scholarly journals Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection

2020 ◽  
Author(s):  
Son T. Luu ◽  
Hung P. Nguyen ◽  
Kiet Van Nguyen ◽  
Ngan Luu-Thuy Nguyen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Hate Speech ◽  
Network Models ◽  
Learning Models ◽  
Neural Network Models ◽  
Speech Detection ◽  
Machine Learning Models

scholarly journals COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS AND REGRESSIONS FOR CAR PRICE PREDICTION

2019 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Random Forest ◽  
Network Models ◽  
Gradient Boosting ◽  
Learning Models ◽  
Neural Network Models ◽  
Boosting Algorithms ◽  
Machine Learning Models

The purpose of the research described in this article is a comparative analysis of the predictive qualities of some models of machine learning and regression. The factors for models are the consumer characteristics of a used car: brand, transmission type, drive type, engine type, mileage, body type, year of manufacture, seller's region in Ukraine, condition of the car, information about accident, average price for analogue in Ukraine, engine volume, quantity of doors, availability of extra equipment, quantity of passenger’s seats, the first registration of a car, car was driven from abroad or not. Qualitative variables has been encoded as binary variables or by mean target encoding. The information about more than 200 thousand cars have been used for modeling. All models have been evaluated in the Python Software using Sklearn, Catboost, StatModels and Keras libraries. The following regression models and machine learning models were considered in the course of the study: linear regression; polynomial regression; decision tree; neural network; models based on "k-nearest neighbors", "random forest", "gradient boosting" algorithms; ensemble of models. The article presents the best in terms of quality (according to the criteria R2, MAE, MAD, MAPE) options from each class of models. It has been found that the best way to predict the price of a passenger car is through non-linear models. The results of the modeling show that the dependence between the price of a car and its characteristics is best described by the ensemble of models, which includes a neural network, models using "random forest" and "gradient boosting" algorithms. The ensemble of models showed an average relative approximation error of 11.2% and an average relative forecast error of 14.34%. All nonlinear models for car price have approximately the same predictive qualities (the difference between the MAPE within 2%) in this research.

Machine Learning Crowdfunding

2020 ◽  
Vol 10 (2) ◽  
pp. 1-11
Author(s):  
Evangelos Katsamakas ◽  
Hao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Topic Modeling ◽  
Network Models ◽  
Unstructured Data ◽  
Text Analytics ◽  
Learning Models ◽  
Neural Network Models ◽  
Machine Learning Models

Crowdfunding is a novel and important economic mechanism for funding projects and promoting innovation in the digital economy. This article explores most recent structured and unstructured data from a crowdfunding platform. It provides an in-depth exploration of the data using text analytics techniques, such as sentiment analysis and topic modeling. It uses novel natural language processing to represent project descriptions, and evaluates machine learning models, including neural network models, to predict project fundraising success. It discusses the findings of the performance evaluation, and summarizes lessons for crowdfunding platforms and their users.

scholarly journals A Web Interface for Analyzing Hate Speech

2021 ◽  
Vol 13 (3) ◽  
pp. 80
Author(s):  
Lazaros Vrysis ◽  
Nikolaos Vryzas ◽  
Rigas Kotsakis ◽  
Theodora Saridou ◽  
Maria Matsiola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Graphical User Interface ◽  
Hate Speech ◽  
Web Interface ◽  
Learning Models ◽  
Speech Detection ◽  
Media Services ◽  
The Web ◽  
Machine Learning Models

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.

First-Break Picking Classification Models Using Recurrent Neural Network

2021 ◽  
Author(s):  
Mohammed Ayub ◽  
SanLinn Kaka
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Contextual Information ◽  
Classification Model ◽  
Superior Performance ◽  
Learning Models ◽  
Neural Network Models ◽  
Minimum Number ◽  
Machine Learning Models

Abstract Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.

scholarly journals A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere

2020 ◽  
Vol 10 (23) ◽  
pp. 8614 ◽  
Author(s):  
Raghad Alshalan ◽  
Hend Al-Khalifa
Keyword(s):  
Neural Network ◽  
Hate Speech ◽  
Characteristic Curve ◽  
Network Models ◽  
Neural Network Models ◽  
Speech Detection ◽  
Language Representation ◽  
Significant Research ◽  
Speech Problem ◽  
Gated Recurrent Units

With the rise of hate speech phenomena in the Twittersphere, significant research efforts have been undertaken in order to provide automatic solutions for detecting hate speech, varying from simple machine learning models to more complex deep neural network models. Despite this, research works investigating hate speech problem in Arabic are still limited. This paper, therefore, aimed to investigate several neural network models based on convolutional neural network (CNN) and recurrent neural network (RNN) to detect hate speech in Arabic tweets. It also evaluated the recent language representation model bidirectional encoder representations from transformers (BERT) on the task of Arabic hate speech detection. To conduct our experiments, we firstly built a new hate speech dataset that contained 9316 annotated tweets. Then, we conducted a set of experiments on two datasets to evaluate four models: CNN, gated recurrent units (GRU), CNN + GRU, and BERT. Our experimental results in our dataset and an out-domain dataset showed that the CNN model gave the best performance, with an F1-score of 0.79 and area under the receiver operating characteristic curve (AUROC) of 0.89.

scholarly journals MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area

2020 ◽  
Vol 12 (6) ◽  
pp. 962 ◽  
Author(s):  
Changyu Liu ◽  
Xiaodong Huang ◽  
Xubing Li ◽  
Tiangang Liang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Snow Cover ◽  
Back Propagation ◽  
Network Models ◽  
Support Vector ◽  
Learning Models ◽  
Neural Network Models ◽  
Fractional Snow Cover ◽  
Machine Learning Models

To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.

scholarly journals On the Impact of Interpretability Methods in Active Image Augmentation Method

2021 ◽  
Author(s):  
Flávio Arthur Oliveira Santos ◽  
Cleber Zanchettin ◽  
Leonardo Nogueira Matos ◽  
Paulo Novais
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Models ◽  
Training Data ◽  
Learning Models ◽  
Neural Network Models ◽  
Wide Range ◽  
The Impact ◽  
Activation Mapping ◽  
Machine Learning Models

Abstract Robustness is a significant constraint in machine learning models. The performance of the algorithms must not deteriorate when training and testing with slightly different data. Deep neural network models achieve awe-inspiring results in a wide range of applications of computer vision. Still, in the presence of noise or region occlusion, some models exhibit inaccurate performance even with data handled in training. Besides, some experiments suggest deep learning models sometimes use incorrect parts of the input information to perform inference. Active image augmentation (ADA) is an augmentation method that uses interpretability methods to augment the training data and improve its robustness to face the described problems. Although ADA presented interesting results, its original version only used the vanilla backpropagation interpretability to train the U-Net model. In this work, we propose an extensive experimental analysis of the interpretability method’s impact on ADA. We use five interpretability methods: vanilla backpropagation, guided backpropagation, gradient-weighted class activation mapping (GradCam), guided GradCam and InputXGradient. The results show that all methods achieve similar performance at the ending of training, but when combining ADA with GradCam, the U-Net model presented an impressive fast convergence.

Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake

2020 ◽  
Vol 36 (3) ◽  
pp. 1166-1187 ◽  
Author(s):  
Shohei Naito ◽  
Hiromitsu Tomozawa ◽  
Yuji Mori ◽  
Takeshi Nagata ◽  
Naokazu Monma ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Aerial Photographs ◽  
Learning Models ◽  
Visual Interpretation ◽  
Damage Classification ◽  
Kumamoto Earthquake ◽  
Machine Learning Models

This article presents a method for detecting damaged buildings in the event of an earthquake using machine learning models and aerial photographs. We initially created training data for machine learning models using aerial photographs captured around the town of Mashiki immediately after the main shock of the 2016 Kumamoto earthquake. All buildings are classified into one of the four damage levels by visual interpretation. Subsequently, two damage discrimination models are developed: a bag-of-visual-words model and a model based on a convolutional neural network. Results are compared and validated in terms of accuracy, revealing that the latter model is preferable. Moreover, for the convolutional neural network model, the target areas are expanded and the recalls of damage classification at the four levels range approximately from 66% to 81%.

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