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Advancements in e-Governance Initiatives: Digitalizing Healthcare in India
In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve ones health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someones general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patients clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within Indias overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of Indias EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
ICT Policy Reforms for Innovation and Economic Development: A Comparative Study of India and China
The widespread adoption of Information and Communication Technologies (ICTs) has become essential for economic and social growth across the world. This paper aims to examine the impact of ICT policies and reforms on the level of economic development and adoption of ICTs in two countries, India and China. Previous studies have shown the positive impact of ICT adoption on economic growth, productivity, and innovation. However, the effectiveness of specific policy measures in promoting ICT adoption and economic development remains ambiguous to the users of ICT. This paper presents a comparative analysis of the ICT policies and reforms implemented in India and China from 2010 to 2021 and their impact on GDP per capita and internet usage. The study aims to identify and analyze the key ICT policies and reforms implemented in the two countries and examine their impact on economic development. The data for this study have been collected from the World Bank indicators database. The sample consists of the two fastest-growing economies in the world, India and China. The data analysis involves conducting descriptive statistics, correlation, and regression analysis to examine the relationship between ICT policies and reforms and their impact on GDP per capita, internet usage, and research and development expenditure. The findings of this study will contribute to the existing literature on the relationship between ICTs and economic development and provide insights into the policy measures that can promote ICT adoption and economic growth in different contexts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Effecient Approach to Detect Fraud Instagram Accounts Using Supervised ML Algorithms
Nowadays social media plays a vital role in different fields including business, economic communication and personal. Many person get profit from the different origins of availability of data from these social media, but cyber-crimes are increasing day by day. A person can generate many fake accounts and hence pretenders can easily be made. Instagram, as one of the popular types of online social media site, carries big information and messages through the posts. Most of the person use Instagram as a digital life marketing place because it is a one of the big social media site. The goal of the research paper is to recognize and stop fake IDs and pages. Because through the professional pages of Instagram, many fake cases and things are occurring present days. So the main thing is to recognize fake pages and fake accounts also. In this paper, we work on various IDs of Instagram. We want to observe an ID is real or not using Machine Learning techniques namely Logistic Regression, Naive Bayes, Support vector machine, Decision tree, Random Forest. 2022 IEEE. -
Meta-analysis of EMF-induced pollution by COVID-19 in virtual teaching and learning with an artificial intelligence perspective
Concerns about the health effects of frequent exposure to electromagnetic fields (EMF) emitted from mobile towers and handsets have been raised because of the gradual increase in usage of cell phones and frequent setting up of mobile towers. The present study is targeted to detrimental effects of EMF radiation on various biological systems mainly due to online teaching and learning processes by suppressing the immune system. During the COVID-19 pandemic, the increased usage of internet due to online education and online office leads to more detrimental effects of EMF radiation. Further inculcation of soft computing techniques in EMF radiation has been presented. A literature review focusing on the usage of soft computing techniques in the domain of EMF radiation has been presented in the article. An online survey has been conducted targeting Indian academic stakeholders (specially teachers, students, and parents termed as population in the paper) for analyzing the awareness towards the biohazards of EMF exposure. 2022 IGI Global. All rights reserved. -
Web Platforms for Fintech Products
Internet marketing and digital marketing are not synonymous in the minds of the majority of the population, yet this may not be true. Given the rise in popularity of digital marketing as a marketing tactic, it is critical to comprehend the distinctions between the two methods. Even while it should be evident that they might be connected, there is very little difference between them. Internet marketing is merely a subclass of digital marketing, as well as the extent of digital marketing encompasses much more than internet marketing. This paper discussed digital marketing technologies, as well as the advantages and disadvantages of employing digital marketing and digital finance tools in general. In order to remain competitive, businesses must overcome obstacles and seize possibilities presented by digital marketing technologies. Lastly, it's critical to prioritise digital marketing and make use of digital finance techniques in order to maintain a good performance without wasting time or money. 2022 IEEE. -
Recent Progress on the Development of Chemosensors
