factor Productivity Growth and Agricultural Research and
Extension : An Analysis of Pakistan ’ s Agriculture , 1960 – 1996. The Pakistan
Development Review, 44(4), 729–746.
II. Amin, K., & Li, J. (2016). Applying Farmer Technology Acceptance Model to
Understand Farmers’ Behavioral Intention to use ICT Based Microfinance
Platform: A Comparative analysis between Bangladesh and China. The
Thirteenth Wuhan International Conference on E-Business—IT/IS Technology
for E-Business, (July), 123. https://doi.org/10.13140/RG.2.1.3832.9363
III. Barker, R., Dawe, D., & Inocencio, A. (2003). Economics of Water Productivity
in Managing Water for Agriculture. Economics of Water Productivity in
Agriculture, 19–35.
IV. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi region
of Andhra Pradesh. International Journal of Innovative Technology and
Exploring Engineering.8(7), pp. 1708-1714.
V. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology.8(4), pp. 1913-
1920.
VI. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards commodities
market with reference to GUNTUR city, Andhra Pradesh.International Journal of
Engineering and Technology(UAE). 7(2), pp. 1104-1106.
VII. Jain, P. (2017). Impact of Demographic Factors : Technology Adoption in. SCMS
Journal of Indian Management, 3(September), 93–102.
VIII. Jin, S., Huang, J., Hu, R., Rozelle, S., Jin, S., Huang, J., … Rozelle, S. (2019).
The Creation and Spread of Technology and Total Factor Productivity in China ’
s Agriculture. Agricultural & Applied Economics Association, 84(4), 916–930.
IX. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite
wellness interventions towards occupational stress. International Journal
of Recent Technology and Engineering.8(1), pp. 2874-2879.
X. Mahadevan, R. (2003). PRODUCTIVITY GROWTH IN INDIAN
AGRICULTURE : THE ROLE OF GLOBALIZATION AND. Asia-Pacific
Development Journal, 10(2), 57–72
XI. Manukonda et al. (2019).What Motivates Students To Attend Guest Lectures?.The International Journal of Learning in Higher Education.Volume 26,
Issue 1. 23-34.
XII. Mittal, S., & Tripathi, G. (2009). Role of Mobile Phone Technology in
Improving. Agricultural Economics Research Review, 22, 451–459.
XIII. Mukherjee, A. N., & Kuroda, Y. (2003). Productivity growth in Indian
agriculture : is there evidence of convergence across states ? Agricultural
Economics, 5150(03), 43–53. https://doi.org/10.1016/S0169-5150(03)00038-0
XIV. Reddy, P. K. (2005). A framework of information technology-based agriculture
information dissemination system to improve crop productivity, 88(12), 1905–
1913.
XV. Shahabinejad, V., & Akbari, A. (2010). Measuring agricultural productivity
growth in Developing Eight. Journal of Development and Agricultural
Economics, 2(9), 326–332.
XVI. Singh, G. (2010). Replacing Rice with Soybean for Sustainable Agriculture in
the Indo-Gangetic Plain of India : Production Technology for Higher
Productivity of Soybean. International Journal of Agricultural Research, 5(5),
259–267. https://doi.org/10.3923/ijar.2010.259.267
XVII. Stiroh, B. K. J. (2019). Information Technology and the U . S . Productivity
Revival : What Do the Industry Data Say ? American Economic Association,
92(5), 1559–1576
XVIII. Sivakoti Reddy, M. (2019).Impact of RSERVQUAL on customer satisfaction: A
comparative analysis between traditional and multi-channel retailing.
International Journal of Recent Technology and Engineering. 8(1), pp. 2917-
2920
XIX. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes. International
Journal of Innovative Technology and Exploring Engineering. 8(7), pp. 1720-
1728
XX. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail service
quality in food and grocery retailing: A comparative study between traditional
and multi-channel retailing. International Journal of Management and Business
Research. 9(2), pp. 68-73.
XXI. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016).Saga of silicon
plate: An empirical analysis on the impact of socio economic factors of farmers
on inception of solar plants. International Journal of Control Theory and
Applications. 9(29), pp. 257-266.
XXII. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving factors
and their impact over employee satisfaction – An empirical evidence from Indian
it industry.International Journal of Mechanical Engineering and Technology.
9(4), pp. 725-732.

View Download

Journal Vol – 14 No -5, October 2019

An Empirical study of Consumer price Index on BSE SENSEX

Authors:

Hymavathi

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00072

October 30, 2019

Abstract:

The Consumer Price Index (CPI) is a measure that examines the weighted average of pricesof a basket of consumergoods and services, such as transportation, food, and medical care. It is calculated by taking pricechanges for each item in the predeterminedbasket of goods and averaging them.The main objective of this study to check howconsumer price index affects the BSE sensex. In this paper null hypothesis is taken and to prove that hull hypothesis is correct for this correlation and regression analysis tools are used for the analysis.

Keywords:

Consumer price Index,Hypothesis,Sensex,

Refference:

I. Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer
satisfaction: A comparative analysis between traditional and multi-channel
retailing. International Journal of Recent Technology and Engineering. 8(1),
pp. 2917-2920.
II. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.
III. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail
service quality in food and grocery retailing: A comparative study between
traditional and multi-channel retailing. International Journal of Management
and Business Research. 9(2), pp. 68-73.
IV. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016). Saga of
silicon plate: An empirical analysis on the impact of socio economic factors
of farmers on inception of solar plants. International Journal of Control
Theory and Applications. 9(29), pp. 257-266.
V. Manukonda et al. (2019). What Motivates Students To Attend Guest
Lectures?. The International Journal of Learning in Higher Education.
Volume 26, Issue 1. 23-34.
VI. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi
region of Andhra Pradesh. International Journal of Innovative Technology
and Exploring Engineering. 8(7), pp. 1708-1714.
VII. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards
commodities market with reference to GUNTUR city, Andhra Pradesh.
International Journal of Engineering and Technology(UAE). 7(2), pp. 1104-
1106.
VIII. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology. 8(4), pp.
1913-1920.
IX. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite
wellness interventions towards occupational stress. International
Journal of Recent Technology and Engineering. 8(1), pp. 2874-2879.
X. Neelima, J., Koneru, K.(2019). Assessing the role of organizational culture in
determining the employee performance – empirical evidence from Indian
pharmaceutical sector. International Journal of Innovative Technology and
Exploring Engineering. 8(7), pp. 1701-1707.

XI. Suhasini, T., Koneru, K. (2019). Employee engagement through HRD
practices on employee satisfaction and employee loyalty: An empirical
evidence from Indian IT industry.
International Journal of Engineering and Advanced Technology. 8(4), pp.
1788-1794.
XII. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving
factors and their impact over employee satisfaction – An empirical evidence
from Indian it industry. International Journal of Mechanical Engineering and
Technology. 9(4), pp. 725-732.

View Download

Journal Vol – 14 No -5, October 2019

Consumers’ Perceptions on Nanotechnology Enabled Cosmetic Products in Conception of Physical Wellness

Authors:

A Sai Manideep, M Siva Koti Reddy, P Srinivas Reddy

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00073

October 30, 2019

Abstract:

Applications of Nanotechnology has widened in diverse fields such as, agrifood processing, food packaging, cosmetics and many more. In this paper we defined a research model constitutes consumers’ willingness to pay for NCPs (Nanotechnology enabled cosmetics products) in fulfillment of physical wellness which is studied from observed variables perceive risk, trust and perceived benefit for the past literature. A total of 139 consumer sample data was taken to conduct the study. It is observed through hierarchical regression that perceived risk is more associated with cosmetics products enabled with nanotechnology and perceived benefit is also a significant predictor i.e., at a benefit forthcoming consumers are comprised to pay for NCPs and followed by trust component in predicting the behavior. It is also observed that consumer’s education qualification (control variable) was having a significant positive association on the behavioral aspect willing to pay for nanotechnology enabled products. Inclusion of a variable educational qualification as control variable the explained variance of the model has increased.

