The novel coronavirus disease (COVID-19) has dramatically affected people’s daily lives worldwide. More specifically, since there is still insufficient access to vaccines and no straightforward, reliable treatment for COVID-19, every country has taken the appropriate precautions (such as physical separation, masking, and lockdown) to combat this extremely infectious disease. As a result, people invest much time on online social networking platforms (e.g., Facebook, Reddit, LinkedIn, and Twitter) and express their feelings and thoughts regarding COVID-19. Twitter is a popular social networking platform, and it enables anyone to use tweets. This research used Twitter datasets to explore user sentiment from the COVID-19 perspective. We used a dataset of COVID-19 Twitter posts from nine states in the United States for fifteen days (from 1 April 2020, to 15 April 2020) to analyze user sentiment. We focus on exploiting machine learning (ML), and deep learning (DL) approaches to classify user sentiments regarding COVID-19. First, we labeled the dataset into three groups based on the sentiment values, namely positive, negative, and neutral, to train some popular ML algorithms and DL models to predict the user concern label on COVID-19. Additionally, we have compared traditional bag-of-words and term frequency-inverse document frequency (TF-IDF) for representing the text to numeric vectors in ML techniques. Furthermore, we have contrasted the encoding methodology and various word embedding schemes, such as the word to vector (Word2Vec) and global vectors for word representation (GloVe) versions, with three sets of dimensions (100, 200, and 300) for representing the text to numeric vectors for DL approaches. Finally, we compared COVID-19 infection cases and COVID-19-related tweets during the COVID-19 pandemic.

Cloud computing has become the cornerstone of modern technology, propelling industries to unprecedented heights with its remarkable and recent advances. However, the fundamental challenge for cloud service providers is real-time workload prediction and management for optimal resource allocation. Cloud workloads are characterized by their heterogeneous, unpredictable, and fluctuating nature, making this task even more challenging. As a result of the remarkable achievements of deep learning (DL) algorithms across diverse fields, scholars have begun to embrace this approach to addressing such challenges. It has become the defacto standard for cloud workload prediction. Unfortunately, DL algorithms have been widely recognized for their vulnerability to adversarial examples, which poses a significant challenge to DL-based forecasting models. In this study, we utilize established white-box adversarial attack generation methods from the field of computer vision to construct adversarial cloud workload examples for four cutting-edge deep learning regression models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Network (1D-CNN) and attention-based models. We evaluate our study with three widely recognized cloud benchmark datasets: Google trace, Alibaba trace, and Bitbrain. The findings of our analysis unequivocally indicate that DL-based cloud workload forecasting models are highly vulnerable to adversarial attacks. To the best of our knowledge, we are the first to conduct systematic research exploring the vulnerability of DL-based models for workload forecasting in the cloud data center, highlighting the inherent hazards to both security and cost-effectiveness in cloud data centers. By raising awareness of these vulnerabilities, we advocate the urgent development of robust defensive mechanisms to enhance the security of cloud workload forecasting in a constantly evolving technical landscape.

Sentiment analysis can largely influence the people to get the update of the current situation. Coronavirus (COVID-19) is a contagious illness caused by the SARS-CoV-2 virus that causes severe respiratory symptoms. The lives of millions have continued to be affected by this pandemic, several countries have resorted to a full lockdown. During this lockdown, people have taken social networks to express their emotions to find a way to calm themselves down. People are spreading their sentiments through microblogging websites as one of the most preventive steps of this disease is the socialization to gain people’s awareness to stay home and keep their distance when they are outside home. Twitter is a popular online social media platform for exchanging ideas. People can post their different sentiments, which can be used to aware people. But, some people want to spread fake news to frighten the people. So, it is necessary to identify the positive, negative, and neutral thoughts so that the positive opinions can be delivered to the mass people for spreading awareness to the people. Moreover, a huge volume of data is floating on Twitter. So, it is also important to identify the context of the dataset. In this paper, we have analyzed the twitter dataset for evaluating the sentiment using several machine learning algorithms, where the random forest algorithm achieved the highest accuracy of 93%. Later, we have found out the context learning of the dataset based on the sentiments.