The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

The fact of appearing of the handheld devices offers forthright entree to the internet and social networking sites. Sentiment analysis and opinion mining is the study of sentiments or opinions shared by different users in social networking sites like, Twitter, Facebook, Reddit, Instagram etc., on diverse social phenomena. In this article, sentiment analysis of different tweets on the ongoing epidemic COVID-19, Corona virus disease is performed. COVID-19 is declared as epidemic by the World Health Organization (WHO) in the mid of March 2020. The statistical and machine learning based analyses are implemented on 40,000 tweets, which were collected in two different mutually exclusive time frames. Tweets are collected from Twitter site between 3/07/2020 to 11/07/2020 and 01/08/2020 to 06/08/2020, using Tweepy python library. Various Python based libraries are applied for data acquisition, data pre-processing and data analysis processes.  As a data pre-processing phase initially sentences are cleaned. Then by calculating the polarity and subjectivity measures tweets are categorized into three groups (viz., negative, neutral, and positive}). Thereafter, in the later phase by applying the Term frequency-inverse document frequency (TF-IDF) feature extraction scheme with the help of uni-gram, bi-gram, and tri-gram techniques different features are extracted to prepare the datasets to feed it into the prediction models. 70% of the datasets are used to train Gaussian Naive Bayes (G-NB), Bernoulli's Naive Bayes (B-NB), Random forest (RF), and Support vector machine (SVM) classifiers to generate different prediction models. Finally, 30% of the data is tested on those learning models. Experimental results suggest that RF and B-NB models performed better than the other two classifier models. The execution computational cost of SVM is very high.

/pdf/covid-19-public-tweets-sentiment-analysis-using-tf-idf-and-40jdd8v2ba.pdf

COVID-19 Public Tweets Sentiment Analysis using TF-IDF and Inductive Learning Models

Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have been successfully applied to Natural Language Processing (NLP), especially in sentiment analysis. NLP can execute numerous functions to achieve significant results through RNN and CNN. Likewise, previous research shows that RNN achieved meaningful results than CNN due to extracting long-term dependencies. Meanwhile, CNN has its advantage; it can extract high-level features using its local fixed-size context at the input level. However, integrating these advantages into one network is challenging because of overfitting in training. Another problem with such models is the consideration of all the features equally. To this end, we propose an attention-based sentiment analysis using CNN and two independent bidirectional RNN networks to address the problems mentioned above and improve sentiment knowledge. Firstly, we apply a preprocessor to enhance the data quality by correcting spelling mistakes and removing noisy content. Secondly, our model utilizes CNN with max-pooling to extract contextual features and reduce feature dimensionality. Thirdly, two independent bidirectional RNN, i.e., Long Short-Term Memory and Gated Recurrent Unit are used to capture long-term dependencies. We also applied the attention mechanism to the RNN layer output to emphasize each word’s attention level. Furthermore, Gaussian Noise and Dropout as regularization are applied to avoid the overfitting problem. Finally, we verify the model’s robustness on four standard datasets. Compared with existing improvements on the most recent neural network models, the experiment results show that our model significantly outperformed the state-of-the-art models.

ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis

Following the recent advances in wireless communication leading to increased Internet of Things (IoT) systems, many security threats are currently ravaging IoT systems, causing harm to information. Considering the vast application areas of IoT systems, ensuring that cyberattacks are holistically detected to avoid harm is paramount. Machine learning (ML) algorithms have demonstrated high capacity in helping to mitigate attacks on IoT devices and other edge systems with reasonable accuracy. However, the dynamics of operation of intruders in IoT networks require more improved IDS models capable of detecting multiple attacks with a higher detection rate and lower computational resource requirement, which is one of the challenges of IoT systems. Many ensemble methods have been used with different ML classifiers, including decision trees and random forests, to propose IDS models for IoT environments. The boosting method is one of the approaches used to design an ensemble classifier. This paper proposes an efficient method for detecting cyberattacks and network intrusions based on boosted ML classifiers. Our proposed model is named BoostedEnML. First, we train six different ML classifiers (DT, RF, ET, LGBM, AD, and XGB) and obtain an ensemble using the stacking method and another with a majority voting approach. Two different datasets containing high-profile attacks, including distributed denial of service (DDoS), denial of service (DoS), botnets, infiltration, web attacks, heartbleed, portscan, and botnets, were used to train, evaluate, and test the IDS model. To ensure that we obtained a holistic and efficient model, we performed data balancing with synthetic minority oversampling technique (SMOTE) and adaptive synthetic (ADASYN) techniques; after that, we used stratified K-fold to split the data into training, validation, and testing sets. Based on the best two models, we construct our proposed BoostedEnsML model using LightGBM and XGBoost, as the combination of the two classifiers gives a lightweight yet efficient model, which is part of the target of this research. Experimental results show that BoostedEnsML outperformed existing ensemble models in terms of accuracy, precision, recall, F-score, and area under the curve (AUC), reaching 100% in each case on the selected datasets for multiclass classification.

/pdf/boostedenml-efficient-technique-for-detecting-cyberattacks-2vyn7l7g.pdf

BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning

Using data mining to track the information spreading on social media about the COVID-19 outbreak

People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users' behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users' behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user's location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages.

/pdf/event-detection-system-based-on-user-behavior-changes-in-1i9dhed2mt.pdf

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.

/pdf/an-analysis-of-image-features-extracted-by-cnns-to-design-yi9ru2wx3u.pdf

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

Big data analytics for critical information classification in online social networks using classifier chains

A Skin Cancer Classification Approach using GAN and RoI-Based Attention Mechanism

In new generation networks, 5G and 6G networks, intelligent mechanisms based on artificial intelligence algorithms are playing a relevant role in the performance improvement at different network levels. In 5G networks, different techniques are used, such as MIMO systems, and its network infrastructure is utilized by diverse services, for instance, vehicular communications. Thus, the description and the tracing of a communication scenario needs a large volume of data. Because of the difficulty to implement actual 5G networks, there is a lack of datasets containing complete 5G scenarios, the data is not enough or contain imbalanced classes to properly train machine learning (ML) algorithms. In this context, we propose a method to increase the amount of data to improve the machine learning performance of some classification models, specifically Random Forest, Multilayer Perceptron, and k-Nearest Neighbors. In the experimental results of the test phase, considering the inclusion of synthetic data, Random Forest, Multilayer Perceptron and k-Nearest Neighbors reached macro F1 scores of 0.9341, 0.9241 and 0.9456, respectively, which are superior to the results obtained when training with the original data only.

Douglas Henrique Silva

Papers

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

Big data analytics for critical information classification in online social networks using classifier chains

A Skin Cancer Classification Approach using GAN and RoI-Based Attention Mechanism

Selection of Beamforming in 5G MIMO scenarios using Machine Learning approach