Keywords:

Nanotechnology,wellness,cosmetic products,perceived benefit,perceived risk,trust,

Refference:

I. Hettler, B. (n.d.). Defining wellness: The six dimensional model of wellness. The
National Wellness Institute. Retrieved from
http://www.nationalwellness.org/index.php?id=391&id_tier=381
II. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi region of
Andhra Pradesh. International Journal of Innovative Technology and Exploring
Engineering. 8(7), pp. 1708-1714.
III. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Effect of worksite
wellness interventions towards occupational stress. International Journal of
Recent Technology and Engineering. 8(1), pp. 2874-2879.
IV. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology. 8(4), pp. 1913-
1920.

V. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards commodities
market with reference to GUNTUR city, Andhra Pradesh. International Journal of
Engineering and Technology(UAE). 7(2), pp. 1104-1106.
VI. Indian cosmetics Industry report (ICIR) 2017. A short perspective document on
the cosmetics retail sector. http://redseer.com/wp-content/uploads/2017/10/118-
Cosmetics-Industry-Report_Final_July2017.pdf. Accessed on May 30th ,2018.
VII. John Besley (2010). Current research on public perceptions of nanotechnology.
Emerging Health Threats Journal, 3:1, 7098, DOI: 10.3402/ehtj.v3i0.7098
VIII. Kim, A.J., Ko, E. (2012). Do social media marketing activities enhance customer
equity?An empirical study of luxury fashion brand. J. Bus. Res. 65 (10) 1480–
1486.
IX. Lisa A. DeLouise (2012). Applications of Nanotechnology in Dermatology,
Journal of Investigative Dermatology (2012) 132, 964–975
X. Nunnally, J.C.(1978). Psychometric Theory, 2nd ed., McGraw-Hill, New York,
NY.
XI. NSDC & KPMG, (2017). “Human Resources and Skill Requirements in Beauty
and Wellness Sector”. Retrieved from
http://www.nsda.gov.in/skill%20gap%20report/sector%20skill%20gap%20report
/Beauty_and_Wellness.pdf , Vol. 4. Accessed on march 10, 2018.
XII. Priyanka Singh &Arun Nanda (2012). Nanotechnology in cosmetics: a boon or
bane?, Toxicological & Environmental Chemistry, 94:8, 1467-1479, DOI:
10.1080/02772248.2012.723482
XIII. Pastrana, H., Avila, A. & Tsai, C.S.J. Nanoethics (2018). Nanomaterials in
Cosmetic Products: the Challenges with regard to Current Legal Frameworks and
Consumer Exposure. Springer Netherlands, 1871-4757, DOI
https://doi.org/10.1007/s11569-018-0317-x
XIV. Roosen, Jutta&Bieberstein, Andrea &Blanchemanche, Sandrine & Goddard,
Ellen &Marette, Stephan &Vandermoere, Frédéric. (2015). Trust and willingness
to pay for nanotechnology food. Food Policy. 52. 10.1016/j.foodpol.2014.12.004.
XV. Siegrist, M. (2000). The influence of trust and perceptions of risks and benefits
on the acceptance of gene technology. Risk Analysis, 20, 195–203.
XVI. Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer satisfaction: A
comparative analysis between traditional and multi-channel retailing.
International Journal of Recent Technology and Engineering. 8(1), pp. 2917-
2920.
XVII. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes. International
Journal of Innovative Technology and Exploring Engineering. 8(7), pp. 1720-
1728.

XVIII. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail service
quality in food and grocery retailing: A comparative study between traditional
and multi-channel retailing. International Journal of Management and Business
Research. 9(2), pp. 68-73.
XIX. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016). Saga of silicon
plate: An empirical analysis on the impact of socio economic factors of farmers
on inception of solar plants. International Journal of Control Theory and
Applications. 9(29), pp. 257-266.
XX. Suhasini, T., Koneru, K. (2019). Employee engagement through HRD practices
on employee satisfaction and employee loyalty: An empirical evidence from
Indian IT industry.International Journal of Engineering and Advanced
Technology. 8(4), pp. 1788-1794.
XXI. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving factors
and their impact over employee satisfaction – An empirical evidence from Indian
it industry. International Journal of Mechanical Engineering and Technology.
9(4), pp. 725-732.

View Download

Journal Vol – 14 No -5, October 2019

Ethics: Its Management and Impact on Work Place

Authors:

Gaurab Kumar Sharma, Princi Gupta, Nisha Singh

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00074

October 30, 2019

Abstract:

This paper tries to investigate the role of ethics in managing organization. This study is divided into three parts, Firstly, the introduction of ethics in management context, secondly, its relevance and challenges in implementing ethics in any institutions and lastly, the ways to get rid of challenges with the help of model in step wise construction.

Keywords:

Business,ethics,workplace,

Refference:

I. Ahad Faramarz GharaMalaki, “professional ethic in civilization of Iran &
Islam”, 2008, Anvar Danish Pub.
II. Dr. Princi Gupta and Nisha Singh A Comparative Study of the Strategies and
Lessons of Two Great Indian Epics: Mahabharata and Ramayana, ISSN
2250-0588, Volume 9, April 2019
III. GhasemVaseghi “The Lessons of ethic Management”, 2005, Amir Kabir
PUB.
IV. HasanKamali,” ManifestatImam of Imam Ali ‘s Management”, 2001,
Modaber pub.
V. Nisha Singh, Dr.Princi Gupta A Study on Effects of Safety and Welfare
Measures on the Motivation of Employees with respect to Balrampur Chini
Mills Limited, International Journal of Research in Engineering, IT and
Social Sciences, ISSN 2250-0588.
VI. Sharma Dr.Gaurab Kumar, Singh Nisha,” Indo-US Trade & Defence
Relations: Opportunities & Challenges for Narendra Modi Government”
April 2019, 4th International Conference On Recent Trends in Humanities,
Technology,
VII. Shames Afagh Yavari, “Professional ethic in Management”2007, Fra pub.

View Download

Journal Vol – 14 No -5, October 2019

Shoppers’ Patronage Behaviour with reference to Online Apparel Retailing

Authors:

M. Uma Devi, Suneel Sankala

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00075

October 30, 2019

Abstract:

Online retail growth drivers are many in number but it all depends on the extent of shopper’s traffic and choice of preference, to achieve this, online stores need to improve on its productivity by ensuring high level of conversion rate from casual visitors to patron customers. This conversion is possible by impacting the patronage behaviour using the variables within the control of the on line retailers. From online shoppers’ perspective, apparel may be a risky product to buy in any one of the online shop due to the uncertainty of apparel quality and non suitability of the various dimensions expected by the shoppers. There are various behavioural theories to explain how an individual forms his intentions, and how intentions relate to actions. Among them the most widely used is multi-attribute model developed by fishbone and ajzen in the year of 1975 i.e., Theory of Reasoned Action and after few years(1985, 1991) ajzen was come up with addition of TRA i.e Theory of Planned Behaviour. The primary purpose of this research study was to identify and investigate the factors and proposed suitable model that affect on-line apparel shoppers’ store patronage behaviour. To attain these objectives, researcher used two diverse tools, i.e., SPSS &AMOS was used for dimension model analysis and structural equation model to test the anticipated hypothesized model.

