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Empirical Assessment of Artificial Intelligence Enablers Strengthening Business Intelligence in the Indian Banking Industry: ISM and MICMAC Modelling Approach
Considering the context of the issue based on literature survey and expert opinion, this study investigates the drivers of Artificial Intelligence (AI) implementation, which further strengthens the Business Intelligence (BI) in taking better decision-making industries in India. For the purpose of serving the objective of examining the enablers towards having a smarter AI ecosystem in banking, the relevance of identified enablers from exhaustive literature survey were discussed with the experts from banking sector and AI professionals. Based on their opinion, 15 final enablers were defined based on the data collected have been put through Interpretive Structural Modelling (ISM) that reveals the binary relationship between the enablers to draw a hierarchical conclusion, and then assess the enablers about their independence, linkage, autonomous character, and dependence based on their calculated driving and dependence power through MICMAC analysis. The ISM and MICMAC integrated approaches have been used to establish interdependence among the enablers of AI in banking in India context. The study reveals that strong algorithms result in building quality AI information, and also the efforts from management related to commitment, financial readiness towards technological advancement, training, and skill development are quite essential in making the baking system smarter and would enable the industry to take better management decision. 2023 selection and editorial matter, Deepmala Singh, Anurag Singh, Amizan Omar & S.B Goyal. -
Empirical estimation of multilayer perceptron for stock market indexes
The return on investment of stock market index is used to estimate the effectiveness of an investment in different savings schemes. To calculate Return on Investment, profit of an investment is divided by the cost of investment. The purpose of the paper is to perform empirical evaluation of various multilayer perceptron neural networks that are used for obtaining high quality prediction for Return on Investment based on stock market indexes. Many researchers have already implemented different methods to forecast stock prices, but accuracy of the stock prices are a major concern. The multilayer perceptron feed forward neural network model is implemented and compared against multilayer perceptron back propagation neural network models on various stock market indexes. The estimated values are checked against the original values of next business day to measure the actual accuracy. The uniqueness of the research is to achieve maximum accuracy in the Indian stock market indexes. The comparative analysis is done with the help of data set NSEindia historical data for Indian share market. Based on the comparative analysis, the multilayer perceptron feed forward neural network performs better prediction with higher accuracy than multilayer perceptron back propagation. A number of variations have been found by this comparative experiment to analyze the future values of the stock prices. With the experimental comparison, the multilayer perceptron feed forward neural network is able to forecast quality decision on return on investment on stock indexes with average accuracy rate as 95 % which is higher than back propagation neural network. So the results obtained by the multilayer perceptron feed forward neural networks are more satisfactory when compared to multilayer perceptron back propagation neural network. Springer International Publishing Switzerland 2016. -
Empirical Study on Categorized Deep Learning Frameworks for Segmentation of Brain Tumor
In the medical image segmentation field, automation is a vital step toward illness detection and thus prevention. Once the segmentation is completed, brain tumors are easily detectable. Automated segmentation of brain tumor is an important research field for assisting radiologists in effectively diagnosing brain tumors. Many deep learning techniques like convolutional neural networks, deep belief networks, and others have been proposed for the automated brain tumor segmentation. The latest deep learning models are discussed in this study based on their performance, dice score, accuracy, sensitivity, and specificity. It also emphasizes the uniqueness of each model, as well as its benefits and drawbacks. This review also looks at some of the most prevalent concerns about utilizing this sort of classifier, as well as some of the most notable changes in regularly used MRI modalities for brain tumor diagnosis. Furthermore, this research establishes limitations, remedies, and future trends or offers up advanced challenges for researchers to produce an efficient system with clinically acceptable accuracy that aids radiologists in determining the prognosis of brain tumors. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Empirical study on The Role of Machine Learning in Stress Assessment among Adolescents
