Yazar "Sönmez, Elena Battini" seçeneğine göre listele

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    Acknowledge of Emotions for Improving Student-Robot Interaction
    (TECH SCIENCE PRESS, 2023-05-25) Han, Hasan; Karadeniz, Oğuzcan; Dalyan, Tuğba; Sönmez, Elena Battini; Sarıoğlu, Baykal
    Robot companions will soon be part of our everyday life and students in the engineering faculty must be trained to design, build, and interact with them. The two affordable robots presented in this paper have been designed and constructed by two undergraduate students; one artifi-cial agent is based on the Nvidia Jetson Nano development board and the other one on a remote computer system. Moreover, the robots have been refined with an empathetic system, to make them more user-friendly. Since automatic facial expression recognition skills is a necessary pre-processing step for acknowledging emotions, this paper tested different variations of Convolutional Neural Networks (CNN) to detect the six facial expressions plus the neutral face. The state-of-the-art performance of 75.1% on the Facial Expression Recognition (FER) 2013 database has been reached by the ensemble voting method. The runner-up model is the Visual Geometry Group (VGG) 16 which has been adopted by the two robots to recognize the expressions of the human partner and behave accordingly. An empirical study run among 55 university students confirmed the hypothesis that contact with empathetic artificial agents contributes to increasing the acceptance rate of robots
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    Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images
    (King Saud bin Abdulaziz University, 2022-09) Sönmez, Elena Battini
    Abstract: The Coronavirus disease is quickly spreading all over the world and the emergency situation is still out of control. Latest achievements of deep learning algorithms suggest the use of deep Convolutional Neural Network to implement a computer-aided diagnostic system for automatic classification of COVID-19 CT images. In this paper, we propose to employ a feature-wise attention layer in order to enhance the discriminative features obtained by convolutional networks. Moreover, the original performance of the network has been improved using the mixup data augmentation technique. This work compares the proposed attention-based model against the stacked attention networks, and traditional versus mixup data augmentation approaches. We deduced that feature-wise attention extension, while outperforming the stacked attention variants, achieves remarkable improvements over the baseline convolutional neural networks. That is, ResNet50 architecture extended with a feature-wise attention layer obtained 95.57% accuracy score, which, to best of our knowledge, fixes the state-of-the-art in the challenging COVID-CT dataset. © 2021 The Authors
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    Development of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Images
    (MDPI, 2023-09) Melek, Ceren Gülra; Sönmez, Elena Battini; Ayral, Hakan; Varlı, Songül
    Abstract: Product recognition on grocery shelf images is a compelling task of object detection because of the similarity between products, the presence of the different scale of product sizes, and the high number of classes, in addition to constantly renewed packaging and added new products’ difficulty in data collection. The use of conventional methods alone is not enough to solve a number of retail problems such as planogram compliance, stock tracking on shelves, and customer support. The purpose of this study is to achieve significant results using the suggested multi-stage end-toend process, including product detection, product classification, and refinement. The comparison of different methods is provided by a traditional computer vision approach, Aggregate Channel Features (ACF) and Single-Shot Detectors (SSD) are used in the product detection stage, and Speed-up Robust Features (SURF), Binary Robust Invariant Scalable Key points (BRISK), Oriented Features from Accelerated Segment Test (FAST), Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB), and hybrids of these methods are used in the product classification stage. The experimental results used the entire Grocery Products dataset and its different subsets with a different number of products and images. The best performance was achieved with the use of SSD in the product detection stage and the hybrid use of SURF, BRISK, and ORB in the product classification stage, respectively. Additionally, the proposed approach performed comparably or better than existing models