In 2019 there was an outbreak of coronavirus pandemic also known as COVID-19. Many scientists believe that the pandemic originated from Wuhan, China, before spreading to other parts of the globe. To reduce the spread of the disease, decision makers encouraged measures such as hand washing, face masking, and social distancing. In early 2021, some countries including the United States began administering COVID-19 vaccines. Vaccination brought a relief to the public; it also generated a lot of debates from anti-vaccine and pro-vaccine groups. The controversy and debate surrounding COVID-19 vaccine influenced the decision of several people in either to accept or reject vaccination. Because of data limitations, social media data, collected through live streaming public tweets using an Application Programming Interface (API) search, is considered a viable and reliable resource to study the opinion of the public on Covid-19 vaccine hesitancy. Thus, this study examines 3 sentiment computation methods (Azure Machine Learning, VADER, and TextBlob) to analyze COVID-19 vaccine hesitancy. Five learning algorithms (Random Forest, Logistics Regression, Decision Tree, LinearSVC, and Naïve Bayes) with different combination of three vectorization methods (Doc2Vec, CountVectorizer, and TF-IDF) were deployed. Vocabulary normalization was threefold; potter stemming, lemmatization, and potter stemming with lemmatization. For each vocabulary normalization strategy, we designed, developed, and evaluated 42 models. The study shows that Covid-19 vaccine hesitancy slowly decreases over time; suggesting that the public gradually feels warm and optimistic about COVID-19 vaccination. Moreover, combining potter stemming and lemmatization increased model performances. Finally, the result of our experiment shows that TextBlob + TF-IDF + LinearSVC has the best performance in classifying public sentiment into positive, neutral, or negative with an accuracy, precision, recall and F1 score of 0.96752, 0.96921, 0.92807 and 0.94702 respectively. It means that the best performance was achieved when using TextBlob sentiment score, with TF-IDF vectorization and LinearSVC classification model. We also found out that combining two vectorizations (CountVectorizer and TF-IDF) decreases model accuracy.

Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy–sadness, trust–disgust, fear–anger, surprise–anticipation, and negative–positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.

The extensive and complex network attacks on smart devices and computers trigger a need for robust and adaptive intrusion detection systems (IDSs). Most of the existing intrusion detection systems are implemented on certain rules where new attacks are not detectable and used outdated datasets such as KDD Cup 99. This paper presents an Artificial Neural Networks (ANN) as a machine learning approach to intrusion detection system for Denial of Service Attacks (DoS) and Distributed Denial of Service Attacks (DDoS). The proposed method used the Bayesian Regularization (BR) backpropagation and scaled conjugate gradient (SCG) descent backpropagation algorithm. The network was trained and tested with subset of the CICID2017 dataset that meets the real world criteria. The features that best characterize each attack and normal network traffic in the input dataset was selected carefully. The result revealed the proposed method successfully detect DoS/DDoS attacks with an accuracy of 99.6% using Bayesian Regularization and 97.7 % in scaled conjugate gradient descent.

An algorithm for designing spreading sequences for an overloaded multicellular synchronous DS-CDMA system on uplink is introduced. The criterion used to measure the optimality of the design is the total weighted square correlation (TWSC) assuming the channel state information known perfectly at both transmitter and receiver. By using this algorithm it is possible to obtain orthogonal generalized WBE sequences sets for any processing gain. The bandwidth of initial generalized WBE signals of each cell is preserved in the extended signal space associated to multicellular system. Mathematical formalism is illustrated by selected numerical examples.

We focus on the detection of Low Rate Denial of Service Attacks (LR DoS) based on their spectral properties. We revised the Fisher g-statistic test and Siegel test for detection of LR DoS attacks such as Shrew and New Shrew attacks. Our results reveal that these attacks can be effectively identified by using Fisher g-statistic test for one dominant periodicity as in the case of Shrew attacks. When multiple (compound) dominant periodicities exist in the case of New Shrew attacks, Siegel test can identify the attack better than Fisher g-statistic test. Our contribution consists in developing a new methodology for rejecting the null hypothesis or identifying LR DoS attacks. Numerical examples for a confidence interval at significance level of α = 0.01 and α = 0.05 are given for Shrew and New Shrew attacks.