Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies

Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies

Author: Kedar Nath Das

Publisher: Springer Nature

Published: 2022-02-12

Total Pages: 758

ISBN-13: 9811668930

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This book presents the collection of the accepted research papers presented in the 1st ‘International Conference on Computational Intelligence and Sustainable Technologies (ICoCIST-2021)’. This edited book contains the articles related to the themes on artificial intelligence in machine learning, big data analysis, soft computing techniques, pattern recognitions, sustainable infrastructural development, sustainable grid computing and innovative technology for societal development, renewable energy, and innovations in Internet of Things (IoT).


Cooperative and Graph Signal Processing

Cooperative and Graph Signal Processing

Author: Petar Djuric

Publisher: Academic Press

Published: 2018-07-04

Total Pages: 868

ISBN-13: 0128136782

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Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience. With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings. - Presents the first book on cooperative signal processing and graph signal processing - Provides a range of applications and application areas that are thoroughly covered - Includes an editor in chief and associate editor from the IEEE Transactions on Signal Processing and Information Processing over Networks who have recruited top contributors for the book


Computer Analysis of Images and Patterns

Computer Analysis of Images and Patterns

Author: Mario Vento

Publisher: Springer Nature

Published: 2019-08-23

Total Pages: 617

ISBN-13: 3030298914

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The two volume set LNCS 11678 and 11679 constitutes the refereed proceedings of the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019. The 106 papers presented were carefully reviewed and selected from 176 submissions The papers are organized in the following topical sections: Intelligent Systems; Real-time and GPU Processing; Image Segmentation; Image and Texture Analysis; Machine Learning for Image and Pattern Analysis; Data Sets and Benchmarks; Structural and Computational Pattern Recognition; Posters.


Speech and Computer

Speech and Computer

Author: Alexey Karpov

Publisher: Springer Nature

Published: 2021-09-22

Total Pages: 856

ISBN-13: 3030878023

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This book constitutes the proceedings of the 23rd International Conference on Speech and Computer, SPECOM 2021, held in St. Petersburg, Russia, in September 2021.* The 74 papers presented were carefully reviewed and selected from 163 submissions. The papers present current research in the area of computer speech processing including audio signal processing, automatic speech recognition, speaker recognition, computational paralinguistics, speech synthesis, sign language and multimodal processing, and speech and language resources. *Due to the COVID-19 pandemic, SPECOM 2021 was held as a hybrid event.


Artificial Intelligence and Evolutionary Computations in Engineering Systems

Artificial Intelligence and Evolutionary Computations in Engineering Systems

Author: Subhransu Sekhar Dash

Publisher: Springer Nature

Published: 2020-02-08

Total Pages: 781

ISBN-13: 9811501998

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This book gathers selected papers presented at the 4th International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, held at the SRM Institute of Science and Technology, Kattankulathur, Chennai, India, from 11 to 13 April 2019. It covers advances and recent developments in various computational intelligence techniques, with an emphasis on the design of communication systems. In addition, it shares valuable insights into advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their application to decision-making and problem-solving in mobile and wireless communication networks.


Computer Vision and Machine Intelligence

Computer Vision and Machine Intelligence

Author: Massimo Tistarelli

Publisher: Springer Nature

Published: 2023-05-05

Total Pages: 777

ISBN-13: 9811978670

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This book presents selected research papers on current developments in the fields of computer vision and machine intelligence from International Conference on Computer Vision and Machine Intelligence (CVMI 2022). The book covers topics in image processing, artificial intelligence, machine learning, deep learning, computer vision, machine intelligence, etc. The book is useful for researchers, postgraduate and undergraduate students, and professionals working in this domain.


Computational Science – ICCS 2021

Computational Science – ICCS 2021

Author: Maciej Paszynski

Publisher: Springer Nature

Published: 2021-06-10

Total Pages: 815

ISBN-13: 3030779610

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The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Deep Learning Driven Self-adaptive hp Finite Element Method” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.


Machine Learning Techniques for Assistive Robotics

Machine Learning Techniques for Assistive Robotics

Author: Miguel Angel Cazorla Quevedo

Publisher: MDPI

Published: 2020-12-10

Total Pages: 210

ISBN-13: 3039363387

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Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.