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ORCIDDevelopment and Analysis of Deep Learning Architectures, 2020
- Witold Pedrycz, Shyi-Ming Chen:
Development and Analysis of Deep Learning Architectures. Studies in Computational Intelligence 867, Springer 2020, ISBN 978-3-030-31763-8 - Raghavan Krishnan, Sarangapani Jagannathan, V. A. Samaranayake:
Direct Error Driven Learning for Classification in Applications Generating Big-Data. 1-29 - Salvatore Graziani, Maria Gabriella Xibilia:
Deep Learning for Soft Sensor Design. 31-59 - Mutlu Avci, Mehmet Sarigül
, Buse Melis Özyildirim:
Case Study: Deep Convolutional Networks in Healthcare. 61-89 - Ankita Singh, Shayok Chakraborty:
Deep Domain Adaptation for Regression. 91-115 - Michelle Karg, Christian Scharfenberger:
Deep Learning-Based Pedestrian Detection for Automated Driving: Achievements and Future Challenges. 117-143 - Omid Ghahabi, Pooyan Safari, Javier Hernando:
Deep Learning in Speaker Recognition. 145-169 - Rami Cohen, Dima Ruinskiy, Janis Zickfeld, Hans IJzerman, Yizhar Lavner:
Baby Cry Detection: Deep Learning and Classical Approaches. 171-196 - Sasanka Potluri, Shamim Ahmed, Christian Diedrich:
Securing Industrial Control Systems from False Data Injection Attacks with Convolutional Neural Networks. 197-222 - Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy:
Deep Learning for Wireless Communications. 223-266 - Andrew Johnston, Angjelo Marku:
Identifying Extremism in Text Using Deep Learning. 267-289
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