Influencer marketing has emerged as a key tactic used by businesses on social media platforms to connect with their target customers. However, there are issues with trust and transparency in this emerging sector, such as phony followers, hidden sponsorships, and inflated engagement numbers. By offering a decentralized, unchangeable record that can authenticate and validate exchanges and transactions between influencers, companies, and followers, blockchain technology offers a potentially useful alternative. The potential for blockchain-enabled reliability and openness in influencer marketing on social media platforms is examined in this paper. This examines at how blockchain might improve trust by preventing the spread of fraudulent followers, confirming influencer-brand partnerships using smart contracts, and validating the legitimacy of measurements of engagement. Furthermore, the study investigates how decentralized identity systems might improve transparency by facilitating influencers' effortless disclosure of sponsorships and identity authentication. The constraints and difficulties of using blockchain in influencer marketing are also covered in this article, including scalability and regulatory considerations. It also outlines new developments and potential paths for using blockchain technology to promote openness and trust in influencer marketing networks. Perhaps this study highlights how blockchain technology is transforming influencer marketing strategies, building stakeholder confidence, and advancing social media advertising transparency.

In terms of the Global Hunger Index, the ranking of India slipped to 101 in 2021 from its 94th position in 2020. The number of billionaires increased to 1007 and the number of millionaires increased by 63% but household incomes of 97% population declined during the Covid-19 pandemic. Even after the pandemic effect, the gap between rich and poor continues to widen fast. The question arises, do leadership theories in focus today have shown the right path? Necessity has arisen to revisit the leadership style followed by Corporates and Political masters and assimilate the Indian culture of ‘Vasudhaiv Kutumbkam’ to develop a comprehensive approach of spiritual transformation where leaders strive to search for a ‘Higher-order purpose of existence’ (HOPE) and embrace for a society centric approach. An understanding is being germinated to give prominence to the Indian culture emanating from scriptures like Bhagwad Gita, Ramayana, etc. It embarks on creating a value-based leadership framework that deals with a higher vision and mission and Rajarshi leadership is among such leadership. This paper is a modest attempt to trace the attributes of Rajarshi leadership in the light of the story of Ramayana wherein Rama (a prince of Ayodhaya in exile) instead of adopting short-cuts or taking the help of Ayodhya to get his wife free, made tie-up with Sugriva (a depressed and coward king hiding in the forest) and prepared an army of monkeys, nomads and downtrodden people of the forest and defeated the powerful well-equipped army of Ravana and established Rama-Rajaya.

Cultural perspectives are pivotal in shaping organizational dynamics, particularly in the context of leadership styles and employee commitment. The goal of this study is to investigate the complex interplay among organizational commitment, transformational leadership, and cultural factors in the US banking sector. The study mentions the significance of cultural factors influencing leadership and organizational commitment, particularly in the US financial sector. However, it does not explicitly reference specific frameworks such as Hofstede’s or Trompenaars’ cultural dimensions. Instead, the paper discusses how leadership behaviors and employee commitment are influenced by broader cultural perspectives but does not operationalize these in terms of named cultural models. The study used a mixed-methods approach, combining qualitative insights from semi-structured interviews with quantitative data from the Multifactor Leadership Survey and the Organizational Engagement Survey. Statistical software such as SPSS facilitates quantitative analysis, while NVivo aids in qualitative analysis. The quantitative analysis encompasses descriptive statistics, multiple regression analysis, ANOVA, and MANOVA, elucidating significant associations between transformational leadership, organizational commitment, and demographic variables. The primary contribution of this paper lies in its thorough examination of cultural influences on leadership and commitment, employing innovative data analysis techniques. Moreover, it provides practical implications for organizational practices within the financial sector. By discerning these cultural nuances, organizations can tailor their strategies to cultivate effective leadership, bolster employee engagement, and foster organizational success. Ultimately, this study offers valuable insights to practitioners, researchers, and policymakers aiming to tackle challenges and capitalize on opportunities within the US financial industry.

Corporate Governance (CG) plays an indispensable role in today's financial environment. In developing countries, it acts as perception creator in addition to fuel for attracting investments from across the world. Better CG has an impact on economies of the countries throughout the globe. In the present study, we identify the impact of corporate governance variables on profitability of private and small finance banks in India. We have used panel data of 25 private and small finance banks in India. The data was taken for the period 20162021.The dissection involves 5 CG variables relating to board independence, women directors, board committees, ownership structure and board meetings. 2 control variables namely firm size and firm age. Return on Asset (ROA) was used as an indicator of bank 's profitability. Based on the diagnostics tests, Ordinary Least Square Regression was applied. The results reflects that board independence with p -value of 0.00 has a positive influence on bank's profitability. Also the F Statistics is at 4.585, which is significant and can sufficiently explain the relationship between corporate governance and Return on Asset. The study can help these banks to improve their profitability by having more independent directors on board with varied experience.

Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed Quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including Quantum Principal Component Analysis (QPCA), Quantum Boltzmann Machines (QBM), and Quantum K-Means Clustering (QKC), by an impressive margin of 25.15%.

Credit scoring is pivotal in financial institutions' risk management. This research integrates XG Boost and neural networks to develop a robust credit scoring model. XG Boost handles structured data and complex relationships, while neural networks excel in learning intricate patterns. The study aims to enhance model interpretability and accuracy. Evaluation using comprehensive metrics reveals the model's effectiveness. The integrated framework achieves 89% accuracy, outperforming traditional approaches. This study highlights the enhanced predictive power of the XG Boost and neural network model. It offers a promising solution to address the limitations of existing credit scoring methods. By leveraging the complementary strengths of both techniques, the model captures diverse features and nuances inherent in credit data. Ultimately, this research contributes to improving decision-making processes in financial institutions, promoting transparency and accuracy in credit scoring.

Multilingual legal document analysis poses unique challenges in the field of Natural Language Processing (NLP) due to the intricacies of legal language and the diverse linguistic landscape of legal texts across jurisdictions. This paper presents an optimization framework designed to enhance the performance of NLP models specifically tailored for multilingual legal document analysis. The proposed framework incorporates advanced techniques in pre-processing, feature engineering, and model architecture to address the complexities inherent in legal language. Leveraging multilingual embeddings and domain-specific knowledge, the model demonstrates improved accuracy in tasks such as named entity recognition, sentiment analysis, and document categorization across a range of languages. Additionally, the optimization framework emphasizes the importance of domain adaptation, acknowledging the nuances and variations in legal terminology across different legal systems. Through a combination of transfer learning and fine-tuning strategies, the model adapts to specific legal domains, ensuring robust performance in diverse legal contexts. Experimental results on a comprehensive dataset of multilingual legal documents validate the effectiveness of the proposed optimization framework. Comparative analyses with baseline models showcase significant improvements in precision, recall, and overall model performance. The findings underscore the potential of the optimized NLP model for applications in legal information retrieval, contract analysis, and legal knowledge management in a multilingual context. This research contributes to the growing body of knowledge in NLP and legal informatics, offering a valuable resource for researchers, practitioners, and developers working on multilingual legal document analysis. The optimized model presented in this paper has the potential to enhance the efficiency and accuracy of automated systems in handling legal texts across diverse linguistic environments.

Purpose Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis. Design/methodology/approach Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers. Findings Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails. Originality/value It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

