Related papers: Evaluating COVID 19 Feature Contributions to Bitco…

Most current assessments use ex post proxies that miss uncertainty and fail to consistently capture the rapid change in bitcoin mining. We introduce a unified, ex ante statistical model that derives expected return, downside risk, and…

COVID-19 is extremely contagious and its rapid growth has drawn attention towards its early diagnosis. Early diagnosis of COVID-19 enables healthcare professionals and government authorities to break the chain of transition and flatten the…

The advent of the COVID-19 pandemic has instigated unprecedented changes in many countries around the globe, putting a significant burden on the health sectors, affecting the macro economic conditions, and altering social interactions…

Accurate forecasts of COVID-19 is central to resource management and building strategies to deal with the epidemic. We propose a heterogeneous infection rate model with human mobility for epidemic modeling, a preliminary version of which we…

The paper studies the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends, Wikipedia pages visits. The pattern of deviation of regression model…

In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121 and the…

In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and…

Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market…

Since the onset of the COVID-19 pandemic in 2020, millions of people have succumbed to this deadly virus. Many attempts have been made to devise an automated method of testing that could detect the virus. Various researchers around the…

In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the…

The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in…

COVID-19 has been a public health emergency of international concern since early 2020. Reliable forecasting is critical to diminish the impact of this disease. To date, a large number of different forecasting models have been proposed,…

Glioblastoma recurrence is largely driven by diffuse infiltration beyond radiologically visible tumor margins, yet standard radiotherapy, the mainstay of glioblastoma treatment, relies on uniform expansions that ignore patient-specific…

We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative…

This study investigates the impact of data source diversity on the performance of cryptocurrency forecasting models by integrating various data categories, including technical indicators, on-chain metrics, sentiment and interest metrics,…

In here, we introduce a novel approach to enhance the accuracy and efficiency of COVID-19 diagnosis using CT images. Leveraging state-of-the-art Transformer models in computer vision, we employed the base ViT Transformer configured for…

Background: Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of affected patients increase rapidly, manual severity assessment becomes a labor-intensive task, and may lead to…

We study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media…

Cardiovascular diseases (CVDs) are one of the most common chronic illnesses that affect peoples health. Early detection of CVDs can reduce mortality rates by preventing or reducing the severity of the disease. Machine learning algorithms…

The worldwide impact of the recent COVID-19 pandemic has been substantial, necessitating the development of accurate forecasting models to predict the spread and course of a pandemic. Previous methods for outbreak forecasting have faced…