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This book addresses theories and empirical procedures for the
application of machine learning and data mining to solve problems
in cyber dynamics. It explains the fundamentals of cyber dynamics,
and presents how these resilient algorithms, strategies, techniques
can be used for the development of the cyberspace environment such
as: cloud computing services; cyber security; data analytics; and,
disruptive technologies like blockchain. The book presents new
machine learning and data mining approaches in solving problems in
cyber dynamics. Basic concepts, related work reviews,
illustrations, empirical results and tables are integrated in each
chapter to enable the reader to fully understand the concepts,
methodology, and the results presented. The book contains empirical
solutions of problems in cyber dynamics ready for industrial
applications. The book will be an excellent starting point for
postgraduate students and researchers because each chapter is
design to have future research directions.
This book provides an introduction to the field of periodic pattern
mining, reviews state-of-the-art techniques, discusses recent
advances, and reviews open-source software. Periodic pattern mining
is a popular and emerging research area in the field of data
mining. It involves discovering all regularly occurring patterns in
temporal databases. One of the major applications of periodic
pattern mining is the analysis of customer transaction databases to
discover sets of items that have been regularly purchased by
customers. Discovering such patterns has several implications for
understanding the behavior of customers. Since the first work on
periodic pattern mining, numerous studies have been published and
great advances have been made in this field. The book consists of
three main parts: introduction, algorithms, and applications. The
first chapter is an introduction to pattern mining and periodic
pattern mining. The concepts of periodicity, periodic support,
search space exploration techniques, and pruning strategies are
discussed. The main types of algorithms are also presented such as
periodic-frequent pattern growth, partial periodic pattern-growth,
and periodic high-utility itemset mining algorithm. Challenges and
research opportunities are reviewed. The chapters that follow
present state-of-the-art techniques for discovering periodic
patterns in (1) transactional databases, (2) temporal databases,
(3) quantitative temporal databases, and (4) big data. Then, the
theory on concise representations of periodic patterns is
presented, as well as hiding sensitive information using
privacy-preserving data mining techniques. The book concludes with
several applications of periodic pattern mining, including
applications in air pollution data analytics, accident data
analytics, and traffic congestion analytics.
This book presents an overview of techniques for discovering
high-utility patterns (patterns with a high importance) in data. It
introduces the main types of high-utility patterns, as well as the
theory and core algorithms for high-utility pattern mining, and
describes recent advances, applications, open-source software, and
research opportunities. It also discusses several types of discrete
data, including customer transaction data and sequential data. The
book consists of twelve chapters, seven of which are surveys
presenting the main subfields of high-utility pattern mining,
including itemset mining, sequential pattern mining, big data
pattern mining, metaheuristic-based approaches, privacy-preserving
pattern mining, and pattern visualization. The remaining five
chapters describe key techniques and applications, such as
discovering concise representations and regular patterns.
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Advances in Model and Data Engineering in the Digitalization Era - MEDI 2022 Short Papers and DETECT 2022 Workshop Papers, Cairo, Egypt, November 21-24, 2022, Proceedings (Paperback, 1st ed. 2022)
Philippe Fournier-Viger, Ahmed Hassan, Ladjel Bellatreche, Ahmed Awad, Abderrahim Ait Wakrime, …
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R2,304
Discovery Miles 23 040
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Ships in 10 - 15 working days
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This volume constitutes short papers and DETECT 2022 workshop
papers, presented during the 11th International Conference on Model
and Data Engineering, MEDI 2022, held in Cairo, Egypt, in November
2022. The 11 short papers presented were selected from the total of
65 submissions. This volume also contains the 4 accepted papers
from the DETECT 2022 workshop, held at MEDI 2022. The volume
focuses on advances in data management and modelling, including
topics such as data models, data processing, database theory,
database systems technology, and advanced database applications.
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Model and Data Engineering - 11th International Conference, MEDI 2022, Cairo, Egypt, November 21-24, 2022, Proceedings (Paperback, 1st ed. 2023)
Philippe Fournier-Viger, Ahmed Hassan, Ladjel Bellatreche
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R1,937
Discovery Miles 19 370
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 11th
International Conference on Model and Data Engineering, MEDI 2022,
held in Cairo, Egypt, in November 2022. The 18 full papers
presented in this book were carefully reviewed and selected from 65
submissions. The papers cover topics such as database systems, data
stream analysis, knowledge-graphs, machine learning, model-driven
engineering, image processing, diagnosis, natural language
processing, optimization, and advanced applications such as the
internet of things and healthcare.