Keywords:

Shoppers’ Patronage Behaviour,Online Apparel Retailing,Theory of Reasoned Action,Theory of Planned Behaviour,

Refference:

I. Allard, T., Babin, B.J. and Chebat, J.C. (2009), “When income matters:
Customers evaluation of shopping malls’ hedonic and utilitarian orientation”,
Journal of Retail Consumer Service, Vol. 16, pp. 40–49.
II. Anderson, N. H. (1974). Cognitive algebra: Integration theory applied to
social attribution In L. Berkowitz (Ed.,), Advances in experimental social
psychology (Vol. 7, pp. l-101). New York: Academic Press.
III. Bilgihan, A. (2016). Gen Y customer loyalty in online shopping: An
integrated model of trust, user experience and branding. Computers in Human
Behavior, 61, 103- 113.
IV. Bilgihan, A. (2016). Gen Y customer loyalty in online shopping: An
integrated model of trust, user experience and branding. Computers in Human
Behavior, 61, 103- 113.
V. Davis, L., Wang, S., &Lindridge, A. (2008). Culture influences on emotional
responses to on-line store atmospheric cues. Journal of Business Research,
61(8), 806–812.
VI. Dawson, S., Bloch, P.H., and Ridgway, N.M. (1990), “Shopping motives,
emotional states, and Retail outcomes”, Journal of Retailing, Vol. 66, No. 4,
pp. 408-427.
VII. Garbarino E, Strabilevitz M (2004). Gender Differences in the Perceived Risk
of Buying Online and the Effects of Receiving a Site Recommendation. J.
Bus. Res. 57: 768-775.

VIII. Gefen, D., Straub, D. W., and Boudreau, M.-C. .Structural Equation
Modeling and Regression: Guidelines for Research
Practice,.Communications of the AIS (4:7), 2000, pp. 1-70.
IX. Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption:
Emerging concepts, methods and propositions. Journal of Marketing, 46(3),
92–101.
X. Johnson, K.K.P., Lennon, S.J., Jasper, C., Damhorst, M.L. and Lakner, H.B.
(2003), “An application of Rogers’ innovation model: use of the internet to
purchase apparel, food, and home furnishing products by small community
consumers”, Clothing and Textiles Research Journal, Vol. 21 No. 4, pp. 185-
96.
XI. K.L. Keller, Conceptualizing, measuring and managing customer based brand
equity, Journal of Marketing 57 (1993) 1–22.
XII. Lysonski, S. and Durvasula, S. (2013), “Consumer decision making styles in
retailing: evolution of mindsets and psychological impacts”, The Journal of
Consumer Marketing, Vol. 30, No. 1, pp. 75-87.
XIII. Magee, M. (2003), “Boom or bust for e-shopping”, The Sunday Tribune,
available at: http:// web.lexis-nexis.com/universe/document (accessed 22
December 2003).
XIV. Osman, M. Z. (1993). A conceptual model of retail image influences on
loyalty patronage behaviour. International review of retail, distribution and
consumer research, 3(2), 133-148.
XV. Pantano, E., &Priporas, C. V. (2016). The effect of mobile retailing on
consumers’ purchasing experiences: A dynamic perspective. Computers in
Human Behavior, 61, 548-555.
XVI. Rowley, M.L. and Okelberry, A. (2000), “Shopping.com: analysis of a
trend”, Journal of Family and Consumer Sciences, Vol. 92 No. 1, pp. 9-10.
XVII. Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer
satisfaction: A comparative analysis between traditional and multi-channel
retailing. International Journal of Recent Technology and Engineering. 8(1),
pp. 2917-2920.
XVIII. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.
XIX. Triandis, H. C. (1977). Interpersonal behavior. Monterey, CA: Brooks/Cole.
XX. V. W. Mitchell, “Consumer perceived risk: Conceptualizations and models,”
European Journal of Marketing, vol. 33, no.1, pp. 164-196, 1999.

XXI. Vijayasarathy, L.R., & Jones, J.M. (2000) ‘Intentions to shop using Internet
catalogues:Exploring the effect of the product types, shopping orientations,
and attitudestoward computers’, Electronic Market, 10(1), 29-38.
XXII. Vroom, V. H. (1964). Work and motivation. New York: Wiley.
XXIII. W. Huang, H. Schrank, and A. J. Dubinsky, “Effect of brand names on
consumers’ risk perceptions of online shopping,” Journal of Consumer
Behavior, vol. 4, no.1, pp. 40-50, 2004.
XXIV. Yim, M.Y., Yoo, S., Sauer, P.L. and Seo, J.H. (2014), “Hedonic shopping
motivation and co- shopper influence on utilitarian grocery shopping in
superstores”, Academy of Marketing Science Journal, Vol. 42, No. 5, pp.
528-544.
XXV. Zeithaml, V. A., Berry, L. L., and Parasuraman, A. .The Behavioral
Consequences of Service Quality,.Journal of Marketing (60:April), 1996, pp.
31-46.

View Download

Journal Vol – 14 No -5, October 2019

HRD – Banks in the ICT Era a Focus on Private sector Banks

Authors:

Ashok Kumar Katta, P. SubbaRao, S. Venkata Ramana

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00076

October 30, 2019

Abstract:

The banking sector in India plays a vital role in the economic growth of the country. Hence, the performance of banks has got a decisive role in controlling the pace of economic development of the whole nation. Performance of banks, in turn, depends on the performance of their human resources (HR) – the most sensitive and most valuable among all resources of an organization. Effective management of HR along with proper adoption and utilization of technological advances particularly those in the field of Information and Communication Technology, (ICT) has become an imperative for banks for their survival and growth. Likewise, thrust on the promotion of bank products particularly using modern philosophies like e-CRM side by side with provision of excellent quality customer service is another imperative. At the centre of all these lies Human Resources (HR); because a well-trained and techno-savvy workforce alone can provide customer service matching with the expectations of today’s discerning customers. As India’s banking sector is passing through a highly turbulent world characterized by VUCA (Volatility, Uncertainty, Complexity, Ambiguity), this paper seeks to study the relative performance of the Old generation Private sector Banks (OPBs) based in Kerala with a focus on their HR productivity and allied HR-related performance parameters.

Keywords:

Old Private sector Banks (OPBs),ICT,CRM,HRM,Employee Productivity,

Refference:

I. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.
II. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.
III. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.

IV. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.
V. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
VI. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
VII. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
VIII. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
IX. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
X. Manukonda et al. (2019). What Motivates Students To Attend Guest
Lectures?. The International Journal of Learning in Higher Education.
Volume 26, Issue 1. 23-34.
XI. Official websites of the Reserve Bank of India (RBI), www.rbi.org.in
XII. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.
XIII. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail
service quality in food and grocery retailing: A comparative study between
traditional and multi-channel retailing. International Journal of Management
and Business Research. 9(2), pp. 68-73.
XIV. Y. V. Rao and Srinivasa Rao Budde. Banking Technology Innovations in
India: Enhancing Customer Value and Satisfaction. Indian Journal of Science
and Technology, Vol 8(33), DOI: 10.17485/ijst/2015/v8i33/78280, December
2015.1-10.

View Download

Journal Vol – 14 No -5, October 2019

Enhancement of Non-Linear Generators to Calculate the Randomness Test for Frequency Property in the Stream Cipher Systems

Authors:

Ibrahim Abdul Rasool Hammood, Ayad Ghazi Naser Alshamri

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00077

October 31, 2019

Abstract:

In this paper, the key generators generated by using (Brüer generator, Geffe generator, and Linear generator), then improved these key generators (Brüerand Geffe). In this research was the focus on the frequency test and then compares the outputs with results in a chi-square.

