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Behavioral Bias as an Instrumental Factor in Investment Decision-An Empirical Analysis
Investment decisions are always complex in nature. Investment assets are volatile in nature there are less volatile, medium volatile and high volatile investment assets in the financial market. In the current study how, the behavioral biases of the investors affecting their investment decisions in the less volatile asset classes is examined using an extensive survey method among the IT professionals in the Bangalore city. The relationship between the demographic variables and behavioral biases is tested. Also, a detailed study is conducted to examine the risk-taking behavior of the investors in the less volatile assets. There are basically three type of investors on the basis of their risk-taking behavior i.e. Risk seeking, Risk Neutral and Risk averse investors. Current study reveals that investors in the less volatile asset classes are very much cautious about the risk factor and therefore they are risk averse in nature. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Enhancing Customer Satisfaction and Sales in Retail Environments: A Personalized Augmented Reality Approach for Dynamic Product Recommendations
This article explores the potential transformative impact of integrating augmented reality (AR) technology with personalized product recommendations in the retail industry. By leveraging ARs ability to overlay digital information onto the physical world, retailers can offer tailored suggestions based on individual preferences, past purchases, and real-time contextual cues, thereby enhancing customer satisfaction and driving sales. Through a comprehensive literature review and empirical analysis, the study investigates user experience, adoption factors, and the long-term effectiveness of AR-deep learning integration in retail settings. Findings reveal significant improvements in customer satisfaction, sales performance, inventory management, and employee productivity with the implementation of AR-Deep Learning technology. Additionally, the article presents an innovative framework that seamlessly integrates AR and deep learning models, demonstrating high accuracy in object recognition, real-time interaction, and enhanced user experience across various industries. While highlighting the studys limitations and areas for further research, this article underscores the importance of customer-centric strategies and technological innovation in optimizing the retail experience and driving business growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Fraud Detection in Credit Card Transaction Using ANN and SVM
Digital Payment fraudulent cases have increased with the rapid growth of e-commerce. Masses use credit card payments for both online and day-to-day purchasing. Hence, payment fraud utilizes a billion-dollar business, and it is growing fast. The frauds use different patterns to make the transactions from the cardholders account, making it difficult for the organization or the users to detect fraudulent transactions. The studys principal purpose is to develop an efficient supervised learning technique to detect credit card fraudulent transactions to minimize the customers and organizations losses. The respective classification accuracy compares supervised learning techniques such as deep learning-based ANN and machine learning-based SVM models. This studys significant outcome is to find an efficient supervised learning technique with minimum computational time and maximum accuracy to identify the fraudulent act in credit card transactions to minimize the losses incurred by the consumers and banks. 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. -
Children's Well-Being in Traditional Vs. Montessori Schools. A test of Self-Determination Theory
The present study is a test of Self -Determination theory, which is well established in the field of education with a huge body of empirical evidence to support its assumptions that when the three universal psychological needs (Autonomy, Competence & Relatedness) of a child are met they will grow and function optimally leading to enhanced well-being. It is evident that Montessori philosophy is overlapping with the components of SDT. This study was conducted to examine the extent to which the three psychological needs are satisfied in Montessori schools in comparison to the Traditional schools. A purposive sample size of 80 children in elementary grades was selected from both Montessori and Traditional schools. Perceived support experienced by the children and their Well-Being was determined to establish the assumption of the SDT. The results showed that children in Montessori schools experienced greater satisfaction of needs when compared to traditional school children. However, the well-being of children from both school types didn???t vary much and the causes can be attributed to factors outside classroom. These findings have some strong implications for policy makers, educators and parents. -
A Comparative Study on Customer??s Expectations and Perceptions on Credit Card Services in Old and New Generation Banks
Card usages have been drastically increasing in India due to the convenience and safety provided by the issuers. At the same time, these issuers are finding it very hard to maintain and gain the market share for this particular service product. In the present scenario credit cards are playing a vital role in every one??s life. Thus, the study has attempted to find out the customer??s expectations and perceptions on credit card services in old and new generation banks. A sample of 225 respondents, who were the users of credit cards in the Bangalore city, was concentrated upon for the study. This was analyzed and tested using Factor analysis, ANOVA and Multiple Linear Regressions. The paper has found the factor the people are expecting more on card services and as well the factor where the users have perceived more. The gap was also analyzed between the expectations and perceptions of the users. The results from the study pointed out the factors the banks have to concentrate upon in order to delight its users and maintain its market share. Hence, businesses should focus on the factors analyzed so as to improve the quality of services provided on cards and to retain the customer base. Keywords: Credit Cards, Customer Expectations, Customer Perceptions,New Generation Banks and Old Generation Bank. -
