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 systematic literature review demonstrates that ML-powered tools can alleviate the burden on healthcare systems. These tools can analyze significant amounts of medical data and potentially improve predictive and preventive healthcare.
In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete
... Show MoreAlzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of
... Show MoreNowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of
... Show MoreCreativity, imagination, and ingenuity are connected with instructing students in literary tasks. The purpose of the study is to determine what teaching activities are most Productive in helping students to comprehend literature. The research design used in the study is descriptive one. The researcher created a survey questionnaire that teachers could complete. It includes twelve learning activities related to teaching literature, such as: conceptual mapping of a poem, short story, or novel; observing and analyzing videos; collaborative discussions about the chosen text; and topical discussions with a partner ,composition of a poem ,analytic writings; playing a role; narrating stories with visual assistance, teacher-student exchanges; atten
... Show MoreThere are numbers of automatic translation services that internet users can choose to automatically translate a certain text, and Google translate is one of these automatic services that proposes over 51 Languages. The present paper sheds light on the nature of the translation process offered by Google, and analyze the most prominent problems faced when Google translate is used. Direct translation is common with Google Translate and often results in nonsensical literal translations, particularly with long compound sentences. This is due to the fact that Google translation system uses a method based on language pair frequency that does not take into account grammatical rules which, in turn, affects the quality of the translation. The
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This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per
... Show MoreThe main factors that make it possible to get the corrosion of reinforcing steel in concrete are chloride ions and the absorption of carbon dioxide from the environment, and each of them works with a mechanism which destroys the stable immunity of rebar in the concrete. In this work the effect of carbon dioxide content in the artificial concrete solution on the corrosion behavior of carbon steel reinforcing bar (CSRB) was studied, potentiostatically using CO2 stream gas at 6 level of concentrations; 0.03 to 2.0 weight percent, and the effect of rising electrolyte temperature was also followed in the range 20 to 50ᴼ C. Tafel plots and cyclic polarization procedures were obeyed to investigate the c
... Show MoreThe steady consumption of fish led many researchers to study it preferences over other foods, especially for radioactivity content. The specific activity concentration (S.A) of natural occurring radioactive materials (NORM) have been measured for Cyprinus carpio fishes collected from several industrial fishes' lakes located in Baghdad governorate using gamma spectroscopy doped with high purity germanium coaxial detector (HPGe). Thirteen fishes' samples were collected from industrial lakes, three samples were collected from cages, and two samples were collected from Trigger River. The last two types of samples were collected in order to compare the results with it. The measured overall averages of S.A for Ra-226, Th-232, and K-40 were 58.
... Show MoreThe current study aimed to detect artificial fraud in chemical additives (formalin, hydrogen peroxide) and to detect thickeners (starch) in imported and local dairy products and to determine their suitability for human consumption due to the high demand for their consumption by members of society, 20 samples of products were collected The local and imported milk in the Iraqi markets of the city of Baghdad consisted of 10 samples of yoghurt and 10 samples of cream. The tests were carried out, which were the detection of chemicals and additive thickeners such as formalin, hydrogen peroxide and starch, respectively, as well as the examination of the microbial load, which included the detection of colon bacteria, yeasts and molds, and the resul
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