Stress is a psychological condition that people who are experiencing difficulties in their social and environmental well-being face, and it can cause several health problems. Young individuals experience major changes during this crucial time, and they are expected to succeed in society. It's critical for people to master appropriate stress management techniques to ensure a smooth transition into adulthood. The transition to new settings, lifestyles, and interactions with a variety of people, things, and events occurs during adolescence. In this study, a dataset was utilized to classify 520 Indian individuals' stress levels into three categories: normal, moderate, and severe. Support Vector Machines, KNN, Decision Trees, Naive Bayes and CNN were among the different classification techniques that were taken into consideration. The CNN Algorithm was found to be the most reliable method for categorizing diseases linked to mental stress. The study's main goal is to create a classification model that can correctly classify a variety of samples into distinct levels of psychological discomfort. 2023 IEEE. -
EmploChain: A Blueprint for Blockchain-Driven Transformation in Employee Life Cycle Management
Integrating blockchain technology into human resource management presents both transformative opportunities and implementation challenges that need to be addressed. This paper proposes a blockchain-based EmploChain Framework, a decentralized ledger approach specifically designed to enable Employee Life Cycle Management by harnessing the potential of blockchain technology. The study looks at the potential benefits of the proposed framework, including increased security, transparency, and automation. The paper also looks at potential limitations like scalability concerns and implementation costs and explores the possible solutions to overcome them. The aim of this research is to provide a thorough understanding of the framework's implications, thereby facilitating informed decisions to implement EmploChain Framework for managing the Employee Life Cycle of an organization.. 2024 IEEE. -
Employee attrition and absenteeism analysis using machine learning methods: Application in the manufacturing industry
HR analytics has been envisaged as recent research trend for providing a comprehensive decision support system to the top level management in terms of employee's performance, recruitment and behaviour analysis. Globally, organizations are using technology to support and ease HR processes. Every organization should give maximum value to every available human resource, and they should minimize the attrition and absenteeism rate and ensure what are the factors that contribute towards employee attrition as well as the causes for workmen absenteeism. The ultimate objective is to correctly identify attrition and absenteeism in order to assist the company to improve retention tactics for key personnel and increase employee satisfaction. Through this chapter, a machine learning-based model is proposed to get quick results for such employee attrition and workmen absenteeism. The model is trained and tested for its accuracy. The result shows that the proposed model has high sensitivity. The managerial implications are also discussed for taking informed decisions. 2023, IGI Global. All rights reserved. -
Employee Attrition, Job Involvement, and Work Life Balance Prediction Using Machine Learning Classifier Models
Employee performance is an integral part organizational success, for which Talent management is highly required, and the motivating factors of employee depend on employee performance. Certain variables have been observed as outliers, but none of those variables were operated or predicted. This paper aims at creating predictive models for the employee attrition by using classifier models for attrition rate, Job Involvement, and Work Life Balance. Job Involvement is specifically linked to the employee intentions to turn around that is minimal turnover rate. So, getting justifiable solution, this paper states the novel and accurate classification models. The Ridge Classifier model is the first one it has been used to classify IBM employee attrition, and it gave an accuracy of 92.7%. Random Forest had the highest accuracy for predicting Job Involvement, with accuracy rate of 62.3%. Similarly, Logistic Regression has been the model selected to predict Work Life Balance, and it has a 64.8% accuracy rate, making it an acceptable classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
EMPLOYEE ENGAGEMENT: ANTECEDENTS AND CONSEQUENCES
