scholarly journals Deep Neural Network for Gender-Based Violence Detection on Twitter Messages

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 807
Author(s):  
Carlos M. Castorena ◽  
Itzel M. Abundez ◽  
Roberto Alejo ◽  
Everardo E. Granda-Gutiérrez ◽  
Eréndira Rendón ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Gender Violence ◽  
Strategic Decision ◽  
Learning Approaches ◽  
Gender Based Violence ◽  
Data Set ◽  
Violence Detection ◽  
Governmental Institutions ◽  
Gender Based

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

Colluding With and Resisting the State: Organizing Against Gender Violence in the U.S.

2021 ◽  
pp. 155708512098760
Author(s):  
Beth E. Richie ◽  
Valli Kalei Kanuha ◽  
Kayla Marie Martensen
Keyword(s):  
Social Protest ◽  
Gender Violence ◽  
Racial Justice ◽  
Gender Based Violence ◽  
Transformative Justice ◽  
Past Half Century ◽  
Gender Based ◽  
Political Praxis ◽  
Past Half ◽  
The U.S

The movements for racial justice, health equity, and economic relief have been activated in the contentious and challenging climate of 2020, with COVID-19 and social protest. In this context, feminist scholars, anti-violence advocates, and transformative justice practitioners have renewed their call for substantive changes to all forms of gender-based violence. This article offers a genealogy of the battered women’s movement in the U.S. from the lived experiences of two longtime activists. These reflections offer an analysis of the political praxis which evolved over the past half century of the anti-violence movement, and which has foregrounded the current social, political, and ideological framing of gender-based violence today. We conclude with a view to the future, focusing on the possibilities for transformative justice and abolition feminism as a return to our radical roots and ancestral histories.

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

scholarly journals Covid-19 detection via deep neural network and occlusion sensitivity maps

2021 ◽  
Author(s):  
Noor Ahmad ◽  
Muhammad Aminu ◽  
Mohd Halim Mohd Noor
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Color Images ◽  
Fine Tuning ◽  
Training Dataset ◽  
Learning Approaches ◽  
Learning Models ◽  
Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

scholarly journals Xenofobia e violência de gênero: uma análise de manchetes veiculadas no webjornalismo brasileiro sobre mulheres venezuelanas

2021 ◽  
Vol 9 (1) ◽  
pp. 319-334
Author(s):  
Mauro Machado do Prado ◽  
Ana Paula de Castro Neves ◽  
Nathália Machado Cardoso Dardeau de Albuquerque
Keyword(s):  
Social Representations ◽  
Gender Violence ◽  
Gender Based Violence ◽  
Symbolic Annihilation ◽  
The Social ◽  
Las Mujeres ◽  
Gender Based ◽  
And Gender ◽  
América Del Sur ◽  
Digital Newspapers

