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Öğe CAD for corneal diseases based on topographical parameters to improve the clinical decision(Aksaray Üniversitesi Fen Bilimleri Enstitüsü, 2021) Al-Salihi, Samer Kais Jameel; Aydın, SezginComputer-Aided Diagnosis is an essential topic in the medical image. it is a sophisticated procedure in medicine that assists physicians in the interpretation of medical images. A Human cornea is the front see-through shield of the eye. It refracts light onto the retina to induce vision. Therefore, any defect in the cornea may lead to vision disturbance. This deficiency is estimated by sets of topographical images measured and assessed by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Corneal images produced by a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosis requires the use of local features in the image. Accordingly, new algorithms proposed in this work to overcome these challenges and improve cornel conditions diagnosing. Firstly, a SWFT algorithm suggested to extract the local features from the corneal images. Wavelet transform used to produce images with different scales instead of using the Difference of Gaussians (DoG) as in the standard SIFT algorithm. Secondly, IG-GLCM algorithm proposed to overcome the drawback of GLCM algorithm known as a time-consuming defect. In IG-GLCM the image gradient is measured in different directions then apply the GLCM to generated images. Thirdly, investigate the use of SIFT with multi-scale subbands of wavelet transform. Finally, new algorithm called Local Information Pattern descriptor suggested to overcome the lack of local binary patterns that loss of information from the image and solve image rotation issue. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast Based Centre (CBC) value, as well as local pattern (LP). The Naive Bayes, KNN, decision tree, and SVM employed as classifiers. The proposed model is trained and tested successfully on a collected dataset which comprises 4848 images of different maps.Öğe Intelligent indoor nitrogen dioxide sensors network based FSO communication(Aksaray Üniversitesi Fen Bilimleri Enstitüsü, 2021) Ali, Mohammed Hussein; Yenice, Yusuf ErkanThe Intelligent Wireless Sensor Network (IWSN) is one of the widely used applications in present time, whereas it is utilized for: 1) sensing of the natural phenomenon or environmental conditions that occur in our world, 2) processing the incoming data from their sensors, 3) presenting a appropriate decision according to the entered data. In this work, an Intelligent Wireless Sensor Network has been designed and implemented, which can be used for applications of carbon monoxide gas sensing, it involves three main parts, the intelligent system, the sensor interface unit, and the wireless communication system. The proposed intelligent system has been utilized to processing the incoming data from carbon monoxide sensors, then presenting the average value of these data. The sensor interface unit has been used for converting the incoming analog signals from the nitrogen dioxide sensors to binary data that should be driven to the intelligent system, where the last one should be saved in FPGA (Field Programmable Gate Array). The wireless communication system has been implemented for transferring the digital data between the sensor node and the intelligent system for remote distances. A Back-Propagation neural network has been utilized as an intelligent system for this work, three layers had been designed in this network, input, single hidden, and output layers. This network has been trained by several training functions, and has used two linear activation functions, the SATLINS function for the hidden layer, and the SATLIN for the output layer. Using TRAINPSO (Particle Swarm Optimization) training function, an optimal result has been also presented, but with reaching the MSE to zero value in 46 iteration using of only three neurons in the hidden layer. A laser FSO(Free-Space-Optical) system has been designed and implemented as a wireless communication unit for the proposed system, cause this technique possesses low power consumption and high bandwidth, directivity, immunity than other techniques for same range of transmission. The ON/OFF keying modulation has been used in this technique for modulating the digital data of the sensor units with an infra-red laser light carrier, which is a powerful widely used modulation technique in the laser communication systems.
