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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. -
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. -
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. -
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. -
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. -
Exploring Investment Behaviour of Working Women for Economic Empowerment
Growth in investments leads to the economic and household upliftment of each person. We can see the presence of women in every area of our economy. They play the role of a teacher, doctor, nurse, engineer, accountant, military officers, entrepreneur, and many more. Now women are more educated; they have their assets in gold and other precious ornaments. They are also aware of various investment schemes available. This study analyzes working women's investment behavior and examines how it is beneficial for our society's economic and household upliftment. The data collection was carried out through 400 respondents using a questionnaire. The study area covers only the Bengaluru Urban population. Five Taluks in the Bengaluru urban were selected for the study. Cluster sampling is followed for selecting samples, and data is collected from each clusters. Eighty samples from each cluster were selected, and data is collected using the survey method. The tools used for data analysis consist of Henry Garrett ranking method, Weighted Average Method, and Percentage Analysis. Risk preferences of the investors were analyzed using the factors derived from the article written by Vashisht and Gupta, 2005. The current study aims at salaried women employees who have a regular income, which they can contribute to savings and investment for the economic and household developments. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Impact of Prolonged Screen Time on the Mental Health of Students During COVID-19
The COVID-19 pandemic has struck every sector around the world, including the education sector. The pandemic has forced educational institutions around the world to close, putting academic calendars in jeopardy. To keep academic activities going, most educational institutes have switched to online learning platforms. However, the lack of e-learning readiness and the current crisis has taken a toll on students mental health significantly. In this study, we hope to understand better students impressions of online education and the impact of prolonged screen time on students mental health. From the responses of 438 students, our study aims to identify the causes of stress in students due to the online mode of education. From eye stress to limited social interaction, all factors leading to poor mental health are considered. Suggestions for addressing the challenges of online education and approaches to create a more successful online learning environment are also provided. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A taxation perspective on how domestic double taxation on corporate taxes affects Indian commerce
This paper examines the impact of domestic double taxation on corporate taxation in India after the abolition of the dividend distribution tax (DDT) and the introduction of the new tax rates and rules in 2020. Domestic double taxation occurs when the same income is taxed twice by the same jurisdiction, such as at the corporate and shareholder level. Using data from the Income Tax Department and the Reserve Bank of India, this paper estimates the effective tax rate on corporate income and dividends in India under the current tax system. It compares it with the previous tax system and the international standards. It also analyses the effect of domestic double taxation on corporate financial decisions, such as the dividend payout ratio, the retained earnings, the debt-equity ratio, and the investment rate. It evaluates the effect of domestic double taxation on corporate tax revenue, tax incidence, and tax efficiency. The authors identify that, between 2019 and 2023, corporate income tax revenue in India increased alongside nominal GDP growth, with a notable positive correlation coefficient between the two variables. The empirical analysis technique involves gathering and analyzing quantitative data to assess the real impact of new tax reforms and reduced corporate tax rates. Finally, this study proposes policy recommendations to mitigate the adverse effects of domestic double taxation and improve India's corporate taxation system and GDP. This paper contributes to the literature by providing updated and comprehensive empirical evidence on domestic double taxation and corporate taxation in India and by offering some insights and suggestions for the policymakers, the tax authorities, the corporate sector, and the academic community. 2025 Malque Publishing. All rights reserved. -
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. -
A critical analysis onto the conundrum of valuation of IP assets as part of liquidation process under IBC, 2016
This study critically examines the valuation of Intellectual Property (IP) assets during the liquidation process under the Insolvency and Bankruptcy Code (IBC), 2016, emphasizing its profound impact on creditor recovery and the efficiency of insolvency proceedings. The research employs a rigorous mixed-methods approach, combining qualitative analysis of high-profile case studies with quantitative assessments of valuation methodologies, such as market, income, and cost approaches. The central research issue revolves around the complexities and challenges of accurately valuing IP assets, which are often unique and lack direct market comparables. The study also scrutinizes the regulatory framework and the pivotal role of registered valuers in ensuring transparent and equitable asset valuation. The findings reveal that precise valuation is not merely a procedural requirement but a critical determinant of liquidation outcomes, influencing the overall recovery rate for creditors and the viability of IP-backed financial instruments. The paper argues that the meticulous calculation of discount rates and adherence to standardized valuation practices can significantly enhance the credibility of the insolvency process, attract more substantial investments, and ultimately lead to superior financial recoveries. This research provides vital insights and robust recommendations for policymakers, legal professionals, and financial analysts, advocating for a more refined and transparent approach to IP asset valuation under the IBC, thereby strengthening India's insolvency framework and market integrity. 2025 Malque Publishing. All rights reserved. -
Navigating personal guarantor liability in insolvency: Transformative and transactional shifts in the legal framework
