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Smart and Secured Ways of E-Payment: Design of New Frameworks
The rapid development in technology and the rapid growth of e-commerce have paved the way for changes in the method of payment. It also gave rise to various hazards while making the e-payment when compared to the normal or standard payment methods it is necessary to make a secured payment. Various initiatives are taken by the government for providing a secure payment system which will be more useful for all the commercial activities done through online. Already various e-payment systems are used by the consumers for paying the amount for the materials purchased. The increasing need of foreign exchange with an effective and efficient electronic payment system is required for making the low value payment. The framework that is been used in the global market and also in virtual marketplace require a complete legal structure which should also have impact on the economy of the mediaeval trade. Rapid development of e-commerce during the recent years has made more changes in the financial and non-financial transactions. In e-commerce, the payment gateway plays an important role in the exchange in ensuring that the transactions occur without any disputes and also maintains the security of the system. Most of the payment gateways used in the e-commerce are provided by rusted third party who will provide monetary information. Due to the increased use of e-commerce and online payment system, there is also any increase in security breaches during the past few years. So, it is necessary to build a new framework that will provide a secured platform for the e-payment system through which the consumers can directly connect to their merchants securely. Most of the third-party providers are also asking for the identity of the customers while making the payment which might even have change of loss of person information of the customer. The new framework should contain an improved security and the data collected should be confident, proper authentication method should be used, and availability of the data and integrity of the data should be maintained. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Women at work: The cultural and creative industries
[No abstract available] -
Classification of financial news articles using machine learning algorithms
The opinion helps in determining the direction of the stock market. Information hidden in news articles is an information treasure which needs to be extracted. The present study is conducted to explore the application of text mining in binning the financial articles according to the opinion expressed inside them. It is discovered that using the tri-n-gram feature extraction process in conjugation with Support Vector machines increases the reliability and precision of the binning process. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Reading behind the tweets: A sentiment Clustering Approach
Market sentiment influence crude oil future prices in direct or indirect way. In order to measure the polarity of market sentiment various techniques has been deployed by industry and academia alike. This pilot study successfully introduced two instruments, namely topic modeling and Sentiment clustering, to unearth the prevailing sentiments behind crude oil future pricesThree main conclusions that can be drawn from empirical results are. First, the K-Means clustering algorithm is an effective technique for sentiment clustering compared to Louveian and MDS clustering techniques. Second sentiment polarity-related positive sentiments have shown more variations in comparison to neutral and negative sentiments. Third It is possible to extract the keywords related to essential factors influencing crude oil prices using the LDA technique under topic modeling 2022 IEEE. -
Insurance Data Analysis with COGNITO: An Auto Analysing and Storytelling Python Library
Data pre-processing has taken an enhanced role with the advent of Machine learning. It is a vital element that forms the encore of the data science and business analytics process. Data pre-processing involves generating descriptive statistical summary, data cleaning, and data manipulation based on inputs gained after the initial analysis. Of late, it has been observed that data science practitioners spend 45% to 50% of their time cleaning and processing the data. Much time can be saved if the data transformation process can be automated. The COGNITO framework helps in performing the automated feature engineering and data storytelling of the dataset based on end-user discretion. The present work discusses the process and results obtained when automated feature engineering was performed on an insurance dataset using COGNITO. 2021 IEEE. -
The Intellectual Structure of Application of Artificial Intelligence in Forecasting Methods: A Literature Review using Bibliometric Thematic Analysis
Crude oil is a valuable asset class which forms the nucleus of the energy core of the transport sector for any country. According to report [1], crude oil helps in meeting 93% of energy needs for the transportation sector globally. It has been projected across various forums that crude oil along with coal and natural gas is going to satisfy world energy needs for the forthcoming years. Consequently, it has been observed that fluctuations in crude oil prices tilt the economies of scale across the world. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Designing an artificial intelligence-enabled large language model for financial decisions
