scholarly journals Bare Skin Image Classification using Convolution Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 138-145
Author(s):  
Jaya Gupta ◽  
◽  
Sunil Pathak ◽  
Gireesh Kumar
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Satellite Image ◽  
Activation Function ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Significant Research ◽  
Research Problems ◽  
Facial Expression Classification ◽  
Expression Classification

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, and plant classification based on images. Here in the paper, the image classification model is applied for identifying the display of daunting pictures on the internet. The proposed model uses Convolution neural network to identify these images and filter them through different blocks of the network, so that it can be classified accurately. The model will work as an extension to the web browser and will work on all websites when activated. The extension will be blurring the images and deactivating the links on web pages. This means that it will scan the entire web page and find all the daunting images present on that page. Then we will blur those images before they are loaded and the children could see them. Keywords— Activation Function, CNN, Images Classification , Optimizers, VGG-19

scholarly journals Image classification model based on spark and CNN

2018 ◽  
Vol 189 ◽  
pp. 03012
Author(s):  
Jiangfeng Xu ◽  
Shenyue Ma
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Great Influence ◽  
Image Data ◽  
Convolution Neural Network ◽  
Classification Model ◽  
Feature Maps ◽  
Single Node ◽  
Network Nodes ◽  
And Performance

Convolution neural network is a commonly used image classification model, but when the network nodes of the training process are too many, it will have a great influence on the training complexity. At the same time, when the size of the image data is large, many problems will appear on the single node, such as convergence slowly, frequently disk reading and writing. In order to overcome the above problems, this paper proposes a distributed convolution neural network based on Spark (Distribution Convolution neural network, Dis-CNN) model. The model first improves the initialization mode of convolution kernel parameters, then eliminates the redundancy of feature maps, and finally optimizes the distributed gradient descent by reducing the synchronous traffic between master and slave, so as to improve the convergence speed and performance. The experimental results show that the model not only improves the accuracy and recall of image classification, but also performs excellent in parallelism.

scholarly journals Identification of Microcontroller Based Objects Using Image Classification in Python

2021 ◽  
Vol 9 (9) ◽  
pp. 87-92
Author(s):  
Zenith Nandy
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Programming Language ◽  
Multiple Testing ◽  
Activation Function ◽  
Convolution Neural Network ◽  
Output Layer ◽  
Deep Learning Neural Network ◽  
Percent Accuracy

Abstract: In this paper, I built an AI model using deep learning, which identifies whether a given image is of an Arduino, a Beaglebone Black or a Jetson Nano. The identification of the object is based on prediction. The model is trained using 300 to 350 datasets of each category and is tested multiple times using different images at different angles, background colour and size. After multiple testing, the model is found to have 95 percent accuracy. Model used is Sequential and uses Convolution Neural Network (CNN) as its architecture. The activation function of each layer is RELU and for the output layer is Softmax. The output is a prediction and hence it is of probability type. This is a type of an application based project. The entire scripting is done using Python 3 programming language. Keywords: image classification, microcontroller boards, python, AI, deep learning, neural network

DUICM Deep Underwater Image Classification Mobdel using Convolutional Neural Networks

2020 ◽  
Vol 12 (3) ◽  
pp. 88-100
Author(s):  
Manimaran Aridoss ◽  
Chandramohan Dhasarathan ◽  
Ankur Dumka ◽  
Jayakumar Loganathan
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Light Propagation ◽  
Learning Algorithm ◽  
Activation Function ◽  
Classification Model ◽  
Low Contrast ◽  
Underwater Image ◽  
Operating Environments ◽  
Absorption And Dispersion

Classification of underwater images is a challenging task due to wavelength-dependent light propagation, absorption, and dispersion distort the visibility of images, which produces low contrast and degraded images in difficult operating environments. Deep learning algorithms are suitable to classify the turbid images, for that softmax activation function used for classification and minimize cross-entropy loss. The proposed deep underwater image classification model (DUICM) uses a convolutional neural network (CNN), a machine learning algorithm, for automatic underwater image classification. It helps to train the image and apply the classification techniques to categorise the turbid images for the selected features from the Benchmark Turbid Image Dataset. The proposed system was trained with several underwater images based on CNN models, which are independent to each sort of underwater image formation. Experimental results show that DUICM provides better classification accuracy against turbid underwater images. The proposed neural network model is validated using turbid images with different characteristics to prove the generalization capabilities.

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