Ebru Aydindag Bayrak

This article presents a brief introduction to big data and big data analytics and also their roles in the healthcare system. A definite range of scientific researches about big data analytics in the healthcare system have been reviewed. The definition of big data, the components of big data, medical big data sources, used big data technologies in present, and big data analytics in healthcare have been examined under the different titles. Also, the historical development process of big data analytics has been mentioned. As a known big data analytics technology, Apache Hadoop technology and its core components with tools have been explained briefly. Moreover, a glance of some of the big data analytics tools or platforms apart from Hadoop eco-system were given. The main goal is to help researchers or specialists with giving an opinion about the rising importance of used big data analytics in healthcare systems.

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and machine learning system used in the healthcare system have been mentioned. The authors have explained the definition of big data, big data samples, and big data analytics. But the main goal is helping researchers or specialists in providing opinion about diagnosing or predicting neurological disorders using intelligent big data analytics and machine learning. Therefore, they focused on the healthcare systems using these innovative ways in particular. The information of platform and tools about big data analytics in healthcare is investigated. Numerous academic studies based on the detection of neurological disorders using both machine learning methods and big data analytics have been reviewed.

The current study has analyzed active fault data along the East Anatolian Fault Zone (EAFZ) applying both manual (classic) and modern versions of the box counting method. The EAFZ active fault dataset used for analysis was taken from the Geoscience Map Viewer and the Drawing Editor from the website of the General Directorate of Mineral Research and Exploration. The study covered an area stretching from Karlıova in the north to Kırıkhan in the south. The fractal analysis of the earthquake surface rupture and the Holocene fault data was performed. Fractal dimensions of the EAFZ active-fault data were calculated for 15 boxes and compared with correlation coefficient values. The calculated fractal dimension values were found to vary with the density of the active-fault data falling into the boxes. The maximum fractal dimension value D1 was determined for Karlıova and its surroundings, which can be associated with the fault density due to the branching geometry.

This article presents a brief introduction to big data and big data analytics and also their roles in the healthcare system. A definite range of scientific researches about big data analytics in the healthcare system have been reviewed. The definition of big data, the components of big data, medical big data sources, used big data technologies in present, and big data analytics in healthcare have been examined under the different titles. Also, the historical development process of big data analytics has been mentioned. As a known big data analytics technology, Apache Hadoop technology and its core components with tools have been explained briefly. Moreover, a glance of some of the big data analytics tools or platforms apart from Hadoop eco-system were given. The main goal is to help researchers or specialists with giving an opinion about the rising importance of used big data analytics in healthcare systems.

İstanbul Üniversitesi | Tarihten Geleceğe Bilim Köprüsü - 1453

Cancer is the common problem for all people in the world with all types. Particularly, Breast Cancer is the most frequent disease as a cancer type for women. Therefore, any development for diagnosis and prediction of cancer disease is capital important for a healthy life. Machine learning techniques can make a huge contribute on the process of early diagnosis and prediction of cancer. In this paper, two of the most popular machine learning techniques have been used for classification of Wisconsin Breast Cancer (Original) dataset and the classification performance of these techniques have been compared with each other using the values of accuracy, precision, recall and ROC Area. The best performance has been obtained by Support Vector Machine technique with the highest accuracy.

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and machine learning system used in the healthcare system have been mentioned. The authors have explained the definition of big data, big data samples, and big data analytics. But the main goal is helping researchers or specialists in providing opinion about diagnosing or predicting neurological disorders using intelligent big data analytics and machine learning. Therefore, they focused on the healthcare systems using these innovative ways in particular. The information of platform and tools about big data analytics in healthcare is investigated. Numerous academic studies based on the detection of neurological disorders using both machine learning methods and big data analytics have been reviewed.

In the analysis of active fault zones, the box counting method was used. In the study, active fault data along the Eastern Anatolian Fault Zone were analyzed with both classic and modern approach of the box counting method. Fault data were analyzed with two different approaches and fractal dimension values were calculated for 15 boxes. Fractal dimension values were varied according to dense faults or rare faults in the determined boxes.