DECODING INTENTIONS TO PURCHASE ORGANIC FOOD PRODUCTS IN AN EMERGING ECONOMY VIA ARTIFICIAL NEURAL NETWORKS
subject: artificial neural networks; green consumerism; green products; organic food; organic food products; sustainability; Sustainable Development Goals
description: Purpose. This study investigates the factors influencing consumers intentions to purchase organic food products in an emerging economy. It addresses the knowledge gap regarding the slower growth of the organic food market in these regions despite the global trend toward environmental sustainability. Methodology / approach. A survey approach involving 350 participants was used. Structural equation modeling (SEM) with SmartPLS 4 and Artificial Neural Network (ANN) with IBM SPSS 28 were used to analyse the impact of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products. Results. The study found significant positive influences of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products, explaining 63.1 % of the variance. Both the analysis approaches (PLS-SEM & ANN) revealed that, health consciousness, followed by awareness of need, emerged as the most important factor related to the intention to purchase organic food products. The results highlight the importance of awareness and personal values in driving pro-environmental behaviour. Originality / scientific novelty. This research offers essential insights into the determinants of organic food purchase intentions in an emerging economy. It emphasises the significance of awareness and personal values in fostering sustainable consumption behaviour, addressing a less explored area in existing literature. Practical value / implications. The findings have important implications for policymakers and marketers. Strategies focused on consumer education about the benefits of organic food can enhance awareness and appeal. Understanding core psychological needs and beliefs that shape consumer motivations can guide the development of effective marketing strategies. The study highlights the strong environmental consciousness among consumers and their desire to protect the envir…
creator: Uikey A.A.; Marak Z.R.; Alsaleh D.; Baber R.
source: Agricultural and Resource Economics, Vol-10, No. 4, pp. 301-328.
publisher: Institute of Eastern European Research and Consulting
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 2414584X
format: Online
language: English
type: Article
coverage: Uikey A.A., Symbiosis International (Deemed University), India; Marak Z.R., Symbiosis International (Deemed University), India; Alsaleh D., Abdullah Al Salem University, Kuwait; Baber R., Christ University, India

Which is the green generation? Amultigroup analysis of millennials and Generation Zs green consumerism
subject: Green consumerism; Green marketing; Green marketing mix; Green purchase intention; Sustainability; Sustainable development goals; Theory of planned behaviour
description: Purpose This study aimed to investigate how components of green marketing mix (GMM), green product (GPD), green price (GPC), green place (GPL) and green promotion (GPM) influence consumer attitudes (ATT), subjective norms (SNM), perceived behavioural control (PBC) and purchase intention (PI) and finally green consumerism (GCM). Design/methodology/approach Using Smart PLS 4 software and PLS-SEM approach, data were analysed for structural relationships among the components of GMM, ATT, SNM, PBC, PI and GCM. The model evaluates hypotheses linking GPD, GPC, GPL and GPM to ATT, SNM and PBC and examines how ATT, SNM and PBC affect PI and GCM. Findings The study revealed that GMM, as a higher-order construct, positively impacts ATT, SNM and PBC, while ATT, SNM and PBC partially mediate the relation between GMM and PI. PI then ultimately results in GCM. The multigroup analysis indicated there is no significant difference between the age groups examined. Research limitations/implications The study may not generalize to all industries or regions. Future research could explore additional factors like cultural or technological influences, and longitudinal studies may be conducted. Practical implications As environmental concerns grow, marketers should focus on consumer attitudes towards green products. Aligning green attributes with consumer values, transparent pricing and multi-channel communication can enhance ATT, SNM and PBC over green purchases, fostering acceptance and intention. Social implications While the findings promote GCM, their broader impact is contingent on genuine environmental practices. Without systemic changes in production and policy, GCM risks perpetuating superficial sustainability narratives. Originality/value This study advances the field by investigating how GMM influences purchase intentions (PI) among Indias urban Millennials and Generation Z, two generations pivotal to shaping sustainable consumption trends in a high-pollution economy. 2025 Emerald…
creator: Uikey, Ashish Ashok; Baber, Ruturaj; Baber, Prerana; Marak, Zericho R.; Mishra, Devvandya
source: British Food Journal;
publisher: Emerald Publishing
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 0007070X;
format: online
language: English
type: Article
coverage: Uikey A.A., Symbiosis Centre for Management Studies, Nagpur Campus, Symbiosis International (Deemed University), Pune, India; Baber R., CHRIST University, Bengaluru, India; Baber P., Symbiosis Law School, Noida, Symbiosis International (Deemed University Pune), Pune, India; Marak Z.R., Symbiosis Centre for Management Studies, Nagpur Campus, Symbiosis International (Deemed University), Pune, India; Mishra D., Jaipuria Institute of Management Jaipur Campus, Jaipur, India

