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001442735 020__ $$a9789811633577$$q(electronic bk.)
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001442735 0247_ $$a10.1007/978-981-16-3357-7$$2doi
001442735 035__ $$aSP(OCoLC)1285601680
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001442735 049__ $$aISEA
001442735 050_4 $$aQ325.5$$b.I58 19th 2020eb
001442735 08204 $$a006.3/1$$223
001442735 1112_ $$aInternational Conference on Machine Learning and Applications$$n(19th :$$d2020 :$$cMiami, FL)
001442735 24510 $$aDeep learning applications.$$nVolume 3 /$$cM. Arif Wani, Bhiksha Raj, Feng Luo, Dejing Dou, editors.
001442735 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001442735 300__ $$a1 online resource (xii, 322 pages : illustrations (some color))
001442735 336__ $$atext$$btxt$$2rdacontent
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001442735 347__ $$bPDF
001442735 4901_ $$aAdvances in intelligent systems and computing,$$x2194-5365 ;$$vvolume 1395
001442735 500__ $$aIncludes author index.
001442735 5050_ $$aDeep Rapid Class Augmentation; a New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting -- A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology -- RGB and Depth Image Fusion for Object Detection using Deep Learning -- Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis -- A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks -- Deep Learning based Time Series Forecasting -- DEAL: Deep Evidential Active Learning for Image Classification -- LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi[1]class Classification -- Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment -- Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience -- Improving the Donor Journey with Convolutional and Recurrent Neural Networks.
001442735 506__ $$aAccess limited to authorized users.
001442735 520__ $$aThis book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
001442735 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 18, 2021).
001442735 650_0 $$aMachine learning$$vCongresses.
001442735 650_6 $$aApprentissage automatique$$vCongrès.
001442735 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001442735 655_0 $$aElectronic books.
001442735 7001_ $$aWani, M. A.$$q(M. Arif),$$eeditor.
001442735 7001_ $$aRaj, Bhiksha,$$eeditor.
001442735 7001_ $$aLuo, Feng,$$eeditor.
001442735 7001_ $$aDou, Dejing,$$eeditor.
001442735 7112_ $$aIEEE International Conference on Machine Learning and Applications$$n(19th :$$d2020)
001442735 77608 $$iPrint version:$$tDeep learning applications. Volume 3.$$dSingapore : Springer, [2022]$$z9811633568$$z9789811633560$$w(OCoLC)1250512742
001442735 830_0 $$aAdvances in intelligent systems and computing ;$$v1395.$$x2194-5365
001442735 852__ $$bebk
001442735 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-3357-7$$zOnline Access$$91397441.1
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001442735 980__ $$aBIB
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