Chemosensors are the chemical structures which convert chemical stimuli into responsive form that can be easily detected, such as change of colour, fluorescence, and other electronic signal. Recently, chemosensors development for detection and monitoring of gases has been growing interest due to the significant importance in environmental and biological systems. Subsequently, the development of chemosensors for detection of various gases is considered to be a significant goal in science and among the all gases, carbon dioxide (CO2) is a major public concern due to its role in global greenhouse warming with environmental pollution. Moreover, quite critical level of CO2 in the modern agricultural, food, environmental, oil and chemical industries is dangerous for living beings to survive such high concentration levels of CO2. Therefore, rapid and selective detection and monitoring of CO2 in the gaseous as well as in the liquid phases provides an incentive for development of new methods. The coverage of this book chapter is divided into different sections according to the use of different types of molecular backbones and the detection pathways. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Deprotection induced modulation of excited state intramolecular proton transfer for selective detection of perborate and ammonia
Acetate protected Naphthalene Coupled Benzothiazole (NCB) has been designed and synthesized for selective detection of perborate (BO3) and ammonia (NH3) based on modulation of excited-state intramolecular proton transfer (ESIPT) process by chemodosimetric deacetylation pathway. In presence of nucleophilic species like BO3 and NH3, acetyl group deprotection of NCB resulted ESIPT within the molecule exhibiting a significant enhancement of absorption and emission signals at 425 nm and 472 nm respectively. The emission enhancement of NCB has been observed by 31-folds and 14-folds in presence of BO3 and NH3 respectively. The selectivity and fast sensitivity of NCB have been shown by the lower detection limit (1.32 M for BO3, 1.74 M for NH3 in UVvis study and 0.60 M for BO3 and 4.39 M for NH3 in fluorescence study) and fast response (rate constants: 12.36 s?1 and 5.54 s?1 for BO3 and NH3 respectively). Analytes induced deacetylation pathway of NCB followed by ESIPT has been clearly demonstrated by theoretical calculation. The test strips based on NCB with BO3 and NH3 are fabricated, which can act as a convenient and efficient test kits for both these analytes. In the practical applications, the sensor NCB can be utilized as low cost food spoilage indicator and soil analysis by fluorometric method. 2024 Elsevier B.V. -
Vehicular Propagation Velocity Forecasting Using Open CV
This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety. 2023 IEEE. -
Wired Highways: The Soul of a Smarter City
The "smart city"concept emerged in the 1990s, characterized by extensive globalization, rapid technological advancement, and the emergence of the knowledge economy. Urban planning professionals began considering how to introduce digital technology into existing city infrastructure to create cities that were faster, efficient, sustainable, and better managed. Innovative thinking has moved away from traditional methods of city planning and management, and has started to challenge the planning systems where governments understood complexity from a bureaucratic perspective. In planning smart cities, local governments are largely relying on using information and communication technologies (ICT) to enhance essential city functions like transportation, energy, water, and public safety. Urbanization is becoming increasingly complex, and the global urban population is growing at unprecedented rates, creating a need of urban systems to be smarter, safer, and more environmentally sustainable. Cities are faced with many complex problems, but urban transportation is one of the most daunting. Existing infrastructure is becoming overwhelmed by congestion, increasing volume of traffic, and safety issues. An innovative way of tackling these problems is through Intelligent Transportation Systems (ITS). ITS is the use of ICT including sensors, software, communication networks, and data analytics in transportation systems to increase efficiency, safety, reliability, and sustainability. By using real-time monitoring, predictive analytics, and data-driven decision-making, ITS creates traffic management systems that improve city response time, decreases congestion and accidents, and increases sustainable or environmental-friendly transport choices. This research recognizes the importance of ITS to smart city development, emphasizing. 2026 IEEE. -
Mechanoluminescence of PolymerOrganic Composites under Strain and Hydrostatic Pressure
The development of underwater-based Internet of Things has drawn much attention to fluorescence sensors for crucial technological advancement. Therefore, the technologies for underwater mechanoluminescence (ML) sensing have created various applications for sensors and self-powered waterproof displays. However, developing single-molecule-based intrinsically adaptive materials capable of responding to multiple stimuli with high sensitivity remains a challenge. Herein, a flexible sensor based on the fluorescent ligand DHN was fabricated using a PVDF polymer matrix to form PVDH, enabling the exploration of external-stimuli-responsive fluorescence enhancement under mechanical strain and underwater pressure. For PVDH, the transition from crystalline to amorphous state by mechanical stimuli boosted its photoluminescence by ?75%, and stretching (strain ?16%) the sensor boosted its photoluminescence by ?145%, which is due to the formation of molecular aggregates in the amorphous state. Additionally, the increase in ML correlated with an output voltage of ?4 V of the fabricated device under mechanical stress. The in-depth density functional theory (DFT) calculations further support the experimental observation by studying the charge distribution and orbital overlapping in the DHN ligand. Furthermore, the film shows clear visualization when subject to underwater pressure and stable fluorescence upon exposure to different impurities. Therefore, this study reveals the use of fluorescence and mechanochromic properties of the ligand for designing advanced underwater sensors for visualization and communication. 2025 American Chemical Society -