Keywords:

Cryptography,Stream Cipher,Frequency,LFSR,

Refference:

I. C. Paar, J. Pelzl, 2010,”Understanding Cryptography”, Springer, Verlag
Berlin Heidelberg.
II. A. Klein, 2013, “Stream Ciphers”, Springer Verlag London.
III. Fardous Eljadi, 2017, “Dynamic Linear Feedback Shift Registers: A Review”,
Kuala Lumpur, Malaysia.
IV. Alice Reinaudo, 2015, “Empirical testing of pseudo random number
generators based on elliptic curves”, Linnaeus University, Sweden.
V. Yassir Nawaz, 2007, “Design of Stream Ciphers and Cryptographic
Properties of Nonlinear Functions”, University of Waterloo, Canada.
VI. Hossam El-din H. Ahmed, Hamdy M. Kalash, and Osama S. Farag Allah,
2005, “An Efficient Chaos-Based Feedback Stream Cipher (ECBFSC) for
Image Encryption and Decryption”, Menoufia University.
VII. M. Robshaw and O. Billet (Eds.), 2008, ” Design of a New Stream Cipher—
LEX”, Springer-Verlag Berlin Heidelberg.
VIII. Roy Ward, Tim Molteno, 2007, “Table of Linear Feedback Shift Registers”,
University of Otago.
IX. Abdullah Ayad Ghazi,Faez Hassan Ali,2018, “Robust and Efficient Dynamic
Stream Cipher Cryptosystem”,Iraqi Journal of Science,University of
Baghdad.
X. Xingyuan Wang, Xiaojuan Wang, Jianfeng Zhao, Zhenfeng Zhang, 2010,”
Chaotic encryption algorithm based on alternant of stream cipher and block
cipher “, Springer, Dalian University of Technology.

XI. Gordon Meiser, 2007,” Efficient Implementation of Stream Ciphers on
Embedded Processors”, Ruhr-University Bochum (Germany).
XII. Rusol M. Shaker Alzewary,Ayad G. Naser Al-Shammar,2016, “Designof
High Efficiency Non-linear Keys Generator Based on Shift Registers”, Iraqi
Journal of Science,University of Baghdad.

View Download

Journal Vol – 14 No -5, October 2019

Estimation the Shape Parameter of (S-S) Reliability of Kumaraswamy Distribution

Authors:

A. S. Mohammed, Alaa M. Hamad, Abbas Najim Salman

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00078

October 31, 2019

Abstract:

In this paper dealt with estimating the reliability in the (S-S) stress-strength of Kumaraswamy function distribution using different estimation methods, Maximum likelihood, Moment method, Shrinkage method depend on to Monte Carlo simulation Comparisons between estimation methods have been using mean square error criteria.

Keywords:

Reliability,Stress-Strength (S-S),Kumaraswamy distribution,Maximum likelihood estimator,Moment estimator and Shrinkage estimator,

Refference:

I. A. N. Salman, T.A. Taha, On Reliability Estimation for the Exponential
Distribution Based on Monte Carlo Simulation, Ibn Al-Haitham Journal for
Pure and Applied science, 10.30526/2017, P.P.(409-419).
II. Dreamlee Sharma, Tapan Kumar Chakrabarty, On Size Biased
Kumaraswamy Distribution, Statistics, Optimization, and Information
Computing, Vol 4, Sep 2016, pp 252-264
III. Kumaraswamy, P. A Generalized probability density function fordoublebounded
random processes. Journal of Hydrology 1980,46(1), 79-88.
IV. Mostafa Mohie Eldin1, Nora Khalil2, Montaser Amein, Estimation of
parameters of the Kumaraswamy distribution based on general progressive type II censoring, American Journal of Theoretical and Applied Statistics,
2014; 3(6): 217-222.
V. Mohamed A. Hussian, Estimation of P[Y<X] for the class of Kumaraswamy-
G distributions, Australian Journal of Basic and Applied Sciences, 7(11) Sept
2013, Pages: 158-169.
VI. Muna Shaker Salman, Comparing Different Estimators of two Parameters
Kumaraswamy distribution, Journal of Babylon University/Pure and Applied
Science/ no.(2)/vol.(25):2017,395-402.
VII. Weerahandi, S., and Johnson, R.A.: Testing reliability in a stress-strength
model when X and Y are normally distributed. Technometrics, 1992; 38: 83–
91.

View Download

Journal Vol – 14 No -5, October 2019

A Novel approach to genome editing using Cellular automata evolutions of adjoints sequences

Authors:

Rama Naga Kiran Kumar. K, Ramesh Babu. I

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00079

October 31, 2019

Abstract:

This paper proposes a novel method for genome editing using cellular automata evolutions of adjoints of Adenine, Thymine, Guanine, and Cytosine. The adjoints of the given a genome sequence are the characteristic binary string sequences. For example, the adjoint of Adenine of a given genome sequence is a binary string consisting of 0’s and 1’s where 1’s corresponds to the presence of Adenine in the genome sequence. So, one can have four adjoint sequences of Adenine, Thymine, Guanine, and Cytosine corresponding to a given genome sequence. Onedimensional three neighborhood binary value cellular automata rules can be applied to an adjoint sequence and the desired number of evolutions could be obtained. This rule is defined by a linear Boolean function and one can have 256 such linear Boolean functions. Genome editing is carried out by superimposing the evolved adjoint sequence on the original genome sequence or on its successive evolutions. In this manner, one can have four ways of genome editing using four adjoint sequences and evolutions.

Keywords:

Genome Editing,Cellular Automata,Evolutions of Adjoints,Linear Boolean functions,

Refference:

I. Carey, Nessa (2015). Junk DNA: A Journey Through the Dark Matter of the
Genome. Columbia University Press. ISBN 9780231170840.
II. Chris M. Rands, Stephen Meader, Chris P. Ponting and Gerton Lunter
(2014). “8.2% of the Human Genome Is Constrained: Variation in Rates of
Turnover across Functional Element Classes in the Human Lineage”. PLoS
Genet 10 (7): e1004525. doi:10.1371 /journal.pgen.1004525.
PMC 4109858. PMID 25057982.
III. Costa, Fabrico (2012). “7 Non-coding RNAs, Epigenomics, and Complexity
in Human Cells”. In Morris, Kevin V. Non-coding RNAs and Epigenetic
Regulation of Gene Expression: Drivers of Natural Selection. Caister
Academic Press. ISBN 1904455948.
IV. Dan Graur, Yichen Zheng, Nicholas Price, Ricardo B. R. Azevedo1,
Rebecca A. Zufall and Eran Elhaik (2013). “On the immortality of
television sets: “function” in the human genome according to the evolutionfree
gospel of ENCODE” (PDF). Genome Biology and Evolution5 (3):
578–90. doi:10.1093/gbe/evt028. PMC 3622293. PMID 23431001.
V. Doolittle, W. Ford (2013). “Is junk DNA bunk? A critique of ENCODE”.
Proc Natl Acad Sci USA110 (14): 5294–5300.
Bibcode:2013PNAS..110.5294D. doi:10.1073/pnas.1221376110.
PMC 3619371. PMID 23479647.
VI. Elgar G, Vavouri T; Vavouri (July 2008). “Tuning in to the signals:
noncoding sequence conservation in vertebrate genomes”. Trends Genet. 24
(7): 344–52. doi:10.1016/j.tig.2008.04.005. PMID 18514361.

