Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network

With increasing interest in hairstyles and hair color, bleaching, dyeing, straightening, and curling hair is being widely used worldwide, and the chemical and physical treatment of hair is also increasing. As a result, hair has suffered a lot of damage, and the degree of damage to hair has been measured only by the naked eye or touch. This has led to serious consequences, such as hair damage and scalp diseases. However, although these problems are serious, there is little research on hair damage. With the advancement of technology, people began to be interested in preventing and reversing hair damage. Manual observation methods cannot accurately and quickly identify hair damage areas. In recent years, with the rise of artificial intelligence technology, a large number of applications in various scenarios have given researchers new methods. In the project, we created a new hair damage data set based on SEM (scanning electron microscope) images. Through various physical and chemical analyses, we observe the changes in the hair surface according to the degree of hair damage, found the relationship between them, used a convolutional neural network to recognize and confirm the degree of hair damage, and categorized the degree of damage into weak damage, moderate damage and high damage.

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End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

BMJ Innovations ◽

10.1136/bmjinnov-2021-000668 ◽

2021 ◽

Vol 7 (2) ◽

pp. 356-362

Author(s):

Harry Coppock ◽

Alex Gaskell ◽

Panagiotis Tzirakis ◽

Alice Baird ◽

Lyn Jones ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Low Cost ◽

Area Under The Curve ◽

Alternative Form ◽

Economic Damage ◽

Audio Signals ◽

Operating Characteristics ◽

Data Set ◽

Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

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Research on Image Recognition of Convolutional Neural Network under Different Computer Data Set Capacities

Journal of Physics Conference Series ◽

10.1088/1742-6596/1744/4/042091 ◽

2021 ◽

Vol 1744 (4) ◽

pp. 042091

Author(s):

Yuanyi Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Recognition ◽

Data Set ◽

Computer Data

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Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

Mathematical Problems in Engineering ◽

10.1155/2020/5795976 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Jian-ye Yuan ◽

Xin-yuan Nan ◽

Cheng-rong Li ◽

Le-le Sun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Environmental Science ◽

Fine Tuning ◽

Classification Model ◽

Classification Models ◽

Data Set ◽

Classification Tasks ◽

Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

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Application of convolution neural network in medical image processing

Technology and Health Care ◽

10.3233/thc-202657 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Hongbo Zhao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Structure ◽

Medical Image ◽

Sampling Methods ◽

Medical Image Processing ◽

Convolution Neural Network ◽

Data Set ◽

Convolution Kernels ◽

Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

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DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

10.36227/techrxiv.12151734.v1 ◽

2020 ◽

Author(s):

Sriram Srinivasan ◽

Shashank A ◽

vinayakumar R ◽

Soman KP

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Intrusion Detection System ◽

Input Data ◽

Detection System ◽

Deep Convolutional Neural Network ◽

Traffic Data ◽

Data Set ◽

Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

International Journal of Extreme Automation and Connectivity in Healthcare ◽

10.4018/ijeach.2020070104 ◽

2020 ◽

Vol 2 (2) ◽

pp. 37-41

Author(s):

Zhixian Chen ◽

Jialin Tang ◽

Xueyuan Gong ◽

Qinglang Su

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Principal Component ◽

Activation Function ◽

Support Vector ◽

Data Set ◽

Novel Approach ◽

Rectified Linear Unit ◽

The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

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Multi-Regional Online Car-Hailing Order Quantity Forecasting Based on the Convolutional Neural Network

Information ◽

10.3390/info10060193 ◽

2019 ◽

Vol 10 (6) ◽

pp. 193 ◽

Cited By ~ 1

Author(s):

Zihao Huang ◽

Gang Huang ◽

Zhijun Chen ◽

Chaozhong Wu ◽

Xiaofeng Ma ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Travel Demand ◽

Short Term Memory ◽

Demand Forecasting ◽

Image Feature ◽

Support Vector ◽

Data Set ◽

Demand Distribution ◽

Demand Forecasting Model

With the development of online cars, the demand for travel prediction is increasing in order to reduce the information asymmetry between passengers and drivers of online car-hailing. This paper proposes a travel demand forecasting model named OC-CNN based on the convolutional neural network to forecast the travel demand. In order to make full use of the spatial characteristics of the travel demand distribution, this paper meshes the prediction area and creates a travel demand data set of the graphical structure to preserve its spatial properties. Taking advantage of the convolutional neural network in image feature extraction, the historical demand data of the first twenty-five minutes of the entire region are used as a model input to predict the travel demand for the next five minutes. In order to verify the performance of the proposed method, one-month data from online car-hailing of the Chengdu Fourth Ring Road are used. The results show that the model successfully extracts the spatiotemporal features of the data, and the prediction accuracies of the proposed method are superior to those of the representative methods, including the Bayesian Ridge Model, Linear Regression, Support Vector Regression, and Long Short-Term Memory networks.

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Recognition and Classification of Broiler Droppings Based on Deep Convolutional Neural Network

Journal of Sensors ◽

10.1155/2019/3823515 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Jintao Wang ◽

Mingxia Shen ◽

Longshen Liu ◽

Yi Xu ◽

Cedric Okinda

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Digestive Disease ◽

Deep Convolutional Neural Network ◽

Mean Average Precision ◽

Average Precision ◽

Deep Convolutional Neural Networks ◽

Data Set ◽

Fine Grained ◽

Digestive Diseases

Digestive diseases are one of the common broiler diseases that significantly affect production and animal welfare in broiler breeding. Droppings examination and observation are the most precise techniques to detect the occurrence of digestive disease infections in birds. This study proposes an automated broiler digestive disease detector based on a deep Convolutional Neural Network model to classify fine-grained abnormal broiler droppings images as normal and abnormal (shape, color, water content, and shape&water). Droppings images were collected from 10,000 25-35-day-old Ross broiler birds reared in multilayer cages with automatic droppings conveyor belts. For comparative purposes, Faster R-CNN and YOLO-V3 deep Convolutional Neural Networks were developed. The performance of YOLO-V3 was improved by optimizing the anchor box. Faster R-CNN achieved 99.1% recall and 93.3% mean average precision, while YOLO-V3 achieved 88.7% recall and 84.3% mean average precision on the testing data set. The proposed detector can provide technical support for the detection of digestive diseases in broiler production by automatically and nonintrusively recognizing and classifying chicken droppings.

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Pulse-Wave-Pattern Classification with a Convolutional Neural Network

Scientific Reports ◽

10.1038/s41598-019-51334-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Gaoyang Li ◽

Kazuhiro Watanabe ◽

Hitomi Anzai ◽

Xiaorui Song ◽

Aike Qiao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Pattern Classification ◽

Pulse Wave ◽

Wave Pattern ◽

Classification Criteria ◽

Physiological Parameters ◽

Physiological Parameter ◽

Data Set ◽

The One

Abstract Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.

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A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images

Remote Sensing ◽

10.3390/rs12061015 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1015 ◽

Cited By ~ 3

Author(s):

Kan Zeng ◽

Yixiao Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Oil Spill ◽

Data Augmentation ◽

Classification Performance ◽

Deep Convolutional Neural Network ◽

Convolutional Network ◽

Data Set ◽

Oil Spill Detection ◽

Processing Framework

Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level.

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