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Regression Analysis on Macroeconomic Factors and Dividend Yield on Bank Nifty Index Returns
The study has examined an impact of macroeconomic variables and dividend yield on Bank NIFTY Index. It analyses the relationship amongst macroeconomic variables and dividend yield. The study used quarterly data from 1 January 2010 to 31 December 2019. It employed statistical measures like regression analysis to analyse the impact of independent variables (macroeconomic factors and dividend yield) on the dependent variable (Bank NIFTY returns) and multicollinearity tests to understand the relationship amongst the independent variables. The observations concluded that GDP, government bond yield and dividend yield have a significant impact on Bank NIFTY returns but CPI does not have a significant impact on Bank NIFTY returns. We can also conclude that all the independent variables are not correlated to each other. The study suggested to policy makers, in India, that they should maintain economic stability through policies of growth that will eventually boost the banking sector and the economy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Regression Analysis using Machine Learning Algorithms to Predict CO2 Emissions
Precise measurement of fuel consumption and emissions plays an important role in evaluating the environmental effects of materials and stringent emission control methods, especially within the transportation sector. This sector represents a substantial contributor to both global greenhouse gas emissions and the release of hazardous pollutants, making accurate assessment imperative for addressing climate change. The primary objective is to construct accurate predictive models that estimate CO2 emissions based on vehicle attributes, fostering a deeper understanding of the environmental impact of vehicular activities. Leveraging the 'CO2 Emissions-Canada.csv' dataset, the paper embarks on an extensive journey of data preprocessing, exploratory data analysis, and model training. These algorithms are meticulously fine-tuned and evaluated through metrics such as R-squared and mean absolute percentage error, rendering insights into their predictive accuracies. In essence, this paper pioneers a pathway towards environmentally responsible mobility solutions, capitalizing on the fusion of data science and environmental conservation. 2024 Bharati Vidyapeeth, New Delhi. -
Regression Approach for Predictive Analysis in Cognitive Decline
Cognitive decline refers to the deterioration of cognitive abilities, including memory, thinking, and reasoning, often associated with aging or neurological disorders like Alzheimer's disease. Machine learning (ML) methods can be used for predicting cognitive decline. Techniques such as Generative Adversarial Networks (GANs), feed-forward neural networks, supervised, and unsupervised learning process and analyse data patterns to forecast cognitive changes. By analyzing large datasets, ML algorithms can identify subtle cognitive shifts and predict future decline, enabling early intervention and personalized healthcare strategies. These diverse ML methods provide valuable tools for understanding, detecting, and potentially mitigating cognitive decline, advancing our ability to address cognitive health challenges. Some of these methods have been discussed later. In this research paper, a model to predict cognitive decline using principles of logical regression is proposed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Regression Test List Sharding in a Distributed Test Environment
One of the major issues during the regression test of the new version of Real Time Operating System (RTOS) is the time involved in test case execution. The main reason being a single embedded system device under test (DUT) is used to execute the test list containing several test cases. This traditional method of regression test also leads to wasted productivity of the other devices at hand that could be otherwise used during this regression test. Hence, in this paper, we propose a technique that aims at reducing the overall regression test cycle time of a newer version of a Real Time Operating System (RTOS) by employing a method known as "test-list sharding"in a distributed test environment. In the proposed work, multiple DUTs are connected to the test server via a communication network. The test server executes the test list containing several test cases and performs the test-list sharding, that is, distributing test cases to different DUTs and executing them in parallel. After the test is executed on the DUT, the test results are sent back to the test server which will summarize all the results. In the proposed work, the sharding is done by distributing the test cases without overloading or under loading any of the DUTs. Test list is sharded in such a way that the same tests are not sent to multiple DUTs. The main advantage of the proposed method is that the test sharding can be easily scalable to accommodate any number of devices that can be connected to the test server. Also, the test list sharding is done in a dynamic way so that the tests are distributed to an idle DUT that has completed a test execution and ready for another test to execute. The comparison study of executing a sample test list sequentially on a single DUT and distributed test system with multiple DUTs is performed. Results obtained showed the performance gain in terms of test cycle time reduction, scalability, equal load distribution and effective resource utilization. 2023 EDP Sciences. All rights reserved. -
