Surendra Reddy Vinta – författare

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following:Developing a smart algorithm to integrate fault detection and classificationAlgorithms to investigate different testing scenarios for various anomalies in electric motorsData fusion to detect and assess electromechanical damageNeural networks for rolling bearing fault diagnosisEvolutionary algorithms to optimize deep learning models for water industry forecastsAI-based anomaly detection and root-cause analysisAn overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

The text explains how explainable artificial intelligence impacts problem-solving and aims to provide practical suggestions across various emerging industries. It further discusses important topics such as the strategic utilization of explainable artificial intelligence in supply chain enhancement, the integral role of explainable artificial intelligence in smart farming and smart cities with the industrial Internet of Things integration.Features:Discusses local interpretable model-agnostic explanations, and Shapley additive explanations for transparent data analysis, modeling, and predictionHighlights the importance of using artificial intelligence (AI) in optimizing processes by studying decision-making interpretability in supply chain optimizationExplains the use of explainable artificial intelligence to optimize supply chains by predicting demand, identifying bottlenecks, and making informed decisions about inventory managementIllustrates the benefit of employing explainable artificial intelligence in optimizing resource utilization, improving decision-making, and creating more efficient and sustainable ecosystemsExplores the integration of explainable artificial intelligence into smart appliances to provide insights into their operations and improve user experienceIt is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electrical and communications engineering, computer science and engineering, and information technology.

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following: Developing a smart algorithm to integrate fault detection and classification Algorithms to investigate different testing scenarios for various anomalies in electric motors Data fusion to detect and assess electromechanical damage Neural networks for rolling bearing fault diagnosis Evolutionary algorithms to optimize deep learning models for water industry forecasts AI-based anomaly detection and root-cause analysis An overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following: Developing a smart algorithm to integrate fault detection and classification Algorithms to investigate different testing scenarios for various anomalies in electric motors Data fusion to detect and assess electromechanical damage Neural networks for rolling bearing fault diagnosis Evolutionary algorithms to optimize deep learning models for water industry forecasts AI-based anomaly detection and root-cause analysis An overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

The text explains how explainable artificial intelligence impacts problem-solving and aims to provide practical suggestions across various emerging industries. It further discusses important topics such as the strategic utilization of explainable artificial intelligence in supply chain enhancement, the integral role of explainable artificial intelligence in smart farming and smart cities with the industrial Internet of Things integration.Features: Discusses local interpretable model-agnostic explanations, and Shapley additive explanations for transparent data analysis, modeling, and prediction Highlights the importance of using artificial intelligence (AI) in optimizing processes by studying decision-making interpretability in supply chain optimization Explains the use of explainable artificial intelligence to optimize supply chains by predicting demand, identifying bottlenecks, and making informed decisions about inventory management Illustrates the benefit of employing explainable artificial intelligence in optimizing resource utilization, improving decision-making, and creating more efficient and sustainable ecosystems Explores the integration of explainable artificial intelligence into smart appliances to provide insights into their operations and improve user experience It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electrical and communications engineering, computer science and engineering, and information technology.