Employee engagement is the emotional connection and dedication that employees feel towards their organisation. It is a term used to describe how dedicated, enthusiastic and involved employees are towards/with their job and the organisation they work for. Antecedents are the variables that influence and contribute towards employee engagement, while consequences are the outcomes linked with employee engagement. Attrition of intellectual capital, disengagement with work, issues of conflict with students, lack of job satisfaction, etc. in the centres of higher education are becoming a burgeoning problem and constructive employee engagement is seen as the solution to these issues. The present study aims to examine the factors responsible for employee engagement as well as the outcomes that are derived due to effective implementation of employee-engagement practices. Data has been collected from 117 faculty members of higher-education institutions from South India using simple random sampling. Data has been analysed with the help of Excel, SPSS and AMOS, using statistical tools like T-test, ANOVA and SEM. The proposed model reflects strong positive association between antecedent variables like autonomy, rewards and recognitions, and fair and equitable treatment and employee engagement, and job satisfaction, organisational commitment and intention to stay as the outcomes of employee engagement. 2024 Published by Faculty of Engineering. -
Employee experience, well-being and turnover intentions in the workplace
Purpose: This study aims to examine the role of employee experience in influencing employee well-being and turnover intentions within organizations. The mediating role of well-being will also be investigated, along with an exploration of whether these relationships differ across genders, specifically in the Indian corporate context. Design/methodology/approach: A descriptive, quantitative study was conducted using structured questionnaires to gather data from 111 employees in the Indian corporate sector. The study used a non-probability judgment sampling method. Data was analyzed through SPSS for descriptive and inferential statistics, and partial least squares was used to explore mediation and model fit. Findings: The study found a significant impact of employee experience on well-being, as well as a negative correlation between both employee experience and turnover intention and well-being and turnover intention. Well-being was found to partially mediate the relationship between employee experience and turnover intention. Gender-based analysis revealed no significant differences in the relationships between these variables for men and women. Originality/value: This research highlights the universal applicability of employee experience as a predictor of well-being and turnover intention, irrespective of gender. By establishing that gender does not moderate these relationships, this study provides new insights challenging traditional assumptions about gender disparities in workplace outcomes. 2024, Emerald Publishing Limited. -
Employee motivation for sustainable entrepreneurship: The mediating role of green hrm
This chapter aims to explore the relationship between employee motivation and sustainable entrepreneurship with a specific focus on the mediating role of green human resource management (HRM). As organizations increasingly recognize the importance of environmental sustainability, understanding the mechanisms through which employee motivation translates into sustainable entrepreneurial behaviors becomes crucial. By integrating concepts from the fields of entrepreneurship, sustainability, and HRM, this study proposes that Green HRM practices play a mediating role in fostering employee motivation for sustainable entrepreneurship. The findings of this research provide valuable insights for organizations seeking to enhance their sustainability efforts by leveraging employee motivation and implementing effective Green HRM strategies. 2023, IGI Global. All rights reserved. -
Employee relations: a comprehensive theory based literature review and future research agenda
This study aims to conduct a systematic and integrative literature review to consolidate the extensive information on employee relations accumulated over the past century, thereby offering new insights into domain-specific phenomena. The research followed a four-phase search strategy in accordance with the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The keyword search utilized terms such as 'employee relations,' 'employee relation,' 'employment relation,' and 'employment relations' in the Scopus and Web of Science databases. By employing an integrative approach along with specific inclusionexclusion criteria, the researchers synthesized articles from leading journals in the field of employee relations, categorizing them based on geographical region, article types, prominent authors and their affiliations, and the most cited research articles. In the final stage, the researchers presented new insights through a conceptual framework utilizing the ADO-TCCM approach, which encompasses antecedents, outcomes, theories, context, methodology, mediators, and moderators of employee relations. This study synthesizes findings and reorganizes key themes into innovative frameworks, providing fresh perspectives on various aspects of employee relations. Ultimately, it offers valuable insights into the critical factors that strengthen long-term employee-employer relationships. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Employees Job Satisfaction, Work-Life Balance, and Health During the Pandemic