Exploring the potential of Andrographis paniculata for developing novel HDAC inhibitors: an in silico approach
Cancer is one of the dreaded diseases of the twentieth century, emerging the major global causes of human morbidity. Cancer research in the last 15 years has provided unprecedented information on the role of epigenetics in cancer initiation and progression. Histone deacetylases (HDACs) are recognized as important epigenetic markers in cancer, whose overexpression leads to increased metastasis and angiogenesis. In the current study, thirty-four (34) compounds from Andrographis paniculata were screened for the identification of potential candidate drugs, targeting three Class I HDACs (Histone deacetylases), namely HDAC1 (PDB id 5ICN), HDAC3 (PDB id 4A69) and HDAC8 (PDB id 5FCW) through computer-assisted drug discovery study. Results showed that some of the phytochemicals chosen for this study exhibited significant drug-like properties. In silico molecular docking study further revealed that out of 34 compounds, the flavonoid Andrographidine E had the highest binding affinities towards HDAC1 (?9.261 Kcal mol?1) and 3 (?9.554 Kcal mol?1) when compared with the control drug Givinostat (-8.789 and ?9.448 Kcal mol?1). The diterpenoid Andrographiside displayed the highest binding affinity (-9.588 Kcal mol?1) to HDAC8 compared to Givinostat (-8.947 Kcal mol?1). Statistical analysis using Principal Component Analysis tool revealed that all 34 phytocompounds could be clustered in four statistical groups. Most of them showed high or comparable inhibitory potentials towards HDAC target protein. Finally, the stability of top-ranked complexes (Andrographidine E-HDAC1 and HDAC3; Andrographiside-HDAC8) at the physiological condition was validated by Molecular Dynamic Simulation and MM-PBSA study. Communicated by Ramaswamy H. Sarma. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
A proposed framework for an appropriate governance system to develop smart cities in India
The Government of India has undertaken a novel step towards building new smart cities as well as transforming some of its existing cities into smart cities. However, tension relating to the governance of smart cities has emerged. Therefore, a mixed-methods approach was used based on a perception survey, case studies, and discussions with stakeholders and experts, to examine the current governance challenges in transforming existing cities into smart cities, and to explore various perspectives to propose a framework for an appropriate governance system for developing smart cities in India. The findings suggested that the current executive-led governance system, with special-purpose vehicles (SPVs) under the control of the state governments as the promoters of smart city development, might not lead to the smart governance system envisaged but, rather, add confusion and conflict, and undermine the constitutionally mandated, legislative-led urban local bodies. The argument in this article is for a people-centric, balanced governance approach with strengthened urban local bodies, enabled by advanced digital technology and the constructive participation of different social solidarities, in which the SPVs would act as the intellectual and executive wing of the urban local bodies. 2023 Regional Studies Association. -
Impacts of Climate Risk and Energy Consumption on Financial Markets: Commodity and Sovereign Shocks in Resource Nations
In this paper, we combined both academic and professional lenses to assess the impact of commodity price shocks and geopolitical uncertainty on sovereign country default risk, fiscal capacity, and financial instability in Commodity Exporting Countries. It uses an interdisciplinary framework with emphasis in international finance, political economy, risk management, and public policy to examines how external shocks, such as geopolitical uncertainty, reinforce sovereign risk and reduce fiscal sustainability. The chapter utilizes a PRISMA-based systematic review of the literature which draws from multiple databases to identify channels through which geopolitical uncertainty leads to price and volatility shocks, increased debt spreads, and reduced fiscal space. By bringing together supports from the disciplines of economics, finance and governance, the chapter calls attention to structural vulnerabilities to export-based economies, as well as policy reforms that reflect adaptive public policy practices. 2026 by IGI Global Scientific Publishing. -
Brand protection in Indias digital economy: trademarks vs. competition regulation
