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Economic growth and higher education in south asian countries: Evidence from econometrics
South Asian economies has witnessed very slow growth over the years and the gap has widened manifold between other nations of Asia particularly East Asian nations and South Asian nations. This paper examines co-integration between the economic growth and reach of higher education in South Asian nations explaining this disparity. The research employed an econometric panel co-integration investigation to analyse the long run relationship of higher education and economic growth among these nations. The research confirmed positive long run causality between the economic growth of the South Asian nations and gross enrolment ratio of higher education. So, if the South Asian nations continue with their existing pattern of paying less attention to higher education by allocating low share of investment on it, poor human capital formation would result in growing further economic disparity between developed and South Asian nations where rich nations would remain richer and poor nations would remain poor with the gap remaining unabridged. This research will serve as an aid to policy makers, educators and financers of South Asian nations to bridge the gap between high-and low-income nations. The focus on the quantum of spending on higher education by the government will help improve the reach of tertiary education and build economic prosperity in these nations. 2020, Sciedu Press. All rights reserved. -
Economic Growth, Automation and Environmental Degradation: An Empirical Evidence from Asian Countries
In the era of Industry 4.0 the increase in population as a result of environmental erosion is the prime concern in the global scenario, Asia as the biggest continent is very much applied to it. In this context assessment of the interrelation relationship between automation, financial development, environmental degradation, and per capita growth of 12 Asian Countries from 1995 to 2022 using the panel ARDL model, in addition to assessing the cause-effect relationship panel causality test also incorporated. As a part of ARDL PMG estimation results demonstrated that capital formation, import automation machinery, urban population growth, and ecological footprint positively impact per capita in the long term. But in this phenomenon, aggregate industrial value added negatively impacts per capita, because of automation labor displacement. Results from the causality test suggest that economic upswing, and urban population growth two-way causal relationship. However, capital formation, value-added, and ecological footprint positively impacted per capita growth. Regarding policy formulation need to formulate the necessary skill development program so that individuals can cope with the new decade of automation, in addition, ecological footprint as an indicator of environmental degradation positively impacts per capita growth, so the government needs to make a strategy at the societal level toward sustainable ecofriendly behavior. 2024 IEEE. -
Economic Growth, Human Resource Development, and Climate Resilience in BRICS A Panel Data Analysis
Present study employs panel data analysis on BRICS economies data spanning from 2000 to 2023, to examine the impact of human resource development, renewable energy consumption, and carbon emissions on GDP per capita of BRICS economies,finding that while human capital strongly drives growth, renewable energy has a positive but weaker effect, and carbon emissions remain tightly linked to GDP, underscoring the persistent reliance on carbon-intensive industries, ultimately highlighting the need for policy shifts toward sustainable growth through education, clean energy transitions, and regulatory frameworks that balance industrialization with climate resilience. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Economic impact of micro loans on the rural women through self help group-bank linkage programme(SBLP) /
Zenith International Journal Of Business Economics And Management Research, Vol.6, Issue 2, pp.98-112, ISSN: 2249-8826. -
Economic Inequalities Amidst Social, Political, and Environmental Crises in BRICS Countries
This study investigates the complex interplay between economic inequalities and the intertwined social, political, and environmental crises within BRICS countries by empirically analyzing panel data from five nations to assess how women's income, political stability, and CO2 emissions influence wealth concentration among the top 10 percent, revealing through rigorous application of pooled OLS, random effects, and fixed effects models that environmental degradation measured by CO2 emissions is the sole significant and robust predictor positively associated with increased wealth disparity, while social and political variables show no statistically significant effects, thereby underscoring the urgent need for integrated policy frameworks in BRICS that prioritize sustainable environmental reforms alongside inclusive socio-political strategies to effectively mitigate growing economic inequalities and promote equitable and sustainable development in these rapidly evolving economies. 2026, IGI Global Scientific Publishing. All rights reserved. -