The liability of personal guarantors under Indias insolvency regime has undergone a significant transformation with the introduction of insolvency provisions under the Insolvency and Bankruptcy Code, 2016 (IBC). This research critically examines the evolving legal framework governing personal guarantors, analysing recent judicial pronouncements and legislative changes that have redefined their rights and obligations in relation to corporate debtors and creditors. The study explores how including personal guarantors in the insolvency process has streamlined creditor recoveries while providing structured financial resolution mechanisms for guarantors. Additionally, the paper assesses the cross-border insolvency framework, comparing India's approach with international insolvency regimes, particularly in the context of harmonisation efforts and global best practices. Through a doctrinal research methodology incorporating statutory analysis, judicial interpretations, and comparative legal studies, the research identifies key challenges, including due process concerns, enforcement inefficiencies, and complexities in cross-border insolvency cases. The findings indicate that while the evolving insolvency framework enhances creditor protection and financial recovery, it raises concerns regarding fairness and procedural safeguards for personal guarantors. The paper advocates for a balanced approach to insolvency laws, calling for targeted reforms to address enforcement gaps and ensure equitable treatment for all stakeholders. By analysing domestic and international perspectives, this study contributes to the discourse on personal guarantor insolvency and offers recommendations to enhance the efficacy and fairness of the resolution process. 2025 Malque Publishing. All rights reserved. -
Advancing Interpretable Machine Learning: Principles, Challenges, and Practical Insights
[No abstract available] -
A taxation perspective on how domestic double taxation on corporate taxes affects Indian commerce
This paper examines the impact of domestic double taxation on corporate taxation in India after the abolition of the dividend distribution tax (DDT) and the introduction of the new tax rates and rules in 2020. Domestic double taxation occurs when the same income is taxed twice by the same jurisdiction, such as at the corporate and shareholder level. Using data from the Income Tax Department and the Reserve Bank of India, this paper estimates the effective tax rate on corporate income and dividends in India under the current tax system. It compares it with the previous tax system and the international standards. It also analyses the effect of domestic double taxation on corporate financial decisions, such as the dividend payout ratio, the retained earnings, the debt-equity ratio, and the investment rate. It evaluates the effect of domestic double taxation on corporate tax revenue, tax incidence, and tax efficiency. The authors identify that, between 2019 and 2023, corporate income tax revenue in India increased alongside nominal GDP growth, with a notable positive correlation coefficient between the two variables. The empirical analysis technique involves gathering and analyzing quantitative data to assess the real impact of new tax reforms and reduced corporate tax rates. Finally, this study proposes policy recommendations to mitigate the adverse effects of domestic double taxation and improve India's corporate taxation system and GDP. This paper contributes to the literature by providing updated and comprehensive empirical evidence on domestic double taxation and corporate taxation in India and by offering some insights and suggestions for the policymakers, the tax authorities, the corporate sector, and the academic community. 2025 Malque Publishing. All rights reserved. -
Assessing the environmental consequences of artificial illumination on marine ecosystems: Legal and ecological perspectives
Fishing has been traditionally practiced using sustainable methods and eco- friendly techniques which ensured the conservation of fish diversity and its population. The traditional fishing techniques used were hand-lining, spearfishing, gleaming, harpoons and spears. However, with technological advancements LED fishing techniques has gained prevalence among fishermen due to their high-intensity LED lights (Light-Emitting Diodes) for fishing. This method enables them to catch fish in larger quantities. Such LED lights can penetrate deeper into the water and illuminate larger areas than traditional methods, disrupting the natural behavior of marine organisms. As a result, this practice leads to a significant decrease in fish populations, adversely affecting marine ecosystems. Although LED fishing offers certain advantages, such as improved efficiency and reduced fuel consumption, it raises serious concerns regarding its socio-legal and environmental impact and highlights the need for effective legal and regulatory frameworks. In this background, the current study explores the socio-economic impacts on fishing communities, which are disproportionately affected by the introduction of this technology. Further, the research delves into the ecological consequences of LED fishing, including its potential to disrupt fish behaviour, alter food chains, and damage sensitive marine ecosystems. It will also analyze the legal and regulatory challenges associated with managing LED fishing and examine the existing Legal framework that is applicable in international and national Contexts. Finally, the paper will propose a framework for sustainable LED fishing. Copyright (c) 2026 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. -
Digital pathways to sustainability: Integrating technology and innovation for a greener future
Digitalization has emerged as a revolutionary element across various sectors and communities, altering conventional practices and creating new avenues for sustainability. This chapter delves into how digital technologies intersect with circular economy principles and sustainable development, emphasizing their contribution to combating climate change and furthering the United Nations' 2030 Sustainable Development Goals (SDGs). By evaluating case studies, theoretical models, and empirical evidence, this chapter investigates the ways in which cutting-edge technologies like artificial intelligence (AI), blockchain, and the Internet of Things (IoT) promote sustainable practices and foster environmental resilience. The chapter concludes by providing practical insights for policymakers, industry executives, and researchers on leveraging digital innovation for a greener future. 2025, IGI Global Scientific Publishing. -
AI-Powered Wheels: Machine Learning Approaches for Predicting Used Car Prices