In the contemporary financial landscape, the integration of deep learning techniques has revolutionized the capabilities of robo-advisors in providing data-driven insights for mutual fund and stock market price prediction. This study explores the application of deep learning methods, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, in empowering robo-advisors to offer personalized investment recommendations and enhance decision-making processes for investors. Leveraging vast amounts of historical financial data, including market trends, asset prices, and economic indicators, LSTM and GRU networks are adept at capturing complex temporal patterns and dependencies within sequential data, enabling more accurate predictions of future stock prices and market trends. By employing these deep learning techniques, robo-advisors can analyze market dynamics in real-time, adapt investment strategies to changing conditions, and provide tailored recommendations aligned with individual investor preferences and risk profiles. Through a comprehensive review of existing literature and empirical studies, this paper evaluates the performance and effectiveness of LSTM and GRU networks in mutual fund and stock market price prediction tasks. The findings suggest that LSTM and GRU networks offer significant advantages over traditional forecasting methods, such as autoregressive models and technical analysis, by effectively capturing long-term dependencies and nonlinear relationships within financial time series data. Moreover, the integration of additional data sources, such as news sentiment analysis and social media trends, further enhances the predictive accuracy and robustness of the models. Overall, the application of deep learning techniques in empowering robo-advisors holds immense potential for revolutionizing investment management practices, democratizing access to financial markets, and empowering investors with actionable insights for informed decision-making. The proposed method is implemented in Python and has an accuracy of about 99.12%.

Artificial Intelligence (AI) in Business Intelligence (BI) is a significant milestone in the way BI is practiced today as it seeks to bring about change in the ways organizations use data to make decisions in their operations. It cast light on the facet of applying advanced machine learning models as well as natural language processing techniques to highly BI operations in this research work. In light of this, this research will spotlight on how NLP and XGBoost, a steep gradient boosting algorithm both work hand in hand in enhancing better prediction and insights from larger data sets. The research uses the NLP tools, such as word embedding and transformer to work with unstructured text data from different sources of businesses. Since NLP eludes useful patterns and trends from textual data, it works synergistically with XGBoost in matters concerning prediction models. XGBoost is used for its ability of high precise in analyze structured data for better prediction results and finding important features that influence business KPIs. Focusing on the qualitative and quantitative analysis of the case and using the NLP and XGBoost, the authors illustrate how the methods increase the accuracy of business forecast, improve the decision-making process, and provide a competitive advantage on the market. Moreover, the study makes efforts to include and elaborate the implementation issue, the data quality and integration issues as well as the possible ways of handling those problems. combining NLP techniques with XGBoost to enhance business intelligence by extracting actionable insights from both structured and unstructured data. It provides optimal decision making by feature engineering, hyperparameters tuning as well as continual learning. This study hence enhances the field of AI BI by revealing new applications and outcomes, which are helpful and informative for the practitioners and academics in the field.

Automated Essay Scoring (AES) represents a dynamic frontier in the realm of educational technology, leveraging advancements in deep learning and explainable artificial intelligence to transform assessment practices. This study undertakes a comprehensive exploration of AES, focusing on the rubric level and uncovering decision-making processes for holistic score predictions. The study also introduces faster SHAP implementations, demonstrating their efficiency in enhancing model interpretability. The approach uses linguistic indicators to quantify text cohesiveness, lexical variation, and complex syntactic structures.. The study evaluates the effect of deep learning using the most recent advances in feature selection, model-agnostic methodologies, and natural language processing. It finds that the addition of hidden layers to a neural network’s architecture increases descriptive accuracy by 10%. The novelty of this study lies in its systematic approach to AES, unraveling the AES black box and providing transparency through explanation models. By addressing the limited exploration of AES at the rubric level, the research contributes to the interpretability of scoring decisions, essential for trust in automated grading systems. Leveraging diverse models, including Deep Neural Networks and ensemble techniques, the research achieves notable accuracy rates, with the ensemble model showcasing an impressive 92.5% accuracy.

