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

Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

Efficient management of treated sewage effluents protects the environment and reuse of municipal, industrial, agricultural and recreational as compensation for water shortages as a second source of water. This study was conducted to investigate the overall performance and evaluate the effluent quality from Al- Rustamiya sewage treatment plant (STP), Baghdad, Iraq by determining the effluent quality index (EQI). This assessment included daily records of major influent and effluent sewage parameters that were obtained from the municipal sewage plant laboratory recorded from January 2011 to December 2018. The result showed that the treated sewage effluent quality from STP was within the Iraqi quality standards (IQS) for disposal and t

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

The agricultural lands that depend on supplementary irrigation methods for winter wheat cultivating in wide areas of the Nineveh province are most vulnerable to climate change concerns. Due to frequent rainfall shortages and the temperature increase recently noticed and predicted by the climate scenarios. Hence important to assess the climate effect on the crop response in terms of water consumption during the periods (2021-2040) and (2041-2060) by using high-resolution data extracted from 6 global climate data GCMs under SSP5-8.5 fossil fuel emission scenarios in changing and fixed CO₂ concentration. And validate the Aqua-Crop model to estimate the yield and water productivity. And gives the RRSME of 7.1- 4.1

It  can be said that the Security of water  in  Basra from the visual task vital strategic issues of concern to the attention of researchers in various attributions and those interested in water, environmental, economic, social, cultural and political affairs ... etc. This view of the great importance of the issue of water in the occupied Basra, which is characterized by parochialism and scarcity, When looking at the sources of our daily lives and in our reality today. We find that millions of people living on the two main exporters Tabaaan oil and water. And depleted oil wealth However Manfred him the most attention because we entered it surpasses all other sources of income, but is not it a littl