scholarly journals Efficacy of Imbalanced Data Handling Methods on Deep Learning for Smart Homes Environments

2020 ◽  
Vol 1 (4) ◽  
Author(s):  
Rebeen Ali Hamad ◽  
Masashi Kimura ◽  
Jens Lundström
Keyword(s):  
Deep Learning ◽  
Imbalanced Data ◽  
Smart Homes ◽  
Data Handling ◽  
Handling Methods

scholarly journals Evaluation of Automatic Speech Recognition Systems

2021 ◽  
Author(s):  
Matheus Xavier Sampaio ◽  
Regis Pires Magalhães ◽  
Ticiana Linhares Coelho da Silva ◽  
Lívia Almada Cruz ◽  
Davi Romero de Vasconcelos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Smart Homes ◽  
The Other ◽  
Learning Models ◽  
Recognition Systems ◽  
Microsoft Azure

Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.

Missing Data Handling Methods

2018 ◽  
pp. 139-185 ◽  
Author(s):  
Craig K. Enders ◽  
Amanda N. Baraldi
Keyword(s):  
Missing Data ◽  
Data Handling ◽  
Handling Methods

Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification

2020 ◽  
Author(s):  
Uzma Batool ◽  
Mohd Ibrahim Shapiai ◽  
Nordinah Ismail ◽  
Hilman Fauzi ◽  
Syahrizal Salleh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Silicon Wafer ◽  
Data Augmentation ◽  
Imbalanced Data ◽  
Training Data ◽  
Defect Classification ◽  
Learning Method ◽  
Test Set

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

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