Mudassir Khan – författare
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Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.
Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systems Discusses the integration of artificial intelligence with machine learning using mathematical modeling Elaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matrices Introduces modern algorithms such as convolutional neural networks and support vector machines Presents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machinesArtificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.
939 kr
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Intelligent systems are technologically advanced machines that perceive and respond to the world around them. Artificial Intelligence and Machine Learning for Smart Community: Concepts and Applications presents the evolution, challenges, and limitations of the application of machine learning and artificial intelligence to intelligent systems and smart communities.
Covers the core and fundamental aspects of artificial intelligence, machine learning, and computational algorithms in smart intelligent systems Discusses the integration of artificial intelligence with machine learning using mathematical modeling Elaborates concepts like supervised and unsupervised learning, and machine learning algorithms, such as linear regression, logistic regression, random forest, and performance evaluation matrices Introduces modern algorithms such as convolutional neural networks and support vector machines Presents case studies on smart healthcare, smart traffic management, smart buildings, autonomous vehicles, smart education, modern community, and smart machinesArtificial Intelligence and Machine Learning for Smart Community: Concepts and Applications is primarily written for graduate students and academic researchers working in the fields of computer science and engineering, electrical engineering, and information technology. Seasonal Blurb: This reference text presents the most recent and advanced research on the application of artificial intelligence and machine learning on intelligent systems. It will discuss important topics such as business intelligence, reinforcement learning, supervised learning, and unsupervised learning in a comprehensive manner.
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Digital Twins and ESG provides essential insight on how integrating cutting-edge Digital Twin technology with ESG practices can transform the understanding of sustainability and propel businesses towards a more transparent, accountable, and responsible future.
Digital Twins and ESG introduces the dynamic world of ESG practices, emphasizing the pivotal role technology plays in shaping and advancing sustainability goals. It introduces readers to the multifaceted world of Digital Twin technology, offering a comprehensive understanding of its historical development and diverse applications across industries. This volume will intricately examine the integration of Digital Twins in ESG metrics and reporting frameworks. Artificial intelligence, machine learning, and blockchain technologies are also discussed as key enablers for achieving ESG goals, providing readers with a glimpse into the potential advancements and breakthroughs that lie ahead. Through detailed analyses and case studies, readers will gain insights into how Digital Twins enhance data collection, monitoring, and reporting, elevating transparency and accountability. Digital Twins and ESG serves as a rallying call, urging businesses to embrace Digital Twins as an integral component of their ESG strategies, ultimately paving the way for a more sustainable and responsible future.
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