Cognitive Computing with Intelligent by S. Aasha Nandhini (.ePUB)+
File Size: 97.7 MB
Cognitive Computing with Intelligent Engineering Platforms by S. Aasha Nandhini, R. Karthick Manoj, D. Lakshmi, Malathy Batumalay
Requirements: .ePUB, .PDF reader, 97.7 MB | True PDF, True EPUB
Overview: Cognitive Computing with Intelligent Engineering Platforms is an explorative study anchored in the transformative role of cloud-based Artificial Intelligence and cognitive computing in advancing smart engineering systems across industrial, urban, energy, and healthcare domains. The volume highlights how AI-driven platforms, Industrial IoT, and cloud infrastructures are reshaping decision-making, control systems and human–machine collaboration. Through focused chapters, the book examines AI-powered control architectures, cognitive technologies for smart manufacturing, and intelligent support systems for operators in next-generation industries. It further addresses collaborative energy management for smart cities using hybrid optimisation and IoT data fusion, advanced Deep Learning models for real-time electric vehicle battery state-of-charge estimation, and cognitive Industrial IoT applications in healthcare for intelligent care delivery. By integrating theoretical models with applied case studies, the book offers a holistic perspective on intelligent, sustainable, and human-centric engineering solutions. Cognitive computing and Artificial Intelligence (AI) are together ushering in a new era of intelligent control systems, offering enhanced accuracy, adaptability, flexibility, and decision-making capabilities for solving complex engineering challenges. A cognitive AI-powered control system combines advanced AI techniques such as Machine Learning, Deep Learning, neural networks, and data analytics with cognitive computing principles like reasoning, perception, and contextual understanding. Engineers must develop skills on the cloud platform of their choice (AWS, Microsoft Azure, Google Cloud) alongside AI frameworks that include TensorFlow and PyTorch. Knowledge of programming languages such as Python and R is becoming indispensable for implementing and customizing AI models. Data management and analytics training are essential for effective handling of large datasets. An engineer requires sufficient knowledge to interpret Machine Learning outcomes and incorporate them into engineering processes.
Genre: Non-Fiction > Tech & Devices
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