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Dental ceramics because of their translucency exemplify the most biologically realistic restorative materials for aesthetic rehabilitation and can be used to estimate dose accumulated as a result of a nuclear accident or attack. In this study, lithium disilicate ceramic obtained from Vivadent Ivoclar, Turkey was studied for its thermoluminescence (TL) properties. The lithium disilicate glass ceramic was irradiated with a 90Sr–90Y β‐source from 10 Gy to 6.9 kGy and the results read on a Harshaw 3500 reader. The TL peak of lithium disilicate ceramic showed sublinearity in the range 12 Gy to 6 kGy. The area under the TL glow curve increased by about 25% by the end of 10th measurement cycle. Fading values were also considered after irradiation. Lithium disilicate ceramic samples underwent 37% fading after 1 h and 59% fading after 1 week. In addition to the experimental study, a software‐based simulation study was also undertaken using a MATLAB system identification tool. Experimental studies are generally time consuming and some materials used for experiments are very expensive. In this study, experimental, and simulation results were compared and produced almost the same outcome with a similarity of more than 98%.

The dysfunction of the cells in the brain that contain the substance known as dopamine, which enables brain cells to interact with each other, results in Parkinson's disease (PD). PD can cause many non-motor and motor symptoms such as speech and smell. One of the difficulties that Parkinson's patients can experience is a change in speech or speaking difficulties. Therefore, the right diagnosis in the early period is important in reducing the possible effects of speech disorders caused by the disease. Speech signal of Parkinson patients shows major differences compared to normal people. In this study, a new approach based on pre-trained deep networks and Long short-term memory (LSTM) by using mel-spectrograms obtained from denoised speech signals with Variational Mode Decomposition (VMD) for detecting PD from speech sounds is proposed. The proposed model consists of four stages. In the first step, the noise is removed by applying VMD to the signals. In the second stage, Mel-spectrograms are extracted from the enhanced sound signals with VMD. In the third stage, pre-trained deep networks are preferred to extract deep features from the Mel-spectrograms. For this purpose, ResNet-18, ResNet-50 and ResNet-101 models are used as pre-trained deep network architecture. In the last step, the classification process is occured by giving these features as input to the LSTM model, which is designed to define sequential information from the extracted features. Experiments are performed with the PC-GITA dataset, which consists of two classes and is widely used in the literature. The results obtained from the proposed method are compared with the latest methods in the literature, it is seen that it has a better performance in terms of classification performance.

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Anahtar Kelimeler Uzun kısa süreli bellek, Diyabet tahmini, Evrişimsel sinir ağ Öz: Diyabet, vücudun yeterli miktarda insülini üretmemesi veya iyi kullanamadığı durumda kan şekerinin normalin üstüne çıkması ile ortaya çıkan bir hastalıktır. Kan şekeri insanların ana enerji kaynağıdır ve bu enerji tüketilen yiyeceklerden gıdalardan gelir. Bu hastalık tedavi edilmez ise ölümcül olabilir. Ancak, erken tanı konulup tedaviye başlandığında tedavisi en olanaklı hastalıklardan biridir. Geleneksel diyabet teşhis süreci zorlu olduğundan, diyabetin klinik ve fiziksel verileri kullanılarak yapay sinir ağı, görüntü işleme ve derin öğrenme gibi sistemler kullanılarak hastalık teşhis edilebilmektedir. Bu araştırmada diyabet teşhisi için derin öğrenmeye dayalı bir model sunulmaktadır. Bu bağlamda Evrişimsel Sinir Ağı (ESA), Uzun Kısa Süreli Bellek (Long-short Term Memory Networks-LSTM) modelinin hibrit kullanımı sınıflandırma için tercih edilmiştir. Ayrıca ESA ve LSTM modelleri deneylerde ayrı ayrı kullanılmıştır. Önerilen modelin performansını değerlendirmek için literatürde yaygın olarak kullanılan Pima Indians Diabetes veri seti kullanılmıştır. En yüksek sınıflandırma başarısı %86,45 olarak ESA+LSTM modelinden elde edilmiştir.