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Introduction to International Financial Economics and Banking
The global financial economics and banking is emerging to be a very important element in an economical setting that becomes increasingly global. The primary objective is to deliberate how the capital flows, foreign exchange markets, interest rate parity and banking regulations have affected financial stability across nations and the economic growth. Background and theoretical structures that define the field of international finance and international banking that interconnect is outlined in the chapter. The chapter sheds some light on the dynamic entanglements between global financial institutions multinationals, central banks, and the overall global capital finance markets, cross-border risk evaluation set ups, and international liquidity indicators for macro-financial connections. The given work can contribute to the main part of the scholarly work by providing a complete image that combines the classical theory with the modern developments, hence, can be of use to scholars, policy makers, and financial professionals dealing with global financial markets. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Blockchain-Enabled ESG Management: Enhancing Data Authenticity and Transparency
Blockchain has a virtual path that is immutable and allows for secure and transparent business transactions and reduces the need for mediators. This chapter outlines the use of blockchain in the management of ESG to boost information integrity and transparency. The purpose is to know how blockchain can potentially contribute to proving some ESG metrics, enhancing the compliance to regulations, and over legalize the reporting on sustainable development. It has been employed by doing a critical analysis of the existing applications of blockchain for ESG reporting in the current fiscal year and studies with cases including expert talk for pronouncing the impact on the data accuracy among the various sectors. According to the results, the ability of blockchains distributed ledger to store data concerning ESG and ensure transparency is resistant to changes. However, problems like compatibility and scalability still have not disappeared and that is why tactical solutions remain relevant. From this study, it can be inferred that there is a potential of blockchain to greatly enhance stakeholders confidence through transparent and exemplary ESG process, which is then likely to enhance organizations ethical behaviour. Besides, it directly focuses on the capacity of blockchain to affect new sustainable company processes and rules and regulations. In conclusion, the use of blockchain technology brings hope in the shift of sustainability governance due to its provision of authenticity and accountability of data, hence promoting better and ethical corporation conduct. It provides valuable information to the stakeholders who want to rely on blockchain to enhance sustainable performance. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Harnessing the Internet of Things for Sustainable Urbanization: A Framework for Resilient Smart Cities
The Internet of Things (IoT) represents a revolutionary technology, which facilitates the emergence of dynamic network of inter-connected devices that can flawlessly communicate and share information. It also merges various technologies, hardware systems, and software frameworks to form an all-embracive ecosystem, which comprises data, people, devices, and smart communication. In a country like India, which exhibits major regional differences in the degree of technology and IoT adoption, it can easily be considered that the idea of IoT has the enormous potential to transform the urban development process and introduce it into the paradigm of improvability, cost-efficient, and maintenance friendly solutions. The issues of smart cities are central to the development of a country and better life of citizens. In India, use of IoT in smart city projects makes it easier to provide core services to constituents, implement transparent governance strategies, and build sustainable urbanization. With the help of IoT, different systems may correlate effectively which will provide a reliable access to the data and the establishment of new opportunities in the form of innovative digital services that would meet the requirements of city residents. The present research paper explores the centrality of IoT in the development of smart cities in India. It discusses the policy framework of IoT in the country, outlines major drivers of and benefits of IoT-based smart city solutions, analyzes consumer preferences and demographics. With this comprehension, the paper would like to suggest practical insights concerning the use of IoT in creating inclusive and technology-driven cities of India. 2025 IEEE. -
Machine Learning-Based Credit Scoring for Personalized and Inclusive Lending in Consumer-Centric Financial Systems
