Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model

2014 ◽  
pp. 103-110 ◽  
Author(s):  
Rozniza Ali ◽  
Bo Jiang ◽  
Mustafa Man ◽  
Amir Hussain ◽  
Bin Luo
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Sem Images

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

scholarly journals Vibration-based classification of road damages: gyroscope data and a simple neural network model

2018 ◽  
Vol 195 ◽  
pp. 012068
Author(s):  
Fergyanto E. Gunawan ◽  
Herriyandi ◽  
Benfano Soewito ◽  
Tuga Mauritsius ◽  
Nico Surantha
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simple Neural Network

Novel complex network model and its application in identifying critical components of power grid

2018 ◽  
Vol 512 ◽  
pp. 316-329 ◽  
Author(s):  
Chong Chen ◽  
Xuan Zhou ◽  
Zhuo Li ◽  
Zhiheng He ◽  
Zhengtian Li ◽  
...  
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Power Grid ◽  
Novel Complex ◽  
Critical Components

scholarly journals Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

A tree-like complex network model

2010 ◽  
Vol 389 (1) ◽  
pp. 171-178 ◽  
Author(s):  
Yuying Gu ◽  
Jitao Sun
Keyword(s):  
Complex Network ◽  
Network Model

The Research of Complex Network on Archives Blog

2011 ◽  
Vol 181-182 ◽  
pp. 14-18
Author(s):  
Yi He
Keyword(s):  
Information Sharing ◽  
Complex Network ◽  
Network Model ◽  
Degree Distribution ◽  
Small World ◽  
Scale Free ◽  
Set Up

At the background of archives blog on Internet, this paper constructs a directed complex network model, and analyzes the network characters such as degree distribution. To verify its efficiency, we collect blogs’ information and set up a complex network..From the analysis result of the simulation and demonstration network, we know that they have the same characters, which show that, the virtual society network has small-world effect and scale-free character compared with real society network. The results indicate that the establishment of archives blog is favor to spread rapidly archives information, improve information sharing efficiency and promote the development of archives technology.

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