Offensive Language Detection in Arabic Social Networks Using Evolutionary-Based Classifiers Learned From Fine-Tuned Embeddings

Social networks facilitate communication between people from all over the world. Unfortunately, the excessive use of social networks leads to the rise of antisocial behaviors such as the spread of online offensive language, cyberbullying (CB), and hate speech (HS). Therefore, abusive\offensive and hate detection become a crucial part of cyberharassment. Manual detection of cyberharassment is cumbersome, slow, and not even feasible in rapidly growing data. In this study, we addressed the challenges of automatic detection of the offensive tweets in the Arabic language. The main contribution of this study is to design and implement an intelligent prediction system encompassing a two-stage optimization approach to identify and classify the offensive from the non-offensive text. In the rst stage, the proposed approach ne-tuned the pre-trainedword embedding models by training them for several epochs on the training dataset. The embeddings of the vocabularies in the new dataset are trained and added to the old embeddings. While in the second stage, it employed a hybrid approach of two classi ers, namely XGBoost and SVM, and a genetic algorithm (GA) to mitigate the drawback of the classi ers in nding the optimal hyperparameter values to run the proposed approach. We tested the proposed approach on Arabic Cyberbullying Corpus (ArCybC), which contains tweets collected from four Twitter domains: gaming, sports, news, and celebrities. The ArCybC dataset has four categories: sexual, racial, intelligence, and appearance. The proposed approach produced superior results, in which the SVM algorithm with the Aravec SkipGram word embedding model achieved an accuracy rate of 88.2% and an F1-score rate of 87.8%.Ministerio Espanol de Ciencia e Innovacion (DemocratAI::UGR) PID2020-115570GB-C2

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using singleand multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.Ministerio Español de Ciencia e Innovación under project number PID2020-115570GB-C22 (DemocratAI::UGR)Cátedra de Empresa Tecnología para las Personas (UGR-Fujitsu

El uso del chatbot como elemento de acción tutorial en la enseñanza universitaria

Abstract: It is of great importance to help and pay attention to students through different educational activities to ensure their participation in class and thus reduce the dropout rate. Traditionally, tutoring activities have been limited to face-to-face sessions in which students pose questions to the teacher. However, in a connected world with many available information systems, innovative tools are needed to facilitate and speed up both the study and the resolution of doubts in a comfortable way. Methods: This paper proposes using a chatbot based tutoring system as a novel educational experience focused on motivating universities students. Results: Besides, we provide a proof-of-concept implementation of a chatbot that answers questions as quickly and accurately possible at any time, in a comfortable way for the students, and at the same time it gathers feedback from the students regarding those topics that need to be explained in class in more detail. Conclusions: This experience is intended to increase the engagement and collaboration of both students and instructors and has helped to decrease the dropout rate in recent years.Resumen: Es de vital importancia ayudar y guiar el aprendizaje de los estudiantes a través de diferentes herramientas y actividades educativas que faciliten su participación en clase y permitan reducir la tasa de abandono. Tradicionalmente, las actividades de tutorización para estudiantes universitarios, se limita a reuniones presenciales en las que los estudiantes plantean preguntas al docente. Sin embargo, dadas las circunstancias actuales y ante un mundo conectado con muchos sistemas de información disponibles, se necesitan herramientas docentes innovadoras que faciliten el aprendizaje y una ágil resolución de dudas. Método: en este trabajo se propone la utilización de un sistema de tutorías, basado en el uso de un chatbot como experiencia educativa novedosa y orientada a motivar y facilitar el aprendizaje en estudiantes universitarios. Resultados: estudio aporta la implementación de un chatbot que responde de forma rápida y precisa, disponible en cualquier momento para solucionar dudas y facilitar el estudio de las materias a los estudiantes. Este chatbot además permite recopilar comentarios de los propios estudiantes sobre los temas que requieren ser explicados en clase con un mayor detalle. Conclusiones: El uso del chatbot tutorial, ha permitido aumentar el compromiso y la colaboración tanto de los estudiantes como de los docentes, disminuyendo la tasa del número de estudiantes que abandonan la asignatura.Ministerio Español de Economía y Competitividad - project TIN2017-85727-C4-2-P -UGR-DeepBio

Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction

Financial distress prediction is crucial in the financial domain because of its implications for banks, businesses, and corporations. Serious financial losses may occur because of poor financial distress prediction. As a result, significant efforts have been made to develop prediction models that can assist decision-makers to anticipate events before they occur and avoid bankruptcy, thereby helping to improve the quality of such tasks. Because of the usual highly imbalanced distribution of data, financial distress prediction is a challenging task. Hence, a wide range of methods and algorithms have been developed over recent decades to address the classification of imbalanced datasets. Metaheuristic optimization-based artificial neural networks have shown exciting results in a variety of applications, as well as classification problems. However, less consideration has been paid to using a cost sensitivity fitness function in metaheuristic optimization-based artificial neural networks to solve the financial distress prediction problem. In this work, we propose ENS_PSONNcost and ENS_CSONNcost: metaheuristic optimization-based artificial neural networks that utilize a particle swarm optimizer and a competitive swarm optimizer and five cost sensitivity fitness functions as the base learners in a majority voting ensemble learning paradigm. Three extremely imbalanced datasets from Spanish, Taiwanese, and Polish companies were considered to avoid dataset bias. The results showed significant improvements in the g-mean (the geometric mean of sensitivity and specificity) metric and the F1 score (the harmonic mean of precision and sensitivity) while maintaining adequately high accuracy.Spanish Government PID2020-115570GB-C2

