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Conference Object Citation - Scopus: 2Big Data on Cloud for Government Agencies: Benefits, Challenges, and Solutions(Assoc Computing Machinery, 2018) Rashed, Alaa Hussain; Karakaya, Ziya; Yazici, AliBig Data and Cloud computing are the most important technologies that give the opportunity for government agencies to gain a competitive advantage and improve their organizations. On one hand, Big Data implementation requires investing a significant amount of money in hardware, software, and workforce. On the other hand, Cloud Computing offers an unlimited, scalable and on-demand pool of resources which provide the ability to adopt Big Data technology without wasting on the financial resources of the organization and make the implementation of Big Data faster and easier. The aim of this study is to conduct a systematic literature review in order to collect data to identify the benefits and challenges of Big Data on Cloud for government agencies and to make a clear understanding of how combining Big Data and Cloud Computing help to overcome some of these challenges. The last objective of this study is to identify the solutions for related challenges of Big Data. Four research questions were designed to determine the information that is related to the objectives of this study. Data is collected using literature review method and the results are deduced from there.Conference Object ANALYSIS OF NEUROONCOLOGICAL DATA TO PREDICT SUCCESS OF OPERATION THROUGH CLASSIFICATION(Assoc Computing Machinery, 2016) Bagherzadi, Negin; Borcek, Alp Ozgun; Tokdemir, Gul; Cagiltay, Nergiz; Maras, H. HakanData mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation.Conference Object Citation - WoS: 2Citation - Scopus: 2Software Engineering in Medical Informatics: a Systematic Literature Review(Assoc Computing Machinery, 2019) Dalveren, Gonca Gokce Menekse; Mishra, DeeptiThis study presents a systematic literature review to provide overall view of the application of Software Engineering (SE) in Medical Informatics (MI) field. Articles published from 2010 to 2019 from seven selected databases ( Emerald, PubMed, IEEE, ACM, Taylor Francis, SAGE and Wiley) were investigated. The existing literature was analyzed, and the emerging areas of research in the medical informatics field have been identified. According to the findings of this study, medical informatics research has been applied in many fields but there is still potential of further research in different areas. Most of the reviewed studies were conducted on data mining, decision support, deep learning and IoT. Also, it can be said that most of the applications are provided as web-based instead of mobile applications. To conclude, the results of this study provides insights to the researchers about the research directions and the gaps in the literature in the MI and SE fields.