The Internet of things and respective skilling is a matter of extensive research. The individuals possessing such skills have paramount advantage over the ones not possessing such skills. Especially in Indian technological landscape, the digital skills and digital inclusion tendencies are gradually transforming yet have not developed to the fullest. The youth foresee immense potential in learning and unlearning the IoT skills and characteristics. The study hence based on validation assessment of Deursen’s Internet of things skills (IoTs) across Indian generation Z will provide insights into the mechanics and dynamics of perceptions with regard to skill-based prowess. The study leverages two types of skills- related to strategic IoT and operations & data analysis in order to ascertain the skill-based dexterity of the youth. The study relies on CFA and SEM modelling to ascertain the impact. The results and findings of the study will give an important insight into their current IoTs skills, areas where they need to improve or wider impacts on education, policy and industry in India. The findings of the study confirm that there are important differences between women and men in terms of their level of education, as well as among students with different levels of knowledge. The results have shown that, as regards shaping the employability discourse, there is support for Internet of Things skills and differences in them. These findings can help stakeholders better prepare the younger generation for the IoT-driven future.

The Internet of Things (IoT) has revolutionized the way devices interact and share information, creating a vast network of interconnected devices. Studyproposed a robust blockchain framework tailored for the unique requirements of IoT environments to address these challenges. The proposed blockchain framework aims to enhance data integrity, security, and efficiency in IoT networks by leveraging the decentralized and tamper-resistant nature of blockchain technology. By incorporating smart contracts, the framework enables autonomous and secure execution of predefined rules, ensuring transparent and trustworthy interactions among IoT devices. The use of consensus algorithms specific to IoT requirements enhances the efficiency of data validation and ensures timely and accurate information dissemination. Furthermore, the framework focuses on scalability to accommodate the growing number of devices in IoT networks without compromising performance. Through the integration of consensus mechanisms and optimized data structures, the proposed solution minimizes the computational overhead associated with blockchain operations in resource-constrained IoT devices. By enhancing security, efficiency, and scalability, the study establishes the development of resilient and trustworthy IoT ecosystems, fostering innovation and growth in the rapidly evolving landscape of interconnected devices. The scalability index of the proposed method is 98.12% when compared with existing methods like ripple, DPOS and tendermint.

Users could interact and have shared experiences in immersive virtual reality (VR) settings, which have great potential for collaborative interfaces. The collaborative interfaces in immersive virtual reality (VR) environments, this research presents an enhanced integration of Graph Neural Networks (GNNs). This approach starts with investigation of the difficulties involved in group interactions in virtual reality environments, highlighting the advanced techniques to capture the subtleties of user behaviour and spatial dynamics. GNN architecture designed for cooperative VR interfaces is shown, enabling real-time analysis and adjustment in response to user input. The GNN-based model creates a dynamic graph of the immersive world by integrating multi-modal input such as, spatial coordinates, and social cues. The GNN enhances user engagement by dynamically adjusting the virtual scene based on patterns of collaborative learning. A range of cooperative situations highlight the model’s flexibility and show how well it can adjust to different human. The integration of reinforcement learning methods, which enable the system to adapt and improve its answers in response to user feedback and task objectives. The effectiveness of this approach is validated by the experimental findings offer, which show increases in overall system performance, cooperation efficiency, of 89.5% when compared with previous methods.

Blockchain is the foundational technology that allows cryptocurrencies like bitcoin to exist. Blockchain technology has been used in numerous domains such as banking, justice, and commerce as part of the fourth industrial revolution since the invention of the steam engine, electricity, and computer technology. People's willingness to adopt technology has been influenced by rapid technological improvement. The traditional education system in developing nations has lately been improved through the implementation of distributed ledger technology. Disruptive technology in education is a key prerequisite for better accountability and exposure. The authors investigated the key factors influencing educational institutions' (knowledge providers) and learners' (knowledge recipients) intentions to use blockchain technology.

This research delves into the transformative impact of technological advancements and the rise of Fintech services on financial institutions operating in the National Capital Region (NCR), India. To assess this impact, a comprehensive questionnaire was formulated using a Likert scale and administered to employees across various financial institutions in the NCR. Factor analysis of the responses unveiled four key factors that collectively accounted for 69.60% of the variance: Operational Efficiency (16.43%), Profitability (15.23%), Productivity (18.69%), and Customer Trust & Perception (19.25%). These factors hold paramount significance in shaping the financial landscape, and managers are urged to prioritize them for strategic decision-making. Embracing technology-driven solutions, process automation, and customer-centric approaches can lead to increased profitability, streamlined operations, and an amplified customer base. The study's scope is restricted to the NCR, making it vital for further research to explore Fintech's impact in broader geographical regions or focus on specific technological innovations. As the financial industry continues to evolve, continuous monitoring and adaptation of these factors will help institutions remain competitive and ensure sustainable growth amidst a rapidly changing landscape.