The impact of the COVID-19 pandemic on private enterprises has been particularly noticeable in the IT and non-ITES sectors. Work came to a complete halt due to the ensuing lockdown, severely affecting businesses and further harming industries like aviation and hospitality. Widespread job losses, shortened workweeks, minimum wage reductions, short-term leave policies, and even company closures have been the results. To understand the extent of these impacts, a descriptive study was conducted online in AprilMay 2021, involving 2439 white-collar workers from various private companies. Convenient sampling methods were used to gather data on the experiences of employees in these sectors during the pandemic. The survey's findings demonstrate a positive but weak association between Work-Life Balance and Health Stress (r?=?0.24, p?<?0.01) and a positive low correlation between Work-Life Balance and Job Satisfaction (r?=?0.23, p?<?0.01). Therefore, work-life balance and job satisfaction among employees were significantly correlated throughout the epidemic. Additionally, there was a negative moderate correlation between Health Stress and Job Satisfaction (r?= ?0.48, p?<?0.01), indicating that as Health Stress decreases, Job Satisfaction increases at moderate levels. The implications of the study were discussed further. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Employer branding on the creation of anticipatory psychological contract
The process of the psychological contract (PC) starts before the employee joins the organisation. The brand of the company remains in the mind of candidates who apply for the job. Considering this relation, this research work is carried out to offer a detailed analysis of the position of employer branding (EB) in the formation of anticipatory psychological contracts (APCs) among millennials. The empirical study was carried out with a sample size of 330 respondents who are studying post-graduation management studies; these students are about to join the corporates. The outcomes of this study show that EB significantly impacts the PC. EB has a strong influence on relational expectations as compared to transactional expectations and employee obligations among potential employees. This paper helps recruitment managers to understand the expectations of potential employees and their beliefs towards their employers during the pre-employment phase. Copyright 2022 Inderscience Enterprises Ltd. -
Employing bioactive compounds derived from Ipomoea obscura (L.) to evaluate potential inhibitor for SARS-CoV-2 main protease and ACE2 protein
Angiotensin converting enzyme 2 (ACE2) and main protease (MPro) are significant target proteins, mainly involved in the attachment of viral genome to host cells and aid in replication of severe acute respiratory syndrome-coronaviruses or SARS-CoV genome. In the present study, we identified 11 potent bioactive compounds from ethanolic leaf extract of Ipomoea obscura (L.) by using GC-MS analysis. These potential bioactive compounds were considered for molecular docking studies against ACE2 and MPro target proteins to determine the antiviral effects against SARS-COV. Results exhibits that among 11 compounds from I. obscura (L.), urso-deoxycholic acid, demeclocycline, tetracycline, chlorotetracycline, and ethyl iso-allocholate had potential viral inhibitory activity. Hence, the present findings suggested that chemical constitution present in I. obscura (L.) will address inhibition of corona viral replication in host cells. 2020 The Authors. Food Frontiers published by NCU, NWU, JSU, ZJU & FAFU and John Wiley & Sons Australia, Ltd. -
Employing Deep Learning in Intraday Stock Trading
Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock's future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR SP 500 index The testing experiments performed on the SPDR SP 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model. 2020 IEEE. -
Empowered learning in school: A scoping review
A high degree of motivation, a sense of commitment, self-efficacy and the ability to make the right choices are the characteristics of empowered learners. With education being seen as preparation for life, educators are increasingly pressured to develop curriculum and pedagogy that assist learners to become empowered. Based on the theoretical framework developed by Arksey and OMalley, the present study reviewed 16 empowered learning intervention studies at the school level published between the years 1995 and 2021 as well as provides an extensive summary of empowered learning enhancing interventions conducted in schools. This study highlights the concept of empowered learning, features and scope of interventions directed towards empowered learning of students at schools and the role of empowered learning in schools. Notwithstanding varied intervention results, the findings of this study indicate that empowered learning interventions produce highly motivated students with a sense of commitment and self-efficacy. This review also identifies the need for more pure experimental studies and a commonly accepted theory on empowered learning as a single concept. 2023, Institute of Advanced Engineering and Science. All rights reserved. -
Empowering Adolescent Emergent Readers in Government Schools: An Exploration of Multimodal Texts as Pathways to Comprehension