TRANSFORMING GREEN TRANSPARENCY INTO GREEN BRAND LOYALTY AND REPURCHASE INTENTIONS: THE ROLE OF BRAND IMAGE AND CREDIBILITY AMONG ELECTRIC VEHICLE USERS
subject: Brand credibility; Brand image; Electric vehicles; Green brand loyalty; Green marketing; Green transparency; Repurchase intention
description: The present study leverages the Stimulus-Organism-Behavior-Consequence (SOBC) framework to investigate how green transparency influences green brand loyalty and repurchase intention among electric vehicle consumers. Specifically, it examines the mediating roles of brand image and brand credibility in the relationships between green transparency, green brand loyalty, andrepurchase intention. Data collected from 386 electric vehicle users were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results reveal that green transparency positively impacts brand image and brand credibility, which subsequently enhances green brand loyalty and repurchase intention. Mediation analysis further highlights brand image and brand credibility as critical mechanisms linking green transparency to green brand loyalty. This study extends the SOBC framework to green marketing, offering theoretical and practical insights into fostering sustainable consumer behavior. By emphasizing the role of green transparency in building credible and compelling brand narratives, the findings guide marketers in cultivating consumer trust and loyalty while supporting policymakers in formulating transparency regulations for a sustainable marketplace. 2025 Journal of Applied Structural Equation Modeling.
creator: Uikey, Ashish Ashok; Baber, Ruturaj; Marak, Zericho R.
source: Journal of Applied Structural Equation Modeling;Volume;9;Issue;1;
publisher: Sarawak Research Society
date: 01-01-2025
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 25904221;
format: online
language: English
type: Article
coverage: Uikey A.A., Symbiosis Centre for Management Studies, Nagpur Campus, Symbiosis International (Deemed University), Pune, India; Baber R., School of Business and Management, Christ University, Bengaluru, India; Marak Z.R., Symbiosis Centre for Management Studies, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

Trust green, pay more: Decoding green brand loyalty and willingness to pay more for electric vehicles through green transparency and green perceived value
subject: Green marketing; Green transparency; Stimulusorganismresponse (SOR); Sustainability; Sustainable development goals
description: The StimulusOrganismResponse framework is applied in this study to explore the impact of Green Transparency (stimuli) and Green Perceived Value (stimuli) on Green Brand Trust (organism) and, subsequently, on Green Brand Loyalty (response) and Willingness to Pay More (response). Self-Brand Connection is examined as a moderator. An online survey was distributed to 557 EV consumers. We employed both PLS-SEM (SmartPLS 4) and CB-SEM (AMOS 29) to test the direct, mediating, and moderating effects, with CB-SEM used as a robustness check for model stability. The results show that both Green Transparency and Green Perceived Value are positive antecedents of Green Brand Trust. Green Brand Trust, in turn, positively influences Green Brand Loyalty and Willingness to Pay More and mediates the effects of the two stimuli. The results also confirm that Self-Brand Connection significantly and positively strengthens the Green Brand Trust?Green Brand Loyalty and Green Brand Trust?Willingness to Pay More relationships. This study establishes Green Brand Trust as a core green consumer behavior mechanism and identity alignment as a catalyst for Green Brand Loyalty and Willingness to Pay More, offering actionable guidance to EV brands for credibility building, customer retention, and sustainable consumption. 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/
creator: Uikey, Ashish Ashok; Marak, Zericho R.; Baber, Ruturaj; Kautish, Pradeep
source: Acta Psychologica;Volume;264;Issue;;Article No.;106598;
publisher: Elsevier B.V.
date: 01-01-2026
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 16918; CODEN: APSOA
format: online
language: English
type: Article
coverage: Uikey A.A., Symbiosis Centre for Management Studies, Nagpur Campus, Symbiosis International (Deemed University), Pune, India; Marak Z.R., Symbiosis Centre for Management Studies, Nagpur Campus, Symbiosis International (Deemed University), Pune, India; Baber R., CHRIST University, Karnataka, Bengaluru, India; Kautish P., Department of Marketing, Institute of Management, Nirma University, Gujarat, Ahmedabad, India

Disentangling the association of PAH molecules with star formation
subject: galaxies: ISM; galaxies: star formation; infrared: galaxies; ISM: molecules; ultraviolet: galaxies
description: Context. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous complex molecules in the interstellar medium and are used as an indirect indicator of star formation. On the other hand, the ultraviolet (UV) emission from young massive stars directly traces the star formation activity in a galaxy. The James Webb Space Telescope (JWST), along with the UltraViolet Imaging Telescope (UVIT), opened up a new window of opportunity to better understand the properties of PAH molecules that are associated with star-forming regions. Aims. We investigate how the resolved scale properties of PAH molecules in nearby galaxies are affected by star formation. Methods. We analyzed the PAH features observed at 3.3, 7.7, and 11.3 m using F335M, F770W, and F1130W images obtained from the JWST. These images helped us identify and quantify the PAH molecules. Additionally, we used UVIT images to assess the star formation associated with these PAH-emitting regions. Our study focused on three galaxies, namely NGC 628, NGC 1365, and NGC 7496, which were selected based on the availability of both JWST and UVIT images. Bright PAH emission regions were identified in the JWST images, and their corresponding UV emission was estimated using the UVIT images. We quantified the star formation properties of these PAH emitting regions using the UVIT images. Furthermore, we investigated the relation between the star formation surface density (?SFR) and the PAH ratios to better understand the impact of star formation on the properties of PAH molecules. Results. Based on the resolved scale study of the PAH-bright regions using JWST images, we found that the fraction of ionized PAH molecules is high in the star-forming regions with high ?SFR. We observed that emission from smaller PAH molecules is higher in star-forming regions with higher ?SFR. Conclusions. Our study suggests that the PAH molecules excited by the photons from star-forming regions with higher ?SFR are dominantly smaller and ionized molecules…
creator: Ujjwal K.; Kartha S.S.; Akhil K.R.; Mathew B.; Subramanian S.; Sudheesh T.P.; Thomas R.
source: Astronomy and Astrophysics, Vol-684
publisher: EDP Sciences
date: 2024-01-01
rights: All Open Access; Hybrid Gold Open Access
relation: ISSN: 46361; CODEN: AAEJA
format: Online
language: English
type: Article
coverage: Ujjwal K., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Akhil K.R., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Subramanian S., Indian Institute of Astrophysics, Sarjapur Road, Koramangala, Bangalore, 560034, India; Sudheesh T.P., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Thomas R., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India

Analysis of Membership Probability in Nearby Young Moving Groups with Gaia DR2
description: We analyze the membership probability of young stars belonging to nearby moving groups with Gaia DR2 data. The sample of 1429 stars was identified from "The Catalog of Suspected Nearby Young Moving Group Stars." Good-quality parallax and proper motion values were retrieved for 890 stars from the Gaia DR2 database. The analysis for membership probability is performed in the framework of the LACEwING algorithm. From the analysis it is confirmed that 279 stars do not belong to any of the known moving groups. We estimated the U, V, W space velocity values for 250 moving group members, which were found to be more accurate than previous values listed in the literature. The velocity ellipses of all the moving groups are well constrained within the "good box," a widely used criterion to identify moving group members. The age of moving group members are uniformly estimated from the analysis of the Gaia color-magnitude diagram with MIST isochrones. We found a spread in the age distribution of stars belonging to some moving groups, which needs to be understood from further studies. 2020. The American Astronomical Society. All rights reserved..
creator: Ujjwal K.; Kartha S.S.; Mathew B.; Manoj P.; Narang M.
source: Astronomical Journal, Vol-159, No. 4
publisher: Institute of Physics Publishing
date: 2020-01-01
rights: All Open Access; Bronze Open Access; Green Open Access
relation: ISSN: 46256
format: Online
language: English
type: Article
coverage: Ujjwal K., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Manoj P., Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India; Narang M., Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai, 400005, India