VII. Ehret CF, De Haller G; De Haller (1963). “Origin, development, and
maturation of organelles and organelle systems of the cell surface in
Paramecium”. Journal of Ultrastructure Research. 9 Supplement 1: 1, 3–42.
doi:10.1016/S0022-5320(63)80088-X. PMID 14073743.
VIII. Gregory TR, Hebert PD; Hebert (April 1999). “The modulation of DNA
content: proximate causes and ultimate consequences”. Genome Res. 9 (4):
317–24. doi:10.1101/gr.9.4.317 (inactive 2015-02-01). PMID 10207154.
IX. Hawkins, JS.; Kim, H.; Nason, JD.; Wing, RA.; Wendel, JF. (Oct 2006).
“Differential lineage-specific amplification of transposable elements is
responsible for genome size variation in Gossypium”. Genome Res 16 (10):
1252–61. doi:10.1101/gr.5282906. PMC 1581434. PMID 16954538.
X. International Human Genome Sequencing Consortium (February 2001).
“Initial sequencing and analysis of the human genome”. Nature 409 (6822):
879–888. Bibcode:2001Natur.409..860L. doi:10.1038/35057062.
PMID 11237011.
XI. Li M, Marin-Muller C, Bharadwaj U, Chow KH, Yao Q, Chen C; Marin-
Muller; Bharadwaj; Chow; Yao; Chen (April 2009). “MicroRNAs: Control
and Loss of Control in Human Physiology and Disease”. World J Surg 33
(4): 667–84. doi:10.1007/s00268-008-9836-x. PMC 2933043.
PMID 19030926.
XII. Mattick JS, Dinger ME (2013). “The extent of functionality in the human
genome”. The HUGO Journal 7 (1): 2. doi:10.1186/1877-6566-7-2.
XIII. Morris, Kevin, ed. (2012). Non-Coding RNAs and Epigenetic Regulation of
Gene Expression: Drivers of Natural Selection. Norfolk, UK: Caister
Academic Press. ISBN 1904455948.
XIV. Marshall CR, Raff EC, Raff RA; Raff; Raff (December 1994). “Dollo’s law
and the death and resurrection of genes”. Proc. Natl. Acad. Sci. U.S.A. 91
(25): 12283–7. Bibcode:1994PNAS…9112283M.
doi:10.1073/pnas.91.25.12283. PMC 45421. PMID 7991619.
XV. Nelson, PN.; Hooley, P.; Roden, D.; Davari Ejtehadi, H.; Rylance, P.;
Warren, P.; Martin, J.; Murray, PG. (Oct 2004). “Human endogenous
retroviruses: transposable elements with potential?”. Clin Exp Immunol 138
(1): 1–9. doi:10.1111/j.1365-2249.2004.02592.x. PMC 1809191.
PMID 15373898.
XVI. Nielsen H, Johansen SD; Johansen (2009). “Group I introns: Moving in new
directions”. RNA Biol 6 (4): 375–83. doi:10.4161/rna.6.4.9334.
PMID 19667762.
XVII. Palazzo, Alexander F.; Gregory, T. Ryan (2014). “The Case for Junk
DNA”. PLoS Genetics10 (5): e1004351. doi:10.1371/journal.pgen.1004351.
ISSN 1553-7404.
XVIII. Pennisi, E. (6 September 2012). “ENCODE Project Writes Eulogy for Junk
DNA”. Science 337 (6099): 1159–1161.
doi:10.1126/science.337.6099.1159. PMID 22955811.
XIX. Petrov DA, Hartl DL; Hartl (2000). “Pseudogene evolution and natural
selection for a compact genome”. J. Hered. 91 (3): 221–7.
doi:10.1093/jhered/91.3.221. PMID 10833048.

XX. Piegu, B.; Guyot, R.; Picault, N.; Roulin, A.; Sanyal, A.; Saniyal, A.; Kim,
H.; Collura, K.; et al. (Oct 2006). “Doubling genome size without
polyploidization: dynamics of retrotransposition-driven genomic expansions
in Oryza australiensis, a wild relative of rice”. Genome Res 16 (10): 1262–
9. doi:10.1101/gr.5290206. PMC 1581435. PMID 16963705.
XXI. Ponting, CP; Hardison, RC (2011). “What fraction of the human genome is
functional?”. Genome Research21: 1769–1776. doi:10.1101/gr.116814.110.
PMC 3205562. PMID 21875934.
XXII. Ponicsan SL, Kugel JF, Goodrich JA; Kugel; Goodrich (February 2010).
“Genomic gems: SINE RNAs regulate mRNA production”. Current
Opinion in Genetics & Development 20 (2): 149–55. doi:10.1016/j.gde.
2010.01.004. PMC 2859989. PMID 20176473.
XXIII. Robin McKie (24 February 2013). “Scientists attacked over claim that ‘junk
DNA’ is vital to life”. The Observer.
XXIV. Sean Eddy (2012) The C-value paradox, junk DNA, and ENCODE, Curr
Biol 22(21):R898–R899.
XXV. Smith MA; et al. (June 2013). “Widespread purifying selection on RNA
structure in mammals”. Nucleic Acids Research 41 (17): 8220–8236.
doi:10.1093/nar/gkt596. PMC 3783177. PMID 23847102.
XXVI. The ENCODE Project Consortium (2012). “An integrated encyclopedia of
DNA elements in the human genome”. Nature 489 (7414): 57–74.
Bibcode:2012Natur.489…57T. doi:10.1038/nature11247. PMC 3439153.
PMID 22955616..
XXVII. Tutar, Y. (2012). “Pseudogenes”. Comp Funct Genomics 2012: 424526.
doi:10.1155/2012/424526. PMC 3352212. PMID 22611337.
XXVIII. Visel A, Rubin EM, Pennacchio LA (September 2009). “Genomic Views of
Distant-Acting Enhancers”. Nature 461 (7261): 199–205.
Bibcode:2009Natur.461..199V. doi:10.1038/nature08451. PMC 2923221.
PMID 19741700.
XXIX. Wahls, W.P.; et al. (1990). “Hypervariable minisatellite DNA is a hotspot
for homologous recombination in human cells”. Cell 60 (1): 95–103.
doi:10.1016/0092-8674(90)90719-U. PMID 2295091.
XXX. Waterhouse, Peter M.; Hellens, Roger P. (25 March 2015). “Plant biology:
Coding in non-coding RNAs”. Nature 520 (7545): 41–42.
doi:10.1038/nature14378.
XXXI. Walters RD, Kugel JF, Goodrich JA; Kugel; Goodrich (Aug 2009).
“InvAluable junk: the cellular impact and function of Alu and B2 RNAs”.
IUBMB Life 61 (8): 831–7. doi:10.1002/iub.227. PMC 4049031.
PMID 19621349.
XXXII. “Worlds Record Breaking Plant: Deletes its Noncoding “Junk” DNA”.
Design & Trend. May 12, 2013. Retrieved 2013-06-04.
XXXIII. Zheng D, Frankish A, Baertsch R; et al. (June 2007). “Pseudogenes in the
ENCODE regions: Consensus annotation, analysis of transcription, and
evolution”. Genome Res. 17 (6): 839–51. doi:10.1101/gr.5586307.
PMC 1891343. PMID 17568002.

 

View Download

Journal Vol – 14 No -5, October 2019

SMART HEALTH CARE SYSTEM USING SENSORS, IOT DEVICE AND WEB PORTAL

Authors:

Suresh S Rao

DOI NO:

https://doi.org/10.26782/jmcms.2019.12.00001

December 28, 2019

Abstract:

Smart health care devices are slowly gaining popularity because of their many advantages over conventional health care system. In the conventional approach, a patient approaches a doctor either in the clinic or hospital. Much of time is spent in patients travel and wait period before he gets approval to meet the doctor. This is much worse for a patient who lives far away and has to spend lots of time in travelling. In general, when a patient first meets the doctor for treatment, he needs to register and then get diagnosed followed by some prescription. After that the patient routinely meets the doctor again leading to travel and wait periods. This will build up lots of stress in the patient especially if he has become weak and if the patient is quite old. The doctor maintains a record of diagnosis and prescription for each patient and this record gets updated on every visit by patient. It may also happen that the doctor may not be available for consultation on certain days due to some emergency or other reasons. This paper suggests a method of handling these issues faced by patient by developing a device and a web portal. The device consists of microcontroller connected to some bio-medical sensors like Temperature, Pulse-Oximeter, ECG, etc. This device can be used to read the patient’s health data on a regular basis and then send it to the Web Server via Wi-Fi module.A Web Portal is also being developed for viewing patient’s data regularly.

