Artificial Intelligence (AI) has emerged as an important technological force in modern asset and portfolio management. The growing availability of financial data, improvement in computational capabilities, and development of machine learning, deep learning, natural language processing, and reinforcement learning have created new opportunities for investment analysis and portfolio decision-making. AI-based systems can process large volumes of structured and unstructured information, identify complex relationships among financial variables, forecast market movements, evaluate investment risks, and dynamically adjust asset allocations. Recent research indicates that AI is influencing not only portfolio optimization but also market forecasting, risk assessment, robo-advisory services, investment research, compliance, and strategic decision-making. .

Efficient irrigation requires timely information about changing field conditions. This paper develops a low-cost smart-irrigation framework that combines an Arduino Uno, a digital temperature–humidity sensor, Internet of Things communication, simple artificial-intelligence methods, and protected relay control. The study follows a design-and-evaluate approach at Ratu Road, Ranchi. Its analytical structure contains 210 time-stamped records, comprising 30 observations per day for seven days. Data quality, overall and day-wise descriptive statistics, environmental demand classes, and functional tests are examined. The illustrative dataset was complete, with an overall mean temperature of 29.81 °C (SD = 1.83) and mean relative humidity of 64.29% (SD = 7.17). Day 4 was warmest (30.91 °C) and least humid (58.90%), while Day 1 was coolest (28.66 °C) and most humid (68.39%). The irrigation-support distribution contained 64 low-, 72 moderate-, and 74 high-demand records..