The digital economy in India has significantly impacted brand protection, with the need for robust trademark protection intensifying to combat issues like counterfeiting, cybersquatting, and unauthorised use. Competition regulation aims to ensure fair market practices, prevent monopolistic behaviour, and foster innovation. This study examines the legislative and judicial framework governing trademarks in India, highlighting key provisions under the Trade Marks Act, of 1999, and their application in the digital context. It also assesses the role of the Competition Commission of India (CCI) in addressing anti-competitive practices. The study identifies tensions and synergies between trademark protection and competition regulation, examining how digital platforms, e-commerce, and social media influence these legal domains. Comparing the study with jurisdictions like the EU and the USA, the paper proposes a balanced approach that harmonises trademark enforcement with competition law principles to ensure brand protection efforts do not stifle competition and innovation in Indias burgeoning digital economy. Recommendations include policy reforms, enhanced cooperation between regulatory bodies, and the adoption of technology-driven solutions to safeguard brands while promoting a competitive and fair digital marketplace. Copyright 2025 Inderscience Enterprises Ltd. -
The Legal and Ethical Guiding Principle of Artificial Intelligence in Smart Healthcare Systems
The relationship between life and technology has been a constant theme throughout history, with healthcare being a significant industry that has seen considerable advancements. The Digital Revolution in the health sector is increasing reliance on emerging technology, including Artificial Intelligence (AI). This chapter explores the advancement of technology in healthcare and its link with Intellectual Property Management. It traces the evolution of AI in healthcare and explores the legal and ethical issues surrounding its implementation in smart healthcare systems. The authors assess existing regulatory frameworks and suggest recommendations for integrating AI into healthcare while adhering to ethical standards and legal limitations. They also discuss the process of creative destruction leading to accelerated innovation, particularly in the rapid ramping up of digital health capabilities. The chapter also addresses significant legal and ethical problems in AI-powered healthcare, such as patient privacy, data security, liability, and algorithmic prejudice. The authors propose guidelines for policymakers, healthcare providers, and AI developers to create AI applications that comply with legal standards and uphold ethical principles, promoting trust and safety in smart healthcare systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Signal-aware deep learningbased respiratory motion prediction for lung tumor management
Introduction: Respiratory motion management in radiotherapy for lung cancer patients remains a significant challenge, as it directly affects accurate tumor targeting. Furthermore, unaccounted tumor motion during treatment planning and delivery can lead to imaging artifacts and biased dose distributions, which compromises the accuracy of image-guided radiotherapy. This issue places clinicians in a dilemma between expanding treatment margins, which increases radiation exposure to healthy tissue or risking reduced targeting precision. Methods: In this work, a hybrid deep learning model composed of dilated convolutional layers, bidirectional long-short term memory layers, and a generative autoencoder module is proposed to jointly model the spatial and temporal characteristics of respiratory motion, while enabling reconstruction of the physiologically coherent respiratory signals. Each architectural component learns complementary motion-related patterns from respiratory signals to support tumor motion prediction. The model performs motion-range classification, captures abnormal breathing patterns across spatial and temporal domains, reconstructs physiologically coherent respiratory cycles, and predicts tumor motion within an algorithmic validation framework. Results: Experimental evaluation demonstrates high motion-range classification performance of 98.37%, including low root-mean square error in motion prediction, while maintaining stable performance across long and complex respiratory signals over multiple breathing cycles. Discussion: This study focuses on algorithmic feasibility and establishes a computational foundation for future clinically calibrated and dosimetrically validated models. The findings indicate that the proposed approach can support future motion-aware radiotherapy planning strategies by improving motion characterization at the algorithmic level. Copyright 2026 Das, J. and Medhi. -
Label Informativeness-Based Minority Oversampling in Graphs (LIMO)
Class imbalance is a pervasive issue in many realworld datasets, particularly in graph-structured data, where certain classes are significantly underrepresented. This imbalance can severely impact the performance of Graph Neural Networks (GNNs), leading to biased learning or over-fitting. The existing oversampling techniques often overlook the intrinsic properties of graphs, such as Label Informativeness (LI), which measures the amount of information a neighbor's label provides about a node's label. To address this, we propose Label Informativenessbased Minority Oversampling (LIMO), a novel algorithm that strategically oversamples minority class nodes by augmenting edges to maximize LI. This technique generates a balanced, synthetic graph that enhances GNN performance without significantly increasing data volume. Our theoretical analysis shows that the effectiveness of GNNs is directly proportional to label informativeness, with mutual information as a mediator. Additionally, we provide insights into how variations in the number of inter-class edges influence the LI by analyzing its derivative. Experimental results on various homophilous and heterophilous benchmark datasets demonstrate the effectiveness of LIMO in improving the performance of node classification for different imbalance ratios, with particularly significant improvements observed in heterophilous graph datasets. Our code is available at https://github.com/smlab-niser/limo. 2025 IEEE. -
