ajay.karanjia95@gmail.com

Customer Relationship Management (CRM) has become a cornerstone of strategic customer engagement and revenue optimization in the tourism sector. This study explores the effectiveness of CRM strategies in Maharashtra’s tourism industry, analyzing their impact on customer satisfaction, retention, and financial performance. A quantitative research approach was employed, collecting data from 600 tourism professionals across four major districts—Mumbai, Pune, Aurangabad, and Nashik. Statistical analyses, including correlation matrices, multiple regression models, and factor analysis, reveal that CRM adoption significantly influences operational efficiency (r = 0.991) and customer retention (r = 0.997). AI-driven automation (r = 0.858) and marketing automation (r = 0.972) enhance engagement, while data security (r = -0.075) and customer engagement (r = -0.229) show weaker direct correlations with revenue growth. Factor analysis highlights four key CRM dimensions: Customer-Centric CRM, Loyalty-Driven CRM, Revenue-Centric CRM, and AI-Integrated CRM, each contributing uniquely to tourism business sustainability. The findings underscore the necessity for a balanced, AI-driven CRM strategy integrating personalization, retention mechanisms, and data-driven decision-making to optimize customer experiences and maximize financial outcomes in Maharashtra’s tourism sector.

This study examines the evolving role of Non-Resident Indians (NRIs) in India’s economic, social, and technological development. It explores NRI contributions through remittances, foreign investments, and entrepreneurship, highlighting their impact on key sectors such as real estate, education, and healthcare. The paper delves into policy frameworks governing NRI engagement and assesses challenges like taxation, dual citizenship, and bureaucratic hurdles. Case studies illustrate successful NRI-driven initiatives in business and philanthropy. The research also investigates brain drain versus brain gain dynamics, emphasizing knowledge transfer and global networking. Emerging digital platforms have facilitated stronger diaspora ties, fostering innovation and cultural exchange. Comparative analysis with other diaspora communities sheds light on best practices for maximizing NRI potential. The study suggests policy recommendations for enhancing NRI participation in nation-building. It underscores the need for inclusive policies that recognize NRIs as strategic partners in India’s progress. The findings contribute to a deeper understanding of transnational ties and their implications for India’s growth trajectory.

Digital banking fraud detection is a dynamic issue because of the nature and sheer number of transactions. Conventional machine learning-based models tend to be challenged with high-dimensional input, real-time processing, and dynamic patterns in fraud. We address these drawbacks by introducing the Graph-Enhanced Transformer Network (GETNet), a mixed deep learning approach combining Graph Neural Networks (GNNs) and Transformer self-attention-based mechanisms for better fraud detection. GETNet identifies transaction relationships through GNNs and uses Transformers for sequential anomaly detection. Experimental results on the PaySim dataset show that GETNet is 99.5% accurate, far superior to traditional approaches like Decision Trees, Support Vector Machines, and Naïve Bayes. The model ensures scalability, flexibility, and real-time detection, which makes it a strong candidate for contemporary banking fraud detection.

More than ever, the healthcare industry witnesses a remarkable transformation with cloud computing becoming a new data-driven era involving healthcare delivery. However, existing methodologies lean heavily on classical encryption algorithms but will alter with emerging and new security threats. This work lies the exhibition of a new paradigm involving NTRU, (N-th degree truncated polynomial ring units), a post-quantum key generation method, and salsa20 for fast data encryption. The entire process involves the generation of keys from the NTRU polynomial-based private-public key pairs and the actual encryption of data using the salsa20 keystream in order for secure data storage inside the cloud environment. The encrypted data is uploaded to the cloud, and decryption is carried out by the authorized receivers using the complete key and the nonce. Results indicated a relationship between the cryptographic processing time, which gradually increased from 2200 ms for a 75-bit key to 3500 ms for a 250-bit key. Further, encryption time went from 2000 ms for a 50-bit key to 16,000 ms for a 300-bit key, while decryption time was observed between 10 ms for a 500-bit key and over 700 ms for a 3500-bit key. Thus, it provides extra strength against quantum attacks yet also balances the performance and makes it viable for future healthcare data protection.

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.