Regression testing on services in mobile applications
The power of mobile devices has increased dramatically in the last few years. The Mobile apps market increases every day and Mobile device has become one of the most important equipment in peoples daily life, which brings us not only convenience of communication, but more and more work and entertainment applications. Mobile testing becomes very crucial as the mobile applications and mobile users are growing rapidly. Test consultants, test specialist, test managers and software engineering researchers are finding ways to do effective verification methods and to ensure reliability of mobile applications. In this paper, we propose regression testing framework on services in Remote Link Lite (RLL) mobile application. In order to perform regression test on mobile application, we have considered RLL application for POC purpose. Research India Publications. -
REGRESSION WITH VOLATILE ERRORS IN THE PRESENCE OF MEASUREMENT ERRORS
This study explores the estimation and testing of regression models with volatile errors when measurement errors are present. The presence of measurement error in models with heteroscedastic disturbances, such as those following an autoregressive conditional heteroscedasticity (ARCH) or Generalized ARCH (GARCH) structure, can lead to biased estimates and misleading inferences. To address this, we develop an estimation framework that accounts for both heteroscedasticity and mismeasured observations, ensuring consistent and asymptotically normal parameter estimates. We estimate the parameters using corrected score estimation and weighted linear regression, which effectively mitigate the impact of measurement error and hetroscedasticity. Additionally, we perform a Likelihood Ratio (LR) test to assess the significance of measurement errors in regression models with volatile errors. Through Monte Carlo simulations, we analyze the bias and efficiency of traditional estimators and demonstrate the robustness of our proposed approach. Finally, the methodology is applied to real-life economic and financial data, illustrating its practical relevance and effectiveness in empirical research. The findings contribute to improving statistical inference in models where measurement error and volatility coexist, ensuring more reliable and accurate parameter estimation. 2025, Gnedenko Forum. All rights reserved. -
Regulating the gig economy: Addressing worker rights in India's quick commerce sector
The study examines the regulatory challenges faced by India's fast- growing quick commerce gig economy, with a focus on the rights and protections of workers. It examines the issue of gig workers and implementation gaps in existing frameworks and highlights the vulnerabilities of migrant workers. The study uses thematic analysis to explore aspects such as working conditions, workers' legal awareness, algorithmic management, and gender inclusion in the workforce. Findings reveal regional disparities in working conditions, discrepancies between legal recognition and practical enforcement, and persistent gender- based inequalities. The research underscores the need for tailored, collaborative efforts among various stakeholders to improve the situation of gig workers. This study aims to enhance the understanding of regulatory issues in the quick commerce sector and offers valuable insights for policymakers, platforms, and researchers to improve working conditions and ensure fair treatment for gig workers in India. 2025, IGI Global Scientific Publishing. All rights reserved. -
Regulating the Rise of Transformers in Global Healthcare: A Legal and International Law Perspective on AI Governance, Ethics, and Data Protection
With advances in healthcare, GPT, BioGPT and MedPaLM are helping doctors diagnose better, choose better treatments and engage with patients. Yet, these uses bring up significant problems related to privacy, understanding what the algorithms do, who is responsible for actions and gaining agreement. The paper examines global regulation on AI-for example, GDPR, HIPAA, India's DPDP Act, OECD AI Principles and UNESCO's AI Ethics Recommendation-and points out where gaps still exist, mainly in places with lower and middle incomes. Using both comparative studies and a focus on policies, it proposes common standards, specially designed rules for AI liability and ways to test ethical considerations. To provide secure, clear and equal use of AI in healthcare markets, the study proposes that countries come together to govern the technology. 2026, IGI Global Scientific Publishing. All rights reserved. -
Regulating the Speed of Innovation: A Legal and Ethical Framework for 6G Deployment in Smart Societies