Memoria de Investigación 2016: Indicadores, Estadísticas y Actividades I+D

Un año más el Vicerrectorado de Investigación y Transferencia pone a disposición pública y abierta los resultados científicos de la Universidad de Granada. Los indicadores que ofrecemos se centran especialmente en la publicaciones indexadas en Web of Science, el estándar de medición internacional, así como en la financiación y obtención de proyectos de I+D. No hemos querido realizar un informe complaciente y por ello las comparaciones con universidades de nuestro entorno son abundantes, de manera que determinemos con exactitud en qué posición estamos. De esta forma cualquier ciudadano o miembro de la comunidad universitaria encontrará en este informe un retrato de nuestra investigación objetivo, transparente y contextualizado. A nivel de resultados la memoria muestra nuestro crecimiento en producción científica internacional, el aumento bruto de la financiación en proyectos estatales de I+D o el incremento de nuestra plantilla investigadores a través de la obtención de contratos posdoctorales.Universidad de Granada. Vicerrectorado de Investigación y Transferenci

Indicadores bibliométricos, 2015: Universidad de Granada

Presentamos el informe Indicadores Bibliométricos 2015 Universidad de Granada, incluido en la Memoria Académica 2014/2015. Consideramos que este nuevo informe constituye un instrumento para verificar objetivamente los resultados de las políticas científicas de la Universidad de Granada. Asimismo, supone un ejercicio de responsabilidad ante la sociedad al dar a conocer con total transparencia nuestros resultados. El informe se compone de los siguientes apartados:0. Material y métodos1. Resultados generales producción e impacto2. Comparativa con otras universidades3. Disciplinas ESI y categorías WoS4. Áreas OECD y universidades5. Altamente citados6. Investigadores en Google Scholar7. Resumen informes bibliométricos8. Estadísticas I+D9. Acción especial: FortalecimientoUniversidad de Granada. Vicerrectorado de Investigación y Transferenci

Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting

Online services depend primarily on customer feedback and communications. When this kind of input is lacking, the overall approach of the service provider can shift in unintended ways. These services rely on feedback to maintain consumer satisfaction. Online social networks are a rich source of consumer data related to services and products. Well developed methods like sentiment analysis can offer insightful analyses and aid service providers in predicting outcomes based on their reviews—which, in turn, enables decision-makers to develop effective strategic plans. However, gathering this data is more challenging on Arabic online social networks, due to the complexity of the Arabic language and its dialects. In this study, we propose an approach to sentiment analysis that combines a neutrality detector model with eXtreme Gradient Boosting and a genetic algorithm to effectively predict and analyze customers’ opinions of an e-Payment service through an Arabic social network. The proposed approach yields excellent results compared to other approaches. Feature analysis is also conducted on consumer reviews to identify influencing keywords.Deanship of Scientific Research, The University of JordanMinisterio espanol de Economia y Competitividad TIN2017-85727-C4-2-

Comparing the Performance of Deep Learning Methods to Predict Companies' Financial Failure

This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades under Project RTI2018-102002-A-I00, in part by the Ministerio de Economia y Competitividad under Project TIN2017-85727-C4-2-P and Project PID2020-115570GB-C22, in part by the Fondo Europeo de Desarrollo Regional (FEDER) and Junta de Andalucia under Project B-TIC-402-UGR18, and in part by the Junta de Andalucia under Project P18-RT-4830.One of the most crucial problems in the eld of business is nancial forecasting. Many companies are interested in forecasting their incoming nancial status in order to adapt to the current nancial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep Learning methods with respect to classi cation tasks, we compare the performance of three well-known Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model of 6 layers) with three bagging ensemble classi ers (Random Forest, Support Vector Machine and K-Nearest Neighbor) and two boosting ensemble classi ers (Adaptive Boosting and Extreme Gradient Boosting) in companies' nancial failure prediction. Because of the inherent nature of the problem addressed, three extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in this study. Thus, ve oversampling balancing techniques, two hybrid balancing techniques (oversamplingundersampling) and one clustering-based balancing technique have been applied to avoid data inconsistency problem. Considering the real nancial data complexity level and type, the results show that the Multilayer Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term Memory and ensemble methods obtained also very good results, outperforming several classi ers used in previous studies with the same datasets.Ministerio de Ciencia, Innovacion y Universidades RTI2018-102002-A-I00Spanish Government TIN2017-85727-C4-2-P PID2020-115570GB-C22European Commission B-TIC-402-UGR18Junta de Andalucia B-TIC-402-UGR18 P18-RT-483

The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings

Paper submitted to the ISSI Conference 2021.We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings