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Author: Liting Wang
Publisher: Springer Nature
ISBN: 9819745500
Category :
Languages : en
Pages : 102
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Book Description
Author: Liting Wang
Publisher: Springer Nature
ISBN: 9819745500
Category :
Languages : en
Pages : 102
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Book Description
Author: Hui Yang
Publisher: #N/A
ISBN: 9813206837
Category : Computers
Languages : en
Pages : 845
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Book Description
The 2016 International Conference on Artificial Intelligence Science and Technology (AIST2016) was held in Shanghai, China, from 15th to 17th July, 2016.AIST2016 aims to bring together researchers, engineers, and students to the areas of Artificial Intelligence Science and Technology. AIST2016 features unique mixed topics of artificial intelligence and application, computer and software, communication and network, information and security, data mining, and optimization.This volume consists of 101 peer-reviewed articles by local and foreign eminent scholars which cover the frontiers and state-of-art development in AI Technology.
Author: David A Landgrebe
Publisher: John Wiley & Sons
ISBN: 0471721255
Category : Science
Languages : en
Pages : 528
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Book Description
An outgrowth of the author's extensive experience teaching senior and graduate level students, this is both a thorough introduction and a solid professional reference. * Material covered has been developed based on a 35-year research program associated with such systems as the Landsat satellite program and later satellite and aircraft programs. * Covers existing aircraft and satellite programs and several future programs *An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Author: Jiangbo Li
Publisher: Bentham Science Publishers
ISBN: 981148578X
Category : Technology & Engineering
Languages : en
Pages : 314
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Book Description
With rapid progress being made in both theory and practical applications, Artificial Intelligence (AI) is transforming every aspect of life and leading the world towards a sustainable future. AI technology is fundamentally and radically affecting agriculture with a move towards smart systems. The outcome of this transition is improved efficiency, reduced environmental pollution, and enhanced productivity of crops. Nondestructive Evaluation of Agro-products by Intelligent Sensing Techniques is a reference which provides readers timely updates in the progress of intelligent sensing techniques used for nondestructive evaluation of agro-products. Chapters, each contributed by experts in food safety and technology, describe existing and innovative techniques that could be or have been applied to agro-products quality and safety evaluation, processing, harvest, traceability, and so on. The book includes 11 individual chapters, with each chapter focusing on a specific aspect of intelligent sensing techniques applied in agriculture. Specifically, the first chapter introduces the reader to representative techniques and methods for nondestructive evaluation. Subsequent chapters present detailed information about the processing and quality evaluation of agro-products (e.g., fruits, and vegetables), food grading, food tracing, and the use of robots for harvesting specialty crops. Key Features: - 11 chapters, contributed by experts that cover basic and applied research in agriculture - introduces readers to nondestructive evaluation techniques - covers food quality evaluation processes - covers food grading and traceability systems - covers frontier topics that represent future trends (robots and UAVs used in agriculture) - familiarizes the readers with several intelligent sensing technologies used in the agricultural sector (including machine vision, near-infrared spectroscopy, hyperspectral/multispectral imaging, bio-sensing, multi-technology fusion detection) - provides bibliographic references for further reading - gives applied examples on both common and specialty crops This reference is intended as a source of updated information for consultants, students and academicians involved in agriculture, crops science and food biotechnology. Professionals involved in food safety and security planning and policymaking will also benefit from the information presented by the authors.
Author: Weitao Chen
Publisher: Springer Nature
ISBN: 9819989973
Category : Computers
Languages : en
Pages : 240
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Book Description
This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance. This book focuses on the following five aspects: 1. Construction of geology remote sensing datasets from multi-level (pixel-level, scene-level, semantic segmentation-level, prior knowledge-assisted, transfer learning dataset), which are the basis of geology interpretation based on deep learning. 2. Research on lithology scene classification based on deep learning, prior knowledge, and remote sensing. 3. Research on lithology semantic segmentation based on deep learning and remote sensing. 4. Research on lithology classification based on transfer learning and remote sensing. 5. Research on inversion of mineral abundance based on the sparse unmixing theory and hyperspectral remote sensing. The book is intended for undergraduate and graduate students who are interested in geology, remote sensing, and artificial intelligence. It is also used as a reference book for scientific and technological personnel of geological exploration.