The expected use of 6G technologies contains unmatched breakthroughs in hyperconnectivity, real-time holographic communication, and AI-supported immersivity. There is, however, in this technological leap, a complex legal-ethical-regulatory issue scene that must be addressed now, worldwide. This chapter provides a cross-disciplinary argument on a missing legal regime that can best govern 6G-enabled ecosystems and especially referring to the governance of the real-time artificial intelligence, XR/VR applications, the privacy consequences surrounding data, and nanobots and human rights concerns in a 6G future. This chapter can be taken as comparative legal, following which the emerging 6G regulatory principles are explored in the example of the European Union (Digital Services Act, AI Act), United States (AI Bill of Rights, FCC policies), and India (Digital Personal Data Protection Act, 2023). It also closes in foreign legal materials such as the Budapest Conference on Cybercrime and General Comment No. 25 (2021) on the right to privacy in the online world of the UN Human Rights Committee. The e-Governance model of Estonia, with significant use of AI and XR to enhance the functionality of the communication network is analyzed as a case study in this view to show the potential, as well as the challenges such hyper-automation can bring about. This chapter is then contrasted with the situation in China, which currently has a 6G surveillance infrastructure and explains why algorithms, mass surveillance, and illegal profiling are risky without some regulation. Besides, this chapter explores transnational data transfer, cyber sovereignty, and cross-border information law enforcement, which require global uniformity by all nations. It is insisted that the precautionary principle and technology impact assessments (TIAs) must be conducted as prerequisites to widespread 6G implementation in smart cities, smart tourism, and healthcare fields. The chapter ends by proposing an International 6G Governance Charter expressing the need to secure legally binding protection of AI-integrated XR systems, the obligation to be transparent, and enforcement-based rights of users within the ultra-fast communication space. 2026 selection and editorial matter, Upinder Kaur, Aparna Kumari, Hemant Kumar Saini, Surbhi B. Khan, and Mariya Ouaissa; individual chapters, the contributors. -
Regulation and innovation in financial markets: The impact of fintech on traditional banking and financial systems blockchain technology
In this chapter, we present the underlying technical principles of distributed ledger technology (DLT) and blockchain technology and outline their practical applications in FinTech. In the recent years, DLT and blockchain technologies in general and cryptocurrencies, in particular, have attracted substantial attention from both researchers and practitioners due to their unique technological features such as the lack of centralized control and high level of anonymity. Because of the disruptive nature, DLT and blockchain have led to the evolution of decentralized applications in multiple domains such as finance, health care, supply chains etc. In this chapter, we first outline basic principles and foundations underpinning the DLT and blockchain technologies. Second, we discuss several applications in the FinTech domain such as cryptocurrencies, smart contracts, risk management, corporate finance, governance, crowdfunding, and derivative markets. 2025, IGI Global Scientific Publishing. All rights reserved. -
Regulation of axisymmetric Rayleigh-Bard convection using boundary temperature coupling of the two circular plates
Controlled delay of regular, chaotic, and periodic regimes of instabilities is studied in the problem of axisymmetric Rayleigh-Bard convection in a vertical cylinder. A feedback control is assumed at the boundaries, which leads to a coupling of the two boundary temperatures. A classical type solution is impossible in such a situation. Hence, a novel series solution procedure is adopted to arrive at the generalized Lorenz model. Due to feedback control, delayed onset of regular convection is observed and the percentage of such a delay as a function of the controller gain parameter, K , is reported. The changes in the pitchfork bifurcation point, the homoclinic orbit, and the Hopf bifurcation point due to feedback control are highlighted with the help of a bifurcation diagram. This diagram shows that the influence of feedback control is to advance the onset of homoclinic bifurcation and delay the onset of Hopf bifurcation. The results indicate that feedback control shows preference for Hopf bifurcation and is antagonistic toward homoclinic bifurcation. The shortening of the time of existence of the strange attractor intermittent with a periodic/quasi-periodic state, which is preceded by the fully periodic motion as K increases is observed using the largest-Lyapunov-exponent plot, the bar-code plot, and the bifurcation diagram. The results coming out of the Kaplan-Yorke dimension reiterates the results depicted by other indicators concerning the influence of K on chaos. The practical importance of the control strategy that is used in the paper is also mentioned in the paper. 2025 Author(s). -
Regulation of cryptocurrency: An Indian perspective
Cryptocurrency is a currency created and held electronically in the virtual world. In the absence of a proper legal framework, cryptocurrencies are unregulated in many countries, including India. This article attempts to delve into the concept of cryptocurrency and the issues and challenges they face. It also discusses the current legal and regulatory framework for cryptocurrencies in India and offer suggestions for their better regulation. 2021 Economic and Political Weekly. All rights reserved. -
