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Insuretech: Saviour of insurance sector in India
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. -
Recruitment Analytics: Hiring in the Era of Artificial Intelligence
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 training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes. Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles. Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidates perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees queries. Resume screening techniques can save the recruiters time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team. 2023 by V. R. Uma, Ilango Velchamy and Deepika Upadhyay. -
Examining women's purchase pattern of casual footwear in accordance with their attitudes and interests
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 like apparels, insurance, real estate etc., but not in the footwear sector. The present study is an attempt to fill this gap. -
Overt dependence of health insurance industry on healthcare system
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 Management and Penerbit Universiti Sains Malaysia, 2021. -
Investigation of Cervical Cancer Detection from Whole Slide Imaging
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. -
A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment
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. -
Reliable monitoring security system to prevent MAC spoofing in ubiquitous wireless network
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. -
A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence
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. -
Jabbar Patel filmmaking - An auteur theory approach /
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. -
Understanding the secular evolution of NGC 628 using UltraViolet Imaging Telescope
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. -
Analysis of Membership Probability in Nearby Young Moving Groups with Gaia DR2
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.. -
Disentangling the association of PAH molecules with star formation
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. UV photons from the star-forming regions could be the reason for the higher fraction of the ionized PAHs. We suggest that the effect of the high temperature in the star-forming regions and the formation of smaller PAH molecules in the star-forming regions might also result in the higher emission in the F335MPAH band. The Authors 2024. -
DECODING INTENTIONS TO PURCHASE ORGANIC FOOD PRODUCTS IN AN EMERGING ECONOMY VIA ARTIFICIAL NEURAL NETWORKS
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 environment. 2024, Institute of Eastern European Research and Consulting. All rights reserved. -
Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)
Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system. 2024 IEEE. -
Perceived organizational support and its influence on employee engagement in informatiom technology organizations
The effective management of Human Resources (HR) or People Resources of an organization through proactive and futuristic design of HR policies is pivotal for an organization s growth. Many of the current challenges faced by businesses globally are owing to industry slowdowns, loss of clientele, lower margins, stiff competition for skilled resources, and high attrition. A diverse set of workforce belonging to different generations having aspirations and expectations galore has entered the corporate sector. This diversity and advent of the generational workforce needs to be taken into consideration while designing HR policies, Reward systems and benefits; as also the factors such as changing family structures and the emergence of a gender equal workforce. Human Resources professionals and organizations are hence tasked with the responsibility of employing different ways and means to fine-tune existing HR strategies and develop new one s that could potentially increase Employee Engagement (EE) and reduce the Intention the Quit (ITQ) among the Information Technology (IT) workforce. The prime emphasis of the current study has been on assessing appropriate HR strategies that can increase engagement and retention at a minimal or no cost to the organization. This study leveraged upon organizational support and care variables such as Perceived Organizational Support (POS), Perceived Supervisor Support (PSS), and Flexible Work Options (FWO) in increasing Employee Engagement (EE) and reducing the Intention to Quit (ITQ). This study goes on to prove that by leveraging upon organizational support and care variables such as POS, PSS, and FWO; organizations can increase the level of engagement of employees, as well as improve employee retention. -
The relationship of workplace flexibility to employee engagement among information technology employees in India
Historically, organizations have been provisioning flexible work arrangement (FWA) options in the workplace to help employees achieve a balance between work obligations and private obligations. We explore the utilization of FWA offerings in the Indian information technology (IT) industry and its relation to employee engagement (EE). Employees working in IT organizations in Bengaluru, India, were approached and data were obtained from 504 participants. The study found that availability of FWA options coincided with their utilization. Disconcertingly, only 7 per cent of the employees were highly engaged, 51 per cent were neither engaged nor disengaged and 41 per cent of the employees were disengaged with their current organization. FWAs were positively related to EE. We find congruence that FWA options lead to better EE warranting further exploration that can guide FWA policies. 2018 SAGE Publications India Private Limited. -
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Elemental abundances in the interstellar medium
One method to investigate the chemical composition of the interstellar medium (ISM) and interstellar dust grains is to conduct interstellar elemental depletion studies, especially of highly abundant species. The role refractory element, silicon (Si) in extinction is not clearly understood and the distribution and evolution of moderately volatile sulfur (S) in the ISM is still an open problem. The key motivation of the work is to investigatethe chemical composition of ISM of our Galaxy, and the formation, processing and distribution of interstellar dust in its different environments, mainly focusing on silicon and sulfur abundances, both in gas and dust.In the work outlined in this thesis, I will be describing the gas and dust phase abundances of Si and S in the interstellar medium using archival observations, and their probable role in the observed extinction. In this work, we also have measured the column density of S II along 9 Galactic sight lines using archival high-resolution observations from the Space Telescope Imaging Spectrograph and determined the abundances of S in both gas and dust phases. Using Archival spectral data towards 131 target stars in the Galaxy, interstellar Si abundances and depletion along those lines of sight has been surveyed. Oscillator strength correction has been performed to account for its improvements, using most recent values. This is an extensive survey done using a much larger data sample compared to previous investigations, but it substantiate the majority of the findings, which show that Si depletion is linked to both the average hydrogen density (n (H)) and the fraction of molecular hydrogen (f(H2)) along the lines of sight. -
Elemental Abundances in the Interstellar Medium
One method to investigate the chemical composition of the interstellar medium (ISM) and interstellar dust grains is to conduct interstellar elemental depletion studies, especially of highly abundant species. The role refractory element, silicon (Si) in extinction is not clearly understood and the distribution and evolution of moderately volatile sulfur (S) in the ISM is still an open problem. The key motivation of the work is to investigate the chemical composition of ISM of our Galaxy, and the formation, processing and distribution of interstellar dust in its different environments, mainly focusing on silicon and sulfur abundances, both in gas and dust. In the work outlined in this thesis, I will be describing the gas and dust phase abundances of Si and S in the interstellar medium using archival observations, and their probable role in the observed extinction. In this work, we also have measured the column density of S II along 9 Galactic sight lines using archival high-resolution observations from the Space Telescope Imaging Spectrograph and determined the abundances of S in both gas and dust phases. Using Archival spectral data towards 131 target stars in the Galaxy, interstellar Si abundances and depletion along those lines of sight has been surveyed. Oscillator strength correction has been performed to account for its improvements, using most recent values. This is an extensive survey done using a much larger data sample compared to previous investigations, but it substantiate the majority of the findings, which show that Si depletion is linked to both the average hydrogen density (n (H)) and the fraction of molecular hydrogen (f(H2)) along the lines of sight. Using this data, the distribution of Si and the variation of dust attributes with Si abundances also has been investigated and found that the linear component of the extinction curve is unrelated to depletion of silicon. -
Evaluation of national rural health mission in Bangalore rural district /
Indian Journal Of Applied Research, Vol.5, Issue 6, pp.836-838, ISSN No: 2249-555X.