Understanding the secular evolution of NGC 628 using UltraViolet Imaging Telescope
subject: galaxies: evolution; galaxies: spiral; galaxies: star formation
description: Secular and environmental effects play a significant role in regulating the star-formation rate and hence the evolution of the galaxies. Since ultraviolet (UV) flux is a direct tracer of the star formation in galaxies, the UltraViolet Imaging Telescope (UVIT) onboard AstroSat enables us to characterize the star-forming regions in a galaxy with its remarkable spatial resolution. In this study, we focus on the secular evolution of NGC 628, a spiral galaxy in the local Universe. We exploit the resolution of UVIT to resolve up to ?63 pc in NGC 628 for identification and characterization of the star-forming regions. We identify 300 star-forming regions in the UVIT far-UV image of NGC 628 using ProFound and the identified regions are characterized using Starburst99 models. The age and mass distribution of the star-forming regions across the galaxy supports the inside-out growth of the disc. We find that there is no significant difference in the star-formation properties between the two arms of NGC 628. We also quantify the azimuthal offset of the star-forming regions of different ages. Since we do not find an age gradient, we suggest that the spiral density waves might not be the possible formation scenario of the spiral arms of NGC 628. The headlight cloud present in the disc of the galaxy is found to be having the highest star-formation rate density (0.23 Myr-1 kpc-2) compared to other star-forming regions on spiral arms and the rest of the galaxy. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
creator: Ujjwal K.; Kartha S.S.; Subramanian S.; George K.; Thomas R.; Mathew B.
source: Monthly Notices of the Royal Astronomical Society, Vol-516, No. 2, pp. 2171-2180.
publisher: Oxford University Press
date: 2022-01-01
rights: All Open Access; Green Open Access
relation: ISSN: 358711; CODEN: MNRAA
format: Online
language: English
type: Article
coverage: Ujjwal K., Department of Physics and Electronics, CHRIST (Deemed to Be University), Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to Be University), Bangalore, 560029, India; Subramanian S., Indian Institute of Astrophysics, Sarjapur Road, Koramangala, Bangalore, 560034, India; George K., Faculty of Physics, Ludwig-Maximilians-Universit, Scheinerstr 1, Munich, D-81679, Germany; Thomas R., Department of Physics and Electronics, CHRIST (Deemed to Be University), Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to Be University), Bangalore, 560029, India

Jabbar Patel filmmaking - An auteur theory approach /
subject: Media Studies
description: Films are a product of the director‟s mind. Through films we convey and understand certain messages by the use of certain symbols and metaphors that reoccur in our surrounding. Studies show that directors have their own individual style or pattern in which they prefer to portray certain elements in the movie.
creator: Ukken, Annette James. - 1324029
source: Master of Science in Communication
publisher: CHRIST (Deemed To Be University)
date: 2015-02-03
contributor: Department of Media Studies
format: PDF
language: English
type: Masters Dissertation

A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence
subject: Gated Recurrent Units (GRU); Long Short-Term Memory (LSTM)network; Multilayer Perception Layer (MLP) architecture; Precipitation; Recurrent Neural Network (RNN)
description: Flood occurs as often as possible happens due to many environmental changes in our planet in the present years. The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies. The various factors that play a major role in the occurrence of rainfall are rise in temperature, humidity level, dew point, pressure in and around the area of concern, wind speed, etc. In order to reduce the number of victims due to flood it is necessary to have a system to predict flood occurrence. In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results. In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors. 2020, Asian Research Association. All rights reserved.
creator: Ullah T.F.; Gnana Prakasi O.S.; Kanmani P.
source: International Research Journal of Multidisciplinary Technovation, Vol-2, No. 5, pp. 8-14.
publisher: Asian Research Association
date: 2020-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 25821040
format: Online
language: English
type: Article
coverage: Ullah T.F., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Gnana Prakasi O.S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Kanmani P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India

Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
subject: ARP spoofing; Broadcast; MAC spoofing; Man-in-the-middle attack; MS; Network trace
description: Ubiquitous computing is a new paradigm in the world of information technology. Security plays a vital role in such networking environments. However, there are various methods available to generate different Media Access Control (MAC) addresses for the same system, which enables an attacker to spoof into the network. MAC spoofing is one of the major concerns in such an environment where MAC address can be spoofed using a wide range of tools and methods. Different methods can be prioritized to get cache table and attributes of ARP spoofing while targeting the identification of the attack. The routing trace-based technique is the predominant method to analyse MAC spoofing. In this paper, a detailed survey has been done on different methods to detect and prevent such risks. Based on the survey, a new proposal of security architecture has been proposed. This architecture makes use of Monitoring System (MS) that generates frequent network traces into MS table, server data and MS cache which ensures that the MAC spoofing is identified and blocked from the same environment. 2019, Springer Nature Singapore Pte Ltd.
creator: Ullas S.U.; Sandeep J.
source: Advances in Intelligent Systems and Computing, Vol-750, pp. 141-153.
publisher: Springer Verlag
date: 2019-01-01
rights: Restricted Access
relation: ISSN: 21945357; ISBN: 978-981131881-8
format: Online
language: English
type: Conference paper
coverage: Ullas S.U., Department of Computer Science, Christ University, Bengaluru, 560029, India; Sandeep J., Department of Computer Science, Christ University, Bengaluru, 560029, India

