Building Embodied Ai Systems The Agents The Architecture Principles Challenges And Application Domains
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Building Embodied AI Systems: The Agents, the Architecture Principles, Challenges, and Application Domains
This book is primed to demystify the emerging and evolving trend of embodied systems. It explains how these unique systems facilitate establishing smarter environments such as multi-specialty hospitals, manufacturing floors, warehouses, retail stores, defense zones, eating joints, entertainment plazas, etc., in detail for the benefit of our esteemed readers. To get a complete and actionable understanding of any mission-critical environment, we must deploy embodied systems. These systems, such as robots, drones, etc., are physical entities that are embedded and empowered with software systems. They interact with the environment in real time, providing context-aware services. There are chapters exclusively delineating the technologies behind the realization and deployment of such enigmatic systems. The prominent industrial use cases are explained in the latter chapters.
Computer Vision and Edge Computing Technologies for the Drone Industry
Computer vision powers critical functions like object detection, classification, and tracking while the drone is airborne. Without computer vision, drones would be unable to autonomously recognize and respond to features like buildings, trees, and diverse terrains. Advances in computer vision enable drones to effectively perform surveillance and security tasks. They analyze visual data to identify suspicious activities, unauthorized access, and enhance threat detection, thus improving decision-making and mission success rates. Computer vision technology is pivotal in developing autonomous navigation and obstacle avoidance in drones. Computer Vision and Edge Computing Technologies for the Drone Industry explores the enhancement of the autonomous capability of drones for operations in dense forests, mountainous regions, or urban settings. It highlights the abilities of computer vision algorithms to enable drones to navigate hazardous environments without human intervention, enabling autonomous flight and collision avoidance. Covering topics such as drone surveillance, traffic management, and industrial applications, this book is an excellent resource for computer scientists, aviation scientists, industrial professionals, professionals, researchers, scholars, academicians, and more.
Improving Threat Detection, Network Security, and Incident Response With AI
Artificial intelligence (AI) strengthens cybersecurity by enhancing threat detection, fortifying network security, and streamlining incident response. Traditional security systems often struggle to manage modern cyber threats. AI addresses this challenge by analyzing data in real-time, identifying patterns and anomalies that may indicate malicious activity. Machine learning algorithms detect attacks and threats faster than humans, allowing organizations to respond proactively. In network security, AI helps in monitoring traffic, predicting vulnerabilities, and automatically implementing protective measures. AI-driven incident response tools assess the breaches, contain threats, and initiate recovery protocols. As cyber threats evolve, integrating AI into security infrastructure is essential for maintaining resilience in the digital age. Improving Threat Detection, Network Security, and Incident Response With AI explores the role of AI in cybersecurity, focusing on its applications in threat detection, malware analysis, network security, and incident response. It examines key AI techniques such as machine learning, deep learning, and natural language processing (NLP) that are transforming cybersecurity operations. This book covers topics such as robotics, software engineering, and behavioral analysis, and is a useful resource for computer engineers, security professionals, academicians, researchers, and data scientists.