Purpose: Artificial intelligence (AI) has profoundly reshaped financial decision-making, introducing a paradigm shift in how institutions and individuals navigate the complex finance landscape. The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics. Design/methodology/approach: The study offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions. The framework excels in handling and analyzing large volumes of numerical and textual data through advanced data mining techniques. Findings: FinSageNet demonstrates exceptional text summarization capabilities, outperforming models like FLAN and GPT-3.5 in Rouge score metrics. The proposed model has shown more accuracy than generic models. Originality/value: The study emphasizes the significance of consistently updating models and adopting a comprehensive approach to integrating AI into financial decisions. This study improves our understanding of how artificial intelligence transforms financial analytics and decision-making processes. 2025, Emerald Publishing Limited. -
Stock Market Trend Analysis on Indian Financial News Headlines with Natural Language Processing
Predicting the stock movement in the real-time scenario has been the most challenging and sophisticated in business. This business is affected by several factors from physical to psychological as well as rational to irrational. So far only few aspects have been taken into account while breaking down the conclusion. Implementing sentiment analysis, a subfield of Natural Language Processing (NLP), from the news, social media or financial document, investors decide whether they should invest for the company. The results have shown a significant and a feasible method for predicting the stock market trend with higher accuracy. The current research has mainly focus on finding the sentiment score from the news headlines and finding the hidden trend from it. Further the trading signals are generated based on the prevailing trend and trends are executed by the automated trading system. Using this algorithm, traders can reduce the manual intervention in the buy and sell decisions related to the stock market. 2021 IEEE. -
Framing and control for sustainability of industries
Purpose: The paper attempts to frame the challenge of managing the transition to a sustainable economy by way of a conceptual model consisting of a zero-footprint regulatory regime and a sustainability fund. Design/methodology/approach: A conceptual model of the sustainable industrial revolution has been developed based on the learnings from industries such as originators (mining), farming, pharmaceuticals, pesticides and chemicals and long-lasting artefacts against an overall perspective. Findings: It is suggested to have an institutional structural mechanism in place to ensure that footprint is minimized through recycling including refurbishing, resale or transformation. This includes management of recycling businesses through execution of a zero-waste regulatory regime that will build and use a sustainability fund. Research limitations/implications: The limitations of the paper are arising out of the topic being an issue of gigantic proportions with immense complexity. An attempt has been made to bring out the inescapability and the imperative of a sustainable industrial revolution. Practical implications: This paper presents practical aspects such as collusion between trash and recycling businesses, land use and social aspects of criticality of public support. If implemented, the suggested model can make a paradigm shift in the way firms, industry and governments can handle the challenge of sustainability. Originality/value: The value of this conceptual paper lies in an attempt to extend the learning organization framework to the concept of a regulatory model for sustainability that is not limited to the definition of a firm but stands extended to industries and to the economics, land use and demographics of the planet. 2021, Emerald Publishing Limited. -
Watching the Watchers: Digital Panopticism in the Age of Algorithmic Culture
The chapter deals with the ideology of Digital Panopticism as basically one of the major features of the algorithmic culture where power is exercised through the routine surveillance, data extraction, and predictive analytics. Using Michel Foucaults idea of the Panopticon as a starting point, the authors suggest that digital systems - for example, social media and algorithmic governance - are control structures that are spread out and therefore it is difficult to distinguish the zones where one is being observed and the zones where one is participating. The chapter links the concept of Digital Panopticism with the theories of the radical surveillance and digital capitalism and also examining the impact of the phenomena on subjectivity, autonomy, and behavior. The authors end the chapter by looking at moral theories and resistance movements to facilitate openness, taking responsibility, and the peoples control in the age of digital technology. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Skilful Leadership and Management: The Importance of Emotional Intelligence
