Coronavirus disease 2019 (COVID-2019) has spread rapidly all over the world and it is known that the most effective way to eliminate the disease is vaccination. Although the traditional vaccine development process is quite long, more than ten COVID-19 vaccines have been approved for use in about a year. The COVID-19 vaccines that have been administered are highly effective enough, but achieving herd immunity is required to end the pandemic. The motivation of this study is to contribute to review the countries’ vaccine policies and adjusting the manufacturing plans of the vaccine companies. In this study, the total number of people fully vaccinated against COVID-19 was forecasted in the US, Asia, Europe, Africa, South America, and the World with the Autoregressive Integrated Moving Average (ARIMA) model, which is a new approach in vaccination studies. Additionally, for herd immunity, the percentage of fully vaccinated people in these regions at the beginning of 2021 summer was determined. ARIMA results show that in the US, Asia, Europe, Africa, South America, and the World will reach 139 million, 109 million, 127 million, 8 million, 38 million, and 441 million people will be fully vaccinated on 1 June 2021, respectively. According to these results, 41.8% of the US, 2.3% of Asia, 17% of Europe, 0.6% of Africa, 8.8% of South America, and 5.6% of the World population will be fully vaccinated people against the COVID-19. Results show that countries are far from the herd immunity threshold level desired to reach for safely slow or stop the COVID-19 epidemic.
Missing values in datasets present an important problem for traditional and modern statistical methods. Many statistical methods have been developed to analyze the complete datasets. However, most of the real world datasets contain missing values. Therefore, in recent years, many methods have been developed to overcome the missing value problem. Heuristic methods have become popular in this field due to their superior performance in many other optimization problems. This paper introduces an Artificial Bee Colony algorithm based new approach for missing value imputation in the four real-world discrete datasets. At the proposed Artificial Bee Colony Imputation (ABCimp) method, Bayesian Optimization is integrated into the Artificial Bee Colony algorithm. The performance of the proposed technique is compared with other well-known six methods, which are Mean, Median, k Nearest Neighbor (k-NN), Multivariate Equation by Chained Equation (MICE), Singular Value Decomposition (SVD), and MissForest (MF). The classification error and root mean square error are used as the evaluation criteria of the imputation methods performance and the Naive Bayes algorithm is used as the classifier. The empirical results show that state-of-the-art ABCimp performs better than the other most popular imputation methods at the variable missing rates ranging from 3 % to 15 %.
Machine learning is a sub field of artificial intelligence which allows forecasting through learning past behaviors and rules from old data. In today's world, machine learning is being used almost in any fields such as education, medicine, veterinary, banking, telecommunication, security, and bio-medical sciences. In human health, although machine learning is generally preferred particularly in predicting diseases and identifying respective risk factors, it is obvious that there are a limited number of publications where this method was applied on veterinary or indicates whether it is correct and applicable. In this review, it was observed that the neural network, logistic regression, linear regression, multiple regression, principle component analysis and k-means methods were frequently used in examined publications and machine learning application in veterinary field upward momentum. Additionally, it was observed that recent developments in the field of machine learning (deep learning, ensemble learning, voice recognition, emotion recognition, etc.) is still new in the field of veterinary. In this review, publications are examined under clustering, classification, regression, multivariate data analysis and image processing topics. This review aims at providing basic information on machine learning and to increase the number of multidisciplinary publications on computer sciences/engineering and veterinary field. Keywords: Machine learning, Artificial intelligence, Veterinary, Computer sience Veteriner Hekimlik Alanında Makine Öğrenmesi Uygulamaları Üzerine Bir Derleme ÖzetMakine öğrenmesi yapay zekanın bir alt çalışma alanı olup eski verilerden geçmiş davranışların ve kuralların öğrenilerek ileriye doğru tahminlerin yapılmasına olanak sağlar. Makine öğrenmesi günümüzde eğitim, tıp, veterinerlik, bankacılık, telekomünikasyon, güvenlik, biyomedikal gibi hemen hemen her alanda kullanılmaktadır. İnsan sağlığında özellikle hastalıkların önceden tahmin edilmesi ve ilgili risk faktörlerinin tespit edilmesinde makine öğrenmesi yöntemleri genellikle tercih edilmekle birlikte hayvan sağlığında doğruluğu ve aynı zamanda ilgili alanda kullanılabilir olup olmadığını belirleyen bu yöntemin uygulandığı çalışmaların sınırlı olduğu görülmektedir. Bu derlemede incelenen çalışmalarda sinir ağları, lojistik regresyon, lineer regresyon, çoklu regresyon, temel bileşen analizi ve k-ortalamalar yöntemlerinden sıklıkla yararlanıldığı ve veterinerlik alanında yapılan makine öğrenmesi çalışmalarının son yıllarda ivme kazandığı gözlemlenmiştir. Ayrıca makine öğrenmesi alanındaki son gelişmelerin (derin öğrenme, kolektif öğrenme, ses tanıma, duygu tanıma, vb.) veterinerlikte yeni yeni uygulandığı gözlemlenmiştir. Bu derlemede çalışmalar kümeleme, sınıflandırma, regresyon, çok değişkenli veri analizi ve görüntü işleme başlıkları altında incelenmiştir. Bu derlemenin amacı makine öğrenmesi ile ilgili temel bilgileri vermek ve bilgisayar bilimleri/mühendisliği ile veterinerlik alanındaki ortak çalışmaları arttırmaktır.
The coronavirus infection outbreak started in W u h a n city, China, in December 2019 and affected more than 200 countries in a year. Th e nu mber of p a ti ents dy in g from and infected with COVID-19 is increasing at an alarming rate in almost all affected countries. One of the most important factors in the COVID-19 death and case rates is the care of intensive care patients. The management of COVID-19 patients who need acute and/ or critical respiratory care has created a significant difficulty for h ealthcare systems worldwide. To prevent the further spread of COVID-19 around the world and to fight the disease, nonclinical computer-aided quick solutions such as artificial intelligence and machine learning are needed. Prediction techniques evaluate past situations and enable predictions about the future situation. In this study, using the dataset created from the data received from the World Health Organization and national database, the numbers of intensive care, intubated patients, and deaths from COVID-19 in Turkey were predicted by the random forest, bagging, support vector regression, classification and regression trees, and k-nearest neighbors machine learning regression methods. In this study, the random forest method has been the most successful algorithm for predicting the number of intensive care patients (r = 0.8698, RMSE = 188.5, MAE = 135.1, MAPE = 13%), the number of intubated patients (r = 0.9846, RMSE = 47.1, MAE = 39.7, MAPE = 9.2%), and the number of deaths (r = 0.9994, RMSE = 1.2, MAE = 0.9, MAPE = 3.5%). The results in this study, it has been shown that machine learning methods, which have been successfully applied in other epidemic diseases, will be successfully applied in the COVID-19 pandemic.
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