After the outbreak of severe acute respiratory syndrome (SARS-2002/2003) and middle east respiratory syndrome (MERS-2012/2014) in the world, new public health crisis, called new coronavirus disease (COVID-19), started in China in December 2019 and has spread all over countries. COVID-19 coronavirus has been global threat of the disease and infected humans rapidly. Control of the pandemi is urgently essential, and science community have continued to research treatment agents. Support therapy and intensive care units in hospitals are also efective to overcome of COVID-19. Statistic forecasting models could aid to healthcare system in preventation of COVID-19. This study aimed to compose of forecasting model that could be practical to predict the spread of COVID-19 in Italy, Spain and Turkey. For this purpose, we performed Auto Regressive Integrated Moving Average (ARIMA) model on the European Centre for Disease Prevention and Control COVID-19 data to predict the number of cases and deaths in COVID-19. According to the our results, while number of cases in Italy and Spain is expected to decrease as of July, in Turkey is expected to decline as of September. The number of deaths in Italy and Spain is expected to be the lowest in July. In Turkey, this number is expected to reach the highest in July. In addition, it is thought that if studies in which the sensitivity and validity of this method are tested with more cases, they will contribute to researchers working in this field. KEYWORDS: COVID-19, pandemi, ARIMA, time series analysis
Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets.
The aim of this study is to compare the least squares (LS) method that lost its function in the case of multicollinearity in regression methods with Ridge Regression (RR) and Principal Components Regression (PCR) which are bias estimators. For this aim, the effect of some body measurements on body weight (BW), body length (BL), height at withers (HW), height at rump (HR), chest depth (CD), chest girth (CG) and chest width (CW) obtained from 59 Saanen kids at weaning period raised at Research Farm of Tokat Gaziosmanpaşa University. Determination coefficient (R2) and mean square error (MSE) values were used to evaluate the estimation performance of the methods. The multicollinearity between height at withers (HW) and height at rump (HR) which were used to estimate body weight was eliminated by using RR and PCR. When R2 and HKO values of the examined methods are compared; It has been shown that RR method have better results of live weight of Saanen goats.
Bu araştırma Tokat Gaziosmanpaşa Üniversitesi araştırma çiftliğinde 3 katlı bir kafes sisteminde gerçekleştirilmiştir. Her kafes bölmesinde 6 tavuk yerleştirilmiş ve her katta 5 tekerrür oluşturulmuştur. 24-42. haftalar arasında toplam 90 Atak-S tavuğundan 9:00 - 13:00 ve 16:00 saatlerinde mevcut yumurtalar toplanmıştır. Deneme süresince 90 tavuktan toplam 1442 yumurta elde edilmiştir. Kafes pozisyonu ile yumurtlama zamanı arasındaki ilişkinin belirlenmesi için uyum analizi uygulanmıştır. Verilerin analizinde SPSS paket programı kullanılmıştır. Sonuçlara göre farklı katlardaki tavukların farklı saatlerde yumurtlamaları istatistiksel olarak önemli bulunmuştur. Uyum analizi sonuçlarına göre değişkenliğin tek boyuttaki açıklama gücü %99,7 olup ikinci boyuttaki açıklama gücünün %0,3 olduğu belirlenmiştir. Birinci boyuttaki kafes katları bakımından değişkenlik incelendiğinde birinci katın açıklama gücünün %65,4 olduğu ikinci boyutta ise ikinci (%56,9) ve üçüncü katın (%41,8) açıklama gücünün daha yüksek olduğu belirlenmiştir. Birinci boyuttaki yumurta toplama saatleri bakımından değişkenlik incelendiğinde saat 13:00‘da toplama için açıklama gücünün %52,8 olduğu ikinci boyutta ise saat 16:00‘daki toplama için açıklama gücünün %79,6 olduğu belirlenmiştir. Sonuç olarak birinci kattaki tavukların genellikle saat 9:00 sıralarında, 2. ve 3. kattaki tavukların ise 13:00 ile 16:00 saatleri arasında yumurtladığı belirlenmiştir. Yumurtaların ortam sıcaklığına göre değişiklik göstermekle birlikte üreticiler tarafından genellikle sabah saatlerinde toplandığı bilinmektedir. Bu nedenle yumurta toplama sıklığının hem ekonomik olarak hem de tüketici sağlığı açısından yeniden düzenlenmesi önem arz etmektedir.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.