A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment
subject: Data Mining; Gaussian Process and Weka; Multi-layer Perceptron; SMO Reg; Stock Price Prediction
description: It is widely agreed that the share price is too volatile to be reliably predicted. Several experts have worked to improve the likelihood of generating a profit from share investing using various approaches and methods. When used in reality, these methods and algorithms often have too low of a success rate to be helpful. The extreme volatility of the marketplace is a significant contributor. This article demonstrates the use of data mining methods like WEKA to study share prices. For this research's sake, we have selected a HCL Tech share. Multilayer perceptron's, Gaussian Process and Sequential minimal optimization have been employed as the three prediction methods. These algorithms that develop optimal rules for share market analysis have been incorporated into Weka. We have transformed the attributes of open, high, low, close and adj-close prices forecasted share for the next 30 days. Compare actual and predicted values of three models' side by side. We have visualized 1step ahead and the future forecast of three models. The Evaluation metrics of RMSE, MAPE, MSE, and MAE are calculated. The outcomes achieved by the three methods have been contrasted. Our experimental findings show that Sequential minimal optimization provided more precise results than the other method on this dataset. 2023 IEEE.
creator: Uma Devi A.; Vishal Kumar R.; Shanmugha Priya P.; Amzad Basha M.S.; Martha Sucharitha M.; Oveis P.M.
source: 2023 5th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-166549360-4
format: Online
language: English
type: Conference paper
coverage: Uma Devi A., SRM Valliammai Engineering College, Department of Management Studies, Chengalpattu, India; Vishal Kumar R., Kalasaligam Academy of Research and Education, Department of Business Administration, Coimbatore, India; Shanmugha Priya P., Sri Krishna College of Technology, Coimbatore, India; Amzad Basha M.S., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Oveis P.M., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India

Investigation of Cervical Cancer Detection from Whole Slide Imaging
subject: Cervical cancer; Classification; Deep Learning; Neural Network; Segmentation
description: Early cancer detection is critical in enhancing a patient's clinical results. Cervical cancer detection from a large number of whole slide images generated regularly in a clinical setting is a complex and time-consuming task. As a result, we require an efficient and accurate model for early cancer diagnosis, especially cervical cancer as it can be fully prevented if detected in an early stage. This study focuses on in-depth writing on current methodologies for cervical cancer segmentation and characterization from the whole cervical slide. It combines the state of their specialty's performance measurement with the quantitative evaluation of cutting-edge techniques. Numerous publications over the last eleven years (2011-2022) clearly outline various cervical imaging methods over multiple blocks. And this review shows different types of algorithms used in each processing stage of detection. The study clearly indicates the advancements in the automation field and the necessity of the same. Published under licence by IOP Publishing Ltd.
creator: Uma N.; Inbanila K.
source: Journal of Physics: Conference Series, Vol-2571, No. 1
publisher: Institute of Physics
date: 2023-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 17426588
format: Online
language: English
type: Conference paper
coverage: Uma N., Department of Electronics & Communication, Christ University, Bangalore, India; Inbanila K., Department of Electronics & Communication, Christ University, Bangalore, India