Traditional approaches to credit-scoring are largely based on rule systems that can be excessively fixed and limited to the ability to reflect individual financial behavior. The article analyzes the effectiveness of machine-learning (ML)- based credit ranking with the hypothesis that they can improve predictive capability and fairness of consumer credit lending. The performance of these algorithms, including supervised methods of learning, e.g., logistic regression, random forests as well as the deep learning, is contrasted to the conventional credit models. Model transparency is provided by SHAP values and other methods explainable by AI. Findings show that practice based on the use of ML outperform traditional methods in risk assessment, especially, through the inclusion of supplementary forms of data in traditional databases based on transaction behavior, virtual footprints, and psychometric signals. Furthermore, ethical standards and moral confidence in ML informed credit decision-making will require regulation-proof and explanatory modelling. Through the research, it is recommended to implement policy measures intended to cause financial institutions, fintech companies, and regulating bodies to implement ML-based credit-scoring technologies, with fairness and predictive effectiveness being reciprocal drivers of financial access and consumer-friendly lending practices. 2025 IEEE. -
A Comparative Analysis of Various Soft Computing Techniques for Indian Stock Market Prediction
Soft computing techniques have been increasingly used for stock market analysis in the past few years because they can capture nonlinear aspects which traditional econometric models do not adequately capture. With different techniques like Artificial Neural Networks, Deep Neural Networks and Stacked Autoencoders available, in this paper, the author tries to determine which of the above methods can model the Indian stock market with higher accuracy. In this study, high-frequency data from Nifty 50 is used, and various feature selection techniques such as PCA and linear regression are used for each of the above machine learning models to predict the Nifty 50 data. Finally, all predictions from the different techniques are compared with the actual index movement and the best method for Nifty 50 is suggested. 2025 Seventh Sense Research Group -
Social capital as a catalyst for leadership excellence: the mediating role of institutional reputation in Indian higher education
Drawing upon social capital theory, this study aims to investigate the impact of social capital on leadership effectiveness through mediating role of institutional reputation in higher educational institution. Data were collected from 310 academic leaders, including HODs, area chairs, and deans, using a structured online questionnaire. The sampling technique used was purposive sampling. Partial least squares structural equation modelling (PLS-SEM) was employed for analysis. The results indicate a significant positive relationship between social capital and leadership effectiveness, highlighting the importance of interpersonal trust, collaborative culture, and professional networks in influencing strategic vision, decision-making, and transformational leadership skills. Moreover, institutional reputation is identified as a partial mediator in this relationship, indicating that robust social capital not only improves direct leadership outcomes but also enhances the perceived credibility and prestige of institutions, hence strengthening leadership legitimacy and influence. This study enhances the sparse empirical literature linking social capital and leadership within the Indian higher education sector and provides pragmatic insights for policymakers and institutional leaders aiming to cultivate trust-based cultures and reputational capital. The study concludes with ideas for cultivating social capital via inclusive governance, faculty involvement, and external collaborations to improve leadership efficacy and ensure sustained institutional success. Copyright 2025 Inderscience Enterprises Ltd. -
An IoT-Based System for Fault Detection and Diagnosis in Solar PV Panels
This abstract describes an IoT-based system for fault detection and diagnosis in solar PV panels. The proposed Fuzzy logic-based fault detection algorithms aims to improve the performance and reliability of solar PV panels, which can be affected by various faults such as shading, soiling, degradation, and electrical faults. The system includes wireless sensor nodes that are deployed on the panels to collect data on their electrical parameters and environmental conditions, such as temperature, irradiance, and humidity. The collected data is then transmitted to a central server for processing and analysis using machine learning algorithms. The system can detect and diagnose faults in real-time, and provide alerts and recommendations to maintenance personnel to take appropriate actions to prevent further damage or downtime. The system has several advantages over traditional manual inspection and maintenance methods, including reduced downtime, lower maintenance costs, and improved energy efficiency. The proposed system has been validated through experimental tests, and the results show that it can accurately detect and diagnose faults in solar PV panels with high reliability and efficiency. 2023 EDP Sciences. All rights reserved. -
Bridging the Skill Gap: Correlating Affective Competencies with Innovation and Technology Preparedness Among Software Professionals
This research focuses on examining the relationship between innovation and technology readiness skills and the affective competencies of IT/ITeS professionals. This research also explores the impact of affective competencies in the work environment. The study highlights how affective competencies like appreciation, motivation, attitudes, and values shape workplace interactions and influence professional success rather than focusing on secondary emotional competencies. A logical connection has been created empirically between affective competencies and their significance in interpersonal relationships, addressing emotional attributes such as feelings, motivation, appreciation, values, and attitudes. This research study is a section of a survey of 457 IT/ITeS professionals, aiming to explore the skills gap analysis on Bloom's paradigms on cognitive, affective, and psychomotor competencies. This article specifically focused on the impact of affective competencies across various IT/ITeS job roles, like Software / Web developers IT/ITeS job roles. The findings reveal a growing realization in the industry: affective competencies are not optional. They are foundational to thriving in dynamic, tech-driven work environments and fostering effective collaboration with teams and clients in the IT/ITeS sector. The Research Publication, www.trp.org.in. -
Internet of Medical Things: Case Studies and Applications in Healthcare
The book presents a comprehensive overview of emerging IoT technologies, IoT devices and sensors and their role in the healthcare industry. It offers recent developments in the creation, application, and impact of new IoMT data security, IoMT devices used in a variety of applications, including telemedicine, monitoring patients, and intelligence-based medical data analysis. It includes discussions on the use of medical data analysis to make various predictions and diagnoses that lead to the right treatment. The security of medical data from Internet of Things (IoT) devices, where there is a risk of data leakage during transmission from the device to the server, is also discussed. Offers Software Application Framework for Sustainable Healthcare Includes feature case studies and real-world examples of IoT devices and technologies in use in healthcare settings Provide insight into the security risks associated with connected medical devices and the measures that can be taken to mitigate these risks. Discusses various integration strategies and technologies, including data interoperability standards, application programming interfaces (APIs), and other tools Contains discussions on emerging IoT technologies, such as blockchain and edge computing, as well as their potential applications in healthcare This book is for researchers, professionals and students interested in the latest advancements on Internal of Medical Things. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
The Impact of Marketing Strategies in the FastFood Industry
This study explores the effectiveness of marketing strategies in the fast-food industry, focusing on digital marketing, promotions, mobile app usage, celebrity endorsements, and perceived value for money. The aim is to provide insights for refining marketing strategies that engage key consumer segmentsMillennials, Gen Z, and Gen X. These cohorts shape consumer behavior, making it crucial for businesses to understand how to effectively engage them. The research examines the relationships between marketing strategies and fast-food consumption frequency and investigates generational differences in consumer responses. A quantitative approach using surveys and questionnaires captures consumer preferences, perceptions, and behaviors. Statistical methods, such as descriptive statistics and Spearmans correlation, are employed for analysis. Findings reveal that digital engagement, promotions, mobile app usage, celebrity endorsements, and perceived value for money significantly influence fast-food purchase frequency. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Approximate Binary Stacking Counters for Error Tolerant Computing Multipliers
To increase the power and efficiency of VLSI circuits, a new, creative multiplying methodology is required. Multiplication is a crucial arithmetic operation for many of these applications. As a result, the newly proposed error-tolerant computing multiplier is a crucial component in the design of approximate multipliers that are both power and gate efficient. We have created approximative multipliers for several operand lengths using this suggested method and a 45-nm library. Depending on their probability, the approximation for the accumulation of changing partial products varies. In compared to approximate multipliers that were previously given, the proposed circuit produces better results. When column-wise generate elements are added to the modified partial product matrix using an OR gate, the output is usually accurate. The amount of energy used, and its silicon area have been considerably reduced in the suggested multiplier when compared to traditional multipliers by 41.92% and 18.47%, respectively. One of the platforms that these suggested multipliers are suitable for is the image processing application. 2024 IEEE. -