Predictive analytics is now an essential tool for dealers, buyers, and sellers due to the used car markets increasing need for precise pricing models. This study compares the capability of Logistic Regression, Random Forest, Linear Regression, Support Vector Machine (SVM), and Gradient Boosting Machines (GBM) for predicting used car pricing. The results demonstrate that Random Forest and Gradient Boosting scored the best accuracy (87%), with Random Forest also demonstrating better precision (90%). Logistic and Linear Regression both achieved comparable accuracy of 85%, with precision scores of 88% and 89%, respectively. SVM, while significantly less accurate (83%) and precise (86%), produced comparable results for high-dimensional data. In terms of training time, Linear Regression (0.0089 seconds) and Logistic Regression (0.0094 seconds) were the fastest, whereas Gradient Boosting (0.8312 seconds) and Random Forest (0.4766 seconds) took much longer. These results demonstrate a trade-off between model complexity, accuracy, and computing efficiency, with simpler models performing better in terms of speed and ensemble models doing better in terms of prediction accuracy. This study presents practical insights to help stakeholders choose machine learning models for predicting used car prices depending on their specific requirements. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Ethical considerations in nanomedicine research involving women
This chapter elaborates social impact and perspectives of advanced technologies as well as critically discuss the ethical issues of womens participation in nano-medicine research. It starts by addressing the issue that women have been under sampled in clinical research but it is moral to take their samples farther into research most notably with certain special health conditions and unique reactions to drug treatments that are likely to be unique to women. One of the most emphasized concepts is informed consent, for the purpose of precisions and clarity in explaining its purpose and usefulness, as well as the probable consequences in the form of some narrowly speculative treatments with particular relevance to certain probabilities remaining beyond the range of perception on the female body after some or other nano-scale procedures. The chapter also talks about the ethical implications in the present strategies to increase the efficacy and safety for women only therapies like nanomedicine; the author urges for stringent preclinical and clinical guidelines which should have examined the following gender variations. It also examines the regulation with guidance and suggests contingency structures that are employed in the management of the ethical use of nanomedicine in women health care. Therefore this chapter tries to develop ethics together with science, incorporate these met ethical considerations into methods and guidelines in research, to contribute to enhancement of beneficial impact of the nanomedicine for womens rights and well-being. 2026 Elsevier Inc. All rights reserved.. -
Rayleigh-benard convection in a dusty newtonian nanofluid with and without coriolis force
Theoretical investigation of the Rayleigh-Bard convection (TRBC) in nanofluid (NF) submerged with dust particles is carried out. Convection in dusty nanofluid is considered between two horizontal free boundaries. Effect of nanoparticles shape is also accounted. The Saffmans dusty fluid model is used to simulate the influence of dust particles, whereas the KVL (Khanafer-Vafai-Lightstone) model is employed to estimate the effective nanofluid properties. The fluid, dust particles and nanoparticles are in the thermal equilibrium state and move with the same velocity. The exact solutions are obtained using Normal Mode Analysis (NMA) method for two different cases namely (1) TRBC in dusty nanofluid (DNF) without Coriolis force (2) TRBC in DNF with Coriolis force. It is established that for the stationary convection, the effect of suspended particles hasten the onset of convection whereas the Coriolis force postpones the onset of convection. 2018 by American Scientific Publishers All rights reserved. -
Tumor Infiltration of Microrobot using Magnetic torque and AI Technique
Because of their surroundings and lifestyle alternatives, human beings, these days be afflicted by a huge style of illnesses. thus, early contamination prediction will become crucial. on the other hand, primarily based just on signs, docs warfare to make correct forecasts. The most challenging issue is accurately forecasting illnesses, which is why machine learning is essential to accomplish this task. To identify concealed patterns within vast amounts of medical data, disease information is processed using data mining techniques. We evolved a extensive contamination prediction primarily based on the affected person's signs. We rent the device getting to know techniques Convolutional Neural network (CNN) and ANFIS to exactly count on sickness (adaptive community-based totally fuzzy inference machine). For an correct forecast, this trendy illness prediction considers the character's way of life picks and fitness history. ANFIS outperforms CNN's set of rules in phrases of popular infection prediction, with an accuracy price of 96.7%. additionally, CNN consumes extra memory and processing energy than ANFIS because it trains and assessments on facts from the UCI repository. The Anaconda notebook is a suitable tool for implementing Python programming as it contains a range of libraries and header files that enhance the accuracy and precision of the process. 2023 IEEE. -
Synthesis and characterization of Cr2AlC MAX phase for photocatalytic applications
MAX phase, a layered ternary carbide/nitride, displays both ceramic and metallic properties, which has significantly attracted the materials research. In this work, Cr2AlC MAX phase powder with high purity was fabricated via a facile and cost-effective pressure-less sintering methodology and utilized for photocatalytic degradation of different organic pollutants for the first time. Various characterization techniques were used for confirming the morphological and other physico-chemical properties of the catalyst. Cr2AlC MAX phase with a low band gap of 1.28 eV has shown 99% efficiency in the degradation of malachite green, an organic pollutant under visible light irradiation. The scavenger studies conclude that, O2?and h+ as the active species during the photocatalytic reaction. Furthermore, the kinetic study revealed that the reaction obeys pseudo-first-order kinetics and can be reused for four cycles without losing the activity. This novel approach can give new insight into the potential application of MAX phase materials in the field of wastewater treatment under visible light irradiation. 2021 Elsevier Ltd