In this paper using computer algebra system a new generalized algorithm is developed to study and generalize the Kaprekar’s operation which can be used for desired numbers of iterations and is also applicable to any n-digits number which is greater than or equal to two.  Existing relevant results are verified with the available results in literature and further extended to examine the difference (kernel) of the obtained number during process with the number obtained in preceding iteration after each step. Sum of the digits of an acquired number obtained after each step is also noticed and found that sum its digits is divisible by 9. A detailed investigation is conducted for all two-digit number and the output acquired is exhibited in tabular form which has not been studied in earlier. An 8-digits number also considered and found that it does not converges to a unique kernel like 3-digits and 4-digits, but follows a regular pattern after initial iteration. Analytical illustrations are provided along with pictorial representations for 2-digits, 3-digits 4-digits and 8-digits number. This algorithm can further be employed for numbers having any number digits. 

Detecting property insurance fraud is critical for reducing financial losses and ensuring fair claim processing. Traditional methods of detecting insurance fraud had several drawbacks, including no feature selection process, no hyper parameter tuning, lower accuracy, and class imbalance problems. To address the aforementioned shortcomings, this paper examines advanced ML (machine learning) techniques for accurately detecting property insurance fraud. To determine the best model for predicting fraudulent activities, this paper tested several machine learning models, including Gradient Boosting, classical ML classifiers, and Stacking Ensemble methods. To address class imbalance and improve model performance, the selected model incorporates proper feature selection, hyper parameter tuning, and SMOTE techniques (synthetic minority over-sampling). The Stacking Ensemble method outperformed the other ML models, achieving an accuracy of 96% and a recall of 94%. The experimental results show that the proposed stacking ensemble-based prediction scheme improves accuracy by 3.4% and recall by 2.7% over previous works. This article also includes a web application for assisting with property insurance fraud, which includes ML-based fraud prediction, question submission, answer checking, and blog post access. According to the findings, more than 54% of users expressed satisfaction with the web application's usefulness for detecting property fraud.

The FinTech sector, an innovative blend of finance and technology, has significantly reshaped financial services by making transactions more efficient and accessible. However, this rapid digitalization has also introduced substantial cybersecurity risks, making the sector an attractive target for cybercriminals. This paper explores the current digital security landscape within the FinTech industry, highlighting prevalent threats such as phishing, malware, and data breaches. It underscores the importance of raising digital security awareness among employees, customers, and other stakeholders to mitigate these risks. The paper analyzes significant case studies and regulatory frameworks and examines the challenges and barriers to implementing effective security measures. It also proposes comprehensive strategies for enhancing digital security awareness, including employee training, customer education, and industry collaboration. The paper concludes with recommendations for future trends and best practices, emphasizing the need for a proactive and collaborative approach to building a secure and resilient FinTech ecosystem.

Simpson's Rule is a widely used numerical integration technique, but it cannot be applied to unequally spaced data. This paper presents a new generalization of Simpson's Rule using both Lagrange and Hermite interpolating polynomials to address this limitation. I provide a geometric interpretation of the method, showing its relationship to the area calculation of a trapezoid and a triangle, where the accuracy is significantly influenced by the chosen interpolating polynomial for midpoint determination. A comprehensive comparative analysis across various functions reveals that the Hermite-based approach consistently exhibits higher accuracy and stability than the Lagrange method, particularly with an increasing number of subintervals. This improved performance stems from the Hermite polynomial's ability to better approximate the function's behavior between data points. The findings highlight the effectiveness of the proposed Hermite-based generalization of Simpson's Rule in improving the accuracy of numerical integration for unequally spaced data, which is commonly encountered in practical applications.

In the software development industry, ensuring software quality holds immense significance due to its direct influence on user satisfaction, system reliability, and overall end-users. Traditionally, the development process involved identifying and rectifying defects after the implementation phase, which could be time-consuming and costly. Determining software development methodologies, with a specific emphasis on Test-Driven Development, aims to evaluate its effectiveness in improving software quality. The study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to comprehensively investigate the impact of Test-Driven Development on various facets of software quality. The survey findings unveil that Test-Driven Development offers substantial benefits in terms of early defect detection, leading to reduced costs and effort in rectifying issues during the development process. Moreover, Test-Driven Development encourages improved code design and maintainability, fostering the creation of modular and loosely coupled code structures. These results underscore the pivotal role of Test-Driven Development in elevating code quality and maintainability. Comparative analysis with traditional development methodologies highlights Test-Driven Development's effectiveness in enhancing software quality, as rated highly by respondents. Furthermore, it clarifies Test-Driven Development's positive impact on user satisfaction, overall product quality, and code maintainability. Challenges related to Test-Driven Development adoption are identified, such as the initial time investment in writing tests and difficulties adapting to changing requirements. Strategies to mitigate these challenges are proposed, contributing to the practical application of Test-Driven Development. Offers valuable insights into the efficacy of Test-Driven Development in enhancing software quality. It not only highlights the benefits of Test-Driven Development but also provides a framework for addressing challenges and optimizing its utilization. This knowledge is invaluable for software development teams, project managers, and quality assurance professionals, facilitating informed decisions regarding adopting and implementing Test-Driven Development as a quality assurance technique in software development.