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Employee attrition is of critical concern to organizations, impacting productivity, workforce planning, and bottom lines. Existing machine learning models falter in capturing the complex temporal dynamics in employee behavior, resulting in poor predictive performance. To overcome this, we suggest a Temporal Fusion Transformer based deep learning approach for employee attrition prediction, which is optimized using Bayesian Optimization. IBM HR Analytics Employee Attrition dataset is utilized, employing extensive data preprocessing such as dealing with missing values, encoding categorical features, and scaling numerical features. The TFT model utilizes multi-head attention, Gated Residual Networks, and variable selection mechanisms to acquire short-term and long-term dependencies. Bayesian Optimization optimizes hyperparameters effectively with reduced computational expense and improved performance. The model offers a 97.5% predictive accuracy, 96.8% precision, 97.2% recall, and an AUC-ROC of 98.1%, significantly better than state-of-the-art machine learning methods available today. Organizations can use it to accurately forecast attrition patterns and adopt retention policies ahead of time, thus minimizing turnover and improving job satisfaction. The research contributes to the field of HR analytics with the development of a new deep learning model dedicated to attrition forecasting. As future research, work will continue with the application of explanation methods, such as SHAP, LIME, and attention visualization, in an effort to support explainability, fairness, and interpretability in AI-based HR decision-making and workforce management.

This study examines the role of domestic commercial banks in financing agro-based enterprises over the period 2020–2024. Through a mixed-method research design that combines quantitative analysis of statistical data with qualitative interviews, the paper evaluates contemporary financing practices, financial products, and lending mechanisms offered by commercial banks, and considers how these practices influence agricultural development. Data sources include central bank reports, commercial bank statements, and agricultural census data. The findings highlight both the progress made in agricultural financing and persistent challenges. Policy recommendations are provided to improve financial products tailored to the agro-sector. Overall, the study contributes to the growing literature on banking and agriculture and offers a comprehensive evaluation aligned with APA 7th edition citation guidelines.

The aim of the empirical study is to identify the factors that influence the profitability of the companies in the pharmaceutical sector listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) of India. The research is based on purely secondary data from top 6 pharmaceutical companies listed on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) of India over 15 years from 2010-2024. To fulfill the objective of the study Quick Ratio (QR), Inventory Turnover Ratio (ITR) Working capital Ratio (WC), and Debt-Equity (DE) are considered independent variables, Price Book Value Ratio (PBV) is regarded as the control variable and Return on Assets (ROA)(Profitability) considered as dependent variable. To identify the factors, that influence the profitability of pharmaceutical companies, the study employees Pearsons Correlation test, Hadri LM Test, Kao and Pedroni test, Hausman test, Breusch-Pagan/Cook-Weisberg test and for the final result apply Random Effect model with the help of Statistical tool Stata-17.00. The result depicts that Inventory Turnover Ratio (ITR), Debt- Equity (DE), and Price Book Value ratio (PBV) are the factors that have a significant impact on the profitability (ROA) of the pharmaceutical companies listed in India.

The empirical result also suggested that Inventory Turnover Ratio (ITR) and Price Book Value ratio (PBV) have a positive influence on the profitability (ROA) but Debt- Equity (DE) has a negative influence on the profitability (ROA) of the pharmaceutical companies listed in India.

Uttarakhand, while being one of India’s largest and fastest-growing economies, faces significant challenges in its economic development. The state’s growth has been uneven and disconnected, lacking uniformity in performance across various sectors. Additionally, the distribution of growth benefits has been selective, favouring certain sectors while leaving others behind. This scenario underscores the urgent need for inclusive growth in Uttarakhand.
To achieve inclusive growth, it is essential to mobilize resources effectively, and financial inclusion plays a pivotal role in this process. Financial inclusion ensures that all individuals have access to financial services, which is crucial for equitable resource generation and distribution. By promoting financial inclusion, Uttarakhand can strive for a more balanced and inclusive economic growth. This approach will ensure that the benefits of economic growth reach all sectors of the economy, contributing to the overall development of the state. In summary, addressing the uneven and selective nature of Uttarakhand’s economic growth through inclusive measures and financial inclusion is vital for sustainable development.

