標(biāo)題: Titlebook: Handbook of Deep Learning Applications; Valentina Emilia Balas,Sanjiban Sekhar Roy,Pijush Book 2019 Springer Nature Switzerland AG 2019 D [打印本頁] 作者: 使醉 時(shí)間: 2025-3-21 18:45
書目名稱Handbook of Deep Learning Applications影響因子(影響力)
書目名稱Handbook of Deep Learning Applications影響因子(影響力)學(xué)科排名
書目名稱Handbook of Deep Learning Applications網(wǎng)絡(luò)公開度
書目名稱Handbook of Deep Learning Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Handbook of Deep Learning Applications被引頻次
書目名稱Handbook of Deep Learning Applications被引頻次學(xué)科排名
書目名稱Handbook of Deep Learning Applications年度引用
書目名稱Handbook of Deep Learning Applications年度引用學(xué)科排名
書目名稱Handbook of Deep Learning Applications讀者反饋
書目名稱Handbook of Deep Learning Applications讀者反饋學(xué)科排名
作者: 卡死偷電 時(shí)間: 2025-3-21 21:39 作者: gene-therapy 時(shí)間: 2025-3-22 02:52 作者: Observe 時(shí)間: 2025-3-22 07:59 作者: 咆哮 時(shí)間: 2025-3-22 09:20
Studien zur Kommunikationswissenschaftdeling. Deep learning models are efficient feature selectors and therefore work best in high dimension datasets. We present major research challenges in feature extraction and selection using different deep models. Our case studies are drawn from gene expression datasets. Hence we report some of the作者: 拘留 時(shí)間: 2025-3-22 16:01
Deep Learning for Document Representation,d sparse. This sparsity and the need to ensure semantic understanding of text documents are the major challenges in text categorization. Deep learning-based approaches provide a fixed length vector in a continuous space to represent words and documents. This chapter reviews the available document re作者: 消散 時(shí)間: 2025-3-22 20:46
Phase Identification and Workflow Modeling in Laparoscopy Surgeries Using Temporal Connectionism ofcene captured by the laparoscopic camera poses additional challenges. These challenges can be overcome by using temporal features in addition to the spatial visual features. A long short-term memory (LSTM) network is used to learn the temporal information of the video. The m2cai16-workflow dataset c作者: 勛章 時(shí)間: 2025-3-22 22:19 作者: 字形刻痕 時(shí)間: 2025-3-23 03:15 作者: Binge-Drinking 時(shí)間: 2025-3-23 09:18
Valentina Emilia Balas,Sanjiban Sekhar Roy,Pijush Provides a concise and structured presentation of deep learning applications.Introduces a large range of applications related to vision, speech, and natural language processing.Includes active researc作者: Benign 時(shí)間: 2025-3-23 13:25
Smart Innovation, Systems and Technologieshttp://image.papertrans.cn/h/image/421137.jpg作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-23 14:30 作者: 歌曲 時(shí)間: 2025-3-23 18:28 作者: 祖先 時(shí)間: 2025-3-24 01:33
https://doi.org/10.1007/978-3-030-11479-4Deep Machine Learning; Deep Neural Network; Deep Belief Network; Restricted Boltzmann Machine; Convoluti作者: 討厭 時(shí)間: 2025-3-24 04:20 作者: 間接 時(shí)間: 2025-3-24 06:34 作者: 油氈 時(shí)間: 2025-3-24 12:20 作者: Eosinophils 時(shí)間: 2025-3-24 15:10
Deep Learning for Scene Understanding,nderstanding is an essential part of this research. It seeks the goal that any image should be as understandable and decipherable for computers as it is for humans. The stall in the progress of the different components of scene understanding, due to the limitations of the traditional algorithms, has作者: Dendritic-Cells 時(shí)間: 2025-3-24 22:40 作者: Humble 時(shí)間: 2025-3-25 00:48 作者: 受辱 時(shí)間: 2025-3-25 03:29
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作者: Intuitive 時(shí)間: 2025-3-25 08:11
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作者: 以煙熏消毒 時(shí)間: 2025-3-25 13:52
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作者: Harness 時(shí)間: 2025-3-25 18:56
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作者: 商品 時(shí)間: 2025-3-25 19:59
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作者: 澄清 時(shí)間: 2025-3-26 02:44 作者: 可忽略 時(shí)間: 2025-3-26 06:42 作者: Leisureliness 時(shí)間: 2025-3-26 10:50
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作者: 凹室 時(shí)間: 2025-3-26 16:37 作者: GROUP 時(shí)間: 2025-3-26 20:11
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作者: FISC 時(shí)間: 2025-3-27 00:03 作者: 粗魯?shù)娜?nbsp; 時(shí)間: 2025-3-27 02:29 作者: 無能力之人 時(shí)間: 2025-3-27 08:24 作者: Clinch 時(shí)間: 2025-3-27 11:10
Sanghamitra Bandyopadhyay,Sriparna Sahaarning has become an efficient solution for learning in the context of supervisioned learning. Deep Learning [.] consists in using Artificial Neural Networks (ANN or NN) with several hidden layers, typically also with a large number of nodes in each layer.作者: irritation 時(shí)間: 2025-3-27 13:40 作者: 委派 時(shí)間: 2025-3-27 18:18 作者: Modify 時(shí)間: 2025-3-27 23:48
