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The numerical nature of financial markets makes market forecasting and portfolio construction a good use case for machine learning (ML), a branch of artificial intelligence (AI). Over the past two decades, a number of academics worldwide (mostly from the field of computer science) produced a sizeable body of experimental research. Many publications claim highly accurate forecasts or highly profitable investment strategies. At the same time, the picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures). We conducted a literature review of 27 academic experiments spanning over two decades and contrasted them with real-life examples of machine learning-driven funds to try to explain this apparent contradiction. The specific contributions our article will make are as follows (1) A comprehensive, thematic review (quantitative and qualitative) of multiple academic experiments from the investment management perspective. (2) A critical evaluation of running multiple versions of the same models in parallel and disclosing the best-performing ones only (“cherry-picking”). (3) Recommendations on how to approach future experiments so that their outcomes are unambiguously measurable and useful for the investment industry. (4) An in-depth comparison of real-life cases of ML-driven funds versus academic experiments. We will discuss whether present-day ML algorithms could make feasible and profitable investments in the equity markets.

COVID-19 has become an international emergency. The use of digital solutions can be effective in managing, preventing, and overcoming the further spread of infectious disease outbreaks. Accordingly, the use of mobile-health (m-health) technologies has the potential to promote public health. This review aimed to study the application of m-health solutions for the management of the COVID-19 outbreak.

The search strategy was done in Medline (PubMed), Embase, IEEE, and Google Scholar by using related keywords to m-health and COVID-19 on July 6, 2020. English papers that used m-health technologies for the COVID-19 outbreak were included.

Of the 2046 papers identified, 16 were included in this study. M-health had been used for various aims such as early detection, fast screening, patient monitoring, information sharing, education, and treatment in response to the COVID-19 outbreak. M-health solutions were classified into four use case categories prevention, diagnosis, treatment, and protection. The mobile phone-based app and short text massaging were the most frequently used modalities, followed by wearables, portable screening devices, mobile-telehealth, and continuous telemetry monitor during the pandemics.

It appears that m-health technologies played a positive role during the COVID-19 outbreak. Given the extensive capabilities of m-health solutions, investigation and use of all potential applications of m-health should be considered for combating the current Epidemics and mitigating its negative impacts.

It appears that m-health technologies played a positive role during the COVID-19 outbreak. Given the extensive capabilities of m-health solutions, investigation and use of all potential applications of m-health should be considered for combating the current Epidemics and mitigating its negative impacts.The Coronavirus disease 2019 (COVID-19) outbreak has been ravaging Iran and other countries with increasing morbidity and mortality. The pathogen spread rapidly and the outbreak caused nationwide anxiety and shock in Iran. To combat the COVID-19 epidemic, the Ministry of Health and Medical Education (MOHME) of Iran introduced several policies and activities, including the use of tele-health services. This letter to the editor uses anecdotal and other records to provide a summary of the activities of MOHME during the COVID-19 outbreak in Iran from February 1 to March 31, 2020. In this commentary, we reviewed the MOHME information site and extended the recommendations offered by MOHME via presenting the existing challenges and a roadmap of the necessary policy requirements. The existing evidence demonstrates that tele-health should have been rapidly implemented as it presents an effective mode of service delivery to reduce morbidity and mortality and decrease the burden on healthcare providers and the health system during the COVID-19 outbreak.Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from January 22, 2020 to January 25, 2021. Selleckchem AZD4547 We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-1ted to KSA.The existence of widespread COVID-19 infections has prompted worldwide efforts to control and manage the virus, and hopefully curb it completely. One important line of research is the use of machine learning (ML) to understand and fight COVID-19. This is currently an active research field. Although there are already many surveys in the literature, there is a need to keep up with the rapidly growing number of publications on COVID-19-related applications of ML. This paper presents a review of recent reports on ML algorithms used in relation to COVID-19. We focus on the potential of ML for two main applications diagnosis of COVID-19 and prediction of mortality risk and severity, using readily available clinical and laboratory data. Aspects related to algorithm types, training data sets, and feature selection are discussed. As we cover work published between January 2020 and January 2021, a few key points have come to light. The bulk of the machine learning algorithms used in these two applications are supervised learning algorithms.