Overt dependence of health insurance industry on healthcare system
subject: Health belief model; Health insurance; Healthcare; Illness and non-illness; Willingness to buy
description: A vast majority of the population in the developing economies remains uninsured. Moreover, the informal sector that employs a larger section of the society is untouched by any of the government scheme. In this study, we use health belief model to examine the factors that induce willingness to buy health insurance among the illness and the non-illness group. A cross-sectional study was conducted on 1,339 participants above 20 years of age of which 351 had contracted illness in the past and 988 had not. Data was collected using questionnaire from four highly populated districts in India. The questionnaire was developed based on the constructs of health belief model. The data was statistically analysed. Kendalls Tau-b correlation technique was used to explore the relationship between perceived vulnerability and product aversion. Logistic regression was used to find out the odds at which each independent variable, categorised based on the health belief model, contributes to willingness to buy. The model was able to predict 15% of the variance for willingness-to-buy among the illness and 27% among the non-illness groups. Findings suggest that the perceived vulnerability reduced product aversion among the illness group. Mere presence of primary and super-specialty hospitals was not sufficient for the illness group to subscribe for health insurance. Income perceptions emerged as a significant predictor among the illness group. Presence of well-established hospital, income perceptions, and subjective norms were significant predictors among the non-illness group. The growth of the health insurance industry largely depends upon the presence of well-established hospitals. In the absence of adequate healthcare facilities, attempts by the insurers to promote insurance covers will become futile. Insurers should also consider alternate segmentation patterns albeit the present socio-demographic pattern, as the health risk experience differs among individuals. Asian Academy of Manag…
creator: Uma V.R.; Ilango V.
source: Asian Academy of Management Journal, Vol-26, No. 1, pp. 197-219.
publisher: Penerbit Universiti Sains Malaysia
date: 2021-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 13942603
format: Online
language: English
type: Article
coverage: Uma V.R., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Bangalore, 560029, India; Ilango V., Department of Computer Application, CMR Institute of Technology, Bangalore, 560037, India

Examining women's purchase pattern of casual footwear in accordance with their attitudes and interests
subject: Attributes; Behavior; Footwear; Regression; Women
description: Purpose: The present study examines the association between the choices of casual footwear attributes of women in accordance with their behavioral pattern. Design/Methodology/Approach: Data was collected from 2365 women through a questionnaire that comprised of two sections. The first section comprised of 50 AIO statements based on which the respondents were profiled according to their behavioural patterns. The second section comprised of selected footwear and store attributes. The consumers were profiled into eleven clusters using factor analysis. The regression scores were used to assign the respondents to the respective components that were extracted through factor analysis. Reliability Test and KMO Test were conducted to check the reliability and adequacy of the sample size. Further, only those variables that qualified the collinearity test were alone subject to regression analysis. Through ANOVA test, it was observed that significant differences existed among the consumers within the clusters. Therefore, the AIO statements were considered as independent variables that were regressed against ten selected footwear attributes. Findings: The Results indicated that consumers with different behaviors had varied preferences towards footwear attributes. Practical Implications: The results of the study indicate that the manufacturers in the footwear sector should revisit their existing strategies and target the consumers on the basis of their behavior as the proliferation of the unorganized sector is very high in this sector. Original Value: There are innumerable literatures that focus on trade policies followed in the footwear market in international countries, treatment of workers in the footwear industry, therapeutic use of footwear, supply chain patterns etc., but hardly any significant study that explores the consumers' behaviour and their association towards their footwear preferences has been conducted. Behavioral segmentation has been used in many other products…
creator: Uma V.R.; Saifil Ali M.I.
source: Indian Journal of Marketing, Vol-43, No. 8, pp. 46-64.
publisher: Associated Management Consultants Pvt. Ltd.
date: 2013-01-01
rights: Restricted Access
relation: ISSN: 9738703
format: Online
language: English
type: Article
coverage: Uma V.R., Department of Commerce, Christ University, Bangalore - 560 029, India; Saifil Ali M.I., School of Management (DASM), Dhaanish Ahmed College of Engineering, Chennai -601301, India

Recruitment Analytics: Hiring in the Era of Artificial Intelligence
subject: Artificial intelligence; Chatbots; Gamification; Recruitment; Resume screening; Virtual interviews
description: Introduction: Traditional recruitment system relied heavily on the applicants curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be misfits. CVs were the only source of candidates data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process. Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages. Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as trai…
creator: Uma V.R.; Velchamy I.; Upadhyay D.
source: The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A, pp. 155-174.
publisher: Emerald Publishing
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-180382028-6; 978-180382027-9
format: Online
language: English
type: Book chapter
coverage: Uma V.R., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Bangalore, India; Velchamy I., Department of Computer Application, CMR Institute of Technology, Bangalore, India; Upadhyay D., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Bangalore, India

LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm
subject: Intersection over Union (IoU) metric; LSTM-MGTO (a technique combining long short-term memory and modified Gorilla troop optimization); Mammary neoplasm; U-Net (a neural network architecture); WDBC and BCCD (two datasets related to breast cancer diagnosis)
description: One of the most prevalent and severe tumors in women, breast cancer, remains a major global health issue despite a notable increase in incidence over the last ten years. It is the second leading cause of cancer-related death among women. Identifying breast cancer in its early stages has the potential to save lives; however, current screening techniques for the illness require several laboratory procedures involving medical experts. Automated solutions with rapid and reliable diagnostic capabilities are needed to minimize human error and expedite breast cancer diagnosis. The projected accuracy of cancer diagnosis remains far from matching the precision offered by existing approaches, even with the research on automated systems for the disease being studied. This work suggests a long short-term memory-based modified Gorilla troop optimization (LSTM-MGTO) method for breast cancer classification in order to address these issues. The Mastectomy Koibra Dataset (BCCD) and Wisconsin Diagnostic Mastectomy (WDBC) datasets were used to test the suggested methods. First, the proposed system employs contrast-limited adaptive histogram equalization (CLAHE) to enhance the quality of digital mammograms. Furthermore, employ a semantic deep learning (SDL) model to extract features. After the feature selection process, a recursive feature elimination technique was implemented to determine the crucial WDBC and BCCD characteristics that are relevant to breast cancer detection. Moreover, recommend a modified U-Net architecture for partitioning in both unmapped and guided contexts. The experimental findings indicate that the newly developed partitioning model surpasses existing advanced techniques, yielding superior results in both Dice and IoU score evaluations. On the WDBC and BCCD datasets, the suggested U-Net segmentation produces maximum Dice scores of 97.65% and 96.24%, respectively. Additionally, the model obtained the greatest IoU scores of 95.43% and 90.65% on the WDBC and BCCD d…
creator: Umamaheswari, D.; Kannan, M.; Mary, I. Priya Stella; Rozario, D. Juliet; Savitha, P. Margaret; Manimekala, B.
source: Network Modeling Analysis in Health Informatics and Bioinformatics;Volume;14;Issue;1;Article No.;17;
publisher: Springer
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 21926662;
format: online
language: English
type: Article
coverage: Umamaheswari D., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Kannan M., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Mary I.P.S., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Rozario D.J., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Savitha P.M., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Manimekala B., Department of Computer Science, Christ University, Karnataka, Bangalore, India

Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning
subject: Convolutional neural networks; Deep learning; Ensemble learning; Image tampering; Particle swarm optimization
description: The field of image processing is crucial for many different applications, including forensic evidence, insurance claims, medical imaging, bio-informatics, artifact collection and more. In many sectors nowadays, digital photographs are regarded as a trustworthy source of information. The manipulation of such photographs leads to a variety of issues. The study presents a method using convolutional neural networks (CNN) combined with modified particle swarm optimization (MPSO) to improve the accuracy of tampering detection. This advancement contributes to improved reliability in fields requiring image authenticity verification, such as forensics and media. The design includes the collection of a dataset comprising both original and tampered images for training and testing the model. A dataset, such as the Media Integration and Communication Center (MICC) dataset, is utilized, which includes various images that have been altered through different tampering techniques. This dataset serves as the foundation for training the CNN and evaluating its performance The findings indicate that the proposed MPSO_CNN method outperforms traditional techniques in terms of precision, accuracy, recall, and F-measure, demonstrating its effectiveness in identifying tampered images. The results highlight the significance of using advanced deep learning techniques for reliable image authenticity verification. 2025, Institute of Advanced Engineering and Science. All rights reserved.
creator: Umamaheswari; Kannan; Rozario, Juliet; Manimekala
source: Bulletin of Electrical Engineering and Informatics;Volume;14;Issue;3;pp.1981-1989
publisher: Institute of Advanced Engineering and Science
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 20893191;
format: online
language: English
type: Article
coverage: Umamaheswari, Department of Computer Science, Christ University, Bangalore, India; Kannan, Department of Computer Science, Christ University, Bangalore, India; Rozario J., Department of Computer Science, Christ University, Bangalore, India; Manimekala, Department of Computer Science, Christ University, Bangalore, India

