Skolyoz için Kapsül Ağları Tabanlı Otomatik Ölçüm Sistemi

Skolyoz, omurganın eğrilmesi ile birlikte omurga genel yapısını deforme eden bir hastalıktır. Skolyoz tanı ve tedavi aşamasında çeşitli yöntemler olmakla birlikte, temel amaç Cobb açısı adı verilen eğrilik açısını azaltarak Skolyoz seviyesini düşürme çerçevesinde şekillenmektedir. Cobb açısı ölçümü esasında uzman tarafından, omurga röntgen filmleri üzerinde manuel olarak gerçekleştirilmektedir. Ancak bu sürecin derin öğrenme gibi bir Yapay Zeka yaklaşımıyla otomatikleştirilmesi hem hasta hem de uzman açısından büyük kolaylık ve kesinlik sağlayacaktır. Açıklamalardan hareketle bu çalışmada, öncelikli olarak Skolyoz ve derin öğrenme odaklı çalışmalar açısından literatürün güncel durumu ele alınmış, ardından Kapsül Ağları (CapsNet) tabanlı bir çözüm ile Cobb açısı ölçümlerinin otomatik bir hale getirilmesi sağlanmıştır. CapsNet çözümünün, ConvNet, BoostNet, RFR ve ResNet-50 modelleri ile karşılaştırılması neticesinde en iyi bulguları CapsNet modelinin verdiği tespit edilmiştir

Cognitive development optimization algorithm based support vector machines for determining diabetes

The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO) and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM) and Cognitive Development Optimization Algorithm (CoDOA) has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF) kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence based diabetes diagnosis, and contributes to the related literature on diagnosis processes

Deep Learning Based Malware Detection Tool Development for Android Operating System

In today's world that called technology age, smartphones have become indispensable for users in many areas such as internet usage, social media usage, bank transactions, e-mail, as well as communication. The Android operating system is the most popular operating system that used with a rate of 85.4% in smartphones and tablets. Such a popular and widely used platform has become the target of malware. Malicious software can cause both material and moral damages to users.In this study, malwares that targeting smart phones were detected by using static, dynamic and hybrid analysis methods. In the static analysis, feature extraction was made in 9 different categories. These attributes are categorized under the titles of requested permissions, intents, Android components, Android application calls, used permissions, unused permissions, suspicious Android application calls, system commands, internet addresses. The obtained features were subjected to dimension reduction with principal component analysis and used as input to the deep neural network model. With the established model, 99.38% accuracy rate, 99.36% F1 score, 99.32% precision and 99.39% sensitivity values were obtained in the test data set.In the dynamic analysis part of the study, applications were run on a virtual smartphone, and Android application calls with strategic importance were obtained by hooking. The method called hybrid analysis was applied by combining the dynamically obtained features with the static features belonging to the same applications. With the established model, 96.94% accuracy rate, 96.78% F1 score, 96.99% precision and 96.59% sensitivity values were obtained in the test data set.</p

EKG Sinyallerini kullanarak Kalp Ritimlerinin Yapay Zekâ ile Sınıflandırılması

Günümüzde teknolojinin hızla ilerlemesi ile birlikte yapay zekâ yöntemleri de birçok alanda sıklıkla kullanılmaktadır. Yapay zekanın önemli kullanım alanlarından birisi de sağlık sektörüdür. Sağlık sektöründe erken teşhis, insan kaynaklı hataların minimuma indirilmesi gibi birçok durumda yapay zekâ yöntemleri kullanılmaktadır. Çalışmada açık kaynak erişimli internet sitesinden (kaggle.com) elde edilen 127710 adet EKG sinyallerine ait veri seti kullanılmıştır. Veri seti 100.710 adet eğitim, 1.500 adet veri de test ve kalan 25.000 adet veri ise doğrulama verisi olarak kullanılmıştır. Eğitim verileri için tasarlanan CNN modeli normal sinüs ritmi, supraventriküler erken atım, erken ventriküler kasılma, ventriküler ve normal atımın karışımı ve sınıflandırılamayan atım olmak üzere toplam beş sınıf için eğitilmiştir. Tasarlanan CNN modelinde hata oranı %5,3, duyarlık oranı %94,4, hassasiyet oranı %94,6, F-değeri ise %94,4 ve %94,7 doğruluk oranı olmak üzere beş farklı performans kriterine göre değerlendirilmiştir

Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.</p

Use of social networking in the Middle East: student perspectives in higher education

This study aims to determine the benefits, risks, awareness, cultural factors, and sustainability, allied to social networking (SN) use in the higher education (HE) sector in Middle Eastern countries, namely Jordan, Saudi Arabia, and Turkey. Using an online survey, 1180 complete responses were collected and analyzed using the statistical confirmatory factor analysis method. The use of SN in the Middle Eastern HE sector has the capacity to promote and motivate students to acquire professional and personal skills for their studies and future workplace; however, the use of SN by tertiary students is also associated with several risks: isolation, depression, privacy, and security. Furthermore, culture is influenced by using SN use, since some countries shifted from one dimension to another based on Hofstede's cultural framework. The study new findings are based on a sample at a specific point in time within a culture. The study findings encourage academics to include SN in unit activities and assessments to reap the benefits of SN, while taking steps to mitigate any risks that SN poses to students. Although other studies in the Middle East examined the use of Learning Management System and Facebook in, HE as a means of engaging students in discussions and communications, however, this study contributes a better understanding of the benefits and risks, awareness, culture, and sustainability, associated with the use of SN in the HE sector in the Middle East. Finally, the paper concludes with an acknowledgment of the study limitations and suggestions for future research

