Face and Expression Recognition

Makine öğrenimi ve nesnelerin internetindeki gelişmeler, kestirimci bakımı sanayi sistemleri için vazgeçilmez bir bileşen hâline getirmiştir. Ancak bu yaklaşımların başarımı, veri işleme süreçlerinin etkinliğine doğrudan bağlıdır. Özellikle veri dengesizliği, öznitelik seçimi ve normalizasyon teknikleri, model perform…

Görsel tanıma sistemlerinde ince taneli sınıflandırma problemleri, birbirine benzer alt kategorilerin ayırt edilmesini gerektirmekte ve bu durum özellikle hava aracı tanıma gibi alanlarda kritik zorluklar ortaya çıkarmaktadır. Mevcut yaklaşımlar genellikle hiyerarşik yapıyı göz ardı ederek düz sınıflandırma stratejile…

This research aims to analyze the applicability and performance of face recognition technologies using different Python-based libraries. Face recognition and comparison tests were performed using the face_recognition, OpenCV and DeepFace libraries, respectively . First, basic face detection application was successfull…

The Transformer model is based on attentional mechanisms to process sequence information independently and utilises positional encoding to add sequence order. In addition to these two features, the model includes various components, such as its layered architecture and a greater number of hyperparameters compared to t…

The Transformer model is based on attentional mechanisms to process sequence information independently and utilises positional encoding to add sequence order. In addition to these two features, the model includes various components, such as its layered architecture and a greater number of hyperparameters compared to t…

The Transformer model is based on attentional mechanisms to process sequence information independently and utilises positional encoding to add sequence order. In addition to these two features, the model includes various components, such as its layered architecture and a greater number of hyperparameters compared to t…

Existing hyperspectral image (HSI) classification (HSIC) methods often fall short in effectively modeling long-range spatial-spectral dependencies and preserving fine-grained relationships across high-dimensional data. To address these limitations, this work introduces DiMAMamba, an innovative architecture that combin…

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In self-supervised robot learning, data is acquired through active interaction with the environment, which is costly. Therefore, sample-efficient exploration is vital. To this end, intrinsic motivation (IM) methods such as learning progress (LP) have been adopted in robot learning, with variable success across tasks;…

Speech emotion recognition (SER) plays a very significant role in creating natural human-computer interfaces (HCI). Recent progress in self-supervised learning (SSL) has contributed to the broader use of pre-trained models instead of traditional spectral or acoustic features in SER. However, there is still limited res…

Veri kümeleme, denetimsiz öğrenme yöntemleri arasında yer alan ve veri setlerinde doğal grupların keşfedilmesini amaçlayan temel bir analiz yöntemidir. Geleneksel kümeleme algoritmaları, özellikle karmaşık yapılı veri setlerinde yeterli başarıyı sağlayamamaktadır. Bu nedenle, meta-sezgisel algoritmalar son yıllarda ve…

A two-stage cascaded direction of arrival (DOA) estimation framework is proposed for parameter estimation under non-ideal array conditions. The framework consists of two modules: contrastive reconstruction network (CRN) and DOA estimation network (DEN). To enhance robustness against array imperfections, CRN leverages…