scholarly journals Convolution neural network and histogram equalization for COVID-19 diagnosis system

2021 ◽  
Vol 24 (1) ◽  
pp. 420
Author(s):  
Bashra Kadhim Oleiwi Chabor Alwawi ◽  
Layla H. Abood
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Training Model ◽  
Histogram Equalization ◽  
Convolution Neural Network ◽  
Training Dataset ◽  
Learning Technology ◽  
Detection Model ◽  
Proposed Model ◽  
Model Training

The coronavirus disease-2019 (COVID-19) is spreading quickly and globally as a pandemic and is the biggest problem facing humanity nowadays. The medical resources have become insufficient in many areas. The importance of the fast diagnosis of the positive cases is increasing to prevent further spread of this pandemic. In this study, the deep learning technology for COVID-19 dataset expansion and detection model is proposed. In the first stage of proposed model, COVID-19 dataset as chest X-ray images were collected and pre-processed, followed by expanding the data using data augmentation, enhancement by image processing and histogram equalization techniuque. While in the second stage of this model, a new convolution neural network (CNN) architecture was built and trained to diagnose the COVID-19 dataset as a COVID-19 (infected) or normal (uninfected) case. Whereas, a graphical user interface (GUI) using with Tkinter was designed for the proposed COVID-19 detection model. Training simulations are carried out online on using Google colaboratory based graphics prossesing unit (GPU). The proposed model has successfully classified COVID-19 with accuracy of the training model is 93.8% for training dataset and 92.1% for validating dataset and reached to the targeted point with minimum epoch’s number to train this model with satisfying results.

scholarly journals Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment

2021 ◽  
Vol 7 (09) ◽  
pp. 1-6
Author(s):  
Adil Hussain Mohammed
Keyword(s):  
Neural Network ◽  
Feature Reduction ◽  
Training Dataset ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Detection Model ◽  
Network Training ◽  
Proposed Model ◽  
Trained Neural Network ◽  
Good Set

Cloud provide support to manage, control, monitor different organization. Due to flexible nature f cloud chance of attack on it increases by means of some software attack in form of ransomware. Many of researcher has proposed various model to prevent such attacks or to identify such activities. This paper has proposed a ransomware detection model by use of trained neural network. Training of neural network was done by filter or optimized feature set obtained from the feature reduction algorithm. Paper has proposed a Invasive Weed Optimization algorithm that filter good set of feature from the available input training dataset. Proposed model test was performed on real dataset, have set sessions related to cloud ransomware attacks. Result shows that proposed model has increase the comparing parameter values.

scholarly journals Potato Leaf Disease Diagnosis and Detection System Based on Convolution Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 254-259
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Potato Crop ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Convolution Neural Network ◽  
Fine Tuning ◽  
Training Dataset ◽  
Potato Leaf ◽  
Model Training

For decades, agriculture has been an essential food source. According to related statics, over 60% of the total earth population mainly depend on agriculture’s sources for their primary feed. Unfortunately, one of the disaster problems that affect badly on agriculture production is plant diseases. There are about 25% of agriculture production lost annually because of plant diseases. Late and Early Blight diseases are one of the most destructive diseases that infect potato crop. Although, the late and inaccurate detection of plant diseases increases the losing percentage for the crop. The main approach of our proposed system is to detect early the plant diseases to decrease the plant’s production losses by using a diagnosis and detection system based on the Convolution Neural Network (CNN). We used CNN to extract the diseases features from the input images of the supported training dataset for classification purposes. For model training, 1700 of potato leaf images were used, then the testing process is done by using approximately 300 images and 100 images for fine tuning and parameters calibration against any biased data. Our proposed CNN architecture archives 98.2% accuracy, which is higher compared with other approaches run on the same dataset.

scholarly journals Automatic Bridge Crack Detection Using a Convolutional Neural Network

2019 ◽  
Vol 9 (14) ◽  
pp. 2867 ◽  
Author(s):  
Hongyan Xu ◽  
Xiu Su ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
Kerang Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Transportation Safety ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Convolutional Network ◽  
Detection Model ◽  
Proposed Model ◽  
Spatial Pyramid Pooling

Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.

scholarly journals Improved Arabic Alphabet Characters Classification Using Convolutional Neural Networks (CNN)

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Nesrine Wagaa ◽  
Hichem Kallel ◽  
Nédra Mellouli
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
State Of The Art ◽  
Optimization Algorithms ◽  
Convolution Neural Network ◽  
Proposed Model ◽  
Augmentation Techniques ◽  
High Recognition Accuracy ◽  
Suitable Change

Handwritten characters recognition is a challenging research topic. A lot of works have been present to recognize letters of different languages. The availability of Arabic handwritten characters databases is limited. Motivated by this topic of research, we propose a convolution neural network for the classification of Arabic handwritten letters. Also, seven optimization algorithms are performed, and the best algorithm is reported. Faced with few available Arabic handwritten datasets, various data augmentation techniques are implemented to improve the robustness needed for the convolution neural network model. The proposed model is improved by using the dropout regularization method to avoid data overfitting problems. Moreover, suitable change is presented in the choice of optimization algorithms and data augmentation approaches to achieve a good performance. The model has been trained on two Arabic handwritten characters datasets AHCD and Hijja. The proposed algorithm achieved high recognition accuracy of 98.48% and 91.24% on AHCD and Hijja, respectively, outperforming other state-of-the-art models.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals An Enhanced Faster-RCNN Based Deep Learning Model for Crop Diseases Detection and Classification

2019 ◽  
Vol 8 (6) ◽  
pp. 4714-4719
Keyword(s):  
Neural Network ◽  
Vital Role ◽  
Convolution Neural Network ◽  
Plant Phenotyping ◽  
Early Disease ◽  
Early Disease Detection ◽  
Proposed Model ◽  
Crop Diseases ◽  
Deep Learning Model ◽  
State Of Art

Recently Plant phenotyping has gained the attention of many researchers such that it plays a vital role in the context of enhancing agricultural productivity. Indian economy highly depends on agriculture and this factor elevates the importance of early disease detection of the crops within the agricultural fields. Addressing this problem several researchers have proposed Computer Vision and Pattern recognition based mechanisms through which they have attempted to identify the infected crops in the early stages.in this scenario, CNN convolution neural network-based architecture has demonstrated exceptional performance when compared with state-of-art mechanisms. This paper introduces an enhanced RCNN recurrent convolution neural network-based architecture that enhances the prediction accuracy while detecting the crop diseases in early stages. Based on the simulative studies is observed that the proposed model outperforms when compared with CNN and other state-of-art mechanisms.

scholarly journals Deep learning framework for material design space exploration using active transfer learning and data augmentation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yongtae Kim ◽  
Youngsoo Kim ◽  
Charles Yang ◽  
Kundo Park ◽  
Grace X. Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Design Space Exploration ◽  
Predictive Power ◽  
Design Space ◽  
Data Augmentation ◽  
Generative Models ◽  
Training Dataset ◽  
Initial Training ◽  
Active Transfer

AbstractNeural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain. In this study, we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations. Results show that our proposed framework can provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training dataset size.

scholarly journals Construction and Application of Indoor Video Surveillance System Based on Human Activity Recognition

2018 ◽  
Vol 232 ◽  
pp. 04024
Author(s):  
Yuchen Wang ◽  
Mantao Wang ◽  
Zhouyu Tan ◽  
Jie Zhang ◽  
Zhiyong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Activity Recognition ◽  
Human Activity ◽  
Monitoring Network ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Convolution Neural Network ◽  
Learning Technology ◽  
Building Monitoring

With the growth of building monitoring network, increasing human resource and funds have been invested into building monitoring system. Computer vision technology has been widely used in image recognition recently, and this technology has also been gradually applied to action recognition. There are still many disadvantages of traditional monitoring system. In this paper, a human activity recognition system which based on the convolution neural network is proposed. Using the 3D convolution neural network and the transfer learning technology, the human activity recognition engine is constructed. The Spring MVC framework is used to build the server end, and the system page is designed in HBuilder. The system not only enhances efficiency and functionality of building monitoring system, but also improves the level of building safety.

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