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G. Venu Ratna Kumari is a Senior Assistant Professor in the Department of Civil Engineering at Vikas College of Engineering and Technology. Her research interests include sustainable engineering, educational technologies, interdisciplinary innovation, and project-based learning. She actively contributes to academic research and student mentoring. Her work emphasizes practical and technology-driven solutions.
The escalation of environmental deterioration, climate change, biodiversity loss, deforestation, pollution, and the unsustainable exploitation of natural resources has heightened the necessity for sophisticated monitoring and conservation techniques. Artificial Intelligence (AI) has emerged as a transformative technology for tackling these difficulties by facilitating real-time environmental monitoring, predictive analytics, automated resource management, and evidence-based conservation planning. Through the integration of machine learning, deep learning, computer vision, remote sensing, Geographic Information Systems (GIS), Internet of Things (IoT), drones, and satellite imagery, AI-driven systems can analyse extensive environmental data, identify ecological changes, forecast environmental risks, and facilitate sustainable resource management. This chapter examines the principles, technology, applications, problems, and future trajectories of AI in environmental monitoring and natural resource conservation, emphasising its contribution to ecological sustainability, biodiversity preservation, and climate resilience.
This chapter offers an in-depth examination of a smart IoT-based internship and placement monitoring tool designed for real-time assessment of student skills and performance analysis. It stresses how important it is to use artificial intelligence, machine learning, and data-driven methods to change how schools work today. The chapter also talks about how real-time analytics, adaptive systems, and predictive modelling can be used in real life to make teaching more successful and keep students interested. To make sure that the implementation is responsible, we carefully look at problems like data quality, scalability, privacy issues, and ethical concerns. In general, this chapter shows how intelligent systems can fill in the holes in traditional education by providing scalable and creative solutions that boost performance, inclusivity, and long-term academic achievement in a variety of learning settings.