VLSI Implementation of Area-Error Optimized Compressor-Based Modified Wallace Tree Multiplier
Approximate multiplier designs can improve their energy efficiency and performance with only a slight loss in accuracy by using approximate arithmetic circuits. This method is appropriate for applications where an approximative answer is acceptable because it uses a range of calculation approaches to those priorities, returning a potentially erroneous result above one that is assured to be exact. The basic idea underlying approximate computing is that, while accurate calculation may require a lot of resources, bounded approximation can result in considerable speed and energy efficiency advantages without sacrificing accuracy. The approximate 4:2 compressor and exact compressors, as well as half adders and full adders, make up the proposed approximate multiplier. The steps of the multiplier architecture are optimised using the recently suggested modified Wallace Tree Multiplier Architecture. When compared to previous designs, the proposed multiplier architecture can generate outcomes with the least amount of inaccuracy. The multiplier architecture is also finished in just two steps. The Modified Wallace Tree Architecture used in the suggested approximate multiplier excels by providing an error rate of 71.80% and a mean error of 173.82. As a result, the mean ? error Product improved by 10%, the error rate improved by 23.3%, and the mean error increased by 31.04%. This is accomplished by the proposed approximate multiplier with a small increase of 22.36% in total power consumption. 2023 IEEE. -
Electromagnetic Radiation-Driven Plastic Degradation and Energy Recovery for Sustainable Waste Management
The persistent accumulation of plastic waste presents a severe global environmental challenge. This study presents a non-thermal photodegradation and energy-recovery system that selectively cleaves 82 5% of CC/CH bonds in polyethene (PE), polypropylene (PP), and polystyrene (PS) within 30 min of UVC (254 nm) exposure. The bond-dissociation energy is harvested via thermoelectric generators (TEGs), delivering 10 W, and via photoelectric cells, yielding 5 W (10 mA.cm- at ? < 2 eV), for a combined recovery of 15 W. Emissions are held below 0.5 ppm VOCs and 0.1 mg.m- microplastics. A lab-scale prototype processes 0.5 kg.h-1 of mixed plastic per 0.1 m reaction area equivalent to 30 Wh.kg-1 of electrical energy and is scalable to 5 kg.h-1 in a pilot module. Real-time FTIR, Raman, and UV-VIS spectroscopy, integrated with an IoT-PID feedback loop, ensures autonomous optimization. Life-cycle assessment indicates a 25% reduction in greenhouse gas emissions compared to conventional recycling methods. A circular-economy framework envisions recovering oligomeric and monomeric fragments for direct reintegration into polymer production. Feature work will implement digital-twin simulations to refine process control, maximize throughput, and ensure long-term system reliability. 2026 by the authors Licensee: Technoscience Publications. -
Performance Evaluation Frameworks in the Context of Indian Microfinance Institutions
The paper conducts a detailed examination of the existing evaluative frameworks for microfinance institutions to gauge the differences and similarities. Efficiency evaluates how MFIs are meeting the performance standards considering time and budget constraints. Outreach evaluates the effectiveness of MFIs in reaching the beneficiaries. Relative efficiency scores were calculated using data envelopment analysis and outreach was measured in five different dimensions (pentagon model). Further, cluster analysis assisted in categorizing the MFIs into five value clusters. The study compares both outreach performance and relative efficiency scores employing ANOVA and correlation analysis. The study was conducted among the Indian context when the sector was hit by crisis during 2010. Paper brought out important insights about the sample. Indian MFIs were found to be more socially efficient, since the social dimension taken into consideration was number of female clients and majority of Indian MFIs has exclusive female focus. The correlation tests found that relative efficiency scores are positively related to depth (poor focus) and length (sustainability) outreach. The results showed that cluster analysis model basing outreach scores was more comprehensive and captured more information compared to the data envelopment model relative efficiency scores. The study is original in its approach in using cluster analysis for outreach performance and in the objective of comparing the two different models. 2019 Aruna Balammal et al., published by Sciendo 2019. -
A study on prediction of health care data using machine learning
Every clinical-decision relies on the doctors experience and knowledge. Perhaps this conventional practice may look appropriate, but it may lead to unpredictable errors, biases, and maximized costs that may affect QoS (Quality-of-Service) given to patients. To help the doctor to save time, the conventional practice to analyze the data for clinical-decision support has to be updated. Machine Learning (ML) and Data Mining (DM) algorithms have applied to have greater and higher predictions. This paper studies a set of ML algorithms by which clinical-predictions are going to be more appropriate and cost-effective. IJSTR 2020. -
