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- BildiriYalnız künye2022Network Traffic Classification with Machine Learning Approaches; [Makine Ogrenmesi Yaklaimlan ile Ag Trafik Siniflandirilmasi]
Eker, Kadir; Eker, Ayse Gul; Mandal, Dilek; Pehlivanoglu, Meltem Kurt; Duru, Nevcihan
Today, detection of malware is often done automatically with artificial intelligence supported systems. In this way, this task, which was previously done manually, can be performed more precisely and more quickly. Darknet traffic is an internet network with many different cybercrime threats. It is also fake traffic observed in empty address space. A globally valid set of Internet Protocol (IP) addresses that are not assigned to any host or device. In this study, a deep learning model and pervasive machine learning classification algorithms are compared using the CIC-Darknet2020 dataset to describe Darknet traffic. The 'Tor' and 'Vpn' classes in the dataset, 'Darknet', 'Non-Tor' and 'Non-Vpn' classes are also specified as harmless. Various adjustments have been made for the unbalanced dataset, which has been performed with multiple classification, various machine learning algorithms. Random Forest Algorithm, Decision Trees and Deep Learning Model were the algorithms with the most successful accuracy values with %97.3, %97.4 %98.2values respectively.
AtıfScopus2
İncele - BildiriYalnız künye2022EEG Signals and Spectrogram with Deep Learning Approaches Emotion Analysis with Images; [Derin Ogrenme Yaklaimlanyla EEG Sinyalleri ve Spektrogram Goruntuleri ile Duygu Analizi]
Eker, Ayse Gul; Duru, Nevcihan; Eker, Kadir
EEG signals are one of the most basic methods used in identifying and analyzing brain activities. Visual representation of EEG signals can be achieved with spectrograms. Spectrograms represent a visual representation of a signal's signal strength over time. In this study, the signals in an EEG dataset containing 'positive', 'negative' and 'neutral' emotion classes were classified with a deep learning model, and then these signals were transformed into a spectrogram image in the dataset with convolutional network model and also with transfer learning (EfficientNet and XceptionNet). Multiple classification was performed with pre-trained models. The success value obtained by the classification of the EEG signals and the success of the visualization in this classification were measured and presented by comparison. While higher accuracy values were achieved in the classification of signals with the deep network model, in metrics such as precision and F1-score, the classification of images with the proposed convolutional network model achieved much higher performance.
AtıfScopus8İncele - MakaleYayıncıda açık erişim2022Anxiety and Depression After Cesarean: Non-pharmacological Evidence Based Practices; [Sezaryen Sonrası Kaygı ve Depresyon: Non-Farmakolojik Kanıt Temelli Uygulamalar]
Terzioğlu, Füsun; Gençbaş, Dercan; Boztepe, Handan; Doğu, Nilgün; Akdeniz, Canberk; Yüceer, Buğse
The purpose of this review is to examine the non-pharmacological evidence-based practices of anxiety and depression after a cesarean section delivery. Postpartum anxiety and depression have increased in the last decade, the rates of postpartum depression are around 13% worldwide. In the postpartum period, women may experience anxiety and depression due to the operation they have undergone; such as being in an unfamiliar environment, facing new technological equipment and encounters with the medical teams, the post-operative pain, the new roles women undertake as mothers. One of the factors that increase anxiety and depression in the postpartum period is the type of delivery. In Turkey, the rate of cesarean delivery is comparatively high, which is approximately 52%. Anxiety and depression after cesarean section develop due to fear and concern such as the complications that may occur during and after the mother's anesthesia, the possibility delaying breastfeeding her baby, and experiencing pain. Non-pharmacological evidence-based applications such as reiki, acupressure, hand and foot massage, yoga, reflexology, aromatherapy, skin to skin care, nursing care protocols were found to be effective in studies conducted to reduce anxiety and depression after cesarean-section. Nurses who have critical roles and responsibilities in pre-and post-cesarean care practices are recommended to include these evidence-based non-pharmacological practices in routine care practices.
AtıfScopus2İncele