Computer Engineering / Bilgisayar Mühendisliği
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Book Part Citation - Scopus: 2Dementia Detection With Deep Networks Using Multi-Modal Image Data(CRC Press, 2023) Yiğit, Altuğ; Işık, Zerrin; Baştanlar, YalınNeurodegenerative diseases give rise to irreversible neural damage in the brain. By the time it is diagnosed, the disease may have progressed. Although there is no complete treatment for many types of neurodegenerative diseases, by detecting the disease in its early stages, treatments can be applied to relieve some symptoms or prevent disease progression. Many invasive and non-invasive methods are employed for the diagnosis of dementia. Computer-assisted diagnostic systems make the diagnosis based on volumetric features (structural or functional) or some two-dimensional brain perspectives obtained from a single image modality. This chapter firstly introduces a broad review of multi-modal imaging approaches proposed for dementia diagnosis. Then it presents deep neural networks, which extract structural and functional features from multi-modal imaging data, are employed to diagnose Alzheimer’s and mild cognitive impairments. While MRI scans are safer than most types of scans and provide structural information about the human body, PET scans provide information about functional activities in the brain. Thus, the setup has been designed to make experiments using both MRI and FDG-PET scans. Performances of multi-modal models were compared with single-modal solutions. The multi-modal solution showed superiority over single-modals due to the advantage of focusing on assorted features. © 2023 selection and editorial matter, Jyotismita Chaki; individual chapters, the contributors.Book Part Citation - Scopus: 2A Survey on Security in Wireless Sensor Networks: Attacks and Defense Mechanisms(IGI Global, 2013) Tekbacak, Fatih; Dalkılıç, Mehmet Emin; Korkmaz, İlker; Dağdeviren, OrhanWireless Sensor Network (WSN) is a promising technology that has attracted the interest of research in the last decade. Security is one of the fundamental issues in sensor networks since sensor nodes are very resource constrained. An attacker may modify, insert, and delete new hardware and software components to the system where a single node, a specific part of the sensing area, and the whole network may become inoperable. Thus, the design of early attack detection and defense mechanisms must be carefully considered. In this chapter, the authors survey attacks and their defense mechanisms in WSNs. Attacks are categorized according to the related protocol layer. They also investigate the open research issues and emerging technologies on security in WSNs.Book Part Citation - Scopus: 1Symmetric Properties of the Syllogistic System Inherited From the Square of Opposition(Birkhäuser, 2017) Kumova, Bora İsmailThe logical square Omega has a simple symmetric structure that visualises the bivalent relationships of the classical quantifiers A, I, E, O. In philosophy it is perceived as a self-complete possibilistic logic. In linguistics however its modelling capability is insufficient, since intermediate quantifiers like few, half, most, etc cannot be distinguished, which makes the existential quantifier I too generic and the universal quantifier A too specific. Furthermore, the latter is a special case of the former, i.e. A subset of I, making the square a logic with inclusive quantifiers. The inclusive quantifiers I and O can produce redundancies in linguistic systems and are too generic to differentiate any intermediate quantifiers. The redundancy can be resolved by excluding A from I, i.e. I-2=I-A, analogously E from O, i.e. O-2=O-E. Although the philosophical possibility of A subset of I is thus lost in I-2, the symmetric structure of the exclusive square (2)Omega remains preserved. The impact of the exclusion on the traditional syllogistic system S with inclusive existential quantifiers is that most of its symmetric structures are obviously lost in the syllogistic system S-2 with exclusive existential quantifiers too. Symmetry properties of S are found in the distribution of the syllogistic cases that are matched by the moods and their intersections. A syllogistic case is a distinct combination of the seven possible spaces of the Venn diagram for three sets, of which there exist 96 possible cases. Every quantifier can be represented with a fixed set of syllogistic cases and so the moods too. Therefore, the 96 cases open a universe of validity for all moods of the syllogistic system S, as well as all fuzzy-syllogistic systems S-n, with n-1 intermediate quantifiers. As a by-product of the fuzzy syllogistic system and its properties, we suggest in return that the logical square of opposition can be generalised to a fuzzy-logical graph of opposition, for 2<n.Book Part Citation - WoS: 4Citation - Scopus: 8Advances in Model-Based Testing of Graphical User Interfaces(Academic Press Inc., 2017) Belli, Fevzi; Beyazıt, Mutlu; Budnik, Christof J.; Tuğlular, TuğkanGraphical user interfaces (GUIs) enable comfortable interactions of the computer-based systems with their environment. Large systems usually require complex GUIs, which are commonly fault prone and thus are to be carefully designed, implemented, and tested. As a thorough testing is not feasible, techniques are favored to test relevant features of the system under test that will be specifically modeled. This chapter summarizes, reviews, and exemplifies conventional and novel techniques for model-based GUI testing.Book Part Citation - WoS: 299Citation - Scopus: 406Introduction To Machine Learning(Humana Press, 2014) Baştanlar, Yalın; Özuysal, MustafaThe machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.