Titlebook: Handbook of Deep Learning Applications; Valentina Emilia Balas,Sanjiban Sekhar Roy,Pijush Book 2019 Springer Nature Switzerland AG 2019 D

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Deep Learning for Document Representation,arameters. Precise and satisfactory document representation is the key to supporting computer models in accessing the underlying meaning in written language. Automated text classification, where the objective is to assign a set of categories to documents, is a classic problem. The range of studies i

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Applications of Deep Learning in Medical Imaging, In particular, convolutional neural network has shown better capabilities to segment and/or classify medical images like ultrasound and CT scan images in comparison to previously used conventional machine learning techniques. This chapter includes applications of deep learning techniques in two dif

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Deep Learning for Marine Species Recognition,application area in the computer vision community. However, with the developments of deep learning, there has been an increasing interest about this topic. In this chapter, we present a comprehensive review of the computer vision techniques for marine species recognition, mainly from the perspective

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Deep Molecular Representation in Cheminformatics, often employed to calculate quantum-chemical descriptors, which are time consuming. Recently, machine learning models have been used for predicting quantum-chemical descriptors because of their computational advantages. However, it is difficult to generate a proper molecular representation for trai

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A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving,he deep learning approach which is attracted much attention in the field of machine learning is given in recent years and an application about semantic image segmentation is carried out in order to help autonomous driving of autonomous vehicles. This application is implemented with Fully Convolution

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Application of Deep Neural Networks for Disease Diagnosis Through Medical Data Sets,d autoencoder network cascaded with a softmax layer. The classifier is trained by applying a special training approach, where each layer of the proposed classifier is trained individually and sequentially. The performance of the proposed classifier is compared with a number of representative classif

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Springer: Deep Learning in eHealth,ers have been performing particularly well for multimedia mining tasks such as object or face recognition and Natural Language Processing tasks such as speech recognition and voice commands. This opens up a lot of new possibilities for medical applications. Deep Learners can be used for medical imag