Keywords:

IoT,ECG,RFID,WSN,BAN,6LOWPAN,Wi-Fi,

Refference:

I. A J Jara, M A Zamora-Izquierdo, and A F Skarmeta, “Interconnection
Framework for mHealth and Remote Monitoring Based on the Internet of
Things”, Selected Areas in Communications,IEEE Journal on, Vol. 31, pp.
47-65, 2013.
II. C Min, S Gonzalez, V Leung, Z Qian, and L Ming, “A 2G-RFID based ehealthcare
system”, Wireless Communications, IEEE, vol. 17, pp. 37-43,
2010.
III. G Broll, E Rukzio, M Paolucci, M Wagner, A Schmidt, H Hussmann
“PERCI: Pervasive Service Interaction with the Internet Of Things”, IEEE
Internet Computing, 13(6), pp 74-81, 2009
IV. G Kortuem, F Kawsar, D Fitton, V. Sundramoorthy, “Smart objects as
building blocks for the Internet of things,” Internet Computing, IEEE, vol. 14,
pp. 44-51, 2010.
V. G Sebestyen, A Hangan, S Oniga, and Z Gal, “eHealth solutions in the
context of Internet of Things,” in Automation, Quality and Testing, Robotics,
2014 IEEE International Conference on, 2014, pp. 1-6.
VI. G Yang, L Xie, M Mantysalo, X Zhou, Z Pang, L D Xu, et al., “A Health-IoT
Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-
Sensor, and Intelligent Medicine Box”, Industrial Informatics, IEEE
Transactions on, vol. 10, pp. 2180-2191, 2014.
VII. H Fang, X Dan, and S Shaowu, “On the Application of the Internet of Things
in the Field of Medical and Health Care”, in Green Computing and
Communications (GreenCom), 2013 IEEE and Internet of Things
(iThings/CPSCom), IEEE International Conference on and IEEE Cyber,
Physical and Social Computing, 2013, pp. 2053-2058.
VIII. J Jin, J Gubbi, S Marusic, M. Palaniswami, “An information framework for
creating a smart city through Internet of Things”, IEEE Internet of Things
Journal, vol. 1, pp. 112-121, 2014.
IX. José Rouillard “Contextual QR Codes”, The Third International IEEE Multi-
Conference on Computing in the Global Information Technology, ICCGI’08,
pp 50-55, 2008.
X. L Atzori, A Lera and G Morabito, “The Internet of Things: A survey,”
Computer Networks, Vol. 54, pp. 2787-2805, 10/28/ 2010.
XI. L Jingran, C Yulu, T Kai and L Junwen, “Remote monitoring information
system and its applications based on the Internet of Things”, in BioMedical
Information Engineering, 2009. FBIE 2009. International Conference on
Future, 2009, pp. 482-485.
XII. L Xu, L Rongxing, L Xiaohui, S Xuemin, C Jiming, and L Xiaodong, “Smart
community: an internet of things application” Communications Magazine,
IEEE, vol. 49, pp. 68-75, 2011.

XIII. Michael Rohs, Beat Gfeller “Using camera-equipped mobile phones for
interacting real-world objects”, Advances in Pervasive Computing, Austrian
Computer Society (OCG); Ferscha A, Hoertner H, Kotsis G (eds.), 2004.
XIV. O Boric-Lubecke, G Xiaomeng, E Yavari, M Baboli, A Singh and V M
Lubecke, “E-healthcare: Remote monitoring, privacy, and security”, in
Microwave Symposium (IMS), 2014 IEEE MTT-S International, 2014, pp. 1-3.
XV. P Castillejo, J F Martinez, J Rodriguez-Molina and A. Cuerva,”Integration of
wearable devices in a wireless sensor network for an Ehealth application”,
Wireless Communications, IEEE, vol. 20, pp. 38-49, 2013.
XVI. P Swiatek and A Rucinski, “IoT as a service system for eHealth”, in e-Health
Networking, Applications & Services (Healthcom), 2013 IEEE 15th
International Conference on, 2013, pp. 81-84.
XVII. Punit Gupta, Deepika Agrawal, Jasmeet Chhabra, Pulkit Kumar Dhir “IoT
based Smart Health Care Kit”, International Conference on Computational
Techniques in Information and Communication Technologies (ICCTICT), pp
237 – 242 ,1-13 March 2016.
XVIII. R S H Istepanian, S Hu, N Y Philip, and A Sungoor, “The potential of
Internet of m-health Things “m-IoT” for noninvasive glucose level sensing”
in Engineering in Medicine and Biology Society,EMBC, 2011 Annual
International Conference of the IEEE, 2011, pp. 5264-5266.
XIX. R Tabish, A M Ghaleb, R Hussein, F Touati, A Ben Mnaouer, L Khriji, et al.,
“A 3G/WiFi-enabled 6LoWPAN-based U-healthcare system for ubiquitous
real-time monitoring and data logging”, in Biomedical Engineering
(MECBME), 2014 Middle East Conference on, 2014, pp. 277-280.
XX. Roy Want “RFID – A key to automating everything”, Scientific American
290, No 1, 2004, pp 56-65.
XXI. S Chen, X Zhu, S Zhang, and J Wang, “A framework for massive data
transmission in a remote real-time health monitoring system,” in Automation
and Computing (ICAC), 2012 18th International Conference on, 2012, pp. 1-
5.
XXII. Till Quack, Herbert Bay, Luc Van Gool “Object Recognition for Internet of
Things”, Internet Of Things, pp 230-246, Springer Berlin Heidelberg, 2008.
XXIII. W Weihua, L Jiangong, W Ling and Z Wendong, “The internet of things for
resident health information service platform research”, in Communication
Technology and Application (ICCTA 2011), IET International Conference
on, 2011, pp. 631-635.
XXIV. X Boyi, X Li Da, C Hongming, X Cheng, H Jingyuan, B Fenglin,
“Ubiquitous Data Accessing Method in IoT-Based Information System for
Emergency Medical Services”, Industrial Informatics, IEEE Transactions on,
vol. 10, pp. 1578-1586, 2014
XXV. Z Wei, W Chaowei, and Y Nakahira, “Medical application on internet of
things”, in Communication Technology and Application (ICCTA 2011), IET
International Conference on, 2011, pp. 660-665.

View Download

Journal Vol – 14 No -6, December 2019

AN ANALYSIS OF AIR COMPRESSOR FAULT DIAGNOSIS USING MACHINE LEARNING TECHNIQUE

Authors:

Prakash Mohan, Manikandan Sundaram

DOI NO:

https://doi.org/10.26782/jmcms.2019.12.00002

December 28, 2019

Abstract:

Machine Fault Diagnosis is an important domain in Mechanical Engineering which concerns about finding fault in the machine parts. Among many techniques to identify and classify the faults, this paper concerns about using machine learning algorithms to distinguish healthy machines fro mtheun healthy machines. Inordertodistinguishthestateofamachine,classificationalgorithmshas to beused.The accuracy of an algorithm depends upon the pattern, that the data set follows. The suitability of the five most commonly used classification algorithm has been discussed. Various transforms can be applied to such sensor data. Here various algorithms have been tested for wave let packet transform. Thea ccuracy of the fit has been measured for all the five algorithms. Hyper-parametertuning has been done to make the fitbetter.