The Taos and Trait Meta-mood on Transpersonal Gratitude: Tracing Their Influences
The mainstream empirical research has always viewed gratitude in its triadic form involving a typical human giver, gift, and receiver. But it is not the same in the case of transpersonal gratitude. Instead, it is directed towards abstract entities beyond self like God, their own state of being, or the cosmos. The previous literature had affirmed that a selfless attitude and better mood could determine overall gratitude. But this relation is not mainly known in the context of this newer form of gratitude. Indian young adults (N = 456) completed scales on transpersonal gratitude, trait meta-mood, and ego-grasping orientationa Taoist concept. The preliminary analysis revealed that the selfless nature was unrelated to transpersonal gratitude. Subsequently, the predictive effect of trait meta-mood on transpersonal gratitude is quantified. The findings explain the distinguishable features of the young adults' populace and positive transpersonal experiences. The need to identify groups, cultural differences, and the utility of interventions on transpersonal gratitude in the future gratitude research is emphasised. 2023, The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India. -
Impact of Learning Functions on Prediction of Stock Data in Neural Network
Digitization has made a vast impact on the modern society. Financial sector is one field where a huge revolution has been experienced because of digitization. Financial data especially time series data is being stored in the digital repositories where it can be used for prediction and analysis. One such data is a stock market data which is a time series data and is generated in a huge amount every second. The stock market data is of great importance as the proper analysis and prediction of data can transform the fate of the global market. Thus the companies and the individuals are looking forward for the development of the automated techniques that can predict stock market data accurately in a real time. In this regard, many researchers developed machine learning techniques such as use of neural network for prediction of stock data. The most common learning function used in neural network is sigmoid function. However, we found that there are many learning functions are available for building neural network. In this paper we are studying the impact of four different learning functions in estimating/predicting the stock value. From the experimental study we found that unipolar sigmoid learning function produced an accuracy of 95.65%, bipolar sigmoid produced an accuracy of 91.34%, tan hyperbolic equation produced an accuracy of 91.02%, and radial base equation produced an accuracy of 87.53%. Clearly, unipolar sigmoid function emerged as the best learning function to build stock data prediction model. The main reason behind its out-performance of unipolar sigmoid is its less complex structure and the 0 to 1 range. 2018 IEEE. -
Unlocking the potential of AI for efficient governance: Innovative approaches of Bahrain
The rapid development and implementation of artificial intelligence (AI) technologies will have significant economic, social, and ethical impacts. Efficient governance is essential to maximize AI's benefits while minimizing its risks. Bahrain is positioning itself as a fintech hub, with AI playing a central role in this transformation. Bahrain's smart governance efforts will be strengthened by integrating AI into public services. E-government efforts will use AI to streamline processes, improve citizen experience, and build a more responsive and efficient public administration. The study provides an overview of how artificial intelligence (AI) is transforming various sectors in Bahrain with innovative approaches to boost productivity, better decision-making, and improve the general quality of services that may also impact the Bahraini economy. Bahrain continues to drive digital innovation, paving the way for a better and more prosperous future and sustainable development. Bahrain's digital transformation has been largely successful thanks to strong government measures. 2024, IGI Global. All rights reserved. -
DOES COVID-19 AFFECT SHARIAH COMPLIANT STOCK? EVIDENCE FROM SELECTED OIC COUNTRIES
This study aims to examine the movements of Islamic stock markets in ten selected OIC (Organization of Islamic Cooperation) countries in relation to Covid-19 cases, providing a comprehensive analysis of market behavior during the pandemic. The countries-Saudi Arabia, Pakistan, Bangladesh, Turkey, Indonesia, Oman, Qatar, UAE, Kuwait, and Bahrain-were chosen based on their large Muslim populations. Data was collected over a one-year period from January 1, 2020, to January 31, 2021, analyzing the relationship between Covid-19 cases and Islamic stock market indices. The study employed co-integration tests to identify long-term relationships and the Vector Error Correction Model (VECM) to explore short-run dynamics. The co-integration test results show a significant long-run relationship between Covid-19 cases and Islamic stock markets in most of the selected OIC countries. Specifically, the Shariah indices in Pakistan, Bangladesh, Turkey, Qatar, UAE, Kuwait, and Bahrain have a positive and significant relationship with Covid-19 cases. Conversely, Saudi Arabia, Indonesia, and Oman exhibit a negative long-term relationship with Covid-19 cases, suggesting a different market response. These results suggest that countries with diversified economies, particularly those relying on natural resources such as oil and agriculture, were more resilient during the pandemic. This study provides novel insights into the unique responses of Islamic stock markets in OIC countries during the pandemic, highlighting regional differences in market behavior and recovery. It suggests that despite the global economic downturn, OIC countries present attractive investment opportunities, particularly due to their swift recovery and resource-based economies, offering a robust portfolio for investors during crises. 2024 by the author(s). -