Developıng Educatıon Software For Fuzzy Logıc and Artıfıcıal Neural Networks

Nowadays, there are many different artificial intelligence techniques that aim to solve real-world problems with the human reasoning structure. Genetic algorithms, fuzzy logic and artificial neural networks are some popular artificial intelligence techniques, which have wide usage ranges. With the support of the computer technology, these techniques can be used easily to solve problems in different fields. In this thesis study, an effective application, which can be used in education of fuzzy logic and artificial neural networks techniques and related research studies, has been developed. With the study, it is aimed to provide a strong “education software”, which can be used for teaching the related techniques and performing sample works about them. The software structure has been developed by combining advantages of object oriented programming techniques via C# programming language. Additionally, this software is one of the uncommon works, which provide Turkish interface.Günümüzde, fiziksel dünyayla özdeşleşmiş problemlerin, insan düşünce yapısıyla çözülmesini amaçlayan, birçok farklı yapay zekâ tekniği bulunmaktadır. Genetik algoritmalar, bulanık mantık ve yapay sinir ağları, yaygın kullanım alanlarına sahip, popüler yapay zekâ tekniklerinden bazılarıdır. Bu teknikler, bilgisayar teknolojisinin de yardımıyla, farklı alanlardaki problemlerin çözümünde kolayca kullanılabilmektedir. Bu tez çalışmasında, bulanık mantık ve yapay sinir ağları tekniklerinin eğitiminde ve bu tekniklerle ilgili araştırma çalışmalarında kullanılabilen, etkili bir uygulama yazılımı geliştirilmiştir. Çalışma sayesinde, ilgili tekniklerin öğretiminde kullanılabilen, örnek çalışmaların yürütülebildiği, güçlü bir “eğitim yazılımı” sunulması amaçlanmaktadır. Yazılım yapısı, C# programlama dili aracılığıyla, nesneye yönelik programlama tekniklerinin avantajları bir araya getirilerek oluşturulmuştur. Ayrıca bu yazılım, ilgili alanda gerçekleştirilmiş olan, Türkçe arayüzlü, ender çalışmalardan birisidir

Design &amp; Development of a Software System for Swarm Intelligence based Research Studies

This paper introduce a software system including widely-used Swarm Intelligence algorithms or approaches to be used for the related scientific research studies associated with the subject area. The programmatic infrastructure of the system allows working on a fast, easy-to-use,&lt;br /&gt;interactive platform to perform Swarm Intelligence based studies in a more effective, efficient and accurate way. In this sense, the system employs all of the necessary controls for the algorithms and it ensures an interactive platform on which computer users can perform studies on a wide spectrum&lt;br /&gt;of solution approaches associated with simple and also more advanced problems

Developıng Educatıon Software For Fuzzy Logıc and Artıfıcıal Neural Networks

Nowadays, there are many different artificial intelligence techniques that aim to solve real-world problems with the human reasoning structure. Genetic algorithms, fuzzy logic and artificial neural networks are some popular artificial intelligence techniques, which have wide usage ranges. With the support of the computer technology, these techniques can be used easily to solve problems in different fields. In this thesis study, an effective application, which can be used in education of fuzzy logic and artificial neural networks techniques and related research studies, has been developed. With the study, it is aimed to provide a strong “education software”, which can be used for teaching the related techniques and performing sample works about them. The software structure has been developed by combining advantages of object oriented programming techniques via C# programming language. Additionally, this software is one of the uncommon works, which provide Turkish interface.Günümüzde, fiziksel dünyayla özdeşleşmiş problemlerin, insan düşünce yapısıyla çözülmesini amaçlayan, birçok farklı yapay zekâ tekniği bulunmaktadır. Genetik algoritmalar, bulanık mantık ve yapay sinir ağları, yaygın kullanım alanlarına sahip, popüler yapay zekâ tekniklerinden bazılarıdır. Bu teknikler, bilgisayar teknolojisinin de yardımıyla, farklı alanlardaki problemlerin çözümünde kolayca kullanılabilmektedir. Bu tez çalışmasında, bulanık mantık ve yapay sinir ağları tekniklerinin eğitiminde ve bu tekniklerle ilgili araştırma çalışmalarında kullanılabilen, etkili bir uygulama yazılımı geliştirilmiştir. Çalışma sayesinde, ilgili tekniklerin öğretiminde kullanılabilen, örnek çalışmaların yürütülebildiği, güçlü bir “eğitim yazılımı” sunulması amaçlanmaktadır. Yazılım yapısı, C# programlama dili aracılığıyla, nesneye yönelik programlama tekniklerinin avantajları bir araya getirilerek oluşturulmuştur. Ayrıca bu yazılım, ilgili alanda gerçekleştirilmiş olan, Türkçe arayüzlü, ender çalışmalardan birisidir

BRAIN. Broad Research in Artificial Intelligence and Neuroscience-Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety

<p>Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it<br> is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or<br> any other movies including intelligent robots – machines threatening the existence of humankind.<br> So, there is a great interest in some alternative research works under the topics of Machine Ethics,<br> Artificial Intelligence Safety and the associated research topics like Future of Artificial Intelligence<br> and Existential Risks. The objective of this study is to provide a general discussion about the<br> expressed research topics and try to find some answers to the question of ‘Are we safe enough in the<br> future of Artificial Intelligence?’. In detail, the discussion includes a comprehensive focus on<br> ‘dystopic’ scenarios, enables interested researchers to think about some ‘moral dilemmas’ and<br> finally have some ethical outputs that are considerable for developing good intelligent systems.<br> From a general perspective, the discussion taken here is a good opportunity to improve awareness<br> on the mentioned, remarkable research topics associated with not only Artificial Intelligence but<br> also many other natural and social sciences taking role in the humankind.</p