Personally Sound: Tapping into Your Talentsn. Automation has offered promised returns of improvements in safety, productivity and reduced costs. Many industry leaders are specifically working on the application of autonomous technology in transportation to produce “driverless” or fully autonomous vehicles. A key technology that has the poten作者: LAPSE 時(shí)間: 2025-3-28 04:08
Ein ?u?erst kaprizi?ses Gegenüberarameters. 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作者: CYN 時(shí)間: 2025-3-28 07:01
https://doi.org/10.1007/978-3-322-91685-3 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作者: 露天歷史劇 時(shí)間: 2025-3-28 14:11
Ein Wort zu Gattung und Schreibweise,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作者: left-ventricle 時(shí)間: 2025-3-28 17:28 作者: 鼓掌 時(shí)間: 2025-3-28 21:16
Die Basics: Begriffe der Stromwirtschaft,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作者: 音樂戲劇 時(shí)間: 2025-3-28 23:23
https://doi.org/10.1007/978-3-658-15164-5 The visual features of a surgical video can be used to identify the surgical phases in laparoscopic interventions. Owing to the significant improvement in performance exhibited by convolutional neural networks (CNN) on various challenging tasks like image classification, action recognition etc., th作者: Misgiving 時(shí)間: 2025-3-29 06:32 作者: Iniquitous 時(shí)間: 2025-3-29 10:14
https://doi.org/10.1007/978-3-663-11691-2d 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作者: 朋黨派系 時(shí)間: 2025-3-29 12:24
Methodisch-methodologischer Ansatz,ventional learning methods such as the error back-propagation is faced with serious obstacles owing to local minima. The layer-by-layer pre-training method has been recently proposed for training these neural networks and has shown considerable performance. In the pre-training method, the complex pr作者: 草率男 時(shí)間: 2025-3-29 16:36
https://doi.org/10.1007/978-3-7091-1075-1ers 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作者: Foolproof 時(shí)間: 2025-3-29 23:31
,Erratum to: Bahnk?rper und Nebenanlagen,as set new standards in the world of prosthetics, be it hearing aids or prosthetic arms, legs or vision, helping paralyzed or completely locked-in users. Not only can one get a visual imprint of their own brain activity but the future of BCI will make sharing someone else’s experience possible. The 作者: figure 時(shí)間: 2025-3-30 01:23 作者: 委派 時(shí)間: 2025-3-30 05:12
Studien zur Kommunikationswissenschaft been made through data mining but there is an increasing research focus on deep learning to exploit the massive improvement in computational power. This chapter presents recent advancements in deep learning research and identifies some remaining challenges as drawn from using deep learning in the a作者: OATH 時(shí)間: 2025-3-30 08:34
Book 2019able attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars..作者: Dappled 時(shí)間: 2025-3-30 15:18 作者: AGONY 時(shí)間: 2025-3-30 16:59 作者: Banister 時(shí)間: 2025-3-30 22:32
Ein Wort zu Gattung und Schreibweise,wo approaches. This chapter examines the attributes and challenges of a number of popular marine species datasets (which involve coral, kelp, plankton and fish) on recognition tasks. In the end, we highlight a few potential future application areas of deep learning in marine image analysis such as segmentation and enhancement of image quality.作者: 起皺紋 時(shí)間: 2025-3-31 03:41
Die Basics: Begriffe der Stromwirtschaft, first trained separately and validation accuracies of these trained network models on the used dataset is compared. In addition, image segmentation inferences are visualized to take account of how precisely FCN architectures can segment objects.作者: LATHE 時(shí)間: 2025-3-31 06:04
,Erratum to: Bahnk?rper und Nebenanlagen,might serve as one of the translation algorithms that converts the raw signals from the brain into commands that the output devices follow. This chapter aims to give an insight into the various deep learning algorithms that have served in BCI’s today and helped enhance their performances.作者: ONYM 時(shí)間: 2025-3-31 11:27
Deep Learning for Scene Understanding,ts of scene understanding. This chapter analyses these contributions of deep learning and also presents the advancements of high level scene understanding tasks, such as caption generation for images. It also sheds light on the need to combine these individual components into an integrated system.作者: 加劇 時(shí)間: 2025-3-31 15:43 作者: 絕種 時(shí)間: 2025-3-31 19:35 作者: Fluctuate 時(shí)間: 2025-3-31 22:46
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving, first trained separately and validation accuracies of these trained network models on the used dataset is compared. In addition, image segmentation inferences are visualized to take account of how precisely FCN architectures can segment objects.作者: Libido 時(shí)間: 2025-4-1 02:46 作者: guzzle 時(shí)間: 2025-4-1 06:12