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Feminism in Practice: Learning from the Barefoot Solar Mamas
The Barefoot College (India) is an NGO working in the fields of education, skills development, health, drinking water, and solar power mainly to train older, rural women who are determined to challenge restrictive gender roles in their respective communities. Since its inception, the NGO has trained over 2,000 rural women as solar engineers across 93 countries worldwide and has brought electricity to over 18,000 homes. Barefoot trainers employ non-normative methods of sharing knowledge such as color coding, sign language, and practical experience. This paper conducts a critical assessment of the Barefoot College Solar Electrification Programme to explore how it empowers illiterate and semi-literate women from remote rural areas around the world to become solar engineers (or Solar Mamas). It utilizes qualitative research methods to analyze this women's empowerment project as a landmark practical application of decolonial feminist theory. The paper contends that the Barefoot approach both challenges and conforms to the Women in Development and Gender and Development approaches of the past. The research is grounded methodologically in feminist praxis and also borrows from the conceptual frameworks of Feminist Political Ecology and Women and the Politics of Place. Stories and personal experiences from Solar Mamas have been highlighted to understand the real world impact of the program. The main findings indicate that the Barefoot College's innovative approach to empower marginalized communities and educate older women is achieved through decentralizing control and demystifying technology. (2024), (Bridgewater State College). All Rights Reserved. -
Effects of Peer Monitoring on Student Stress Level of College Students Based on Multi-Layer Perceptron Approach
The classroom is just one of many places where the proposed approach encounter stress. Previous studies have shown that college students experience high rates of stress. It is not known if the Student Stress Inventory-Stress Manifestations (SSI-SM) is useful in identifying stressors and evaluating stress manifestations among college students. To this end, it was created a college-specific version of the Student Stress Inventory-Stress Manifestations (SSI-SM) and administered it to students to determine its validity and reliability. These procedures comprise the proposed technique and include preprocessing, feature selection, and model training. It uses Normalization as a preprocessing approach. The term' normalization' refers to the procedure of rescaling or modifying data so that all categories have the same variance. The proposed approach employed linear discriminant analysis as a means of selecting features. The models are then trained using MLP after information gain has been used to choose relevant features. The proposed approach achieves better results than the two leading alternatives, CNN and RNN. 2024 IEEE. -
Exchange rate, stock price and trade volume in US-China trade war during COVID-19: An empirical study
This article aims to examine the influence of international trade wars on the majority of stock market operations, both directly and indirectly affected. The impact of the trade war on the exchange rates of the participating countries was similarly negative. This article seeks to trace the conversion standards' footprints in the United States, China, and India using several indexes such as the Shanghai Composite Index, Dow Jones index, and Nifty 50. The cost of closing down various indices on a daily basis, as well as the conversion standard upsides of the participating currencies, are all examined in this study. Furthermore, utilizing the OLS and GARCH models, this work provides insights into measuring the uncertainties about the impact of exchanging scale on financial exchange. According to the findings of OLS, changes in the swapping scale have had a minor impact on the daily closing costs of stock records in the individual countries. The conversion standard, on the other hand, has a major impact on trade volumes in all three stock markets. When compared to the SSE and DJI equities, the GARCH model predicts that the contingent shift will be less shocking, resulting in a smaller impact on Nifty trade volume. To replicate the impact of trade wars during the Covid-19 crisis, the final results imply that data from domestic and international financial transactions must include securities market transactions. Author This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). -
Actualizing The Inner Self : Impact of An Online Signature Strengths Intervention On Well-Being
The PERMA Theory of Well-being states that exercising signature strengths one s most newlineprominent character strengths enhances five distinct dimensions of well-being, namely, newlinepositive emotions, engagement, relationships, meaning, and accomplishment. The present study tests this theory by examining the impact of an online signature strengths intervention on each of the aforementioned dimensions of well-being and overall well-being using an explanatory sequential mixed method experimental research design. The quantitative phase of the study implemented a randomized controlled trial (RCT) of the intervention with a wait-list control newlinegroup. A total of 82 participants recorded their levels of well-being and its dimensions at pretest and post-test using a standardized tool. Out of the 82 participants, 42 participants were in the experimental group and 40 participants in the wait-list control group. A one-month followup measure of well-being was also taken among participants in the experimental group to determine the long-term effectiveness of the intervention. Focus Group Discussions (FGDs) were conducted in the qualitative phase of the study among participants in the experimental group to explore the subjective experiences and mental processes underlying the identification and utilization of signature strengths. Results demonstrated medium to large increases in all the dimensions of well-being except for the dimension of engagement which did not show a newlinesignificant increase at either time points. Qualitative findings validated the quantitative findings and revealed important mental and emotional mechanisms underlying the experience of utilizing signature strengths, thereby providing a deeper insight into the nature and working of the intervention. Findings of the study carry far-reaching implications for organizations as well as educational and healthcare institutions to empower individuals to function optimally by utilizing their inner potential and experience the peak of well-being in all domains of life. -