Keywords:

Principal Component Analysis,Support Vector Machine,Fault Prognosis,Air Compressor,

Refference:

I. Ali J, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015)
Application of empirical mode decomposition and artificial neural
network for automatic bearing fault diagnosis based on vibration signals.
Applied Acoustics89:16– 27.
II. II. C S, B D, S, Manivannan K (2014) Bearing fault diagnosis using
wavelet packet transform, hybrid PSO and support vector machine, vol
97. https://doi.org/10.1016/j.proeng.2014.12.329.
III. C W, Y R, M, Cheng Y (2016) Fault diagnosis for rotating machinery: A
method based on image processing. PLoS. ONE 11(10):1–22
IV. DH, MSC, Song G, RenL,LiH (2015)A review of damage detection methods
forwindturbineblades.SmartMaterialsandStructures24(3):033001,https://doi.org/10.1088/0964-1726/24/3/033001
V. Desmet A, et al. (2017) Leak detection in compressed air systems using
unsupervised anomaly detection techniques pp 1–10
VI. Devendiran S(2016)Vibration Based Condition Monitoring and Fault Diagnosis Technologies
for Bearing and Gear Components AReview11(6):3966–3975
VII. F D, S S, F, Pecht M (2017) Current Noise Cancellation for Bearing Fault
DiagnosisUsingTime-Shifting.IEEETransactionsonIndustrialElectronics
46:1–1
VIII. FM. Arkkio, A. Roivainen J(2014) Electrical Fault Diagnosis for an
Induction Motor Using an Electromechanical FEModel
IX. Fengtao, Song L, Zhang L, Li H (2011) Fault Diagnosis for
Reciprocating Air Compressor Valve Using P-V Indicator Diagram and
SVM
X. G Y, T Z, T, Cao L (2018) A multiscale noise tuning stochastic
resonance for fault diagnosis in rolling element bearings. Chinese
Journal of Physics 56(1):145–157
XI. H S, Ben S, Bacha K, Zeadally S (2015) Smart wireless sensor networks
for online faults diagnosis in an induction machine. Computers and
Electrical Engineering 41:226–239
XII. H Y, Lee WS, Wu CY (2014) Automated fault classification of
reciprocating compressors from vibration data: A case study on
optimization using a genetic algorithm
XIII. HeM, HeD, Bechhoefer E(2016) Using Deep Learning Based Approaches for
Bearing Fault Diagnosis with AE Sensors
XIV. JL,MW, K,SunL (2015a) Mechanical Fault Diagnosis for HV Circuit Breakers Based on
Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine.
https://doi.org/10.1155/2015/101757
XV. J NV, D, Kim JM (2015b) Accelerating 2d fault diagnosis of an induction
motorusingagraphicsprocessingunit.InternationalJournalofMultimedia
and Ubiquitous Engineering 10(1), https://doi.org/10.14257/ijmue.
2015.10.1.32
XVI. K Z, C X, Fang JQ, Zheng PF, Wang J (2017) Fault Feature
Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs
Network. International Journal of Rotating Machinery 2017, URL
https://doi.org/ 10.1155/2017/9602650
XVII. Kavathekar S, Upadhyay N, Kankar PK (2016) Fault Classification of
Ball Bearing by Rotation Forest Technique. Procedia Technology
23:187–192

XVIII. M,BeloiuR(2014) Faultsdiagnosisforelectricalmachinesbasedonanalysis
of motorcurrent
XIX. M, Ushakumari S (2011) Incipient fault detection and diagnosis of
induction motor using fuzzy logic, vol 2013
XX. M A, M R, M, Ehtiwesh I (2010) A combined practical approach to
condition monitoring of reciprocating compressors using IAS and
dynamicpressure. World Academy of Science, Engineering and
Technology63(3):186–192
XXI. Omid M (2016) An intelligent approach
XXII. Prakash A (2014) A review on machine condition monitoring and fault
diagnostics using wavelet transform
XXIII. Q . A S, L S, G, Shao L (2015) Vibration sensor based intelligent fault
diagnosis system for large machine units in petrochemical industries.
InternationalJournalofDistributedSensorNetworks2015,URLhttps://doi.
org/10.1155/2015/239405
XXIV. R,Sugumaran V (2015) Fault diagnosis of automobile hydraulic brake system
using statistical features and support vector machines, vol 52
XXV. R D, S S, K R, Verma NK, Salour A (2016) Generating feature sets for
fault diagnosis using denoising stacked auto-encoder. https://doi.org/10.
1109/ICPHM.2016.7542865
XXVI. RP,S,Jennions IK (2013) Rotor dynamic faults: Recent advances in diagnosis
and prognosis. International Journal of Rotating Machinery 2013,
https://doi.org/10.1155/2013/856865
XXVII. S,Zhou D (2016) Study on a New Fault Diagnosis Method Based on Combining
Intelligent. Technologies11(6):61–72
XXVIII. S E, H J, K, Shahzad T (2017a) Vibration Feature Extraction and
Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey.
Asia Pacific Journal of Multidisciplinary Research5(51):103–110
XXIX. S G, A PJ, Kulkarni JV (2015a) Fault Diagnosis of Bearing of Electric
Motor Using Wavelet Transformand Fault Classification Based on Support Vector.
Machine2(5):41–46
XXX. S L, Z, Hu K (2017b) Traction inverter open switch fault diagnosis based
onchoice-Williamsdistributionspectralkurtosisandwavelet-packetenergy
Shannonentropy.Entropy19(9),https://doi.org/10.3390/e19090504
XXXI. S M, Tan ACC, Mathew J (2015b) A review of prognostic techniques for
non- stationary and non-linear rotating systems, vol 62
XXXII. Shaheryar A, Yin XC, Ramay WY (2017) Robust Feature Extraction on Vibration Data under Deep-Learning Framework:
An Application for Fault Identification in Rotary. Machines International Journal of Computer Applications 167(4):975–8887, URL-https://pdfs.semanticscholar.org/6866/4737a162cfacf3f51d5dd3b7435f2ef9b698.pdf
XXXIII. T, Wu Z (2015) A vibration analysis based on the wavelet entropy method of a scroll compressor
XXXIV. T L, X, Tan ACC (2017a) Fault diagnosis of rolling element bearings
based on Multiscale Dynamic Time Warping. Measurement: Journal of the International Measurement Confederation,
95355(366):10–1016
XXXV. T S, M K, P, Ramachandran KI (2014) Fault diagnosis of automobile gearbox
basedonmachinelearningtechniques,vol97.URLhttps://doi.org/10.1016/
j.proeng.2014.12.452
XXXVI. T V, AlThobiani F, Tinga T, Ball A, Niu G (2017b) Single and combined
faultdiagnosisofreciprocatingcompressorvalvesusingahybriddeepbelief
network. vol 0, URLhttps://doi.org/10.1177/0954406217740929
XXXVII. Verma NK, Sevakula RK, DixitS, Salour A (2016) Intelligent Condition Based
Monitoring Using Acoustic Signals for Air Compressors. IEEE Transactions on Reliability65(1):291–309
XXXVIII. XL,S,HuJ(2017)Improving Rolling Bearing Fault Diagnosis by DS Evidence
Theory Based FusionModel
XXXIX. Y,Al-khassaweneh M (2014) Fault Diagnosis inInternal Combustion Engines
Using Extension Neural Network. IEEE Transactions on Industry
Applica-tions61(3):1434–1443
XL. Y, Benjelloun K (2016) Sleeve Bearing Fault Diagnosis and
Classification Zhao R (2016) Deep Learning and Its Applications to
Machine Health Monitoring: A Survey. vol 14, pp 1–14

View Download

Journal Vol – 14 No -6, December 2019

WEB MINING USING K-MEANS CLUSTERING AND LATEST SUBSTRING ASSOCIATION RULE FOR E-COMMERCE

Authors:

Rudra Prasad Chatterjee, Kaustuv Deb, Sonali Banerjee, Atanu Das, Rajib Bag

DOI NO:

https://doi.org/10.26782/jmcms.2019.12.00003

December 28, 2019

Abstract:

User latency plays a significant role in e-commerce. This latency can be minimized by a priori predicting and fetching probable web pages for web users to run the e-commerce activities. Those prediction techniques are normally supported by clustering, classification and some association rules based on the data set of web logs of navigations, searching and attached web links with the e-commerce web pages. This paper proposes an integrated web page prediction technique by analyzing web users’ previous navigational behavior. K-means clustering and latest substring association rule are considered for developing the proposed method of ecommerce web page prediction. The proposed method is evaluated by analyzing the precisions values of the output clusters using the proposed prediction technique.

