The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

The purpose of this study is to describe the extent and nature of informal tenure practices in urban areas in Iraq, through undertaking a rapid assessment in Baghdad city. The UN-HABITAT 2008 publication Secure Land Rights for All discusses the importance of access and rights to land throughout the developing world. Secure land rights are critical to development and poverty reduction, and the greatest challenge in providing secure land rights are in urban areas, where overcrowding can lead to a number of informal tenure practices ranging from individually unregistered or unauthorised housing, to large informal settlements. Access to land is a fundamental basis for human shelter, food production, and other economic activity. Secur

The aim of this research is to identify the availability of visual thinking skills in the chemistry textbook scheduled for the third intermediate grade for the academic year (2020-2021) in the Republic of Iraq. The study sample consisted of all (85) images contained in the chemistry course for the third intermediate grade, which are (85) form using the curriculum.  Analytical descriptive A list of visual thinking skills was prepared, and the unit of form was adopted as a unit of analysis and repetition as a unit of counting, and frequencies and percentages were used for statistical treatment, and validity and reliability were calculated.  And using the Holste equation.  The following results were reached: The skill

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The