Bilgi teknolojilerinde yüksek hızda yaşanan gelişmeler ve internet kullanımının çok yaygın hale gelmesi ile birlikte, çeşitli platformlarda biriken verinin çeşitliliği ve hacmi de artmıştır. Büyük veri kavramı ile ifade edilen bu verilerin işlenmesi ve anlamlı bilgilerin elde edilmesi, önemli sonuçlar elde edilebilmesine imkân vermektedir. Bu çalışmada, büyük veri analizinde yapay zekâ ve makine öğrenmesi tekniklerinin kullanımı tartışılmıştır. Başlıca yapay zekâ ve makine öğrenmesi teknikleri hakkında bilgiler verilerek, bu tekniklerin büyük verilerle yapılan uygulamalarından örnekler verilmiştir. Başlıca olarak; kümeleme, sınıflandırma, yapay sinir ağları, metin ve web madenciliği, fikir madenciliği ve duygu analizi alanlarında büyük verilerle yapılan çalışmalar anlatılmıştır.

Objective:Type 1 diabetes (T1D) is the most common cause of diabetes in childhood but type 2 diabetes (T2D) and maturity onset diabetes of the young (MODY) are emerging as noteworthy causes of diabetes at young ages. The aim is to determine the distribution, trends and clinical features of the different types of diabetes in childhood in one tertiary center.Methods:The records of children and adolescents aged 0-18 years who were diagnosed as “diabetes/persistent hyperglycemia” between January 1999 and December 2016, were reviewed. Clinical and laboratory characteristics of the patients at diagnosis and type of diabetes were recorded.Results:Children and adolescents aged 0-18 years who were diagnosed "diabetes/persistent hyperglycemia" between January 1999 and December 2016, were reviewed. Clinical and laboratory characteristics of the patients at the diagnosis and type of diabetes were recorded.The mean ± standard deviation age of 835 patients (48.7% females) at diagnosis was 8.8±4.4 years. Eighty-four percent of the patients were diagnosed as T1D, 5.7% as T2D, 5.3% as clinical MODY and 5% as being cases of other types of diabetes. The frequency of diabetic ketoacidosis (DKA) and severe DKA in T1D were 48.4% and 11.6%, respectively. Fourteen patients (29.2%) with T2D presented with ketosis and two of them (4.2%) had DKA at diagnosis. Antibody positivity was 83.1% in T1D and 14.8% in T2D. A statistically significant increase in the frequency of T2D has clearly been demonstrated in recent years with a frequency of 1.9%, 2.4% and 7.9% in 1999-2004, 2005-2010 and 2011-2016, respectively (p<0.001). In MODY, genetic analysis was performed in 26 (59%) patients and HNF1A and GCK gene mutations were detected in 3 (11.5%) and 14 (53.8%) patients, respectively.Conclusion:Although the most frequent cause of DM is T1D in childhood, a trend towards increase in the frequency of T2D in recent years is notable in our population.

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Дане дослідження присвячене вивченню вразливостей і заходів інформаційної безпеки в Industry 4.0. Пропонується план безпеки, який пропорційний організаційним структурам ідентифікації протоколів кібербезпеки для Industry 4.0. Завдяки запропонованому підходу може бути забезпечена безпека інформації, а цінна інформація захищена від атак. ключові слова: Четверта промислова революція (Industry 4.0), інформаційна безпека, безпека кібер-системи, управління ризиками.

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Abstract:Chronic kidney disease is a prolonged disease that damages the kidneys and prevents the normal duties of the kidneys. This disease is diagnosed with an increase of urinary albumin excretion lasting more than three months or with significant reduction in a kidney functions. Chronic kidney disease can lead to complications such as high blood pressure, anemia, bone disease and cardiovascular disease. In this study we have been investigated to determine the factors that decisive for early detection of chronic kidney disease, launching early patients treatment processes, prevent complications resulting from the disease and predict of disease. The study aimed diagnosis and prediction of disease using the data set that composed of data of 250 patients with chronic kidney disease and 150 healthy people. First, the chronic kidney disease data was classified with machine learning algorithms and then training and test results were analysed. The estimation results of chronic kidney disease were compared with similar data and studies.