This theory presents a multi-layered investigation of consumer loyalty concerning e-banking administrations. In the quickly advancing computerized scene, understanding consumer loyalty is vital for e-banking suppliers. This study utilizes a complete methodology, using both quantitative and subjective strategies. Quantitative information is gathered through studies, zeroing in on elements, for example, site convenience, exchange speed, security, client care, and administration unwavering quality. Subjective information is assembled from top-to- bottom meetings, investigating clients’ close-to-home reactions and discernments. Through factual examination and topical coding, the review recognizes key drivers and hindrances influencing consumer loyalty. Discoveries uncover nuanced bits of knowledge into the e-banking experience, revealing insight into regions for development. The complex structure gives a comprehensive view, empowering e-banking foundations to improve their administrations and designer contributions to more readily meet client assumptions. At last, this exploration adds to the streamlining of e-banking administrations and offers a significant asset for professionals and scientists in the field. This research is used secondary data to find the experience of the user and use this data to train the model. The accuracy of the model after training is found in this stage which improves the research and makes the research appropriate. The multidimensional analysis improves the accuracy of finding customer satisfaction. This project uses machine learning techniques to find the accuracy of the model and to solve the issue to improve customer satisfaction.

The widespread adoption of digital payment platforms has revolutionized financial transactions by enhancing convenience, security, and efficiency. This study provides a comparative analysis of Google Pay and other fintech solutions in Bangalore, Karnataka, India, focusing on user demographics, platform preferences, satisfaction levels, challenges, and future expectations. Data were collected from 500 respondents using a structured questionnaire, followed by advanced statistical analyses, including Chi-Square tests, ANOVA, and correlation analysis.
 
The results indicate that demographic factors have no significant impact on platform preference. Instead, transaction success rates, security, and customer support are the primary determinants of user satisfaction. The study also identifies key challenges, such as technical issues and security concerns, and proposes enhancements for future fintech innovations. These insights contribute to a deeper understanding of digital payment adoption and user behavior, offering valuable guidance to policymakers and fintech developers in improving financial technology solutions.

This research paper focuses on the digital transformation in the finance sector which has revolutionized the working of the equity stock market. Online trading apps have made trading in equity stocks convenient and easily accessible. Investors have a plethora of choice to select among the competitive online trading apps who are continuously striving to provide most user friendly and efficient platform to the investors. In this study, the aim is to understand the investors satisfaction with regards to factors such as user interface, speed of transactions, security measures and customer support provided by various online trading apps. The study aimed to identify the challenges faced by the investors while using online trading app and also to find the factors which discourage investors to stay away from using online trading app and continue with the traditional broker-led trading. The study is conducted using primary data collected from 112 investors through questionnaire using convenient sampling method. Descriptive statistical techniques, chi-square tests and ANOVA are used for the purpose of analysis. The study indicated that young and educated professionals preferred to adopt online trading platforms and resolving technical and service challenges will further lead to increase in the satisfaction level and higher adoption rates among the broader population.

This study investigates the impact of the Goods and Services Tax (GST) on textile retailers in Malappuram District, Kerala. The research reveals that GST has increased fabric prices, reduced sales, and led to a decrease in offers, advertisement, and stock levels. Despite not affecting profit significantly, GST has caused practical difficulties in implementation, required external consultancy services, and necessitated special training for accounting staff. To mitigate these negative effects, retailers must adapt their business strategies, reduce prices, implement customized discounts, and adopt advanced accounting systems. The study suggests that increasing retailer awareness about GST and providing proper training to accounting staff can help navigate GST complexities. The research contributes to the existing literature on GST and its impact on various industries, providing valuable insights into the challenges faced by textile retailers.

This study explores the relationship between crowdfunding, crowd-investing, and startups and their impact on India’s economic landscape. The authors analyse existing literature to emphasise how these mechanisms expand capital availability for new ventures. The investigation highlights the role of startups in driving economic growth through job creation, attracting investments, and fostering innovation. Additionally, the authors discuss how startups promote social inclusion and bridge socioeconomic gaps by ensuring that technological advancements reach remote areas. The authors illustrate how startups can reshape societal structures and enhance well-being, positioning India as a global economic powerhouse. Stakeholders can foster collaboration and drive sustainable development by understanding the integration between crowdfunding, crowd-investing, and startups.
In conclusion, this paper emphasises the transformative effects of crowdfunding and crowd-investing on India’s economy, advocating for an environment that nurtures innovation and investment for a prosperous and inclusive future.

In recent years, China’s green bond market has developed dramatically and is playing an increasingly important role in the global green finance sector. This paper selects all the green bonds that have been issued in mainland China from 2016 to 2022 in the Wind database as the initial sample, and through data screening and organizing, 734 valid samples were finally obtained to be used in the empirical analysis through constructing a multiple regression model, to deeply study which factors will affect the interest rate of the green bond issuance. The empirical results show that: (1) The green bond issuance (coupon) rate will change positively with the changes of the benchmark interest rate and the issuance period. (2) The green bond issuance (coupon) rate will change negatively with the issuance scale, the GDP per capita level of the issuer’s region, the debt rating, and the third-party authentication status. (3) Since most green bond issuers in China are state-owned enterprises, the empirical analysis at the current stage could not reveal any significant relationship between the coupon rate of green bonds issued in the form of public offerings with the nature of their issuers.