O presente trabalho consiste em um estudo qualitativo das representações sociais de imigrantes venezuelanas na América do Sul no período de 2016 a 2019, a partir de manchetes de notícias divulgadas em jornais digitais brasileiros. O objetivo é verificar a ocorrência ou não de veiculações que constituam de forma explícita ou implícita uma violação à dignidade e aos direitos dessas mulheres, ao fomentar ou incitar a xenofobia e a violência de gênero na sociedade através de palavras, frases ou expressões capazes de provocar um aniquilamento simbólico. Para tanto, realizou-se um estudo bibliográfico e documental acerca das vulnerabilidades sociais presentes nos processos imigratórios contemporâneos, que foi consubstanciado com a análise de conteúdo (BARDIN, 2009), em abordagem qualitativa, de manchetes publicadas em jornais digitais brasileiros. A partir da análise realizada, foi possível inferir que estes veículos de comunicação vêm frequentemente descrevendo a migração venezuelana como um problema, mas em conotação negativa, sem o cuidado de descrição do contexto de forma mais clara e abrangente da questão a ser noticiada.   Xenofobia y violencia de género: un análisis de los titulares de las mujeres venezolanas en el periodismo web brasileño El presente trabajo consiste en un estudio cualitativo de las representaciones sociales de los inmigrantes venezolanos en América del Sur en el período de 2016 a 2019, a partir de titulares de noticias publicados en periódicos digitales brasileños. El objetivo es verificar la ocurrencia o no de colocaciones que constituyan explícita o implícitamente una violación a la dignidad y derechos de estas mujeres, al promover o incitar la xenofobia y la violencia de género en la sociedad a través de palabras, frases o expresiones capaces de provocar una aniquilación simbólica. Para ello, se realizó un estudio bibliográfico y documental sobre las vulnerabilidades sociales presentes en los procesos migratorios contemporáneos, el cual fue fundamentado con análisis de contenido (BARDIN, 2009), en un enfoque cualitativo, de titulares publicados en diarios digitales brasileños. Del análisis realizado, se pudo inferir que estos medios de comunicación han venido describiendo muchas veces la migración venezolana como un problema, pero en una connotación negativa, sin preocuparse por describir de manera más clara y completa el contexto del tema a reportar. Palabras clave: Derechos humanos de la mujer. La violencia de género. Xenofobia. Periodismo web.   Xenophobia and gender violence: an analysis of headings broadcasted in brazilian webjornalism on venezuelan women The present work consists of a qualitative study of the social representations of Venezuelan immigrants in South America in the period from 2016 to 2019, based on news headlines published in Brazilian digital newspapers. The objective is to verify the occurrence or not of placements that explicitly or implicitly constitute a violation of the dignity and rights of these women, by promoting or inciting xenophobia and gender violence in society through words, phrases or expressions capable of provoking a symbolic annihilation. To this end, a bibliographic and documentary study was carried out on the social vulnerabilities present in contemporary immigration processes, which was substantiated with content analysis (BARDIN, 2009), in a qualitative approach, of headlines published in Brazilian digital newspapers. From the analysis carried out, it was possible to infer that these media outlets have often been describing Venezuelan migration as a problem, but in a negative connotation, without taking care to describe the context more clearly and comprehensively of the issue to be reported. Keywords: Women’s human rights. Gender-based violence. Xenophobia. Webjournalism.

Digital Technologies and Interventions Against Gender-Based Violence in Rural Areas

2021 ◽  
pp. 105756772110404
Author(s):  
Andrea Adams ◽  
Suzanne G. Lea ◽  
Elsa M. D’Silva
Keyword(s):  
Rural Communities ◽  
Rural Areas ◽  
Digital Technologies ◽  
Gender Violence ◽  
Gender Based Violence ◽  
Indian Women ◽  
Women And Girls ◽  
Gender Based ◽  
Culture Of Silence ◽  
Common Barriers

This study reports experiences of combining digital technologies and facilitated interventions to address gender-based violence in rural areas. The methodology was based on the Safecity platform with a combination of communicative methods, digital technologies, and participant-led interventions to address gender-based violence in the State of Bihar and the Satara district in rural India. The findings indicate that the most common barriers to creating change in rural communities include patriarchal mindsets that foster a culture of silence around women's rights, lack of education, digital illiteracy, and lack of access to digital tools and services. Notwithstanding these obstacles, rural Indian women and girls participated in an intervention to create a new narrative informed by technological solutions that addressed gender violence in their communities.

scholarly journals Combatting Intimate Partner Violence: Representations of Social and Healthcare Personnel Working with Gender-Based Violence Interventions

2020 ◽  
Vol 17 (15) ◽  
pp. 5543 ◽  
Author(s):  
Marcella Autiero ◽  
Fortuna Procentese ◽  
Stefania Carnevale ◽  
Caterina Arcidiacono ◽  
Immacolata Di Napoli
Keyword(s):  
Intimate Partner Violence ◽  
Partner Violence ◽  
Intimate Partner ◽  
Gender Violence ◽  
World Health ◽  
Organizational Level ◽  
Healthcare Personnel ◽  
Gender Based Violence ◽  
Integrated Networks ◽  
Gender Based

