Öz Tanımlama sistemleri son derece güvenilir kişisel veriler kullanılarak tasarlanmaktadır. Doğruluk oranı ve güvenilirlik bu sistemlerin en temel parametreleridir. Elektroensefalografi (EEG) sinyali zamana, içsel ve çevresel faktörlere bağlı olarak değişir. Yapılan çalışmalar sonucunda EEG sinyalinin tanımlama sistemlerinde kullanılabilirliği teyit edilmiştir. Çevresel etkiler en aza indirildiğinde vücut tarafından üretilen sinyallerin kişiselleştirilmiş sinyaller olduğu anlaşılmaktadır. Uzun Kısa Süreli Bellek (LSTM) yönteminin zaman serilerinde başarılı sonuçlar verdiği bilinmektedir. Bu çalışmada derin öğrenme tekniklerinden biri olan LSTM yöntemi kullanılarak bir tanımlama sistemi tasarlanmıştır. LSTM kullanılmadan once, EEG bazı işlemler ile frekans alt bileşenlerine bölünür. Bu ayrılan frekans alt bileşenlerinin korelasyon analizi ile delta dalgasının kullanılmasına karar verilmiştir. Hazırlanan system farklı koşullar altında incelenmiştir. Üç farklı eğitim serisi üzerinde 200 test yapılmıştır. En yüksek doğruluk oranı %89,5'tir. Ortalama doğruluk oranı %86,292'dir. Hazırlanan system farklı koşullar altında çalışacak şekilde tasarlanmıştır. Sistem çeşitli optimizasyon algoritmalrı kullanılarak gelişime açıktır.

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Atrial fibrillation (AF) is a frequently encountered heart arrhythmia problem today. In the method followed in the detection of AF, the recording of the Electrocardiogram (ECG) signal for a long time (1-2 days) taken from people who are thought to be sick is analyzed by the clinician. However, this process is not an effective method for clinicians to make decisions. In this article, various artificial intelligence methods are tested for AF detection on long recorded ECG data. Since the ECG data is a time series, a hybrid model has been tried to be created with the Long Short Term Memory (LSTM) algorithm, which gives high results in time series classification and regression, and a hybrid method has been developed with the Extreme Gradient Boosting algorithm, which is derived from the Gradient Boosting algorithm. To improve the accuracy of the LSTM architecture, the architecture has been strengthened with an Attention-based block. To control the performance of the developed hybrid Attention-based LSTM-XGBoost algorithm, a public data set was used. Some preprocessing (filter, feature extraction) has been applied to this data set used. With the removal of these features, the accuracy rate has increased considerably. It has been proven to be a consistent study that can be used as a support system in decision-making by clinicians with an accuracy rate of 98.94%. It also provides a solution to the problem of long ECG record review by facilitating data tracking.

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Cryptocurrencies are popular today even though they do not have a physical form with their high profit rates and increasing usage day by day. However, the volatility of cryptocurrencies is higher than physical currencies. These volatilities change with the effect of social media rather than changes in exchange rates of physical currencies. For this reason, in this study, using Twitter data, one of the most widely used social media tools, real-time analysis on the values of four cryptocurrencies with the highest market value and the change in the estimated success compared to classical approaches were examined. The basic steps of this study: Obtaining Twitter data and financial data, performing sentiment analysis using Twitter data, making predictions on MM-LSTM architecture. The approach is aimed to be a predictive method open to online learning. Various filter steps were applied to remove the effect of bot users on Twitter that could prevent the prediction performance on the created data set, and the effect of the method on accuracy rate was tried to be reduced by eliminating the activity of bot accounts.