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Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering
The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of patterns and the categorization of data into specific sets. Predictive modeling and optimization methods allow unknown events to be categorized based on statistics and classifiers input by researchers. The Handbook of Research on Predictive Modeling and Optimizat...
Machine Learning Concepts for Beginners
The book "Machine Learning Concepts for Beginners- Theory and Applications" provides the in-depth knowledge in the field of Machine Learning to graduate, post graduate and research scholars. Basically, machine learning is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
Mastering Disruptive Technologies
About the Book: The book is divided into 4 modules which consist of 21 chapters, that narrates briefly about the top five recent emerging trends such as: Cloud Computing, Internet of Things (IoT), Blockchain, Artificial Intelligence, and Machine Learning. At the end of each module, authors have provided two Appendices. One is Job oriented short-type questions with answers, and the second one provide us different MCQs with their keys. Salient Features of the Book: Detailed Coverage on Topics like: Introduction to Cloud Computing, Cloud Architecture, Cloud Applications, Cloud Platforms, Open-Source Cloud Simulation Tools, and Mobile Cloud Computing. Expanded Coverage on Topics like: In...
Mastering Artificial Intelligence and Machine Learning
The book “Mastering Artificial Intelligence and Machine Learning” provides the in-depth knowledge in the field of Artificial Learning, Expert Systems, Natural Language Processing, Deep Learning, Machine Learning etc., to the graduate, post graduate and research scholars. When we talk about Artificial Intelligence, it often evokes a world of robots or futuristic technologies. However, Artificial Intelligence is already part of our daily lives. It is impacting the business world more. Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. Machine learning is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
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...
Big Data in Engineering Applications
This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.
Handbook of Probabilistic Models
Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. S...
Climate Change Adaptation in Pacific Countries
This book showcases vital lessons learned from research, field projects and best practice examples with regard to climate change adaptation in countries throughout the Pacific region, a part of the planet that is particularly vulnerable to and affected by climate change.The book's primary goals are to document the wealth of experiences in the region available today, to encourage cross-sector interactions among the various stakeholders in the region, and to help transfer results to other countries and regions. Accordingly, it gathers a set of papers presented at a symposium on climate change adaptation held in Fiji in July 2016, focusing on "Fostering Resilience and Improving the Quality of Life". In these contributions, local and international experts present a variety of initiatives showing how Pacific countries are coping with the many problems associated with climate change, including initiatives in education and awareness work taking place across the region, operational aspects and their implications for policy-making, and challenges in urban and rural areas.
Translating the Paris Agreement into Action in the Pacific
This book provides an insight into how a country contributes to the GHG emissions reductions required to keep global warming within the limits set by the Paris Agreement arrived at COP21 in 2015. It shows what actions are needed for the implementation plan that Fiji will use to satisfy its quota (i.e. its Nationally Determined Contribution or NDC) of the total GHG emissions reductions. It is a primary resource material for those who wish to obtain an understanding of the science behind climate change mitigation. It reveals the behind-the-scenes action that takes place to convert the rhetoric of climate change into the action on the ground that actually reduces the GHG emissions and global warming. The book also presents a critique of methods adopted by nations in meeting their NDCs to emissions reductions as agreed at the Paris Agreement, and suggests improvements.
Information Integration and Web Intelligence
This book constitutes the refereed conference proceedings of the 25th International Conference on Information Integration and Web Intelligence, iiWAS 2023, organized in conjunction with the 21st International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM2023, held in Denpasar, Bali, Indonesia, during December 4-6, 2023. The 24 full papers and 24 short papers presented in this book were carefully reviewed and selected from 96 submissions. The papers are divided into the following topical sections: business data and applications; data management; deep and machine Learning; generative AI; image data and knowledge graph; recommendation systems; similarity measure and metric; and topic and text matching.
