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.
Knowledge of the distribution of the rock mechanical properties along the depth of the wells is an important task for many applications related to reservoir geomechanics. Such these applications are wellbore stability analysis, hydraulic fracturing, reservoir compaction and subsidence, sand production, and fault reactivation. A major challenge with determining the rock mechanical properties is that they are not directly measured at the wellbore. They can be only sampled at well location using rock testing. Furthermore, the core analysis provides discrete data measurements for specific depth as well as it is often available only for a few wells in a field of interest. This study presents a methodology to generate synthetic-geomechani
... Show MoreThe current research aims to verify the role of strategic intelligence as an explanatory variable in organizational success as a respondent variable in the colleges of the University of Fallujah, the research community. (Dean, Associate Dean, Section Head, Division Officer, Unit Officer), The researcher used the questionnaire as the main tool to collect data that included (50) items, in addition to using personal interviews and field observations as aids in data collection. The researcher relied on statistical programs (SPSS V.25; Excel V (16) In the treatment and analysis of data through the use of the most appropriate statistical methods (arithmetic mean, standard deviation, difference coefficient, determinatio
... Show MoreThe emphasis of Master Production Scheduling (MPS) or tactic planning is on time and spatial disintegration of the cumulative planning targets and forecasts, along with the provision and forecast of the required resources. This procedure eventually becomes considerably difficult and slow as the number of resources, products and periods considered increases. A number of studies have been carried out to understand these impediments and formulate algorithms to optimise the production planning problem, or more specifically the master production scheduling (MPS) problem. These algorithms include an Evolutionary Algorithm called Genetic Algorithm, a Swarm Intelligence methodology called Gravitational Search Algorithm (GSA), Bat Algorithm (BAT), T
... 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
... Show MoreImage recognition is one of the most important applications of information processing, in this paper; a comparison between 3-level techniques based image recognition has been achieved, using discrete wavelet (DWT) and stationary wavelet transforms (SWT), stationary-stationary-stationary (sss), stationary-stationary-wavelet (ssw), stationary-wavelet-stationary (sws), stationary-wavelet-wavelet (sww), wavelet-stationary- stationary (wss), wavelet-stationary-wavelet (wsw), wavelet-wavelet-stationary (wws) and wavelet-wavelet-wavelet (www). A comparison between these techniques has been implemented. according to the peak signal to noise ratio (PSNR), root mean square error (RMSE), compression ratio (CR) and the coding noise e (n) of each third
... 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 MoreIn 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 Moreأن عملية التعلم لازالت تسير بنفس الاسلوب المتبع الذي لا يعتبر المتعلمة محور اساسي في عملية التعلم مما سبب ظهور الملل وانخفاض الرغبة لدى المتعلمات للتعلم لغياب الحافز, ولكون المهارات الاساسية بكرة السلة كالمناولة الصدرية والطبطبة بتغير الاتجاه والتصويب السلمي تعد من المهارات المهمة في اللعبة تم اجراء هذه الدراسة الذي يهدف الى اعداد منهج تعليمي قائم على انموذج التعلم البنائي والتعرف على تأثيره في بعض ا
... Show More