This exploratory study, which was part of a larger investigation into multimodality, looked at the comprehension levels of 62 Grade 8 students from two government schools who were identified as emerging readers out of a group of 118 students. Through observations and interactions with teachers and students, the potential for multimodal texts to enhance comprehension was highlighted. The study specifically compared the effectiveness of a digital comic (Text A) and an audio-visual text (Text B) in enabling comprehension among these emergent readers. Participants were instructed to narrate the content and share their interpretations of these texts, with their responses recorded and analyzed. Feedback revealed a marked preference for Text B among 45 of the 62 emergent readers assessed. Employing theoretical frameworks related to comprehension, language production, multimodality, and task structure, this research concentrated on the subset of 45 students who favored Text B. The findings underscore the importance of aligning instructional materials with students preferred learning modalities, suggesting that such alignment enhances comprehension. The study proposes a refined approach to literacy education policy, advocating for the inclusion of diverse modalities to better meet the varied learning needs of students. 2024 Association of Literacy Educators and Researchers. -
Empowering BRICS economies: The crucial role of green finance, information and communication technologyand innovation in sustainable development
This study delves into the crucial role of green finance, information and communication technology (ICT), technological innovation, and renewable energy in the Brazil, Russia, India, and China (BRICS) countries from 2000 to 2021. The findings highlight the importance of green finance in reducing the ecological footprint and promoting eco-friendly initiatives, sustainable practices, environmental technology innovation, and heightened environmental awareness. This means 1% increase in green related finance has reduced ecological footprint by 0.72% in BRICS economies. Additionally, technological innovation and the consumption of renewable energy play a significant role in enhancing environmental sustainability. Conversely, the study reveals that ICT has a considerable impact on the ecological footprint, but the interaction effect with green finance helps to mitigate its negative effects and improve the environmental quality. Meanwhile, non-renewable energy, gross domestic product (GDP) per capita, and urbanization have an adverse effect on the environment. To strengthen green finance in BRICS countries, governments can establish comprehensive policy frameworks that prioritize sustainability and create a conducive climate for incentivizing investment in environmentally friendly endeavors. 2024 ERP Environment and John Wiley & Sons Ltd. -
Empowering E-commerce: Leveraging Open AI and Sentiment Analysis for Smarter Recommendations
Online product reviews are pivotal in shaping consumer purchasing decisions in today's digital era. Leveraging the wealth of sentiment-rich data available through these reviews, this research proposes an approach to enhance product recommendation systems. This study integrates sentiment analysis techniques into the recommendation process to provide users with more personalized and insightful product recommendations. By analyzing the sentiment expressed in user-generated content, such as reviews and ratings, this system aims to capture not only the explicit preferences but also the underlying sentiments and emotions of users towards products. Furthermore, this system utilizes OpenAI and the power of Langchain to develop a chatbot interface, enabling users to interact naturally and receive personalized product recommendations based on their preferences and sentiment analysis. Through experimentation on real-world datasets, this paper evaluates the effectiveness and performance of the sentiment-enhanced recommendation system compared to traditional recommendation methods. The results demonstrate the potential of sentiment analysis in improving the relevance, accuracy, and user satisfaction of product recommendations. 2024 IEEE. -
Empowering families: Strategies for effective child and adolescent treatment
This chapter examines the importance of family involvement in infant and adolescent psychological practices and interventions. It emphasizes the significance of involving families in the therapeutic process, which leads to enhanced treatment outcomes, increased parent and carer satisfaction, and a higher probability of long-term success. It discusses evidence-based interventions that effectively engage and empower parents and carers, such as parent education, family therapy, and parent-child interaction therapy. Despite the numerous advantages, family involvement may face cultural differences, language barriers, stigma, and lack of awareness. Mental health professionals must adopt culturally sensitive approaches, combat stigma, and provide ongoing support and education to surmount these obstacles. When families are actively involved and empowered, they establish a strong support network beyond therapy sessions, promoting resilience and positive change for the child's development and well-being. 2024, IGI Global. All rights reserved.