Detecting abusive Instagram comments in Turkish using convolutional Neural network and machine learning methods

2021 ◽  
Vol 174 ◽  
pp. 114802
Author(s):  
Habibe Karayiğit ◽  
Çiğdem İnan Acı ◽  
Ali Akdağlı
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Methods ◽  
Machine Learning Methods

Landslide susceptibility mapping based on convolutional neural network and conventional machine learning methods

2021 ◽  
Author(s):  
Rui Liu ◽  
Xin Yang ◽  
Chong Xu ◽  
Luyao Li ◽  
Xiangqiang Zeng
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Conventional Machine

Abstract Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced Convolutional Neural Network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected the Jiuzhaigou region in Sichuan Province, China as the study area. A total number of 710 landslides and 12 predisposing factors were stacked to form spatial datasets for LSM. The ROC analysis and several statistical metrics, such as accuracy, root mean square error (RMSE), Kappa coefficient, sensitivity, and specificity were used to evaluate the performance of the models in the training and validation datasets. Finally, the trained models were calculated and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine-learning based models have a satisfactory performance (AUC: 85.72% − 90.17%). The CNN based model exhibits excellent good-of-fit and prediction capability, and achieves the highest performance (AUC: 90.17%) but also significantly reduces the salt-of-pepper effect, which indicates its great potential of application to LSM.

scholarly journals Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 170
Author(s):  
Muhammad Wasimuddin ◽  
Khaled Elleithy ◽  
Abdelshakour Abuzneid ◽  
Miad Faezipour ◽  
Omar Abuzaghleh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Signal Analysis ◽  
Neural Network Modeling ◽  
Ecg Signal ◽  
Analysis Model ◽  
Learning Methods ◽  
Machine Learning Methods

Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.

scholarly journals Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hua Xie ◽  
Minghua Zhang ◽  
Jiaming Ge ◽  
Xinfang Dong ◽  
Haiyan Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Data Representation ◽  
Air Traffic ◽  
Deep Convolutional Neural Network ◽  
Basic Unit ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Traffic Scenario

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity (SOC) is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image (MTSI), is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors.

scholarly journals Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sorayya Rezayi ◽  
Niloofar Mohammadzadeh ◽  
Hamid Bouraghi ◽  
Soheila Saeedi ◽  
Ali Mohammadpour
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Acute Lymphoblastic Leukemia ◽  
Convolutional Neural Network ◽  
Lymphoblastic Leukemia ◽  
Leukemic Cells ◽  
Learning Methods ◽  
Convolutional Network ◽  
Machine Learning Methods

Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2 ∗ 2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.

scholarly journals Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

2022 ◽  
Vol 14 (2) ◽  
pp. 321
Author(s):  
Rui Liu ◽  
Xin Yang ◽  
Chong Xu ◽  
Liangshuai Wei ◽  
Xiangqiang Zeng
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Conventional Machine

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.

scholarly journals Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

2020 ◽  
Vol 10 (20) ◽  
pp. 7097
Author(s):  
Leevi Annala ◽  
Sami Äyrämö ◽  
Ilkka Pölönen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stochastic Model ◽  
Convolutional Neural Network ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Physical Parameters ◽  
Learning Methods ◽  
Model Inversion ◽  
Machine Learning Methods

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer perceptron, lasso, stochastic gradient descent, and linear support vector machine regressors, we find the convolutional neural network to be the most accurate in the inversion task.

Hand Gesture Recognition Based on sEMG Signal and Convolutional Neural Network

2021 ◽  
pp. 2151012
Author(s):  
Ziyi Su ◽  
Handong Liu ◽  
Jinwu Qian ◽  
Zhen Zhang ◽  
Lunwei Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Hand Gestures ◽  
Learning Methods ◽  
Semg Signal ◽  
Machine Learning Methods ◽  
Semg Signals

Recently, deep learning has become a promising technique for constructing gesture recognition classifiers from surface electromyography (sEMG) signals in human–computer interaction. In this paper, we propose a gesture recognition method with sEMG signals based on a deep multi-parallel convolutional neural network (CNN), which solves the problem that traditional machine learning methods may lose too much useful information during feature extraction. CNNs provide an efficient way to constrain the complexity of feedforward neural networks by weight sharing and restriction to local connections. Sophisticated feature extraction is to be avoided and hand gestures are to be classified directly. A multi-parallel and multi-convolution layer convolution structure is proposed to classify hand gestures. Experiment results show that in comparison with five traditional machine learning methods, the proposed method could achieve higher accuracy.

scholarly journals Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples

2018 ◽  
Vol 8 (9) ◽  
pp. 1573 ◽  
Author(s):  
Vladimir Kulyukin ◽  
Sarbajit Mukherjee ◽  
Prakhar Amlathe
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Feature Engineering ◽  
Learning Methods ◽  
Machine Learning Methods

Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing samples were separated from the validation samples by beehive and location, a shallower raw audio convolutional neural network with a custom layer outperformed three deeper raw audio convolutional neural networks without custom layers and performed on par with the four machine learning methods trained to classify feature vectors extracted from raw audio samples. On a more challenging dataset of 12,914 audio samples where the training and testing samples were separated from the validation samples by beehive, location, time, and bee race, all raw audio convolutional neural networks performed better than the four machine learning methods and a convolutional neural network trained to classify spectrogram images of audio samples. A trained raw audio convolutional neural network was successfully tested in situ on a low voltage Raspberry Pi computer, which indicates that convolutional neural networks can be added to a repertoire of in situ audio classification algorithms for electronic beehive monitoring. The main trade-off between deep learning and standard machine learning is between feature engineering and training time: while the convolutional neural networks required no feature engineering and generalized better on the second, more challenging dataset, they took considerably more time to train than the machine learning methods. To ensure the replicability of our findings and to provide performance benchmarks for interested research and citizen science communities, we have made public our source code and our curated datasets.

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