Machine Learning Paradigms: Advances in Data Analytics

Tsihrintzis, George A.; Sotiropoulos, Dionisios N.; Jain, Lakhmi C.

doi:10.1007/978-3-319-94030-4_1

Cited by 14 publications

(12 citation statements)

References 2 publications

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“…Other common activation functions used in artificial neural network applications are the rectified linear unit (ReLU), the leaky ReLU, and the hyperbolic tangent (see, e.g. [5,37]). The process of training an artificial neural network as ( 17) is performed by the minimization of a given functional J(Θ 1 , φ 1 , Θ 2 , φ 2 , .…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations

Brevis

Muga

Zee

2021

Computers & Mathematics with Applications

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Section: Artificial Neural Networkmentioning

confidence: 99%

A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations

Brevis

Muga

Zee

2021

Computers & Mathematics with Applications

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“…The term "Machine Learning" dates back to 1959, as it was introduced in a nominal paper by Samuel [7]. Today, Machine Learning has grown into a multi-disciplinary approach of very active and intense research worldwide [8][9][10][11][12][13][14], which aims at incorporating learning abilities into machines. More specifically, the aim of Machine Learning research and applications is to enhance machines with mechanisms, methodologies, procedures and algorithms that allow them to become better and more efficient at performing specific tasks, either on their own or with the help of a supervisor/instructor.…”

Section: Editorial Notementioning

confidence: 99%

Introduction to Advances in Machine Learning/Deep Learning-Based Technologies

Tsihrintzis

Virvou

Jain

2021

Learning and Analytics in Intelligent Systems

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The field of Machine Learning and its sub-field of Deep Learning are most active areas of research in Artificial Intelligence, as researchers worldwide continuously develop and announce both new theoretical results and innovative applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its readers to some of the most significant recent advances in Machine Learning/Deep Learning-based technologies. At the same time, the book aims at honouring Professor Nikolaos G. Bourbakis, an outstanding researcher in this area who has contributed significantly to the development of Machine Learning/Deep Learning-based technologies. As such, the book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent progress in Machine Learning/Deep Learningbased technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their areas of interest.

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“…Besides traditional economic figures, all sorts of ad-44 ditional and diverse data are currently collected and, to 45 name just few, include data related to mobility, traffic, 46 GPS, outdoors activity, travel, planning, user habits, 47 shopping, customer behavior, entertainment, social in-48 teractions, education, medicine, biology, health, foren-49 sics, or energy consumption [5,7].…”

mentioning

confidence: 99%

Book Review

2021

IDT

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The term 4 th Industrial Revolution (4 th IR) is used 10 to describe the new era into which human civilization 11 is entering [1] and which transforms human societal 12 structure into, so-called, Society 5.0 [2,3]. Previous IRs 13 in human history have depended on coal and water to 14 drive steam engines and oil to drive internal combustion 15 engines (1 st IR), the Mass Production Line (2 nd IR) and 16 advances in Electronics and Information Technology 17 (3 rd IR). In contrast, the 4 th IR is characterized by ex-18 tensive synergy between physical, digital, biological 19 and energy sciences and technologies, with all these 20 scientific disciplines having data as their common and 21 uniting thread [4,5]. 22 -Which networks and protocols need to be devel-81 oped to transmit data efficiently? 82 -What potential legal issues arise when dealing with 83 data, and how can they be legislated? 84 To address these and other related challenges, new 85 and interdisciplinary approaches need to be devised, 86 which have given rise to a multidisciplinary scientific 87 and technological field named Data Science. Data Sci-88 ence emerges as a discipline that combines research 89

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Machine Learning Paradigms: Advances in Data Analytics

Cited by 14 publications

References 2 publications

A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations

A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations

Introduction to Advances in Machine Learning/Deep Learning-Based Technologies

Book Review

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