scholarly journals Application of convolutional neural networks in optical text recognition to junk data filtering

2021 ◽  
Vol 2127 (1) ◽  
pp. 012024
Author(s):  
T E Razumov ◽  
D V Churikov ◽  
O V Kravchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Text Recognition ◽  
Data Filtering ◽  
Service Architecture ◽  
C Language ◽  
Software Application ◽  
Fully Connected

Abstract In this paper, the problem of constructing a model for detecting and filtering unwanted spam messages is solved. A fully connected convolutional neural network (FCNN) was chosen as the model of the classifier of unwanted emails in email. It allows you to divide emails into two categories: spam and not spam. The main result of the research is a software application in the C++ language, which has a micro-service architecture and solves the problem of image classification. The app can handle more than 106 requests per minute in real-time.

scholarly journals Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 70
Author(s):  
Elena Solovyeva ◽  
Ali Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Computational Cost ◽  
High Accuracy ◽  
Activation Functions ◽  
Fully Connected ◽  
Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

scholarly journals Research on improved wavelet convolutional wavelet neural networks

2020 ◽  
Author(s):  
Jing-Wei Liu ◽  
Fang-Ling Zuo ◽  
Ying-Xiao Guo ◽  
Tian-Yue Li ◽  
Jia-Ming Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
High Performance ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Deep Learning Algorithm ◽  
Main Work ◽  
Good Ability ◽  
Fully Connected

AbstractConvolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.

scholarly journals Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibin Chang ◽  
Ying Cui
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classification Algorithm ◽  
Large Set ◽  
Data Set ◽  
Multilabel Learning ◽  
Average Maximum

More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR-10 data set, which is much higher than the traditional CNN algorithm.

scholarly journals Convolutional neural network for image classification based on transfer learning technique

2021 ◽  
Author(s):  
Ghassan Mohammed Halawani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Learning Networks ◽  
The Neural Network ◽  
Learning Technique ◽  
Common Architecture

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.

scholarly journals Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation

2021 ◽  
Vol 7 ◽  
pp. e497
Author(s):  
Shakeel Shafiq ◽  
Tayyaba Azim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Discrimination Performance ◽  
Input Image ◽  
Data Sets ◽  
Discrimination Power ◽  
Level Information ◽  
Fully Connected

Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling layers whose goal is to preserve local and low-level information of the input image and down sample it to avoid overfitting. Inspired from the functionality of local binary pattern (LBP) operator, this paper proposes to induce discrimination into the mid layers of convolutional neural network by introducing a discriminatively boosted alternative to pooling (DBAP) layer that has shown to serve as a favourable replacement of early maxpooling layer in a convolutional neural network (CNN). A thorough research of the related works show that the proposed change in the neural architecture is novel and has not been proposed before to bring enhanced discrimination and feature visualisation power achieved from the mid layer features. The empirical results reveal that the introduction of DBAP layer in popular neural architectures such as AlexNet and LeNet produces competitive classification results in comparison to their baseline models as well as other ultra-deep models on several benchmark data sets. In addition, better visualisation of intermediate features can allow one to seek understanding and interpretation of black box behaviour of convolutional neural networks, used widely by the research community.

scholarly journals Tensorflow Based Image Classification using Advanced Convolutional Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 994-998
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Programming Language ◽  
Convolutional Neural Networks ◽  
Input Data ◽  
Research Study ◽  
Testing Data

In this Research study image identifications will be done by the help of Advanced CNN (Convolutional Neural Networks with Tensorflow Framework. Here we use Python as a main programming language because Tensorflow is a python library. In this study input data mainly focuses on Plants categories by the help of leaves for identifications. Selecting CNN is the best approach for the training and testing data because it produces promising and continuously improving results on automated plant identifications. Here results are divided in terms of accuracy and time. Using advanced CNN results are above 95% while on others accuracy is below 90% and taking much time than this.

scholarly journals Dog Breed Prediction using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 318-322
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Graphical Representation ◽  
Outcome Analysis ◽  
Dog Breed ◽  
The One

Deep learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks (CNNs) gives single often used method for image classification and detection. In this exertion, we define a CNNbased approach for spotting dogs in per chance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm (CNN) gives good analysis accuracy for all the tested datasets

scholarly journals Plant leaf identification system using convolutional neural network

2021 ◽  
Vol 10 (6) ◽  
pp. 3341-3352
Author(s):  
Amiruzzaki Taslim ◽  
Sharifah Saon ◽  
Abd Kadir Mahamad ◽  
Muladi Muladi ◽  
Wahyu Nur Hidayat
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
White Background ◽  
Identification System ◽  
Plant Identification ◽  
Software Product ◽  
And Training ◽  
Random Background

This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.

RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

2020 ◽  
Vol 69 (1) ◽  
pp. 378-383
Author(s):  
T.A. Nurmukhanov ◽  
◽  
B.S. Daribayev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Input Data ◽  
Optimization Methods ◽  
Text Recognition ◽  
Activation Functions

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.

Deep learning for quark–gluon plasma detection in the CBM experiment

2020 ◽  
Vol 35 (33) ◽  
pp. 2043002 ◽  
Author(s):  
Fedor Sergeev ◽  
Elena Bratkovskaya ◽  
Ivan Kisel ◽  
Iouri Vassiliev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Quark Gluon Plasma ◽  
Heavy Ion ◽  
Gluon Plasma ◽  
Ion Collisions ◽  
Validation Set ◽  
Fully Connected

Classification of processes in heavy-ion collisions in the CBM experiment (FAIR/GSI, Darmstadt) using neural networks is investigated. Fully-connected neural networks and a deep convolutional neural network are built to identify quark–gluon plasma simulated within the Parton-Hadron-String Dynamics (PHSD) microscopic off-shell transport approach for central Au+Au collision at a fixed energy. The convolutional neural network outperforms fully-connected networks and reaches 93% accuracy on the validation set, while the remaining only 7% of collisions are incorrectly classified.

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