The ADHD awareness ecosystem in India is undergoing a transformative shift, yet significant gaps remain in recognition, diagnosis, and structured support systems. Our research aims to identify systemic gaps in ADHD awareness while addressing challenges faced by awareness-driven startups and existing remedial frameworks. The study focuses on understanding lack of awareness, social stigma, misconceptions, shortage of proper facilities, and privacy concerns within society. It also proposes practical solutions to strengthen ADHD screening, early identification, and access to appropriate treatment in India. The research is descriptive in nature and is based on primary data collected through structured questionnaires and interviews from 107 respondents in the KDMC region. A purposive sampling technique was employed for the proposed research. Collected data were analyzed using both quantitative and qualitative methods. The current study also suggests an innovative model to enhance ADHD awareness, improve screening mechanisms, and promote accessible treatment support systems.

Cloud-based platforms have greatly simplified the training and deployment of machine learning models. Yet, the ease of use of these platforms has come at the cost of limited visibility into the operational mechanisms of the system. This research seeks to address the issue of balancing ease of use with the need to have a better insight into the operation of the systems. In this research, we have carried out a structured comparative evaluation of the ease of use of the Amazon SageMaker platform, as well as an enhanced version of the platform with better observability. The ease of use of the two configurations was evaluated based on four different parameters, namely the granularity of the logs, the ease of access of the metrics, the ability to respond to alerts, as well as the ease of diagnosis of faults. Through the analysis of the configurations, the enhanced version of the platform was found to have better ease of use without a proportional increase in the cost of operation.

The HR system had gone through multiple stages of evolution as it first started as fully manual process then it developed itself and easied E-documentation process by introducing tools like hris erp, etc reducing workload and improving efficiency among the employees. Even with such advancement in HR technologies companies still require more use of HR intelligence system. Our study help to understand the advantages of HR technologies and also understanding the limitation where we can adopt more technology This study aims to analyze how information system operates systematically within the human resource department of an organization. It also tries to understand how intelligent HR systems optimize the use of various resources such as human capital, financial resources, and technological tools. Additionally, the study focuses on analyzing the processes, policies, and structured framework involved in organizational development systems.

Market volatility is a reality in contemporary economic environments, and it affects the way managers process information, make risk assessments, and make strategic decisions. This paper investigates the effect of market volatility on managerial decision-making, using the behavioral economics approach, and it highlights the importance of psychological factors, emotions, and cognitive limitations in managerial decision-making. The paper investigates how factors such as uncertainty, loss aversion, overconfidence, herding, and risk perception influence decision-making during market instability. The study also investigates how managers strike a balance between short-term survival and long-term organizational success when faced with changes in demand, prices, or financial conditions. The study uses both theoretical and practical approaches to identify the patterns of decision-making under pressure and to point out the behavioral aspects that can be both helpful and harmful to business success. The results of the study are expected to provide insights into the human aspects of strategic decision-making and to provide frameworks for improving decision quality in dynamic market environments.

Startups play a vital role in accelerating India’s economic development by contributing to job creation, innovation, capital formation, and regional growth. They support the expansion of new industries, enhance productivity, and encourage competition, which ultimately strengthens the country’s GDP and overall economic structure. The rise of startups in sectors such as fintech, e-commerce, edtech, agritech, and healthcare has created new market opportunities, increased digital adoption, and attracted both domestic and foreign investments. By promoting entrepreneurship and supporting small-scale innovation, startups also contribute to balanced regional development and reduce dependence on traditional employment sectors. This research paper focuses specifically on the contribution of startups to India’s economic development. The study will use both primary and secondary data collection methods. Primary data will be collected through surveys and questionnaires from Young Entrepreneurs, Students , and other relevant respondents . Secondary data will be gathered from government reports, startup databases, research journals, and publications from organizations such as DPIIT, NASSCOM, and RBI. The paper will also evaluate the strengths and weaknesses of startups in the context of economic development. Key strengths include employment generation, innovation-driven growth, increased foreign investment, and the development of new business ecosystems. However, startups also face weaknesses such as high failure rates, funding challenges, regulatory barriers, and limited access to skilled resources in certain regions.

Analyzing primary survey data of gig workers and consumers and secondary data from NITI Aayog the paper delineates how income growth, business expansion, and unemployment correlate with patterns of gig employment. Our two-sample t-test reveals that higher income leads to significant consumption on complement services based on platform, while business scale increases demand for gig workers. But they also find evidence of income volatility, skill underutilization, and high job-switching intentions, which suggest a weakness in overall long-term employment stability. Income insecurity and limited social protection are a big focus for human wellbeing while the gig model promotes flexible labor allocation and rapid platform growth from a business perspective. Policy support, skill alignment, income stabilization mechanisms and will provide sustainable development of gig economy.

Artificial Intelligence (AI) is rapidly transforming financial systems across the globe, particularly in emerging markets where traditional risk assessment methods often fail due to data limitations and market volatility. This study examines the role of AI-based financial risk prediction models in improving decision-making, enhancing credit evaluation, and minimizing investment uncertainty in developing economies. The research explores how machine learning algorithms, predictive analytics, and big data integration contribute to identifying potential financial risks such as credit defaults, market fluctuations, and fraud detection. Emerging markets often face challenges such as inconsistent financial records, lack of transparency, and dynamic regulatory environments, making AI-driven solutions highly relevant. The study highlights the effectiveness of AI in improving forecasting accuracy, reducing human bias, and enabling proactive risk management. However, it also considers limitations such as data privacy concerns and technological barriers. The findings suggest that AI-based risk prediction systems can significantly strengthen financial stability and support sustainable economic growth in emerging markets.