Since the financial crisis, investors have given greater regard to environmental, social, and governance (ESG) factors when making stock market choices. To achieve sustainable development, these elements are fundamental. This research paper aims to assess the impact of risk profile, investing decision-making, and asset familiarity on the preferences of individual stock market investors for ESG investing by using investment behaviour as a mediator. According to the paper, there is a growing trend in investments that consider environmental, social, and governance (ESG) concerns. The study's methodology included secondary data sources as well as primary data obtained using a Google form poll, which yielded 400 legitimate responses. To examine the sample size, the programme G*Power 3.1.9.2 was used. A total of 400 responses were included into the research, with the programme determining a sample size of 262 individuals. People from all throughout India filled out the survey. To construct the structural equation model, the Smart PLS programme was used. The validity and reliability of the constructs were evaluated using Confirmatory Composite Analysis. Use of confirmatory composite analysis allowed for evaluation of the constructs' dependability. With Cronbach's Alpha, Composite Reliability, and Rho A values of 0.7, the validity and reliability tests utilised in this study were powerful. The study's results provide light on how ESG factors would influence investing choices in the long run. When drafting legislation pertaining to the stock market, authorities would have the option to include investor preferences.

In the post-pandemic era, client preparedness, motivation for technological adoption, and appreciation for technology-enabled financial services all need substantial study and analysis. The study investigates the factors that impact customer participation in financial institutions’ fin-tech offerings. To investigate the factors that influence customers’ engagement in fin-tech services, a well-structured questionnaire was sent to 423 respondents through a suitable sample approach. SPSS-25 and SEM analysis were used to determine the determinants and important variables. The factor analysis found that six variables accounted for 72.04% of the variation. Following SEM regression analysis, it was shown that Trust (β =0.44, P =0.000), User Experience (β =0.31, P =0.000), and the regulatory framework (β =0.29, P =0.000) all had a substantial influence on customer engagement. The findings of the study will aid all strategic planners in developing strategies and policies to increase consumer engagement in the provision of financial services.

The COVID-19 outbreak has drastically changed the life of every person and has infected people in 185 countries. Since no vaccine has been developed for this disease so far, lockdown, work from home, and social distancing and only a few essential services were allowed to open. Lockdown and restricted movement of people was the only solution to control this crisis. These steps taken by all the countries have stopped all the commercial activities which left all businesses, banks, and financial institutions to count losses and cost. The big question which has emerged that whether e-collaboration between banks and technology continues to be the key to success for finding solutions to the problems in this new environment which COVID-19 has created. This chapter examines the way the digital banking collaboration between banks and Fintech can resolve the problems provided by the COVID-19 pandemic and control the impending economic fallout in India and across the world.

Tenable progress,' according to the UN, is progress that meets present needs without endangering the ability of future generations to meet their own needs.This essay makes a significant point. The relevance of all three of these features is acknowledged in this study, despite its emphasis on environmental sustainability. Physical entities and their data are often used interchangeably when discussing electronic copies, which suggests that there is a link and barrier for effective data flow. For the long term viability of DT-based systems, this article provides a (SLR). A large number of DT-related papers were also selected because they addressed the researched SLRs and were judged relevant to the study's goals. There were so many problems and obstacles in the articles that were selected and analysed: Research on the benefits of is lacking; the benefits of are not well defined; DTs may help to cost reduction or enhance decision-making, but this is uncertain; internetpractice should be improved and more integrated. Furthermore, our research has not been able to uncover a publication that only covers DTs in connection to situational sustainability.