Emotional intelligence (EI) has become more important in the study of organisational behaviour, particularly in relation to management and effective leadership. EI is the ability to identify, understand, and control ones own emotions as well as those of others. Those with high EI find it easier to navigate complex social interactions, build strong relationships, and resolve conflicts. EI is the ability to recognise, manage, and evaluate emotions. The ability to express ones emotions in a healthy way and to empathise with others is a sign of great emotional intelligence in a leader, and it will enhance both performance and workplace relationships. The study employed a range of machine learning (ML) methods, such as ANN, BRDT, Naive Bayes, and Random Forest, to predict EI based on behaviour credits. ML approaches have become more and more common. The results showed that the BRDT has the accuracy of 98.3 which is higher in all other machine learning models and gives better results. Seven behavioural attributes and seven additional individual attributes made up the prediction dataset. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Artificial Intelligence in Mental Wellbeing in a Unit of Healthcare Industry in India
Artificial Intelligence is of great help in healthcare sector in saving the human lives from deadly diseases like cancer, brain tumour etc. The present conceptual paper emphasise over a prominent unit of health care which is mental health care. Method: The paper envisages the role of artificial intelligence in identifying and curing mental disorders. For this the literature was collected from the database of scopus, WOS, government websites, newspaper articles and WHO. Results: Further the results revealed that with the use of AI enabled technology, (Machine learning, Deep learning, Natural Language Processing, Teletherapy, Computer vision), the medical professionals are able to diagnose and cure the mental disorders accurately and at early stage. With the government assistance for research centers, the inclusion of AI in mental healthcare can prove to be a great help for the wellbeing of mankind specifically in a developing country like India. 2026 by IGI Global Scientific Publishing. All rights reserved. -
AI and human collaboration in tourism: a framework for scalable, authentic, and engaging content
This study examines the effectiveness of AI-generated content in tourism marketing by comparing it to human-generated narratives. While AI enhances scalability and factual accuracy, its ability to replicate emotional engagement and cultural authenticity remains unexplored. Using the Information Quality Framework (IQF), the study employs readability analysis, sentiment analysis, and thematic analysis to assess AI- and human-generated content. AI-generated travel narratives were sourced from large language models, while human content came from tourism blogs and vlogs. Findings reveal that AI-generated content is well-structured and highly readable but lacks emotional depth and trust-building elements. Sentiment analysis shows stronger emotional responses in human narratives, while thematic analysis highlights richer cultural insights. The study proposes a Hybrid AI-Human Collaboration Model, leveraging AI's efficiency with human creativity. These insights contribute to AI ethics, tourism storytelling, and digital marketing, offering practical recommendations for integrating AI into tourism content creation. 2025 Asia Pacific Tourism Association. -
Designing an artificial intelligence-enabled large language model for financial decisions
Purpose Artificial intelligence (AI) has profoundly reshaped financial decision-making, introducing a paradigm shift in how institutions and individuals navigate the complex finance landscape. The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics. Design/methodology/approach The study offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions. The framework excels in handling and analyzing large volumes of numerical and textual data through advanced data mining techniques. Findings FinSageNet demonstrates exceptional text summarization capabilities, outperforming models like FLAN and GPT-3.5 in Rouge score metrics. The proposed model has shown more accuracy than generic models. Originality/value The study emphasizes the significance of consistently updating models and adopting a comprehensive approach to integrating AI into financial decisions. This study improves our understanding of how artificial intelligence transforms financial analytics and decision-making processes. 2025 Emerald Publishing Limited -
From Nodes to Notables a Graph Theoretic Framework for Uncovering Emerging Influencers
Identification of new effects in social networks is important for effective digital marketing, information dissemination and community engagement. This article introduces a new graph-theoretical framework designed to systematically reveal impressively by analyzing structural network properties. Our function benefits from the main centrality matrix - including degrees, beach, proximity and self vector center - to evaluate the effect of users in the complex network. In addition, we use social identity algorithms such as the Luven and label suggestions to identify opinions of opinions and important contacts in networking groups. The processing of data affecting recent progress in Graph Neural Network (GNNS) is integrated to limit the impressive identity in order to detect the treatment and the fine effect pattern. Experimental results accurately demonstrate the efficiency of our hybrid approaches in influencing pinpointing, leading to actionable insight into targeted marketing campaigns and the effect of impact monitoring is increased. The challenges related to interpretation and dynamic network situations are discussed for future research. 2026 IEEE. -
