Machine Learning and Data Mining in Aerospace Technology

Machine Learning and Data Mining in Aerospace Technology PDF Author: Aboul Ella Hassanien
Publisher: Springer
ISBN: 3030202127
Category : Technology & Engineering
Languages : en
Pages : 232

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Book Description
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.

Machine Learning and Data Mining in Aerospace Technology

Machine Learning and Data Mining in Aerospace Technology PDF Author: Aboul Ella Hassanien
Publisher: Springer
ISBN: 3030202127
Category : Technology & Engineering
Languages : en
Pages : 232

Get Book Here

Book Description
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.

Metalearning

Metalearning PDF Author: Pavel Brazdil
Publisher:
ISBN: 9783030670252
Category : Artificial intelligence
Languages : en
Pages :

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Book Description


AI and Blockchain Optimization Techniques in Aerospace Engineering

AI and Blockchain Optimization Techniques in Aerospace Engineering PDF Author: Vignesh, U.
Publisher: IGI Global
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 272

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Book Description
The amalgamation of artificial intelligence (AI), optimization techniques, and blockchain is revolutionizing how to conceptualize, design, and operate aerospace systems. While optimization techniques are pivotal in streamlining aerospace processes, security challenges have recently surfaced. AI and Blockchain Optimization Techniques in Aerospace Engineering delves into the transformative impact of technologies on various facets of the aerospace industry, offering a multidimensional solution to overcome security concerns and enhance the overall efficiency of aerospace systems The book explores how machine learning reshapes aerospace systems by automating complex tasks through self/reinforced learning methods. From air traffic data analysis to flight scheduling, geographical information, and navigation, machine learning has become an indispensable tool, offering valuable insights that enhance aerospace operations. Simultaneously, blockchain technology, with its inherent characteristics of decentralization and tamper-proof ledgers, ensures transparency, accountability, and security in transactions, providing an innovative approach to data integrity and system resilience. Designed for technology development professionals, academicians, data scientists, industrial experts, researchers, and students, the book offers a panoramic view of the latest innovations in the field.

Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)

Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) PDF Author: Aboul-Ella Hassanien
Publisher: Springer Nature
ISBN: 3030442896
Category : Technology & Engineering
Languages : en
Pages : 880

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Book Description
This book presents the proceedings of the 1st International Conference on Artificial Intelligence and Computer Visions (AICV 2020), which took place in Cairo, Egypt, from April 8 to 10, 2020. This international conference, which highlighted essential research and developments in the fields of artificial intelligence and computer visions, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into sections, covering the following topics: swarm-based optimization mining and data analysis, deep learning and applications, machine learning and applications, image processing and computer vision, intelligent systems and applications, and intelligent networks.

Machine Intelligence and Data Science Applications

Machine Intelligence and Data Science Applications PDF Author: Vaclav Skala
Publisher: Springer Nature
ISBN: 9811923477
Category : Technology & Engineering
Languages : en
Pages : 909

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Book Description
This book is a compilation of peer reviewed papers presented at International Conference on Machine Intelligence and Data Science Applications (MIDAS 2021), held in Comilla University, Cumilla, Bangladesh during 26 – 27 December 2021. The book covers applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, speech and gesture analysis, etc. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber physical system and smart agriculture, etc. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.

Machine Learning and Data Mining

Machine Learning and Data Mining PDF Author: Abhishek Mehta
Publisher:
ISBN: 9783330003873
Category :
Languages : en
Pages : 144

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Book Description


Machine Intelligence and Emerging Technologies

Machine Intelligence and Emerging Technologies PDF Author: Md. Shahriare Satu
Publisher: Springer Nature
ISBN: 303134619X
Category : Computers
Languages : en
Pages : 597

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Book Description
The two-volume set LNICST 490 and 491 constitutes the proceedings of the First International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, hosted by Noakhali Science and Technology University, Noakhali, Bangladesh, during September 23–25, 2022. The 104 papers presented in the proceedings were carefully reviewed and selected from 272 submissions. This book focuses on theoretical, practical, state-of-art applications, and research challenges in the field of artificial intelligence and emerging technologies. It will be helpful for active researchers and practitioners in this field. These papers are organized in the following topical sections: imaging for disease detection; pattern recognition and natural language processing; bio signals and recommendation systems for wellbeing; network, security and nanotechnology; and emerging technologies for society and industry.

Fusion of Machine Learning Paradigms

Fusion of Machine Learning Paradigms PDF Author: Ioannis K. Hatzilygeroudis
Publisher: Springer Nature
ISBN: 3031223713
Category : Technology & Engineering
Languages : en
Pages : 204

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Book Description
This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.

Applications of Big Data in Large- and Small-Scale Systems

Applications of Big Data in Large- and Small-Scale Systems PDF Author: Goundar, Sam
Publisher: IGI Global
ISBN: 1799866750
Category : Computers
Languages : en
Pages : 377

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Book Description
With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems.

Proceedings of International Joint Conference on Advances in Computational Intelligence

Proceedings of International Joint Conference on Advances in Computational Intelligence PDF Author: Mohammad Shorif Uddin
Publisher: Springer Nature
ISBN: 9819914353
Category : Technology & Engineering
Languages : en
Pages : 729

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Book Description
This book gathers outstanding research papers presented at the 6th International Joint Conference on Advances in Computational Intelligence (IJCACI 2022), held in hybrid mode during October 15 – 16, 2022. IJCACI 2022 is jointly organized by Jahangirnagar University (JU), Bangladesh and South Asian University (SAU), India. The book presents the novel contributions in areas of computational intelligence and it serves as a reference material for advance research. The topics covered are collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal and natural language processing.