Intimate partner violence (IPV) has been declared a global epidemic by the World Health Organization. Although the attention paid to both the perpetrators and victims of gender-based violence has increased, scientific research is still lacking in regard to the representations of operators involved in interventions and management. Therefore, the following study explores how the representations of operators affect how gender violence can be managed and combatted through an ecological approach to this phenomenon, in addition to highlighting the roles of organizational-level services and their cultural and symbolic substrates. In total, 35 health and social professionals were interviewed and textual materials were analyzed by thematic analysis. The evidence suggests that services contrasting gender-based violence utilize different representations and management approaches. The authors hope that these differences can become a resource, rather than a limitation, when combatting gender-based violence through the construction of more integrated networks and a greater dialogue among different services, in order to make interventions designed to combat gender-based violence more effective.

scholarly journals Deep Neural Network Algorithm Feedback Model with Behavioral Intelligence and Forecast Accuracy

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1465
Author(s):  
Taikyeong Jeong
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Training Data ◽  
Series Data ◽  
Context Aware ◽  
Data Set ◽  
Feedback Model ◽  
Data Connection

When attempting to apply a large-scale database that holds the behavioral intelligence training data of deep neural networks, the classification accuracy of the artificial intelligence algorithm needs to reflect the behavioral characteristics of the individual. When a change in behavior is recognized, that is, a feedback model based on a data connection model is applied, an analysis of time series data is performed by extracting feature vectors and interpolating data in a deep neural network to overcome the limitations of the existing statistical analysis. Using the results of the first feedback model as inputs to the deep neural network and, furthermore, as the input values of the second feedback model, and interpolating the behavioral intelligence data, that is, context awareness and lifelog data, including physical activities, involves applying the most appropriate conditions. The results of this study show that this method effectively improves the accuracy of the artificial intelligence results. In this paper, through an experiment, after extracting the feature vector of a deep neural network and restoring the missing value, the classification accuracy was verified to improve by about 20% on average. At the same time, by adding behavioral intelligence data to the time series data, a new data connection model, the Deep Neural Network Feedback Model, was proposed, and it was verified that the classification accuracy can be improved by about 8 to 9% on average. Based on the hypothesis, the F (X′) = X model was applied to thoroughly classify the training data set and test data set to present a symmetrical balance between the data connection model and the context-aware data. In addition, behavioral activity data were extrapolated in terms of context-aware and forecasting perspectives to prove the results of the experiment.

scholarly journals Covid-19 detection via deep neural network and occlusion sensitivity maps

2021 ◽  
Author(s):  
Noor Ahmad ◽  
Muhammad Aminu ◽  
Mohd Halim Mohd Noor
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Color Images ◽  
Fine Tuning ◽  
Training Dataset ◽  
Learning Approaches ◽  
Learning Models ◽  
Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

scholarly journals Deep Neural Networks for the Classification of Bank Marketing Data using Data Reduction Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 4373-4378
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Reduction ◽  
Deep Neural Network ◽  
Training Data ◽  
Neural Network Classifier ◽  
Data Set ◽  
Reduction Techniques ◽  
Marketing Data ◽  
Bank Marketing

The amount of data belonging to different domains are being stored rapidly in various repositories across the globe. Extracting useful information from the huge volumes of data is always difficult due to the dynamic nature of data being stored. Data Mining is a knowledge discovery process used to extract the hidden information from the data stored in various repositories, termed as warehouses in the form of patterns. One of the popular tasks of data mining is Classification, which deals with the process of distinguishing every instance of a data set into one of the predefined class labels. Banking system is one of the realworld domains, which collects huge number of client data on a daily basis. In this work, we have collected two variants of the bank marketing data set pertaining to a Portuguese financial institution consisting of 41188 and 45211 instances and performed classification on them using two data reduction techniques. Attribute subset selection has been performed on the first data set and the training data with the selected features are used in classification. Principal Component Analysis has been performed on the second data set and the training data with the extracted features are used in classification. A deep neural network classification algorithm based on Backpropagation has been developed to perform classification on both the data sets. Finally, comparisons are made on the performance of each deep neural network classifier with the four standard classifiers, namely Decision trees, Naïve Bayes, Support vector machines, and k-nearest neighbors. It has been found that the deep neural network classifier outperforms the existing classifiers in terms of accuracy

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