Application of Artificial Neural Networks in Geoinformatics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Application of Artificial Neural Networks in Geoinformatics write by Saro Lee. This book was released on 2018-04-09. Application of Artificial Neural Networks in Geoinformatics available in PDF, EPUB and Kindle. This book is a printed edition of the Special Issue "Application of Artificial Neural Networks in Geoinformatics" that was published in Applied Sciences
Application of Artificial Neural Networks in Geoinformatics
Application of Artificial Neural Networks in Geoinformatics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Application of Artificial Neural Networks in Geoinformatics write by Saro Lee. This book was released on 2018. Application of Artificial Neural Networks in Geoinformatics available in PDF, EPUB and Kindle. Application of Artificial Neural Networks in Geoinformatics.
Artificial Neural Networks and Evolutionary Computation in Remote Sensing
Artificial Neural Networks and Evolutionary Computation in Remote Sensing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Neural Networks and Evolutionary Computation in Remote Sensing write by Taskin Kavzoglu. This book was released on 2021-01-19. Artificial Neural Networks and Evolutionary Computation in Remote Sensing available in PDF, EPUB and Kindle. Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.
Geophysical Applications of Artificial Neural Networks and Fuzzy Logic
Geophysical Applications of Artificial Neural Networks and Fuzzy Logic - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Geophysical Applications of Artificial Neural Networks and Fuzzy Logic write by W. Sandham. This book was released on 2013-06-29. Geophysical Applications of Artificial Neural Networks and Fuzzy Logic available in PDF, EPUB and Kindle. The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 1940s and 1960s, respectively, the most rapid progress has only been achieved in the last fifteen years. This has been due to significant theoretical advances in our understanding of ANNs and FL, complemented by major technological developments in high-speed computing. In geophysics, ANNs and FL have enjoyed significant success and are now employed routinely in the following areas (amongst others): 1. Exploration Seismology. (a) Seismic data processing (trace editing; first break picking; deconvolution and multiple suppression; wavelet estimation; velocity analysis; noise identification/reduction; statics analysis; dataset matching/prediction, attenuation), (b) AVO analysis, (c) Chimneys, (d) Compression I dimensionality reduction, (e) Shear-wave analysis, (f) Interpretation (event tracking; lithology prediction and well-log analysis; prospect appraisal; hydrocarbon prediction; inversion; reservoir characterisation; quality assessment; tomography). 2. Earthquake Seismology and Subterranean Nuclear Explosions. 3. Mineral Exploration. 4. Electromagnetic I Potential Field Exploration. (a) Electromagnetic methods, (b) Potential field methods, (c) Ground penetrating radar, (d) Remote sensing, (e) inversion.
Artificial Neural Networks in Agriculture
Artificial Neural Networks in Agriculture - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Neural Networks in Agriculture write by Sebastian Kujawa. This book was released on 2021-11-11. Artificial Neural Networks in Agriculture available in PDF, EPUB and Kindle. Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.