Economic Insights: The Computational Intelligence Perspective on Finance
Using technological advancements and shifting risk landscapes as a driving force, this abstract investigates the revolutionary approaches that have reshaped risk mitigation in contemporary contexts. Introducing a new era of proactive risk management has been made possible by the combination of artificial intelligence (AI), machine learning (ML), and predictive analytics. Organizations are able to recognize patterns and anticipate potential risks with an accuracy that has never been seen before, thanks to these technologies, which analyze vast datasets. By extracting valuable insights from unstructured data sources, natural language processing (NLP) and sentiment analysis broaden the scope of risk assessment with their respective capabilities. Blockchain technology improves both transparency and security, particularly in the realm of financial transactions, thereby lowering the likelihood of fraudulent activity. Cloud computing makes dynamic risk modeling easier to accomplish, which in turn makes it possible to simulate real-time scenarios. The cumulative effect of these innovations not only improves the efficiency of risk reduction, but it also helps organizations develop risk management frameworks that are more agile and resilient. When navigating the complexities of a risk landscape that is constantly shifting, it is essential to strike a balance between technological advancements, ethical considerations, and transparency. 2026 by Apple Academic Press, Inc. -
Economic policy uncertainty and corporate inventory holding: evidence from emerging economies
Purpose: This study aims to investigate how economic policy uncertainty (EPU) influences the inventory levels of 6,150 companies in ten emerging economies, specifically Chile, Brazil, China, Colombia, Hong Kong, India, Mexico, Pakistan, Russia and Singapore. Design/methodology/approach: The data used in this study is of quarterly frequency from 2004Q1 to 2020Q4 collected from the COMPUSTAT global database. To estimate the coefficients, this study has employed a two-step GMM model. Findings: We have discovered new evidence indicating a curvilinear relationship, specifically an inverted U-shaped pattern, between economic policy uncertainty (EPU) and corporate inventory holdings. These findings remain resilient when subjected to various rigorous tests. Furthermore, we observe that firms with lower financial constraints are capable of increasing their inventory holdings to a significant extent in the presence of high economic policy uncertainty (EPU) than firms facing higher financial constraints. Originality/value: Our research adds to the expanding body of literature that explores the impact of economic policy uncertainty on firm-level decision-making. We provide fresh evidence regarding the correlation between economic policy uncertainty and inventory holding, specifically focusing on emerging economies worldwide. Furthermore, we make a valuable contribution to the existing literature by examining whether the association between economic policy uncertainty and inventory holdings is influenced by the extent of financial constraints. 2025, Emerald Publishing Limited. -
Economic resilience in the face of pandemic: a holistic mathematical analysis of the pandemic in India
COVID-19 was initiated in 2020 and caused an immediate threat to global countries in terms of both economic and health influences. In this present work, we extend the Susceptible-Infected-Recovered (SIR) model by considering two new variables, gross domestic product or GDP (G) and unemployment (U), to study the impact of this epidemic on the Indian economy during the 20202023 period. Since our extended SIR model includes two novel compartments, which are GDP and unemployment rate, we can now explore in more detail the sophisticated relationship between health and economic matters. The framework allows us to investigate the following consequences: how changes in the infection rate affect the economy and how changes in GDP and unemployment translate into the spread of this contagion. These visualizations are based on real-time quarterly data and provide full knowledge of the interaction between health and economic dynamics during the COVID-19 crisis in India. Government initiatives and regulations are also reviewed for their efficiency to contain the virus while taming the economic cost. Real-world results are contrasted with the care to find the strengths and weaknesses of the policies that come out with the underlying assumptions in the model. This paper, in other words, deploys an in-depth analysis of the convoluted links between economics, policy, and public health in the face of a pandemic with a geographic focus in India. 2025 by the authors. -
Economic Sustainability, Mindfulness, and Diversity in the Age of Artificial Intelligence and Machine Learning