This book provides an introduction to the field of periodic pattern
mining, reviews state-of-the-art techniques, discusses recent
advances, and reviews open-source software. Periodic pattern mining
is a popular and emerging research area in the field of data
mining. It involves discovering all regularly occurring patterns in
temporal databases. One of the major applications of periodic
pattern mining is the analysis of customer transaction databases to
discover sets of items that have been regularly purchased by
customers. Discovering such patterns has several implications for
understanding the behavior of customers. Since the first work on
periodic pattern mining, numerous studies have been published and
great advances have been made in this field. The book consists of
three main parts: introduction, algorithms, and applications. The
first chapter is an introduction to pattern mining and periodic
pattern mining. The concepts of periodicity, periodic support,
search space exploration techniques, and pruning strategies are
discussed. The main types of algorithms are also presented such as
periodic-frequent pattern growth, partial periodic pattern-growth,
and periodic high-utility itemset mining algorithm. Challenges and
research opportunities are reviewed. The chapters that follow
present state-of-the-art techniques for discovering periodic
patterns in (1) transactional databases, (2) temporal databases,
(3) quantitative temporal databases, and (4) big data. Then, the
theory on concise representations of periodic patterns is
presented, as well as hiding sensitive information using
privacy-preserving data mining techniques. The book concludes with
several applications of periodic pattern mining, including
applications in air pollution data analytics, accident data
analytics, and traffic congestion analytics.
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Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, July 19-22, 2022, Proceedings (Paperback, 1st ed. 2022)
Hamido Fujita, Philippe Fournier-Viger, Moonis Ali, Yinglin Wang
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R3,676
Discovery Miles 36 760
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed proceedings of the
35th International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems, IEA/AIE 2022, held in
Kitakyushu, Japan, in July 2022. The 67 full papers and 11 short
papers presented were carefully reviewed and selected from 127
submissions. The IEA/AIE 2022 conference focuses on focuses on
applications of applied intelligent systems to solve real-life
problems in all areas including business and finance, science,
engineering, industry, cyberspace, bioinformatics, automation,
robotics, medicine and biomedicine, and human-machine interactions.
This book presents an overview of how machine learning and data
mining techniques are used for tracking and preventing diseases. It
covers several aspects such as stress level identification of a
person from his/her speech, automatic diagnosis of disease from
X-ray images, intelligent diagnosis of Glaucoma from clinical eye
examination data, prediction of protein-coding genes from big
genome data, disease detection through microscopic analysis of
blood cells, information retrieval from electronic medical record
using named entity recognition approaches, and prediction of
drug-target interactions. The book is suitable for computer
scientists having a bachelor degree in computer science. The book
is an ideal resource as a reference book for teaching a graduate
course on AI for Medicine or AI for Health care. Researchers
working in the multidisciplinary areas use this book to discover
the current developments. Besides its use in academia, this book
provides enough details about the state-of-the-art algorithms
addressing various biomedical domains, so that it could be used by
industry practitioners who want to implement AI techniques to
analyze the diseases. Medical institutions use this book as
reference material and give tutorials to medical experts on how the
advanced AI and ML techniques contribute to the diagnosis and
prediction of the diseases.
This book presents an overview of how machine learning and data
mining techniques are used for tracking and preventing diseases. It
covers several aspects such as stress level identification of a
person from his/her speech, automatic diagnosis of disease from
X-ray images, intelligent diagnosis of Glaucoma from clinical eye
examination data, prediction of protein-coding genes from big
genome data, disease detection through microscopic analysis of
blood cells, information retrieval from electronic medical record
using named entity recognition approaches, and prediction of
drug-target interactions. The book is suitable for computer
scientists having a bachelor degree in computer science. The book
is an ideal resource as a reference book for teaching a graduate
course on AI for Medicine or AI for Health care. Researchers
working in the multidisciplinary areas use this book to discover
the current developments. Besides its use in academia, this book
provides enough details about the state-of-the-art algorithms
addressing various biomedical domains, so that it could be used by
industry practitioners who want to implement AI techniques to
analyze the diseases. Medical institutions use this book as
reference material and give tutorials to medical experts on how the
advanced AI and ML techniques contribute to the diagnosis and
prediction of the diseases.
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Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Kitakyushu, Japan, September 22-25, 2020, Proceedings (Paperback, 1st ed. 2020)
Hamido Fujita, Philippe Fournier-Viger, Moonis Ali, Jun Sasaki
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R4,693
Discovery Miles 46 930
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed proceedings of the
33rd International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems, IEA/AIE 2020, held in
Kitakyushu, Japan, in September 2020. The 62 full papers and 17
short papers presented were carefully reviewed and selected from
119 submissions. The IEA/AIE 2020 conference will continue the
tradition of emphasizing on applications of applied intelligent
systems to solve real-life problems in all areas. These areas
include are language processing; robotics and drones; knowledge
based systems; innovative applications of intelligent systems;
industrial applications; networking applications; social network
analysis; financial applications and blockchain; medical and
health-related applications; anomaly detection and automated
diagnosis; decision-support and agent-based systems; multimedia
applications; machine learning; data management and data
clustering; pattern mining; system control, classification, and
fault diagnosis.
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Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad, India, December 17-20, 2019, Proceedings (Paperback, 1st ed. 2019)
Sanjay Madria, Philippe Fournier-Viger, Sanjay Chaudhary, P. Krishna Reddy
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R1,614
Discovery Miles 16 140
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 7th
International Conference on Big Data analytics, BDA 2019, held in
Ahmedabad, India, in December 2019.The 25 papers presented in this
volume were carefully reviewed and selected from 53 submissions.
The papers are organized in topical sections named: big data
analytics: vision and perspectives; search and information
extraction; predictive analytics in medical and agricultural
domains; graph analytics; pattern mining; and machine learning.
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