Secured Cloud Computing for Medical Database Monitoring Using Machine Learning Techniques
A growing number of people are calling on the health-care industry to adopt new technologies that are becoming accessible on the market in order to improve the overall quality of their services. Telecommunications systems are integrated with computers, connectivity, mobility, data storage, and information analytics to make a complete information infrastructure system. It is the order of the day to use technology that is based on the Internet of Things (IoT). Given the limited availability of human resources and infrastructure, it is becoming more vital to monitor chronic patients on an ongoing basis as their diseases deteriorate and become more severe. A cloud-based architecture that is capable of dealing with all of the issues stated above may be able to provide effective solutions for the health-care industry. With the purpose of building software that would mix cloud computing and mobile technologies for health-care monitoring systems, we have assigned ourselves the task of designing software. Using a method devised by Higuchi, it is possible to extract stable fractal values from electrocardiogram (ECG) data, something that has never been attempted previously by any other researcher working on the development of a computer-aided diagnosis system for arrhythmia. As a result of the results, it is feasible to infer that the support vector machine has attained the best classification accuracy attainable for fractal features. When compared to the other two classifiers, the feed forward neural network model and the feedback neural network model, the support vector machine excels them both. Furthermore, it should be noted that the sensitivity of both the feed forward neural network and the support vector machine yields results that are equivalent in quality (92.08% and 90.36%, respectively). 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of Systematic Trade-offs between Military and Healthcare Expenditure alongside GDP Growth of Select Asian and Western Exporting Economies in the 21st Century
This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality between GDP growth and healthcare expenditure, reflecting economic stability and balanced allocation patterns. In contrast, developing Asian nations display fragmented and volatile relationships due to resource constraints and inefficiencies. Outlier analysis reveals country-specific dynamics, such as conflict-driven spending in Afghanistan and Myanmar and growth-focused strategies in China. Temporal trends show that economic crises, like the COVID-19 pandemic, significantly disrupt GDP growth but have limited long-term effects on healthcare or military expenditures. Clustering analysis identifies distinct groups of nations, shaped by economic capacity and geopolitical pressures. The findings emphasize the need for tailored policy frameworks to balance national security and human well-being, particularly in developing nations facing structural challenges. For sustainable development, policies must align resource allocation with economic priorities, geopolitical contexts, and societal needs. 2025, Binghamton University Libraries. All rights reserved. -
Complex Systems Mapping of Fiscal Growth Dynamics at Strategic Maritime Chokepoints Using Time-Series Slopes
This study examines how maritime and trading states allocate public resources between defence, health, and economic growth around three strategic chokepoints the Strait of Malacca, the Strait of Hormuz, and the Suez Canal. The analysis extends the classic guns versus butter framing by treating defence and health spending as co-evolving components of an interconnected fiscal-growth system. Using World Development Indicators data (1999-2024), trend slopes are estimated for military spending (% of GDP), healthcare spending (% of GDP), and GDP growth (annual %). Two derived indicators are computed, a defence-to-health slope ratio (military slope/health slope) and a fiscal-balance proxy (health slope - military slope). Augmented Dickey-Fuller tests are used to assess stationarity (unit-root behaviour), and Granger causality tests to examine whether GDP growth temporally precedes changes in spending shares. Hormuz chokepoint states show non-negative health slopes (e.g., UAE +0.1199) alongside negative GDP growth slopes in some cases (e.g., Qatar -0.4754). Suez chokepoint states exhibit negative defence slopes (e.g., Egypt -0.0899) with comparatively small or negative health slopes (e.g., Egypt -0.0211). The United States is included as an external benchmark because it is the largest trading nation by monetary trade volume and is directly or indirectly coupled to chokepoint flows; it shows health +0.1758 and military -0.0116 (ratio -0.0657). These quantified configurations support chokepoint-specific fiscal regimes and provide a compact visual map of security, health, growth dynamics in a small integrated complex systems. @ Binghamton (The ORB), 2026.