Insuretech: Saviour of insurance sector in India
subject: Consumer Attitude; Fintech; Health Insurance; Insurance Sector; InsurTech; Structural Equation Model
description: Technology in finance has propelled financial literacy and inclusiveness and may give the insurance sector an edge to reach its potential consumers. The current study aimed to identify the role of Fintech in transforming the insurance sector and improving the penetration rate in India. With the descriptive research design, the study collects the primary data through a survey technique targeting the general public and personnel in the insurance sector as a study population. A conceptual model is proposed to understand the interlink between consumer attitude towards Insurance, factors influencing their decision, and the role of Fintech in bridging the gap in insurance penetration. This study focuses on three areas, namely health insurance, life insurance, and vehicle insurance. The study's findings reveal that the insurtech will significantly improve the efficiency of the insurance sector which will result in significant financial performance. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad.
creator: Umasankar M.; Desai K.; Padmavathy S.
source: Emerging Trends and Innovations in Industries of the Developing World: A Multidisciplinary Approach, pp. 1-6.
publisher: CRC Press
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-100381954-7; 978-103260103-8
format: Online
language: English
type: Book chapter
coverage: Umasankar M., Christ University, Bangalore, India; Desai K., Christ University, Bangalore, India; Padmavathy S., Kongu Engineering College, Erode, India

Ban or boon: Consumer attitude towards plastic bags ban
subject: Consumer attitude; Environment; Plastic; Purchase behavior; Structural equation modelling
description: In Tamil Nadu, the state government has imposed a ban on plastic bags two years ago. This has created a major impact of the day to day life of common people. Though it has positive effect on the environment, the common public had different perception as a consumer. This paper aimed at studying the consumer attitude towards the ban on plastic bags. A descriptive research design adopted to address the various dimension of consumer perception towards the ban on plastic ban. A sample size of 400 respondents was selected on the basis of systematic random sampling technique to collect data through structured questionnaire. For conducting the survey, consumers of retail shops in urban and rural places were chosen as target respondents. The collected data were analyzed with the help of statistical tools such as ANOVA, t-Test, Correlation, Linear Regression and Structural equation modelling and the interpretation reported. The result revealed that only 34 percentage of respondent were aware the environmental impact of plastic bags. About 71 percentage of consumers reported that they have faced difficulties in their day to day life due to plastic ban. 2021 American Institute of Physics Inc.. All rights reserved.
creator: Umasankar M.; Padmavathy S.; Radhikaashree M.; Pavithra V.
source: AIP Conference Proceedings, Vol-2387
publisher: American Institute of Physics Inc.
date: 2021-01-01
rights: All Open Access; Bronze Open Access
relation: ISSN: 0094243X; ISBN: 978-073544146-0
format: Online
language: English
type: Conference paper
coverage: Umasankar M., Department of Professional Studies, Christ (Deemed to be University); Padmavathy S., Department of Management Studies, Kongu Engineering College; Radhikaashree M., Dr.MGR Educational and Research Institute; Pavithra V., Department of Management Studies, Kongu Engineering College

Influence of manufacturing process on distribution of MWCNT in aluminium alloy matrix and its effect on microhardness
subject: Metal matrix composites; MWCNT; Powder metallurgy; Spark plasma sintering
description: Nano composites are finding increased focus and their influence on improving the matrix properties are very attractive. But the success is fully dependent on the uniform distribution and dispersion of nano reinforcements in the matrix. Manufacturing process was found to have greater role in distribution of the reinforcements. The liquid processing and solid processing like SPS and hot coining found to have different effect on the matrix due to the nature of reinforcements. Current study focussed on the microstructure study using Back scattered images and the microhardness with and without reinforcements. MWCNT was occupying the particle boundary. Hot coining was found to distribute MWCNT on the particle surface as well as on the particle boundary. Clustering was absent and resulted in improved hardness in comparison with casting as well as spark plasma sintering. 2018 Trans Tech Publications, Switzerland.
creator: Umasankar V.; Thomas S.
source: Materials Science Forum, Vol-928 MSF, pp. 32-37.
publisher: Trans Tech Publications Ltd
date: 2018-01-01
rights: Restricted Access
relation: ISSN: 2555476; ISBN: 978-303571296-4; CODEN: MSFOE
format: Online
language: English
type: Conference paper
coverage: Umasankar V., School of Mechanical and Building Sciences, VIT University, Chennai, India; Thomas S., Department of Mechanical and Automobile Engineering, Christ University, Bangalore, India