Regulatory and strategic challenges of patent evergreening in the MedTech industry: An analysis of competition law implications
Patent evergreening is a strategy used by pharmaceutical companies to extend their market exclusivity through minor modifications of existing drugs, often without significant therapeutic advancements. This practice raises concerns about access to affordable medicines, particularly in developing countries like India, where high drug prices impact public health. A comparative analysis with the European Union (EU) reveals that while India relies on patent law restrictions, the EU employs competition law under Article 102 of the Treaty on the Functioning of the European Union (TFEU) to regulate evergreening. Cases such as AstraZeneca v. European Commission demonstrate the EU's effects-based approach to curbing anti-competitive patent strategies. This study highlights the gaps in India's regulatory framework, emphasizing the need for greater coordination between the Indian Patent Office (IPO) and the Competition Commission of India (CCI), and adopting an effects-based approach are crucial to preventing evergreening while ensuring both innovation and consumer welfare. 2025, IGI Global Scientific Publishing. All rights reserved. -
Regulatory Challenges and Compliance in Decentralized Finance (DeFi): Comparative Study Between India and USA
Decentralized Finance (DeFi) is an emerging force transforming the global financial landscape by leveraging blockchain technology to eradicate the middlemen and assist peer- to- peer financial transactions. However, a decentralized and pseudonymous nature brings a big challenge in its regulation and compliance, especially in Know Your Customer (KYC) and Anti- Money Laundering (AML) regulations, market misconduct and adaptation, new cryptocurrency innovations, and safety and security issues. This paper comparatively analyses the two regulatory frameworks, compliance mechanisms, technical adaptations, and measures of cybersecurity regulating DeFi in India and the United States. By examining the salient regulatory challenges and compliance strategies in both jurisdictions, this study aims to provide insights that help foster a balanced regulatory environment that promotes innovation without undermining financial stability or consumer protection. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Regulatory challenges and compliance in federated learning (FL) for financial applications
The financial sector is increasingly turning toward artificial intelligence (AI) for applications such as fraud detection, credit scoring, and risk management. But that makes it contrary to the regulatory environment. New data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the Digital Personal Data Protection Act (DPDPA) in India impose stringent conditions on data residency, minimization, and sovereignty. This chapter argues that traditional centralized AI systems which require sensitive data to be collected for processing at one site simply do not sit well with these legal requirements, thereby creating massive compliance risks for financial institutions. By way of an extensive architectural study and practical application, this chapter demonstrates that the very basic functions of a traditional AI system tend to contravene prohibitions on cross-border transfers of data. Instead, we propose Federated Learning (FL) as a compliance-by-design solution that solves this sticking point. In other words, by inverting the discredited approachand bringing the algorithm to the data rather than the other way aroundFL ensures that practitioners in different institutions and jurisdictions collaborate on model training without sharing raw data. Only aggregated and anonymized updates on the model are sentinherently complying with certain data residency and data minimization principles. Besides advocating for FL as a core compliant innovation pathway, this chapter also touches on a number of regulatory uncertainties and other potential issues arising from this technology, such as liability, model security, and a need for industry-wide standards. To this end, the chapter clearly states that the adoption of privacy-preserving technologies such as FL has become integral. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. -
Regulatory data protection for global economy of biopharmaceuticals: comparative legal analysis with focus on innovative biopharma in India
This provides a new global economy of biopharmaceuticals with an exclusive right over clinical data, meaning that no other person or persons may use them for a specified period. This study, therefore, offers a critical analysis of complementary protection granted to biopharmaceuticals by patents and regulatory data protection (RDP) globally with respect to innovation, competition, and access to medicines. This study probes the effectiveness and the challenge RDP is making using statistical analysis, financial modelling, and comparative analysis of the regulatory framework in Central Drugs Standard Control Organization (CDSCO), Food and Drug Administration (FDA), and Emergency Market Authorization (EMA). The justification for this combination is that RDP fosters innovation due to the protection of clinical trial investments, which provides a drive for the introduction of innovative biologics but does not inhibit the launch of biosimilars. With RDP, though they are very different in what they do, patents have created an enabling environment to make sustainable innovation in biopharmaceuticals accessible. International regulatory hurdles have to emerge so that a balance that advances both innovation and affordability becomes the norm within biopharma. Copyright 2025 Inderscience Enterprises Ltd. -