Asynchronous Method of Oracle: A Cost-Effective and Reliable Model for Cloud Migration Using Incremental Backups
Cloud Computing has reached a new level in flexibility to provide infrastructure. The proper migration method should be chosen for better cost management and to avoid overpayments to unused resources. So, the migrations from On-Premises to cloud infrastructure is a challenge. The migration can be done in synchronous or asynchronous modes. The synchronous method is mostly used to minimize downtime while doing the cloud migrations. The asynchronous methods can do the migrations in offline mode and very consistently. This paper addresses various issues related to the synchronous mode of Oracle while doing highly transactional database migrations. The proposed methodology provides a solution with a combination of asynchronous and incremental backups for highly transactional databases. This proposed method will be a more cost-effective and reliable model without compromising consistency and integrity. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Compatible Hexadecimal Encryption-Booster Algorithm for Augmenting Security in the Advanced Encryption Standard
Among the most prominent encryption algorithms, Advanced Encryption Standard ranks first. Even so, many familiar characters can be seen when an AES encrypted file is opened. As of today, there have been very few contributions to research on suppressing known characters in AES encrypted files. It is possible to identify encrypted files not only by their name and content, but also by their size. As a result, hackers can identify files at source and target locations by comparing their sizes. In this paper, a methodology is presented to address these two research gaps. As a result of the proposed algorithm, almost all characters are transformed into an unintelligible format not only for humans, but also for computer interpreters. As an additional benefit, the proposed method makes the encrypted file appear smaller and conceals its actual size. The proposed Encryption Booster algorithm is also easily integrated with Advanced Encryption Standard. 2023 IEEE. -
Assimilating sense into disaster recovery databases and judgement framing proceedings for the fastest recovery
The replication between the primary and secondary (standby) databases can be configured in either synchronous or asynchronous mode. It is referred to as out-of-sync in either mode if there is any lag between the primary and standby databases. In the previous research, the advantages of the asynchronous method were demonstrated over the synchronous method on highly transactional databases. The asynchronous method requires human intervention and a great deal of manual effort to configure disaster recovery database setups. Moreover, in existing setups there was no accurate calculation process for estimating the lag between the primary and standby databases in terms of sequences and time factors with intelligence. To address these research gaps, the current work has implemented a self-image looping database link process and provided decision-making capabilities at standby databases. Those decisions from standby are always in favor of selecting the most efficient data retrieval method and being in sync with the primary database. The purpose of this paper is to add intelligence and automation to the standby database to begin taking decisions based on the rate of concurrency in transactions at primary and out-of-sync status at standby. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Cloud databases: A resilient and robust framework to dissolve vendor lock-in
Vendor lock-in has become a major concern in cloud computing. The term vendor lock-in describes situations where the subscriber cannot move data or services to another cloud vendor. This is due to heavy data volumes, high network bandwidth costs, dependencies, or unacceptable downtime. The proposed vendor lock-in dissolution practice migrates the database effectively in noticeably less time, regardless of database size and with a nominal network bandwidth requirement. Through this new practice, databases can be migrated to very remote regions, even across continents. A real-time implementation of the proposed method presented in this paper. 2024 The Author(s) -
Flexible and cost-effective cryptographic encryption algorithm for securing unencrypted database files at rest and in transit
To prevent unauthorized access to the databases and to ensure that the data of the databases is protected from intruders and insiders, the data is being encrypted at the storage locations. The same goal is achieved with Transparent Data Encryption, a feature that can be found in almost all database products. However, it has been observed that the non-datafiles are being ignored and there is no standard encryption for them like there is for datafiles. Moreover, there was no standard algorithm to encrypt them without relying on third-party tools. Therefore, This study provides a robust algorithm to perform the encryption. This presentation also describes the importance of non-datafiles encryption, and how some non-datafiles can pose a threat to data and infrastructure without encryption. The practical implementation of the non-data file encryption algorithm shows the authentic results. Further, unlike existing algorithms, the proposed algorithm gives the file owner full control over the encryption logic. In the encryption process, two levels of encryption logics are combined with a passcode lock, while the same combination of two levels of reversing encryption and passcode is used in the decryption process to convert encoded data back into text format. 2022 The Author(s) -
Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors.