Using Shallow and Deep Learning to Automatically Detect Hate Motivated by Gender and Sexual Orientation on Twitter in Spanish

The increasing phenomenon of “cyberhate” is concerning because of the potential social implications of this form of verbal violence, which is aimed at already-stigmatized social groups. According to information collected by the Ministry of the Interior of Spain, the category of sexual orientation and gender identity is subject to the third-highest number of registered hate crimes, ranking behind racism/xenophobia and ideology. However, most of the existing computational approaches to online hate detection simultaneously attempt to address all types of discrimination, leading to weaker prototype performances. These approaches focus on other reasons for hate—primarily racism and xenophobia—and usually focus on English messages. Furthermore, few detection models have used manually generated databases as a training corpus. Using supervised machine learning techniques, the present research sought to overcome these limitations by developing and evaluating an automatic detector of hate speech motivated by gender and sexual orientation. The focus was Spanish-language posts on Twitter. For this purpose, eight predictive models were developed from an ad hoc generated training corpus, using shallow modeling and deep learning. The evaluation metrics showed that the deep learning algorithm performed significantly better than the shallow modeling algorithms, and logistic regression yielded the best performance of the shallow algorithms.

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Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm

Intelligent Data Analysis ◽

10.3233/ida-194747 ◽

2020 ◽

Vol 24 (5) ◽

pp. 1065-1086

Author(s):

Kudakwashe Zvarevashe ◽

Oludayo O. Olugbara

Keyword(s):

Neural Network ◽

Deep Learning ◽

Learning Algorithm ◽

Convolution Neural Network ◽

Machine Learning Techniques ◽

Speech Emotion Recognition ◽

Modern World ◽

Emotional Speech ◽

Deep Learning Algorithm ◽

Speech Database

Speech emotion recognition has become the heart of most human computer interaction applications in the modern world. The growing need to develop emotionally intelligent devices has opened up a lot of research opportunities. Most researchers in this field have applied the use of handcrafted features and machine learning techniques in recognising speech emotion. However, these techniques require extra processing steps and handcrafted features are usually not robust. They are computationally intensive because the curse of dimensionality results in low discriminating power. Research has shown that deep learning algorithms are effective for extracting robust and salient features in dataset. In this study, we have developed a custom 2D-convolution neural network that performs both feature extraction and classification of vocal utterances. The neural network has been evaluated against deep multilayer perceptron neural network and deep radial basis function neural network using the Berlin database of emotional speech, Ryerson audio-visual emotional speech database and Surrey audio-visual expressed emotion corpus. The described deep learning algorithm achieves the highest precision, recall and F1-scores when compared to other existing algorithms. It is observed that there may be need to develop customized solutions for different language settings depending on the area of applications.

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An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Wireless Communications and Mobile Computing ◽

10.1155/2021/6079582 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Sumaya Sanober ◽

Izhar Alam ◽

Sagar Pande ◽

Farrukh Arslan ◽

Kantilal Pitambar Rane ◽

...

Keyword(s):

Deep Learning ◽

Inflation Rate ◽

Learning Algorithm ◽

Credit Cards ◽

Fraud Detection ◽

Machine Learning Techniques ◽

Internet Commerce ◽

Deep Learning Algorithm ◽

Financial Conditions ◽

Learning Techniques

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

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Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211052027 ◽

2021 ◽

pp. 036119812110520

Author(s):

Fiorella Mete ◽

David J. Corr ◽

Michael P. Wilbur ◽

Ying Chen

Keyword(s):

Machine Learning ◽

Ad Hoc ◽

Learning Algorithm ◽

Classification Tree ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Classification Framework ◽

Learning Techniques ◽

Tree Methods ◽

Step Algorithm

Collecting information on heavy trucks and monitoring the bridges which they regularly cross is important for many facets of infrastructure management. In this paper, a two-step algorithm is developed using bridge and truck data, by deploying sequentially unsupervised and supervised machine learning techniques. Longitudinal clustering of bridge data, concerning strain waveforms, is adopted to perform the first step of the algorithm, while image visual inspection and classification tree methods are applied to truck data concurrently in the second step. Both bridge and truck traffic must be monitored for a limited, yet significant, amount of time to calibrate the algorithm, which is then used to build a classification framework. The framework provides the same benefits of two data collection systems while only one needs to be operative. Depending on which monitoring system remains available, the framework enables the use of bridge data to identify the truck’s profile which generated it, or to estimate bridge response given the truck’s information. As a result, the present study aims to provide decision-makers with an effective way to monitor the whole bridge-traffic system, bridge managers to plan effective maintenance, and policymakers to develop ad hoc regulations.

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Review on Various Machine Learning and Deep Learning Techniques for Prediction and Classification of Quotidian Datasets

Recent Advances in 3D Imaging, Modeling, and Reconstruction - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-5294-9.ch014 ◽

2020 ◽

pp. 296-323

Author(s):

Anisha M. Lal ◽

B. Koushik Reddy ◽

Aju D.

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Training Data ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Supervised Methods ◽

Regression Techniques

Machine learning can be defined as the ability of a computer to learn and solve a problem without being explicitly coded. The efficiency of the program increases with experience through the task specified. In traditional programming, the program and the input are specified to get the output, but in the case of machine learning, the targets and predictors are provided to the algorithm make the process trained. This chapter focuses on various machine learning techniques and their performance with commonly used datasets. A supervised learning algorithm consists of a target variable that is to be predicted from a given set of predictors. Using these established targets is a function that plots targets to a given set of predictors. The training process allows the system to train the unknown data and continues until the model achieves a desired level of accuracy on the training data. The supervised methods can be usually categorized as classification and regression. This chapter discourses some of the popular supervised machine learning algorithms and their performances using quotidian datasets. This chapter also discusses some of the non-linear regression techniques and some insights on deep learning with respect to object recognition.

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The Study of Noise Effect on CNN-Based Deep Learning from Medical Images

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.3.1036 ◽

2021 ◽

Vol 11 (3) ◽

pp. 202-207

Author(s):

Kittipat Sriwong ◽

◽

Kittisak Kerdprasop ◽

Nittaya Kerdprasop

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Medical Images ◽

Research Work ◽

Health Science ◽

Learning Performance ◽

Machine Learning Techniques ◽

Disease Symptoms ◽

Deep Learning Algorithm ◽

Learning Techniques

Currently, computational modeling methods based on machine learning techniques in medical imaging are gaining more and more interests from health science researchers and practitioners. The high interest is due to efficiency of modern algorithms such as convolutional neural networks (CNN) and other types of deep learning. CNN is the most popular deep learning algorithm because of its prominent capability on learning key features from images that help capturing the correct class of images. Moreover, several sophisticated CNN architectures with many learning layers are available in the cloud computing environment. In this study, we are interested in performing empirical research work to compare performance of CNNs when they are dealing with noisy medical images. We design a comparative study to observe performance of the AlexNet CNN model on classifying diseases from medical images of two types: images with noise and images without noise. For the case of noisy images, the data had been further separated into two groups: a group of images that noises harmoniously cover the area of the disease symptoms (NIH) and a group of images that noises do not harmoniously cover the area of the disease symptoms (NNIH). The experimental results reveal that NNIH has insignificant effect toward the performance of CNN. For the group of NIH, we notice some effect of noise on CNN learning performance. In NIH group of images, the data preparation process before learning can improve the efficiency of CNN.

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