The sustainability of artificial intelligence (Al) and machine learning (ML) requires human diversity and mindfulness. This chapter discusses the various ways in which AI and ML can interact with humans to improve society, e.g., in filing copyrights or design patents or increasing mindfulness. AI and ML could educate weavers and farmers about their legal rights, cultivation methods, banking processes, and the harmful effects of tobacco consumption and other health-related issues. AI and ML could help teach mindfulness. ML can measure additional biofeedback. Music, mathematics, and art may benefit from AI and machine learning. Human-technology relations and the blue-green deployment model can be used to maintain two independent infrastructures or duplicate feature stores. It is possible to cultivate mindfulness and an awareness of diversity and communal harmony through AI and machine learning, as AI and machine learning can infer the emotional and cognitive states of the people with whom they interact. By leveraging the entire process of visualization, reading, and listening with AI, machine learning, and beyond, the digital future has the potential to incorporate real-time emotions and feelings. This would entail emotional responses on both ends and a variety of other technologies and users. 2024 Taylor & Francis Group, LLC. -
Economic, Political, and Demographic Drivers of Social Isolation: Exploring the Role of Digital Literacy and Migration in Shaping Social Isolation - A Qualitative Study
This chapter examines the impact of migration and digital literacy on social isolation amongst workers. Migration can disrupt established social networks, making it challenging for an individual to establish and build new connections. The research employed a qualitative approach, and data were collected through semi-structured interviews with migrant workers residing in Bengaluru. The findings provide contextual information on the causes of social isolation and help acquire more knowledge on how migration and digital literacy relate and influence social isolation. It prioritises individual experience over statistical data, with an increased understanding of the drivers of social isolation. Advanced digital literacy, on the other hand, can reduce social isolation by enabling migrants to maintain connections with their immediate family, access information, and develop innovative social networks. The research study's findings had a significant impact on policies and employers, highlighting the importance of social integration and mental health. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Economics of Farming in Mahatwar, Uttar Pradesh
Recent policy efforts have focussed on transforming eastern Uttar Pradesh, an acknowledgement of the relative backwardness of the regions agricultural development. Despite this, there has been little discussion in the literature of agrarian relations and their implications for the economics of farming. Taking Mahatwar village in eastern Uttar Pradesh as a case study, this article examines disparities across socio-economic classes in incomes and the costs of cultivation. We found substantial inequality, with landlord and big capitalist farmer households earning nearly 30 times the annual income of lower peasant and manual worker households. These disparities arise primarily from differences in costs: poor peasant and manual worker households bear a disproportionate rental burden, rely excessively on family labour, and use much of their produce for self-consumption. Our findings highlight the need for rent reduction and yield enhancement, along with support measures such as minimum support prices (MSPs), to provide meaningful incomes to low-income farmers. 2025, Tulika Books. All rights reserved. -
Econophysical bourse volatility-Global Evidence
Financial Reynolds number (Re) has been proven to have the capacity to predict volatility, herd behaviour and nascent bubble in any stock market (bourse) across the geographical boundaries. This study examines forty two bourses (representing same number of countries) for the evidence of the same. This study finds specific clusters of stock markets based on embedded volatility, herd behaviour and nascent bubble. Overall the volatility distribution has been found to be Gaussian in nature. Information asymmetry hinted towards a well-discussed parameter of 'financial literacy' as well. More than eighty percent of indices under consideration showed traces of mild herd as well as bubble. The same indices were all found to be predictable, despite being stochastic time series. In the end, financial Reynolds number (Re) has been proved to be universal in nature, as far as volatility, herd behaviour and nascent bubble are concerned. 2020 Bikramaditya Ghosh et al., published by Sciendo 2020. -
Ecotourism a Sustainable Development Approach: A Case Study of Bandipur Forest
