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Kamlesh Kelwade is an Associate Professor in the Department of Computer Science & Engineering at Anjuman College of Engineering & Technology, India. His contribution focuses on Generative Artificial Intelligence and next-generation computing applications.
Predictive analytics has become a crucial element of contemporary engineering systems as it allows engineers to convert historical and real-time data into practical forecasts. The swift advancement of sensors, Internet of Things (IoT) devices, cloud computing, digital twins, and artificial intelligence has led to the accessibility of substantial quantities of engineering data. Machine learning (ML) offers computational methodologies for identifying patterns within datasets and constructing prediction models without the need to manually code every relationship between input variables and results. This chapter elucidates the essential principles of predictive analytics grounded on machine learning and explores frequently utilised supervised, unsupervised, and ensemble learning methods for engineering purposes The section elaborates on the predictive analytics process, encompassing data preparation, feature engineering, model training, validation, performance assessment, interpretability, and deployment. The study investigates applications in predictive maintenance, manufacturing, structural health assessment, energy systems, renewable energy forecasting, transportation, civil infrastructure, environmental surveillance, and industrial IoT. Ultimately, the chapter addresses obstacles like data integrity, model transparency, concept evolution, computing demands, cybersecurity threats, and ethical implications. The section illustrates how machine learning-based predictive analytics can facilitate dependable, effective, sustainable, and astute engineering decision-making.
Generative Artificial Intelligence (GenAI) has surfaced as a revolutionary framework in contemporary computing, allowing machines to create novel material instead of solely categorising or forecasting pre-existing data. In contrast to traditional Artificial Intelligence systems that mostly emphasise analytical functions, generative models are capable of creating text, photos, audio, video, software code, synthetic data, designs, and multimodal material. This section offers an in-depth analysis of Generative AI for future computing technologies. It examines the principles of generative models, transformer frameworks, foundational models, prompt optimisation, retrieval-enhanced generation, multimodal cognition, artificial intelligence agents, edge and cloud infrastructure, software development, cybersecurity, healthcare systems, intelligent urban environments, Internet of Things, education, robotics, digital replicas, and computational science. The section additionally explores difficulties related to hallucination, privacy, security, bias, explainability, computational expenses, intellectual property issues, and ethical AI. Ultimately, novel research trajectories are introduced for the advancement of efficient, reliable, autonomous, multimodal, and human-centered generative computing systems.