scholarly journals Classification of Restlessness Level by Deep Learning of Visual Geometry Group Convolution Neural Network with Acoustic Speech and Visual Face Sensor Data for Smart Care Applications

2020 ◽  
Vol 32 (7) ◽  
pp. 2329
Author(s):  
Ing-Jr Ding ◽  
Nai-Wei Zheng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Sensor Data ◽  
Visual Geometry

scholarly journals Convolutional Neural Network for Classification of Android Applications Represented as Grayscale Images

2019 ◽  
Vol 8 (12S) ◽  
pp. 835-843
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Performance ◽  
Smart Phone ◽  
Convolution Neural Network ◽  
Android Malware ◽  
Android Applications ◽  
Conventional Solution ◽  
Grayscale Images

A rapid dissemination of Android operating system in smart phone market has resulted in an exponential growth of threats to mobile applications. Various studies have been carried out in academia and industry for the identification and classification of malicious applications using machine learning and deep learning algorithms. Convolution Neural Network is a deep learning technique which has gained popularity in speech and image recognition. The conventional solution for identifying Android malware needs learning based on pre-extracted features to preserve high performance for detecting Android malware. In order to reduce the efforts and domain expertise involved in hand-feature engineering, we have generated the grayscale images of AndroidManifest.xml and classes.dex files which are extracted from the Android package and applied Convolution Neural Network for classifying the images. The experiments are conducted on a recent dataset of 1747 malicious Android applications. The results indicate that classes.dex file gives better results as compared to the AndroidManifest.xml and also demonstrate that model performs better as the image become larger.

scholarly journals Visualization and Classification of Diseases using Deep Learning Based Convolution Neural Network

2020 ◽  
Vol 937 ◽  
pp. 012053
Author(s):  
D.A Janeera ◽  
P. Amudhavalli ◽  
P Sherubha ◽  
S.P Sasirekha ◽  
P. Anantha Christu Raj ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Classification Of Diseases

Understanding Image Classification Using TensorFlow Deep Learning - Convolution Neural Network

2019 ◽  
Vol 3 (2) ◽  
pp. 19-37
Author(s):  
Vinit Kumar Gunjan ◽  
Rashmi Pathak ◽  
Omveer Singh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Fine Tuning ◽  
Learning Technology ◽  
The Neural Network ◽  
Learning Technique ◽  
Classification Of Images ◽  
Initial Classification

This article describes how to establish the neural network technique for various image groupings in a convolution neural network (CNN) training. In addition, it also suggests initial classification results using CNN learning characteristics and classification of images from different categories. To determine the correct architecture, we explore a transfer learning technique, called Fine-Tuning of Deep Learning Technology, a dataset used to provide solutions for individually classified image-classes.

scholarly journals Using Convolution Neural Network for Defective Image Classification of Industrial Components

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hao Wu ◽  
Zhi Zhou
Keyword(s):  
Neural Network ◽  
Experimental Study ◽  
Deep Learning ◽  
Large Scale ◽  
Intelligent System ◽  
Convolution Neural Network ◽  
Lens Distortion ◽  
Proposed Model ◽  
Industrial Cameras

Computer vision provides effective solutions in many imaging relation problems, including automatic image segmentation and classification. Artificially trained models can be employed to tag images and identify objects spontaneously. In large-scale manufacturing, industrial cameras are utilized to take constant images of components for several reasons. Due to the limitations caused by motion, lens distortion, and noise, some defective images are captured, which are to be identified and separated. One common way to address this problem is by looking into these images manually. However, this solution is not only very time-consuming but is also inaccurate. The paper proposes a deep learning-based artificially intelligent system that can quickly train and identify faulty images. For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task. In order to eliminate the chances of overfitting, the proposed model also employed Dropout technology to adjust the network. The experimental study reveals that the system can precisely classify the normal and defective images with an accuracy of over 91%.

Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning

2019 ◽  
Vol 33 (09) ◽  
pp. 1954027 ◽  
Author(s):  
Dexiang Zhang ◽  
Jingzhong Kang ◽  
Lina Xun ◽  
Yu Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Bilateral Filter ◽  
Convolution Neural Network ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Edge Information

In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this paper also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available data sets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.

scholarly journals Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0925
Author(s):  
Asroni Asroni ◽  
Ku Ruhana Ku-Mahamud ◽  
Cahya Damarjati ◽  
Hasan Basri Slamat
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Classification ◽  
Deep Learning Neural Network ◽  
Voice Data ◽  
The Voice

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

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