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Article Citation - WoS: 38Citation - Scopus: 47Understanding Key Skills for Information Security Managers(Elsevier Sci Ltd, 2018) Haqaf, Husam; Koyuncu, MuratInformation security management is a necessity for all institutions and enterprises that regard company information as valuable assets. Developing, auditing and managing information security depends upon professional expertise in order to achieve the desired information security governance. This research seeks the key skills required for the position of information security management as well as the methods to develop these skills through professional training programs. The study adopts the Delphi method which requires building a list of items through a literature survey and involves experts with certain expertise to modify the list until a consensus on less than 20% of the items is reached. Through completing three rounds of the Delphi technique - data collection, relevance voting and ranking sixteen skills are shortlisted as the key skills. In the final list, the majority belong to core information security skills, and the top two skills belong to project/process management skills and risk management skills, indicating the importance of these skills for the information security manager role. In addition, a series of related professional training programs and certifications are surveyed, the outcome of which highlights a number of most comprehensive and appropriate programs to develop these determined skills.Article Citation - WoS: 33Citation - Scopus: 41Visual and Auditory Data Fusion for Energy-Efficient and Improved Object Recognition in Wireless Multimedia Sensor Networks(Ieee-inst Electrical Electronics Engineers inc, 2019) Koyuncu, Murat; Yazici, Adnan; Civelek, Muhsin; Cosar, Ahmet; Sert, MustafaAutomatic threat classification without human intervention is a popular research topic in wireless multimedia sensor networks (WMSNs) especially within the context of surveillance applications. This paper explores the effect of fusing audio-visual multimedia and scalar data collected by the sensor nodes in a WMSN for the purpose of energy-efficient and accurate object detection and classification. In order to do that, we implemented a wireless multimedia sensor node with video and audio capturing and processing capabilities in addition to traditional/ordinary scalar sensors. The multimedia sensors are kept in sleep mode in order to save energy until they are activated by the scalar sensors which are always active. The object recognition results obtained from video and audio applications are fused to increase the object recognition performance of the sensor node. Final results are forwarded to the sink in text format, and this greatly reduces the size of data transmitted in network. Performance test results of the implemented prototype system show that the fusing audio data with visual data improves automatic object recognition capability of a sensor node significantly. Since auditory data requires less processing power compared to visual data, the overhead of processing the auditory data is not high, and it helps to extend network lifetime of WMSNs.Review Citation - WoS: 7Citation - Scopus: 9A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques(Mdpi, 2023) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, RoohallahSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.Article Citation - WoS: 16Citation - Scopus: 25An Intelligent Fuzzy Object-Oriented Database Framework for Video Database Applications(Elsevier, 2009) Ozgur, Nezihe Burcu; Koyuncu, Murat; Yazici, AdnanVideo database applications call for flexible and powerful modeling and querying facilities, which require an integration or interaction between database and knowledge-based technologies. It is also necessary for many real life video database applications to incorporate uncertainty, which naturally occurs due to the complex and subjective semantic content of video data. In this study, firstly, we introduce a fuzzy conceptual data model to represent the semantic content of video data. For that purpose, UML (unified modeling language) is utilized and extended to represent uncertain information along with video specific properties. Secondly, we present an intelligent fuzzy object-oriented database framework for video database applications. The introduced fuzzy conceptual model is used in this framework, which provides modeling of complex and rich semantic content and knowledge of video data including uncertainty. Moreover, it supports various flexible queries including (fuzzy) semantic, temporal and (fuzzy) spatial queries, based on the video data model. We think that the presented conceptual data model and the framework can be used for any video database application. (C) 2009 Elsevier B.V. All rights reserved.Article Citation - WoS: 9Citation - Scopus: 11Food Index: a Multidimensional Index Structure for Similarity-Based Fuzzy Object Oriented Database Models(Ieee-inst Electrical Electronics Engineers inc, 2008) Yazici, Adnan; Ince, Cagri; Koyuncu, MuratA fuzzy object-oriented data model is a fuzzy logic-based extension to an object-oriented database model that permits uncertain data to be explicitly represented. The fuzzy object-oriented database (FOOD) model is one of the proposed models in the literature to handle uncertainty in object-oriented databases. Several kinds of fuzziness are dealt with in the FOOD model, including fuzziness at attribute level and between object and class and between class and superclass relations. The traditional index structures do not allow efficient access to both crisp and fuzzy objects for fuzzy object-oriented databases since they are not efficient enough in processing both crisp and fuzzy queries. In this study, we propose a new index structure, namely a FOOD index (FI), to deal with different kinds of fuzziness in fuzzy object-oriented databases and to support multidimensional indexing. In this paper, we describe this proposed index structure and show how it supports various types of flexible queries, and evaluate its performance for exact, range, and fuzzy queries.
