No abstract

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

In this paper, sentiment analysis of two critical events is presented using machine learning (ML) techniques. COVID-19 has put immense pressure across the globe and sentiment analysis of data from Twitter using ML techniques has become a hot topic. We extract the COVID-19 and Expo2020 data from twitter. First, we evaluate the Twitter data of these two significant events for sentiment analysis and then use the classification algorithm to find out the usefulness of the proposed methodology. A hybrid approach that uses supervised learning model Support Vector Machine (SVM) combined with Bayes Factor Tree Augmented Naive Bayes (BFTAN) technique is proposed to accurately classify the input tweet while keeping in mind the different challenges of sentiment analysis. Our study has four main contributions: a) hybrid classification techniques are thoroughly explored for sentiment analysis, b) a novel hybrid classification approach is proposed for sentiment analysis, c) a new Twitter dataset related to COVID-19 that can be used for future research, d) empirical study to show that the hybrid-classification approach can achieve comparable performance in improving accuracy, identifying the polarity of comparative sentences, distinguishing the intensity of opinion words, considering negative words, and handling sarcasm as well. The experimental results show that the proposed approach is robust in producing correct classification results with the tradeoff of poor time efficiency. Also, the accuracy of the proposed model is comparable to other classifiers, which is encouraging. Class distribution of each dataset demonstrates that more than 60% of tweets are negative.

Background: An increased CA 125 in conjunction with a pelvic mass is, although, strongly indicative of ovarian cancer, there are a number of other benign diseases that may be linked to a pelvic mass and a higher CA 125. Meigs syndrome is an uncommon condition in women under the age of 30. It consists of a triad of benign fibrous ovarian tumors, ascites, and pleural effusion. When the tumor is removed, the symptoms got resolve completely within two weeks. Case Presentation: It is a case of a 15-year-old girl with fibroma, along with a review of the literature. Although the cause of the fluid accumulations is unknown, it seems to be linked to lymphatic blockage. Abdominal distension, pain, cough, pleuritic chest pain, vomiting, fever, and weight loss are all presenting symptoms. Conclusion: This case report concludes that although a pelvic mass with elevated CA 125 is strongly indicative of malignancy, other illnesses, particularly Meigs syndrome and pseudo-Meigs syndrome in young women presenting with a pleural effusion, should always be evaluated as a differential diagnosis. The fluid buildup typically disappears within two weeks of the tumor being removed.