Keywords:

Web page prediction,K-Means Clustering,Latest Substring Association Rule,Subsequence Association Rule,Substring Association Rule,

Refference:

I. A. Anitha , “A New Web Usage Mining Approach for Next Page Access
Prediction”, Int. Journal of Computer Applications, 8 (11), 7-10,2010.
II. C. Gutierez-Soto, “On The Reuse of Past Searches in Information Retrieval:
Study of two Probabilistic Algorithm”, Int. Journal of Information System
Modeling and Design. 6 (2). 72-92,2015.
III. C. Ray, and P. Das, “An Outlier Detection Based on Frequent Pattern”, Int.
Journal of Engineering Research & Technology (IJERT), 2(11), 2810-2814,
2013.
IV. G. Khodabandelou, C. Hug, and C. Salinesi, “Mining User’s Intents from
logs”, Int. Journal of Information System Modeling and Design, 6(2), 43-71,
2015.

V. G. Shrivastava , K. Sharma , and A. Bawankan, “A new framework semantic
web technology based e-learning”, In Environment and Electrical
Engineering (EEEIC), 2012 11th International Conference on (pp. 1017-
1021).
VI. G. Shrivastava, and V. Bhatnagar, “Secure Association Rule Mining for
Distributed Level Hierarchy in Web”, International Journal on Computer
Science and Engineering, 3(6), 2240-2244,2011.
VII. G. Shrivastava, and V. Bhatnagar, “Analyses of Algorithms and Complexity
for Secure Association Rule Mining of Distributed Level Hierarchy in Web”,
International Journal of Advanced Research in Computer Science, 2(4), 2011.
VIII. I. Chhabra, G. Suri, “Data Science and Knowledge Discovery through Data
Mining Paradigms”, Journal of Mechanics of Continua and
Mathematical Sciences, 14(2), 167-173, 2019
IX. J. Chembath, and T. Fredrik, “Ensemble System Using Hybrid Next Web
Page Prediction Algorithm Combining Clustering, Markov Model With
Associative and Longest Common Subsequence Classifiers”, Int. Journal of
Pure and Applied Mathematics, 119(12), 5329-15339,2018.
X. K. Avrachenkov, V. Dobrynin , D. Nemirovsky, S. K. Pham and E.
Smirnova, “Pagerank based clustering of hypertext document collections ”, In
Proc. of the 31st Annual Int. ACM SIGIR Conf. on Research and
Development in Information Retrieval, 873-874, ACM,2008.
XI. K. Sharma, G. Shrivastava, and D. Singh, “Risk Impact of Electronics-
Commerce Mining: A Technical Overview”, International Conference on
Computing Communication and Information Technology, pp 96-100, 2012.
XII. K. Sharma and G. Shrivastava, “Public Key Infrastructure and Trust of Web
Based Knowledge Discovery”, International Journal of Engineering, Sciences
and Management, 4(1), 56-60,2011.
XIII. K. Sharma, G. Shrivastava, and V. Kumar, “Web mining: Today and
tomorrow”. Electronics Computer Technology (ICECT), 2011 3rd
International Conference on (Vol. 1, pp. 399-403).
XIV. K. Sharma, and V. Bhatnagar, “Private and Secure Hyperlink Navigability
Assessment in Web Mining Information System”, International Journal on
Computer Science and Engineering, 3(6), 2245-2250,2011.
XV. O. P. Mandal , and H. K. Azad, “Web Access Prediction Model Using
Clustering and Artificial Neural Network”, Int. Journal of Engineering
Research and Technology, 3(9),195-199, 2014.
XVI. P. Bhart , D. Shukla , D. S. Saini and A. Gaur, “Composite Model of Web
Page Prediction Using Page Ranking Algorithm and Markov Model”, 4th Int.
Conf. on Recent development in Engineering Science, 125-13,2017.

XVII. P. Sampath and M. Prabhavathy, “Web Page Access Prediction using Fuzzy
Clustering by Local Approximation Memberships (FLAME) Algorithm”,
ARPN Journal of Engineering and Applied Sciences, 10(7), 3217-3220,
2015.
XVIII. R. Geetharamani, P. Revathy and S. G. Jacob, “Prediction of Users Webpage
Access Behaviour Using Association Rule Mining”, Sadhana, 40(8), 2353-
2365,2015.
XIX. R. P. Chatterjee , M. Ghosh , M. K. Das , and R.Bag, “Webpage Prediction
Using Latest Substring Association Rule Mining”, In Foundations and
Frontiers in Computer, Communication and Electrical Engineering: Proc. of
the 3rdInt. Conf. C2E2, Mankundu, West Bengal, India, 15th-16th January,
2016. (p. 157), CRC Press.
XX. Sunita, V. Rana, “Improved probable clustering based on data
dissemination for retrieval of web URLs”, Journal of Mechanics of
Continua and Mathematical Sciences, 14(5), 285-294, 2019.

View Download

Journal Vol – 14 No -6, December 2019

ON A CERTAIN SUBCLASS OF HARMONIC UNIVALENT FUNCTIONS DEFINED Q-DIFFERENTIAL OPERATOR

Authors:

B. RAVINDAR, R. B. SHARMA, N. MAGESH

DOI NO:

https://doi.org/10.26782/jmcms.2019.12.00004

December 28, 2019

Abstract:

The concepts of q-analysis has numerous applications in different subfields of science such as optimal control, ordinary fractional calculus, geometric function theory, qintegral and q-difference equations. In this paper we define certain subclasses of harmonic univalent functions in the open unit disk U  {zC : | z |  1} by utilizingqdifferential operator and obtain coefficient bounds and extreme points for the functions in this class.

Keywords:

q-differential operator,Harmonic function,Salagean operator,univalent function,

Refference:

I. J. Clunie as well as T. Sheil-Small, Harmonic univalent functionalities, Ann.
Acad. Sci. Fenn.Ser. A I Mathematics. 9 (1984 ), 3-25.
II. J. M. Jahangiri, Accordant features starlike in the device disk, J. Mathematics.
Anal. Appl. 235( 1999 ), no. 2, 470-477.
III. J. M. Jahangiri, Harmonic univalent features determined next to q-calculus
drivers, Int. J.Mathematics. Anal. and also Appl. 5 (2018 ), no. 2, 39-43.
IV. J. M. Jahangiri, G. Murugusundaramoorthy as well as K. Vijaya, Salagean-type
harmonicunivalent functionalities, South west J. Pure Appl. Arithmetic. (2002 ),
no. 2, 77-82.
V. H. Lewy, On the non-vanishing of the Jacobian in a specific one-to-one
applyings,Bull. Amer. Mathematics. Soc. 42 (1936 ), no. 10, 689-692.
VI. S. D. Purohit and R. K. Raina, Certain subclasses of analytic functions
associated with fractional q-calculus operators, Math. Scand. 109 (2011), no. 1,
55-70.
VII. B. Ravindar and R. Bharavi Sharma, On a subclass of harmonic univalent
functionsassociated with the differential operator, International Journal of
Engineering and Technology (UAE). 7 (2018), no. 3.3, 146-151.
VIII. B. Ravindar and R. Bharavi Sharma and N. Magesh, On a subclass of harmonic
univalent functions defined by Ruscheweyh q- differential operator, AIP
Conference Proceedings. 2112 (2019), 020018.
IX. R. Bharavi Sharma and B. Ravindar, On a subclass of harmonic univalent
functions defined by convolution and integral convolution, International Journal
of Pure and Applied Mathematics, 117 (7) 2017, pp. 135-145.
X. R. Bharavi Sharma and B. Ravindar, On a subclass of harmonic univalent
functions, Journal of Physics: Conf. Series 1000 (2018) 012115, doi:
10.1088/1742- 6596/1000/1/012115.

View Download

Journal Vol – 14 No -6, December 2019