Bandipur Tiger Reserve is geographically speaking, it is an ecological confluence since the Western and Eastern Ghats intersect here, making this region unique and exceptional in terms of its flora and fauna. The community land areas of all the border settlements as well as the nearby notified and unnotified forests have been included in the buffer of this tiger reserve. The scrub jungle along the park's eastern boundaries is made up of stunted trees, scattered bushes, and open grassland patches. The Eco-tourism activity is run in the two Ranges of Bandipur (54 km2) and GS Betta (28 km2), covering a total area of 82.00 km2, or around 9.40% of the Reserve's total size. From the above analysis, it could be concluded that the government should provide that there are administrative facilities, halting facilities, etc. just next to National Highway 67, which cuts through the eco-tourism region. Additionally, the village community people agree that the regions where some Private Tourist Resorts have situated border the Kundu Range's Eco-tourism area. The Reserve benefits from having almost year-round operations. The usual methods of stopping poaching, such as arresting and prosecuting offenders, have obviously failed; conservation education aiming at altering local attitudes will greatly reduce the ongoing threats to the integrity of biological systems in the Bandipur forest. Operationalizing sustainable ecotourism within protected areas ultimately relies on management and operations that maximize the industry's potential positive advantages while minimizing its negative ones. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Edge and Fog Computing in Cyber-Physical Systems
The benefits of cyber-physical system advances include low latency and high bandwidth data processing in areas such as automotive, healthcare, and business automation. Traditional environments are often located in centralized and remote locations and cannot meet the demand. Edge computing and cloud computing have become fundamental concepts that will bring computing closer to the center of the data. Edge computing can reduce latency and bandwidth consumption by processing data on or near IoT devices. Fog computing adds another layer to this by distributing work and storage across multiple nodes, thus providing a scalable and flexible infrastructure. This article discusses the principles, benefits, and challenges of integrating edge and cloud computing into a CPS environment. It leverages the power of proximity-based edge computing and the centralized capabilities of cloud computing to provide scalable, instantaneous responses to CPS applications or time to optimize services. The demonstration shows a variety of things from smart cities to the use of IoT in healthcare in CPS. The article also covers some specific security and privacy issues and future directions in distributed computing, including the role of AI and 5G, which are supposed to offer additional resources in various applications. 2025 IEEE. -
Edge Attention Module for Object Classification
A novel edge attention-based Convolutional Neural Network (CNN) is proposed in this research for object classification task. With the advent of advanced computing technology, CNN models have achieved to remarkable success, particularly in computer vision applications. Nevertheless, the efficacy of the conventional CNN is often hindered due to class imbalance and inter-class similarity problems, which are particularly prominent in the computer vision field. In this research, we introduce for the first time an Edge Attention Module (EAM) consisting of a Max-Min pooling layer, followed by convolutional layers. This Max-Min pooling is entirely a novel pooling technique, specifically designed to capture only the edge information that is crucial for any object classification task. Therefore, by integrating this novel pooling technique into the attention module, the CNN network inherently prioritizes on essential edge features, thereby boosting the accuracy and F1-score of the model significantly. We have implemented our proposed EAM or 2EAMs on several standard pre-trained CNN models for Caltech-101, Caltech-256, CIFAR-100 and Tiny ImageNet-200 datasets. The extensive experiments reveal that our proposed framework (that is, EAM with CNN and 2EAMs with CNN), outperforms all pre-trained CNN models as well as recent trend models Pooling-based Vision Transformer (PiT), Convolutional Block Attention Module (CBAM), and ConvNext, by substantial margins. We have achieved the accuracy of 95.5% and 86% by the proposed framework on Caltech-101 and Caltech-256 datasets, respectively. So far, this is the best results on these datasets, to the best of our knowledge. All the codes along with graphs, and their classification reports are shared on an anonymous GitHub link: https://anonymous.4open.science/r/Object-Classification-7BE5. 2025 IEEE. -
Edge Computing and Real-Time Analytics as the Next Frontier for Big Data in IT Companies
