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    Detection of correct pregnancy status in lactating dairy cattle using MARS data mining algorithm
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Canga, Demet; Boga, Mustafa
    In this study, it is aimed to determine pregnancy outcomes by using multivariate adaptive regression splines (MARS) algorithm for classification type problems. For this purpose, data obtained from a private dairy farm in the Konya region of Turkiye in 2020 were used to determine pregnancy outcomes in holstein dairy cattle. It has been determined how to perform statistical analyses on solving classification-type problems with the MARS algorithm and how to use R packages (caret and earth) by creating an R script file. After the analysis, the MARS estimation equation was created and in finding the probability of being pregnant: While lactation period, cow age, number of lactations, insemination number, and total lactation milk yield variables are important, it was seen that 7-day mean milk yield and last lactation milk yield were not significant. Using the train function of the caret package, the number of terms that produce the highest accuracy and the degree of interaction are determined. Goodness-of-fit tests of the optimum model were calculated. Within the scope of the evaluation of the generalization ability of the model, training and test sets were created, the classification success graph of the MARS algorithm, the model building phase were summarized, and the generalization ability of the established model was measured. When the pregnancy status is taken as a positive reference, the correct classification rate (sensitivity) of the animal with positive pregnancy status was found to be 0.9574:the correct classification rate (specificity) of pregnant animals was found to be 0.8370. The overall classification ratio of the training set (accuracy) was found to be 0.8777. The area under the ROC curve (AUC) was found to be 0.947, which indicates that the optimum specificity value is close to 1.
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    Sensitive Livestock and Information Communication Technology Applications to Prevent the Spread of Covid-19
    (2023) Kılıç, Hatice Nur; Boga, Mustafa; Canga, Demet
    The epidemic disease called COVID-19 (SARS-CoV-2) has affected the whole world. With the spread of the epidemic, various measures such as distance education, home-office, and especially movement restrictions, have been tried to be taken. These measures have increased people's demand for healthy food. The formation of food safety awareness among in consumers has revealed the necessity of control of the controlling food chain (production, storage, transportation of products, etc.). In this process, animal products gained importance, especially as people paid more attention to their nutrition compared to previous years. Especially in this process, animal production should be systematically sustainable in order to meet the increasing animal protein needs of people. In this review, it aims to compile sensitive livestock systems in order to ensure the sustainability of animal production, the production of healthier animals and the production of the obtained products within the framework of food safety rules, with the cessation of mobility due to the measures taken under quarantine and social distance in the COVID-19 epidemic. Thus, in addition to reducing the human workforce during the epidemic process, the data collected with modern animal husbandry will prevent diseases, and facilitate the diagnosis and treatment processes in the event of a disease. With the use of information and communication technologies (ICT), which have an important place in this system, the data obtained through the modern livestock system can be easily processed, managed, and shared, thus reducing the possibility of disease transmission during the pandemic process.
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    Yumurta Verimi Üzerine Bazı Özelliklerin Etkisinin Regresyon Analiz Yöntemlerinden Bagging Mars ile Belirlenmesi ve R Uygulaması
    (2020) Canga, Demet; Boğa, Mustafa
    Çalışmada, BAGGING ile MARS birleştirilerek elde edilen hibrid bir yaklaşımın yumurta tavuklarındanelde edilen bir veri setinde sıcaklık ve nemin yumurta verimi, kırık/çatlak yumurta, ölen hayvan sayısıve yem tüketimi üzerine etkisinin olup olmadığı test edilmiştir. Çalışmada Adana ili ÇukurovaBölgesinde bulunan özel bir hayvancılık işletmesinden 2018 yılına ait yumurta üretim işletmesinden eldeedilen veriler kullanılmıştır. Araştırmada, ortalama 60 haftalık yaşta olan Lohman ırkı tavuklardan eldeedilen veri seti kullanılmıştır. Oluşan MARS ve Bagging MARS algoritmasında regresyonproblemlerinin daha güçlü bir çözümünü sunmak için R STUDIO programındaki earth (enhancedadaptive regression through hinges) ve caret (classification and regression training), mda (MixtureDiscriminant Analysis) paketleri kullanılmıştır. Bagging MARS tekniğinin tahmin performansı,bootstrap örnek sayısı olan B değeri 3 alınarak uyum iyiliği kriterleri ile değerlendirilmiştir. Çalışmada,MARS ve bagging MARS analizi kullanarak sıcaklık ve nemin yumurta verimi, kırık/çatlak yumurta,ölen hayvan sayısı ve yem tüketimi üzerine etkisi araştırılmıştır. Yumurta verimi üzerine, akşamsıcaklığının(t3) etkisinin olduğu fakat sabah(t1) ve öğle(t2) sıcaklıklarının ise etkisinin olmadığıbelirtilmiştir. Kırık/çatlak yumurta ve ölen hayvan hayvan sayısı 5 den az/ hafta olmasından dolayı,MARS ve Bagging MARS modellerinde bu değişkenlere tahmin denkleminde yer verilmemiştir. Yemtüketiminin ise, her iki modelde de pozitif bir katkısının olduğu görülmüştür.