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Record Nr. |
UNINA9910483362503321 |
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Titolo |
Deep Learning Applications / / M. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh, editors |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , [2020] |
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©2020 |
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ISBN |
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Descrizione fisica |
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1 online resource (178 pages) : illustrations |
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Collana |
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Advances in Intelligent Systems and Computing, , 2194-5357 ; ; 1098 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Big data |
Control engineering |
Robotics |
Mechatronics |
Computational Intelligence |
Machine Learning |
Big Data |
Control, Robotics, Mechatronics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Trends in Deep Learning Applications -- Optimization Strategies -- Quasi-Newton Optimization Methods -- Application to Deep Reinforcement Learning -- Medical Image Segmentation using Deep Neural Networks with Pre-trained Encoders -- Enabling Robust and Autonomous Material handling in Logistics through applied Deep Learning Algorithms -- Performance metric -- Dataset creation -- Detecting Work Zones in SHRP2 NDS Videos Using Deep Learning Based Computer Vision -- Deep Learning Framework and Architecture Selection -- Action Recognition in Videos Using Multi-Stream Convolutional Neural Networks -- Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimers disease. |
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Sommario/riassunto |
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This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers. |
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