Meta-heuristic algorithms have been significantly applied in addressing various real-world prediction problem, including in disease prediction. Having a reliable disease prediction model benefits many parties in providing proper preparation for prevention purposes. Hence, the number of cases can be reduced. In this study, a relatively new meta-heuristic algorithm namely Barnacle Mating Optimizer (BMO) is proposed for short term dengue outbreak prediction. The BMO prediction model is realized over real dengue cases data recorded in weekly frequency from Malaysia. In addition, meteorological data sets were also been employed as input. For evaluation purposes, error analysis relative to Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Deviation (MAD) were employed to validate the performance of the identified algorithms which includes the comparison between BMO against Moth Flame Optimizer (MFO) and Grey Wolf Optimizer (GWO) algorithms. Upon simulation, the superiority is in favour to BMO by producing lower error rates.
Concentrated research topic in the study of key variables in the work of the inspectors general offices , which are in the application of quality management standards audit work and reduce the incidence of corruption. It highlights the importance of current research in being a serious attempt aimed at highlighting the role of the importance of standards of quality management audit work , because they represent a router and leader of the accountant or ( Sergeant ) in the performance of his work and the extent of compliance with these standards , as well as highlight the role of quality audit in reducing the incidence of corruption , of during the professional performance of Higher auditors and determine the responsibilities entrus
... Show MoreThe 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
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Coronavirus has affected many people around the world and caused an increase in the number of hospitalized patients and deaths. The prediction factor may help the physician to classify whether the patient needs more medical attention to decrease mortality and worsening of symptoms. We aimed to study the possible relationship between C reactive protein level and the severity of symptoms and its effect on the prognosis of the disease. And determine patients who require closer respiratory monitoring and more aggressive supportive therapies to avoid poor prognosis. The data was gathered using medical record data, the patient's medical history, and the onset of symptoms, as well as a blood sample to test the
... Show MoreBackground:Breast carcinoma is the most common malignant tumor and the leading cause of carcinoma death in women, with more than 1,000,000 cases occurring worldwide annually.(1) as a matter of fact , the mortality rate for breast carcinoma changed very little from the 1930s to the early 1990s, because of the combined action of earlier diagnosis and improved therapy .<br />Materials and methods: The prospective study included 500 cases of breast carcinoma who went total mastectomy , between October 2006- April 2007, where taken from private pathology laboratory , sections(4microns) are taken and stained with H&E stain and over-reviewed.<br />Results: Clinicopathological analysis of the 500 cases of breast carcinoma, includ
... Show MoreBack ground: This is a prospective study of Head injury in Najaf.
Aim: to study the causes & out come & way of transferring the rat to the hospital & best way to investigate them.
Patients & methods:A prospective analytical study of 200 cases of Head injury patients, who were admitted to Saddam Teaching Hospital , in Najaf between 18 t" of November 1996 and 1st of September 1998.
Results: All age groups were included in this study, male to female ratio was 4:1 and the highest incidence was seen at the age group below 14 years. The two most common causes of
head injury were road traffic accident (RTA)(51 %) and assault (22%),of RTA pedestrians accounted for (87.25%). RTA accounted of (8
Background: Brain abscess is collection of pus in the brain parrynchima surrounded by a true capsule. Usually diagnosed by CT & MRI, & treated surgically by drainage by burr hole, or excision.
Objective: evaluate our work with brain abscess.
Patient& method: 74 Patients collected in the specialized surgical hospital neuro-surgical department, from Jan. 1995 till Jan. 2005 treated surgically, all cases fully evaluated clinically & radiologically & then evaluation of the surgical procedure.
Results: there is a slight male predominance & prevalence more in the 1st 2decades of life mostly in children with cong. heart disease, headache was the most common presenting feature, with other signs of infection diagn
In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc
... Show MoreIn this paper, we study some cases of a common fixed point theorem for classes of firmly nonexpansive and generalized nonexpansive maps. In addition, we establish that the Picard-Mann iteration is faster than Noor iteration and we used Noor iteration to find the solution of delay differential equation.
The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed a
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