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Dhananjaya Reddy is an Associate Professor of Mathematics at Government Degree College, Karvetinagaram, Andhra Pradesh. He possesses extensive academic experience in mathematics, statistics, educational assessment, and academic administration. His research interests include quantitative analysis, educational evaluation, and learning analytics. He
This chapter offers a thorough examination of AI-integrated feedback and evaluation systems aimed at fostering continual enhancement in educational results. It stresses how important it is to use artificial intelligence, machine learning, and data-driven methods to change how schools work today. The conversation talks on important frameworks, algorithms, and technological methods that help with better monitoring, prediction, and optimisation of outcomes relating to students. The proposed methods support personalised learning, timely intervention, and smart academic decision-making by using big data sets and smart analytics. To make sure that the implementation is responsible, we carefully look at problems like data quality, scalability, privacy issues, and ethical concerns. 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.
Precision agriculture and intelligent farming are revolutionising traditional farming methods through the use of machine learning (ML), Internet of Things (IoT) technology, remote sensing, robotics, and data-informed decision-making. Conventional farming methods frequently depend on standardised management techniques that may overlook the regional heterogeneity of soil properties, crop development, water resources, pest prevalence, and weather patterns. Machine learning facilitates the examination of diverse agricultural data to aid with localised decision-making, resource efficiency, crop surveillance, and yield forecasting. This chapter elucidates the essential principles of precision agriculture and intelligent farming, explores significant agricultural data repositories, and analyses prevalent machine learning and deep learning methodologies. It presents a cohesive framework that links field sensors, remote sensing platforms, data processing systems, predictive models, and apps for farmers. The section additionally addresses model assessment, implementation obstacles, data compatibility, interpretability, economic viability, and ecological sustainability.