Author: J. K. Mandal
Publisher: Springer
ISBN: 981106427X
Category : Computers
Languages : en
Pages : 616
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Book Description
The two volume set CCIS 775 and 776 constitutes the refereed proceedings of the First International Conference on Computational Intelligence, Communications, and Business Analytics, CICBA 2017, held in Kolkata, India, in March 2017. The 90 revised full papers presented in the two volumes were carefully reviewed and selected from 276 submissions. The papers are organized in topical sections on data science and advanced data analytics; signal processing and communications; microelectronics, sensors, intelligent networks; computational forensics (privacy and security); computational intelligence in bio-computing; computational intelligence in mobile and quantum computing; intelligent data mining and data warehousing; computational intelligence.
Author:
Publisher:
ISBN:
Category : Military intelligence
Languages : en
Pages : 644
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Book Description
Author: Mikhail Z. Zgurovsky
Publisher: Springer
ISBN: 3319351621
Category : Technology & Engineering
Languages : en
Pages : 389
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Book Description
This monograph is dedicated to the systematic presentation of main trends, technologies and methods of computational intelligence (CI). The book pays big attention to novel important CI technology- fuzzy logic (FL) systems and fuzzy neural networks (FNN). Different FNN including new class of FNN- cascade neo-fuzzy neural networks are considered and their training algorithms are described and analyzed. The applications of FNN to the forecast in macroeconomics and at stock markets are examined. The book presents the problem of portfolio optimization under uncertainty, the novel theory of fuzzy portfolio optimization free of drawbacks of classical model of Markovitz as well as an application for portfolios optimization at Ukrainian, Russian and American stock exchanges. The book also presents the problem of corporations bankruptcy risk forecasting under incomplete and fuzzy information, as well as new methods based on fuzzy sets theory and fuzzy neural networks and results of their application for bankruptcy risk forecasting are presented and compared with Altman method. This monograph also focuses on an inductive modeling method of self-organization – the so-called Group Method of Data Handling (GMDH) which enables to construct the structure of forecasting models almost automatically. The results of experimental investigations of GMDH for forecasting at stock exchanges are presented. The final chapters are devoted to theory and applications of evolutionary modeling (EM) and genetic algorithms. The distinguishing feature of this monograph is a great number of practical examples of CI technologies and methods application for solution of real problems in technology, economy and financial sphere, in particular forecasting, classification, pattern recognition, portfolio optimization, bankruptcy risk prediction under uncertainty which were developed by authors and published in this book for the first time. All CI methods and algorithms are presented from the general system approach and analysis of their properties, advantages and drawbacks that enables practitioners to choose the most adequate method for their own problems solution.
Author: Guan Gui
Publisher: Springer Nature
ISBN: 3030364054
Category : Computers
Languages : en
Pages : 494
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Book Description
This two-volume set LNICST 301 -302 constitutes the post-conference proceedings of the Third EAI International Conference on Advanced Hybrid Information Processing, ADHIP 2019, held in Nanjing, China, in September 2019. The 101 papers presented were selected from 237 submissions and focus on hybrid big data processing. Since information processing has acted as an important research domain in science and technology today, it is now to develop deeper and wider use of hybrid information processing, especially information processing for big data. There are more remaining issues waiting for solving, such as classification and systemization of big data, objective tracking and behavior understanding in big multimedia data, encoding and compression of big data.
Author: Maliheh Eftekhari
Publisher: Frontiers Media SA
ISBN: 2832549713
Category : Science
Languages : en
Pages : 246
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Book Description
Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis
