Ayurveda, as a holistic system of medicine originating in ancient India, has gained recognition for its emphasis on balance and harmony within the body, mind, and spirit. This ancient healing practice has evolved over centuries and incorporates the concept of doshas – the biological energies that govern an individual's physical and mental well-being. Ayurveda focuses on bringing balance to the doshas through various treatments, including herbal medicines, lifestyle practices, and therapies. In recent years, Ayurveda has experienced significant growth and recognition in India, with the government promoting it as a mainstream healthcare system. Additionally, Ayurveda practices such as yoga and meditation have gained international acclaim. However, with the integration of modern technology, Ayurveda is poised to undergo further advancements. This research study explores the integration of artificial intelligence (AI) and machine learning techniques in Ayurvedic treatment, particularly in the Saharanpur and Dehradun regions. With personalized and holistic healthcare being the cornerstone of Ayurveda, the use of AI and machine learning algorithms can improve accuracy in diagnosis and treatment. Leveraging large datasets, these algorithms can identify patterns and correlations that may not be immediately evident to human practitioners. The majority of respondents in the study have been practicing Ayurveda treatment consultancy for 1-3 years (60%), while 40% had more than 3 years of experience. 70% of respondents believed that AI-driven diagnosis approaches are more effective than generalized approaches in Ayurveda. It was revealed that all respondents used AI tools primarily for Sparshana diagnosis, with no utilization reported for Darshana or Prashna. 80% of respondents believed that the integration of AI into Ayurveda has the potential to greatly improve various aspects of the practice. 50% of respondents were satisfied, and 40% were very satisfied with the current AI-driven diagnosis, while 10% expressed neutrality. These findings indicate a general acceptance and optimism towards AI in Ayurveda diagnosis, with recognition of its potential benefits and effectiveness. However, further research is needed to explore specific factors contributing to satisfaction and neutrality, as well as any concerns or areas for improvement.

This paper examines the intricate relationship between exchange rate policies and stock market performance, focusing on Kuwait and Saudi Arabia in the Gulf region. It investigates the historical evolution of exchange rate regimes in both countries, analyzing their unique policy objectives. Using empirical analysis, the research explores the correlation between exchange rates and stock market indices, employing statistical methods and regression analysis to identify patterns. Comparative assessments reveal distinct patterns in stock market performance, shedding light on the effectiveness of different exchange rate systems in shaping economic dynamics. The findings contribute to the literature by offering insights into the nuanced relationship between exchange rate policies and stock markets, with implications for policymakers, investors, and financial analysts. By focusing on Kuwait and Saudi Arabia, two pivotal economies, the study addresses a gap in the literature, providing a comparative perspective. Policymakers can use the findings to understand the potential impact of exchange rate policies on economic indicators. Investors can leverage the conclusions to inform their decision-making, considering the nuances of exchange rate dynamics. The study finds that both countries peg their currencies to major world currencies, primarily the USD, and identifies a strong correlation between the Kuwait Stock Index and the Saudi Tasi Index.