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Ms. A. Sindhu Devi is associated with the Department of Computer Science and Business Systems at Jerusalem College of Engineering, Chennai, Tamil Nadu, India. Her academic interests include Machine Learning, Artificial Intelligence, forecasting models, data analytics, and intelligent computing applications. She is actively engaged in teaching and research focusing on AI-driven predictive technologies and their applications in solving real-world challenges.
This chapter offers an exhaustive examination of machine learning methodologies for the early identification and forecasting of student stress and cognitive-behavioral alterations. It stresses how important it is to use artificial intelligence, machine learning, and data-driven methods together to change how schools work today. The talk focuses on important frameworks, algorithms, and technology methods that make it easier to keep an eye on, forecast, and enhance student results. The suggested methods support personalised learning, timely intervention, and smart academic decision-making by using massive datasets and smart analytics. The part that new technologies play in making education systems suitable for the future is also spoken about. The chapter shows how intelligent systems can fill in the gaps in traditional education by providing scalable and creative solutions that boost performance, inclusivity, and long-term academic achievement in a variety of learning settings.
Forecasting is essential for facilitating informed decision-making in numerous practical sectors, such as healthcare, agriculture, banking, manufacturing, transportation, energy, meteorology, smart urban development, and supply chain logistics. The growing accessibility of extensive datasets produced by Internet of Things (IoT) devices, cloud computing infrastructures, and digital systems has hastened the use of Machine Learning (ML)-driven forecasting models. In contrast to traditional statistical forecasting methods, machine learning algorithms possess the capability to autonomously identify intricate nonlinear correlations, extract concealed patterns from both historical and real-time data, and perpetually enhance predictive precision. Sophisticated algorithms including Linear Regression, Decision Trees, Random Forest, Support Vector Regression (SVR), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer models have exhibited exceptional efficacy in predictive analytics. This chapter delineates the principles, framework, predictive models, applications, obstacles, and prospective research avenues of machine learning-driven forecasting for astute real-world decision-making support.