Hybrid Imaging and Visualization: Employing Machine Learning with Mathematica - Python - Tapa dura

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Joseph Awange is a professor of Environmental Geoinformatics at the Department of Land Surveying and Geo-Informatics (LSGI, The Hong Kong Polytechnic University) having previously worked at Curtin University (Australia). His research interests are sensing of environmental changes employing geospatial and AI techniques, hydroclimate, climate variability/change, climate extremes and mathematical geosciences (algebraic & numerical solutions). He obtained his BSc and MSc degrees in Surveying (University of Nairobi, Kenya). Joseph was awarded a merit scholarship by the German Academic Exchange Program (DAAD), which facilitated his obtaining a second MSc degree and PhD in Geodesy at Stuttgart University (Germany) under the supervision of the late Prof Erik W. Grafarend (one of the world’s renown geodesists). In 2002-2004, he was awarded the prestigious Japan Society of Promotion of Science (JSPS; Japan) Fellowship to pursue postdoctoral research in Environmental Geodesy at Kyoto University. His research output has been globally recognized through prestigious Awards (i) Alexander von Humboldt (AvH) Fellowships (2008-2011 Ludwig Leichhardt Memorial Fellowship for experienced researchers, 2015 & 2019 AvH support in acknowledgment of his scientific achievements in his specific academic field and in recognition of his contribution towards scientific cooperation with German colleagues), (ii) 2015 JSPS Fellowship, and (iii), Brazil Frontier of Science (Brazil). Joseph who holds a Postgraduate Diploma in Environmental Impact Assessment (EIA) from Murdoch University (Australia) is currently serving as the Acquisition Manager for Springer Nature (world-leading research, educational and professional publisher) in charge of Earth Sciences, Geography, and Environment books. He attained International Editorial role in Springer Earth Science Books and has authored more than 25 scholarly books with the prestigious Springer publisher and more than 250 peer-reviewed high-impact journal publications.

Béla Paláncz is emeritus professor of computer science at the Technical University of Budapest, Hungary. He received his D.Sc. from the Hungarian Academy of Sciences in 1993 and has a background in education and research of mathematical modeling and numeric-symbolic computation as well as machine learning algorithms and experience abroad including at RWTH (Aachen, Germany), Imperial College (London), and Wolfram Research (USA), Research Institute for Symbolic Computation (RISC) Johannes Kepler University ( Linz, Austria) and recently Curtin University, School of Earth and Planetary Sciences (Perth, Australia) and University of Canterbury, Department of Mechanical Engineering (Christchurch, New Zealand). He can be contacted at the Department of Geodesy and Surveying, Faculty of Civil Engineering, Budapest University of Technology and Economics, H-1111 Budapest, Műegyetem rkp.3., Hungary.

Lajos Völgyesi graduated as a geophysicist from the Faculty of Natural Sciences of Eötvös Loránd University in Budapest. Currently, he is Professor Emeritus at the Department of Geodesy and Surveying of the Faculty of Civil Engineering of the Budapest University of Technology and Economics. He is a member of the Hungarian Academy of Sciences. His main research interests are in the fields of physical and theoretical geodesy, mathematical geosciences and geophysics. He has significant scientific achievements in the fields of time variation of the geoid and the explanation of the physical background of major geoid anomalies, the geophysical foundations of 4-dimensional geodesy and the study of Earth rotation phenomena. He also does researches in astronomical geodesy and, together with his physicist colleagues, has started an important series of experiments to prove the so-called weak equivalence principle by remeasuring the Eötvös experiment. His research results have been published in about 200 papers and numerous international presentations and scientific conferences. He has authored and co-authored several textbooks, including two award of excellence for his textbook Geophysics.

De la contraportada

This second edition of the book that targets those in computer algebra and artificial intelligence introduces Black Hole algorithm that is essential for optimizing hyperparameters, an important task in machine learning where mostly, stochastic global methods are used as well as ChatGPT, a novel and in the last few years, very popular Generative AI technology. In addition, fisher discriminant, a linear discriminant that can provide an optimal separation of objects, and the conversion of time series into images thereby making it possible to employ convolution neural network to classify time series effectively are presented.