Titlebook: Deep Learning in Data Analytics; Recent Techniques, P Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman Book 2022 Springer Nature Switzerland

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書目名稱Deep Learning in Data Analytics影響因子(影響力)




書目名稱Deep Learning in Data Analytics影響因子(影響力)學科排名




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書目名稱Deep Learning in Data Analytics網絡公開度學科排名




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書目名稱Deep Learning in Data Analytics被引頻次學科排名




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書目名稱Deep Learning in Data Analytics年度引用學科排名




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書目名稱Deep Learning in Data Analytics讀者反饋學科排名

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H.-S. Kim,L. Wiedeman,H. Helvajiansed learning algorithm used in this chapter. Finally, the pre-trained deep convolutional neural network (DCNN) model is used. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEP) corpus dataset. In this dataset, human body movement expressing the five archetypical emotions

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SPER

Introduction: An Overview of the Book stage, a deep ranking support vector machine (SVM) is used to define a consistent feature label, which will be served as input to a CNN. It reduces the number of channels in the following CNN and allows it to converge on extended detailed segmentation of the optic discs and vessels. In order to gai

笨拙處理

https://doi.org/10.1007/978-1-349-26109-3ed in an increased life span of patients with genetic disabilities. Thus, the affected persons can now live up?to a higher age. The current study aimed to discover hidden patterns from congenital heart databases for future medical diagnosis using a clustering technique to find secret ways. The desig

Hallowed

Hallowed

一小塊

Vorarbeiten zur empirischen Analyse,n concept of security in cloud computing. It also throws light on security to sensitive data, all the four-level authentication authorization, data security, network security, cloud security. Increasing threat of security in the growing demand of clouds is becoming a main issue. This chapter also de

Pastry

2197-6503 in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications..978-3-030-75857-8978-3-030-75855-4Series ISSN 2197-6503 Series E-ISSN 2197-6511

原告

A Study on Discrete Action Sequences Using Deep Emotional Intelligencesed learning algorithm used in this chapter. Finally, the pre-trained deep convolutional neural network (DCNN) model is used. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEP) corpus dataset. In this dataset, human body movement expressing the five archetypical emotions