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Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions
Academic Paper from the year 2017 in the subject Engineering - Power Engineering, language: English, abstract: The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV) panels based on a self-organizing feature map (SOFM) model. The proposed model is compared with other models, including the multi-layer perceptron (MLP) and support vector machine (S...
Intelligent Internet of Things for Smart Healthcare Systems
The book focuses on developments in artificial intelligence (AI) and internet of things (IoT) integration for smart healthcare, with an emphasis on current methodologies and frameworks for the design, growth, implementation, and creative use of such convergence technologies to provide insight into smart healthcare service demands. Concepts like signal recognition, computation, internet of health stuff, and so forth and their applications are covered. Development in connectivity and intelligent networks allowing for social adoption of ambient intelligence is also included. Features: •Introduces Intelligent IoT as applicable to the key areas of smart healthcare. •Discusses computational in...
Solar Photovoltaic Power Intermittency and Implications on Power Systems
Solar photovoltaic (PV) systems have experienced a tremendous increase in installed capacity in the past decade. Although solar PV power is environmentally friendly and can be used to extend the life of fossil fuel reserves, it is of an intermittent nature. Through nine chapters, this book provides an understanding of solar PV systems’ power output intermittency and its impacts on power systems. The first part of the book highlights the source of the variability of both solar irradiation and PV systems’ output, while the second highlights the impact of factors such as temperature and dust on PV systems’ output variability through case studies. The third part highlights the impacts of PV systems’ output variability of the design and operation of power systems. This book will appeal to students, researchers, operators, engineers, designers, and policymakers working on solar PV or electric power systems design, planning, or operation.
Artificial Intelligence Technology in Healthcare
Artificial Intelligence Technology in Healthcare: Security and Privacy Issues focuses on current issues with patients’ privacy and data security including data breaches in healthcare organizations, unauthorized access to patients’ information, and medical identity theft. It explains recent breakthroughs and problems in deep learning security and privacy issues, emphasizing current state-of-the-art methods, methodologies, implementation, attacks, and countermeasures. It examines the issues related to developing artifiicial intelligence (AI)-based security mechanisms which can gather or share data across several healthcare applications securely and privately. Features: Combines multiple te...
Healthcare Solutions Using Machine Learning and Informatics
Healthcare Solutions Using Machine Learning and Informatics covers novel and innovative solutions for healthcare that apply machine learning and biomedical informatics technology. The healthcare sector is one of the most critical in society. This book presents a series of artificial intelligence, machine learning, and intelligent IoT-based solutions for medical image analysis, medical big-data processing, and disease predictions. Machine learning and artificial intelligence use cases in healthcare presented in the book give researchers, practitioners, and students a wide range of practical examples of cross-domain convergence. The wide variety of topics covered include: Artificial Intelligen...
Machine Learning and Deep Learning Techniques for Medical Image Recognition
Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of ma...
Smart Agritech
Agriculture has always been a vital sector of the global economy, providing food and raw materials for industries and households. However, with the growing population, changing climate conditions, and limited resources, the agriculture sector is facing numerous challenges. To address these challenges, farmers and agricultural companies are turning to advanced technologies such as Robotics, Artificial Intelligence (AI), and the Internet of Things (IoT). This exciting new volume provides a comprehensive overview of the latest technological advances in agriculture, focusing on the use of these three cutting-edge technologies. The book will explore the potential benefits of these technologies in improving agricultural efficiency, productivity, and sustainability. Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.
Future Health Scenarios
Digital technologies is a major emerging area to invest and research in new models of health management. Future health scenarios are constituted by technologies in health and clinical decision-making systems. This book provides a unique multidisciplinary approach for exploring the potential contribution of AI and digital technologies in enabling global healthcare systems to respond to urgent twenty-first-century challenges. Deep analysis has been made regarding telemedicine using big data, deep learning, robotics, mobile and remote applications. Features: Focuses on prospective scenarios in health to predict possible futures. Addresses the urgent needs of the key population, socio-technical ...
Computational Intelligence in Communications and Business Analytics
This two-volume set constitutes the refereed proceedings of the 5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023, held in Kalyani, India, during January 27–28, 2023. The 52 full papers presented in this volume were carefully reviewed and selected from 187 submissions. The papers present recent research on intersection of computational intelligence, communications, and business analytics, fostering international collaboration and the dissemination of cutting-edge research.
Federated Deep Learning for Healthcare
This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information. Features: Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. Investigates privacy-preserving methods with emphasis on data security and privacy. Discusses healthcare scaling and resource efficiency considerations. Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.