Rehabilitation and Reintegration of Children in Conflict with Law: An Exploratory Study of Karnataka and Telangana
The current study analyses the effect that rehabilitation and reintegration of children in conflict with law has on the life of the children. It traces the history of deviant behavior of the child and the causes that lead to deviancy. For the purpose of this study, two institutions were selected from Karnataka and Telangana, because of geographical and social-economic similarities. A sample of 10 (5 from each state) children who had undergone rehabilitation and reintegration process were selected between the age of 12-18 years and interview was conducted. The interview shed light on the rehabilitation and reintegration process that exists in the two states and their institutions from the perspective of the participants and their struggles and challenges was also recorded. Thematic analysis by Braun was loosely followed. The analysis brought out major themes in the experience of the participants and their changes that they had undergone. The research concludes by listing out the characteristics of the respective institutions in the state of Karnataka and Telangana and their impact on the participant in the aspect of rehabilitation and reintegration. -
RehabPal: Automated Physical Therapy Assistance
This innovative research project transforms automated physical therapy support by combining 2D and 3D pose assessment approaches with tailored feedback systems. Utilizing Mediapipe technology, the apparatus attentively observes patients movements in a two-dimensional space and gives realtime data regarding their gait, exercise compliance, and range of motion. Accurate data on joint angles and body segment alignment are provided by the system's sophisticated 3D posture estimation algorithms, which enhance tracking precision. The system incorporates customized feedback systems that include individual patients goals, progress, and conditions and delivery and reward schemes. By increasing user engagement and adherence, the integration of gamification elements has the potential to revolutionize automated physical therapy support. This comprehensive approach aims to enhance patients quality of life while simultaneously enhancing long-term rehabilitation outcomes by providing more conveniently accessible, affordably priced, and specially designed therapies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Reimagining Automotive Manufacturing with Generative AI: A Technolology-Organization-Environment Perspective
Generative AI (Gen-AI) is transforming industries, and the automotive sector is no exception. Auto Component Manufacturing (AutoCM) organizations face strong pressure to adopt Gen-AI for competitiveness, yet little research has examined adoption factors in this context. This study investigates the determinants of Gen-AI adoption in AutoCM firms using the Technology-Organization-Environment framework. A model was developed and tested through survey data from 490 managers, validated via PLS-SEM. The findings reveal key predictors of adoption: perceived intelligence, data quality, absorptive capacity, IT infrastructure, top management commitment, vendor support, competitive pressure, and government support. Results further show that adoption intention drives potential use of Gen-AI, but this relationship is weakened by concerns over Gen-AI hallucination. This research identifies crucial drivers of Gen-AI adoption in AutoCM, extends TOE framework literature, and provides actionable insights for practitioners to strategically harness Gen-AIs transformative potential in the sector. 2025 The Author(s). Published with license by Taylor & Francis Group, LLC. -
Reimagining Career Growth for Gen Z and Millennials: Role of Career Lattices in Enhancing Employee Engagement
Problem: Widespread disengagement among the working professionals and changing career expectations of Generation Z and Millennials pose a risk to talent management, retention, and performance in the IT sector. Solution: This quantitative study examines how employees perceived competence translates into engagement via multi-directional career development pathways (career lattice, career-goal progress, professional ability improvement, and promotion) using a survey of 304 IT professionals and PLS-SEM. Results show that employees who felt more competent reported higher engagement, and this relationship was strengthened when organisations provided career lattice pathways such as skill development, lateral mobility, and goal progress. Stakeholders: The findings identify actionable levers for organizational leaders, HRD practitioners, and talent managers to design adaptive career frameworks that increase engagement among Gen Z and Millennial employees. Implication: HRD leaders and talent managers should prioritise flexible, skills-based mobility and career-goal supports to boost Gen Z and Millennials engagement. The Author(s) 2026