Edge computing combined with real-Time analytics is rapidly transforming the way the IT companies can use big data to make smarter and quicker decisions. Centralized model of clouds traditional clouds are being put under stress owing to an explosive increase in data volume, latency sensitive applications as well as bandwidth limitations. Edge computing also makes computation much closer to data sources and allows real-Time analytics that can mitigate latency by orders of magnitude, increase data security and achieve instant insights at a scale. This paradigm shift gives power to IT firms to maximize operations, individualize services and allow agile reactions in a dynamic scenario like smart infrastructure, IoT implementations, and AI-based systems. Edge computing can also be used to offload processing to the edge thus reducing traffic in the core network as well as enabling distributed intelligence. Moreover, edge systems, coupled with AI models, make IT infrastructures perform predictive analytics at the source and become less dependent on backhaul links. This hybridizing process is a paradigm shift in the serious research of big data strategies, and in a future where competitive advantage will rest on latency, context-sensitivity, and localized smarts. 2025 IEEE. -
Edge computing for smart disease prediction treatment therapy
Healthcare systems are increasingly seeking to match patients' pace of life and be personalized, as they are demanding more advanced products and services. The only solution for collecting and analyzing health data in realtime is an edge computing (EC) environment, coupled with 5G speeds and modern computing techniques. The technology in healthcare is currently being used to develop smart systems that can expedite the diagnosis of disease and provide precise and timely treatment. The automated hospital monitoring system and medical diagnosis system enable doctors to monitor and diagnose patients from a variety of locations, including hospitals, workplaces, and homes and provide transportation options. As a result, overall doctor visits are reduced as well as patient care is improved. More than 162 billion healthcare IoT devices are expected to be used worldwide by 2021 thanks to the internet of things (IoT) sensors and applications for general healthcare. With edge intelligence (EI), wearable devices with sensors, like smartwatches or smartphones, and gateway devices, such as microcontrollers, can form edge nodes: smart devices with sensors, as well as gateway devices with sensors, can act as edge nodes. Smart sensor devices are typically installed at a greater distance from personal computers (PCs) and servers, which can be utilized in fog computing (FC). In healthcare, EC and FC are used to deliver reliable, low-latency, and location-aware healthcare services by utilizing sensors located within users' reach. Recently, many researchers have proposed using hierarchical computing for the distribution and allocation of inference-based tasks among edge devices and fog nodes, which could lead to an increase in computing power and compute capability of edge devices. For disease prediction, this chapter discusses a variety of EC techniques. 2024 Apple Academic Press, Inc. All rights reserved. -
Edge Computing in Aerial Imaging A Research Perspective
Internet of Drones (IoD) is a field that has a vast scope for improvement due to its high adaptability and complex problem statements. Aerial vehicles have been employed in various applications such as rescue operations, agriculture, crop productivity analysis, disaster management, etc. As computing and storage power have increased, satellite imaging and drone imaging have become possible, with vast datasets available for study and experiments. The recent work lies in the edge computing sector, where the captured aerial images are processed at the edge. Our paper focuses on the algorithms and technologies that easily facilitate aerial image processing. The applications and their architectures are focused on which can efficiently function using aerial processing. The various research perspectives in aerial imaging are concentrated on paving the way for further research. 2024 Scrivener Publishing LLC. All rights reserved. -
Edge criticality in signed graphs admitting a Roman dominating function
A Roman dominating function(RDF) on a signed graph S = (G, ?) is a function f: V (S) ? {0, 1, 2} such that f(N[v]) ? 1 for every vertex v ? V (S) and any vertex v with f(v) = 0 has a neighbour u ? N + P (v) having f(u) = 2, where f(N[v]) = f(v) + ?u?N(v) ?(uv)f(u). The weight of an RDF is ?(f) = ?v?V f(v) and the minimum weight among all the RDFs on S is called the Roman domination number, ?R(S). In this article we explore the concept of edge criticality in signed graphs admitting an RDF by examining the signed graphs S such that ?R(S+uv) < ?R(S), for any pair of non-adjacent vertices u and v of S, such that the edge uv is positive. This work is licensed under https://creativecommons.org/licenses/by/4.0/ -
Edge incident 2-edge coloring of graphs
The edge incident 2-edge coloring of a graph G is an edge coloring of the graph G such that not more than two colors are assigned to the edges incident to an edge e = uv in G. In other words, for every edge e in G, the edge e and all the edges that are incident to the edge e is in at most two different color classes. The edge incident 2-edge coloring number ?n2(G) is the maximum number of colors in any edge incident 2-edge coloring of G. The main objective of this paper is to study the edge incident 2-edge coloring concept and apply the same to some graph classes. Besides finding the exact values of these parameters, we also obtain some bounds. World Scientific Publishing Company.