Optimizing Cybersecurity in Digital Domain Through Proactive Cyber Monitoring
In todays linked digital landscape, cybersecurity is a top priority for individuals, organizations, and governments alike. As cyber threats grow in sophistication and frequency, the necessity for proactive and comprehensive defense strategies become more pressing. This study paper goes into the topic of improving cybersecurity through proactive cyber monitoring, providing an in-depth analysis of both hacker approaches and defense strategies. The study takes a multifaceted approach, starting with a thorough examination of common hacking strategies used by cyber enemies. By deconstructing popular attack routes such as phishing, virus propagation, and social engineering, the article sheds light on the complexities of cyber threats and hostile actors strategies for exploiting system vulnerabilities. Building on this foundation, the study investigates proactive cyber monitoring as a proactive defensive measure. Organizations can improve their cybersecurity posture by using advanced monitoring technologies, anomaly detection algorithms, and threat intelligence feeds to identify and mitigate possible threats before they become full-scale attacks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Regression Analysis for Longitudinal Aging Study in India Data
This paper examines the Longitudinal Aging Study in India (LASI) and its role in providing valuable insights into the health, social, psychological, and economic well-being of the older Indian population. The paper examines the use of dependent independent variables in a multiple linear regression model, tests assumptions of linearity, and examines the significance of the overall model and the individual variables. There are 190 variables in the dataset being used. This paper presents the results of comparing the regression models obtained through basic, forward, and stepwise selection methods where the model obtained using the stepwise selection method, when all the linearity assumptions are satisfied, explains 86.51% of the variation in the dependent variable and the Adjusted R-squared of the model is 0.8374. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Global Anti-Discrimination Law and AI Concerning Imposter Syndrome and Legal Frameworks: Gender, Diversity, and Intersectional Bias in Professional Advancement Technology
In the age of artificial intelligence, strong anti-discrimination laws are important for more than just following the rules. It includes social, ethical and economic issues of interest to technologists and non-technologists alike. The regulations would help reduce the chance that AI perpetuates stereotypes that limit women and other groups, based on preconceived ideas. Lost impostor syndrome, behind-thescenes and careers being ruined by AI-bias fuel psychological damage to self-esteem, job satisfaction, retention etc.AI bias If bias exits in neural networks that has emerged in the workforce, " then its best to circumvent such bias before problems arise. In that chapter, it examines how international antidiscrimination law links to AI, with examples of impostor syndrome. It also explores the intersection of this technology with global anti-discrimination laws, and in doing so, reveals significant legal and sociotechnical implications. 2026 by IGI Global Scientific Publishing. All rights reserved. -
An Explainable AI Techniques for Advancing Diabetes Prediction Using Machine Learning
Researchers have developed an automated system to identify diabetes risk. This system combines data from two sources: a collection of female patients in Bangladesh and an expanded dataset from a local textile factory. The expanded dataset includes information from 203 additional patients. The system uses several techniques to improve its accuracy. It first identifies the most important factors for predicting diabetes, then employs a special model to estimate insulin levels. It also addresses challenges like imbalanced data (where one outcome is more common) and explains its predictions using artificial intelligence techniques. This system achieved the superlative results has an 81.0% accuracy rate, 0.812 F1 score, and 0.844 Area Under the Curve (AUC).. These metrics indicate strong performance in identifying diabetes risk. 2025 IEEE. -
Enhanching the Performance Metrics of Overlay Network for QoS in Media Transfer Using Genetic Algorithm
Quality of Service (QoS) of real time video applications is difficult to realize in wireless mobile networks because of the limited resource availability. Software-Defined Networking (SDN) Overlay networks are becoming popular to solve routing, traffic engineering and QoS due to the rapid increase in the adoption and investment in SDN. The SDN market size is projected to grow by a double-digit CAGR within the next decade and reached the low tens of billions USD in 2023, which shows a positive adoption of the industry. Real-time streaming and live content demand have also risen to an all-time high - the live-streaming market is growing at an average rate of about -20-23% CAGR through 2030, and the role of QoS in high-volume media is becoming more and more relevant. 2025 IEEE.