The impact of AI and agile HR on talent acquisition and onboarding in the service industry
The high speed and competition levels in the service sector make it imperative to have a workforce that is capable of responding to rapid change. This research studies the synergistic impact of the utilization of Artificial Intelligence and Agile Human Resources in the service sector in enhancing the talent acquisition and onboarding process. Transforming the present study into a quest to see how these two transformative forces interact in the quest for identifying their potential for increasing efficiency and effectiveness, along with the employee experience. It investigates how AI can contribute to Agile HR in order to make talent acquisition easier. This involves using AI-based tools in talent sourcing, candidate screening, and assessment, and automating other routine tasks in order to set HR free for more strategic activities. The study also investigates the application of AI in personalizing onboarding, improving employee engagement, and accelerating time-to-productivity. Best practices and challenges are identified by analyzing case studies of service organizations that successfully put both AI and Agile HR into practice. This paper covers the ethical issues associated with AI and the requirement for human judgment in the process of talent acquisition and onboarding. Ultimately, the chapter will contribute to an understanding of how AI and Agile HR can combine in order to build a competitive advantage for service organizations. This will involve the optimization of talent acquisition and onboarding in a manner that enables the building of high-performing teams in order to deliver better customer satisfaction and drive business growth. 2026 Pushan Kumar Dutta, Amarnath Padhi, Sulagna Das, Vinod Kr Sharma and Poshan Yu. All rights reserved. -
Fractional and memory effects on wave reflection in pre-stressed microstructured solids with dual porosity
The present work investigates the influence of fractional-order derivative and memory-dependent derivative on the behavior of various waves reflected at the free surface of a size-dependent, pre-stressed, microstructured thermoelastic solid with a dual porosity framework. A generalized MooreGibsonThomson (MGT) model, incorporating higher-order terms and memory effects, is adopted to describe the complex heat transfer behavior within the material. A nonlocal framework based on Eringen's theory is utilized to derive the basic relations of the considered medium. An examination of the non-dimensionalized governing equations is conducted employing the normal mode technique to provide accurate solutions. The research demonstrates the presence of six separate wave modes that travel at varying speeds within the medium. The energy and amplitude ratios of reflected waves are determined by applying suitable boundary conditions. The influence of varying incidence angles on the reflected wave energy distribution is investigated numerically and visualized using MATLAB software. The study reveals that the energy ratios of the reflected waves are sensitive to the fractional-order parameter, kernel functions, initial stress, and nonlocality parameter. The analysis suggests a conservative reflection process, indicating minimal energy loss during reflection. Key findings and their implications for relevant scenarios are presented in the conclusion. Comparisons with existing models for certain cases demonstrate good agreement, supporting the validity of the present model. 2025 Elsevier Masson SAS -
KleinGordon nonlocal dynamics of porous piezo-thermoelastic medium with surface irregularity under fractional-order modified LS model
The miniaturization of devices alongside advances in thermal management technologies necessitates the generalization of heat conduction and thermal elastic coupling to faithfully represent material responses at ultrashort temporal scales. Motivated by viscoelastic mechanical analogies, this work develops an analytical framework for investigating vibrational behavior in an orthotropic, size-dependent piezo-thermoelastic substrate featuring voids, modeled within the Modified LordShulman (MLS) thermoelasticity theory augmented by fractional derivatives. Employing the KleinGordon nonlocal elasticity formulation, the governing equations of motion are rigorously derived. The normal mode method facilitates the examination of coupled thermoelectro-mechanical excitation phenomena. Emphasis is placed on a corrugated interface contiguous to a vacuum, where comprehensive boundary conditions encompassing thermal, electrical, mechanical, and stress equilibria are imposed to determine fundamental field variables. The study systematically evaluates the influence of pivotal parameters, including temporal evolution, nonlocality characteristics, and spatial coordinates, on the thermomechanical and electrical responses, with outcomes substantiated through detailed graphical representations. Although previous investigations have addressed vibrations in porous piezo-thermoelastic media under varying theoretical constructs, the current research uniquely elucidates the dynamic response of a size-dependent porous piezo-thermoelastic medium with a corrugated surface within the fractional-order modified LordShulman framework, marking a significant advancement in the modeling of smart microstructured materials. The Author(s), under exclusive licence to Springer Nature B.V. 2026.