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Classification of Arrhythmias Based on Artificial Intelligence From Electrocardiography Signals
No full textHeart rhythms that differ from the normal sinus rhythm occurs as a result of the heart beating in a certain order and at regular intervals in electrocardiography (ECG) signals, are called arrhythmias. Irregularities in the rhythm can be an indicator of abnormal condition or even vital risks. Consequently, the detection and accurate classification of arrhythmias are critically important. The classification of arrhythmias using machine learning models trained with features obtained from ECG signals has been a subject of interest for researchers for many years. However, the application of deep learning techniques which eliminate the need for signal processing and feature extraction steps is also prominent research topic. Comparing the performance of these methods and determining the model parameters are essential for better results. In this thesis, two different rhythm classification methods are developed with the analysis of ECG signals and the performance of these methods are evaluated. The first method involves the rhythm classification with using machine learning models trained with features obtained from Heart Rate Variability (HRV) analysis. The second method classify rhythms by spectrogram and scalogram images as input to Convolutional Neural Networks (CNNs) without requiring signal preprocessing and feature extraction. The performance results of these two classification methods were compared. MIT-BIH Arrhythmia Database is used in the study. Normal Sinus Rhythm (N), Atrial Fibrillation (AFIB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Ventricular Bigeminy (B), Pacemaker Rhythm (P) are classified. In the classification performed with HRV-based features, firstly the noise removal process is applied to the signal. R wave peaks are detected on the filtered signals. HRV analysis is performed with R wave peaks and features are extracted. These features are then used to train machine learning models, including Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Ensemble Learning methods. In deep learning, CNN-based classification, spectrogram and scalogram images are generated from the denoised ECG signals. These images are then input into CNN architectures such as GoogleNet, ResNet-50, AlexNet, and SqueezeNet to assess their classification performance. It is determined how the performance of different neural networks performed when trained with scalogram and spectogram images. In addition, the rhythm classification performance performed with feature extraction and machine learning models is compared with the classification performance performed using deep learning models. In the HRV analysis, the highest accuracy value in arrhythmia classification performed with a two-minute signal length is obtained with the ensemble learning model with 87.91%. In the five-minute signal length, 90.48% accuracy is achieved with the SVM. In the classification performed using spectrogram images, the maximum accuracy is 91.90% with AlexNet. In the classification performed with scalogram images, the maximum accuracy is obtained as 92.85% with the GoogleNet. These findings demonstrate that scalogram-based CNN models achieve superior classification performance, highlighting their potential for accurately detecting and classifying arrhythmias in ECG signals. The observed findings show that these evaluated models provide high performance in classifying arrhythmias in ECG signals.Elektrokardiyografi (EKG) sinyallerinde kalbin belirli bir düzende ve düzenli aralıklarla atması sonucu oluşan normal sinüs ritimi dışında gözlenen ritimler aritmi olarak adlandırılır ve atımın bozuklukları normal olmayan bir durumun hatta hayati risklerin göstergesi olabilir. Bu nedenle aritmilerin bulunması ve hangi tür aritmi olduğunun belirlenmesi önemlidir. EKG sinyallerinden elde edilen öznitelikler ile eğitilen makine öğrenmesi modelleri ile aritmilerin sınıflandırılması uzun yıllardır araştırmacılar tarafından ilgi çeken konulardan olmuştur. Bununla birlikte uzun süren ön sinyal işleme adımlarının ve öznitelik çıkarma adımlarının olmadığı derin öğrenme tekniklerinin kullanılması da güncel konular arasındadır. Bu yöntemlerin performanslarının karşılaştırılması ve kullanılan model parametrelerinin belirlenmesi aşamaları önem taşımaktadır. Bu tez çalışmasında, EKG sinyallerinin analizi ile iki farklı ritim sınıflandırma yöntemi geliştirilerek bu yöntemlerin performansı değerlendirilmiştir. İlk yöntem kalp hızı değişkenliği analizi ile elde edilen özniteliklerle eğitilen makine öğrenmesi modellerinin kullanıldığı sınıflandırma yöntemidir. Sinyal ön işleme ve öznitelik çıkarımı olmadan EKG sinyallerinin spektogram ve skalogram görüntülerinin evrişimsel sinir ağlarına girdi olarak verilerek sınıflandırılması ikinci yöntemdir. Bu iki sınıflandırma yönteminin performans sonuçları karşılaştırılmıştır. Çalışmada MIT-BIH Aritmi Veri Tabanı kullanılmıştır. Çalışmada normal sinüs ritmi, atriyal fibrilasyon, sol dal bloğu, sağ dal bloğu, ventriküler begemini, pacemaker ritim sınıflandırılan ritimlerdir. Kalp hızı değişkenliği analizi tabanlı özniteliklerle gerçekleştirilen sınıflandırmada öncelikle sinyalde gürültü giderme işlemi uygulanmıştır. Filtrelenmiş sinyaller üzerinden R dalgası tepe noktalarının bulunması gerçekleştirilmiştir. R dalgası tepe noktaları ile kalp hızı değişkenliği analizi yapılmış ve öznitelikler çıkarılmıştır. Sonrasında ise makine öğrenmesi modelleri bu öznitelikler ile eğitilmiş ve model performansları değerlendirilmiştir. Tez kapsamında Karar Ağacı, Destek Vektör Makinesi, K-En Yakın Komşu, Topluluk Öğrenme modelleri çalışılmıştır. Derin öğrenme, evrişimsel sinir ağları tabanlı sınıflandırmada ise EKG sinyallerinde gürültü giderme işlemi yapılarak spektogram ve skalogram görüntüleri elde edilmiştir. Bu görüntüler farklı sinir ağlarına girdi olarak verilmiştir. Tez kapsamında GoogleNet, ResNet-50, AlexNet ve SqueezeNet çalışılan evrişimsel sinir ağlarıdır. Farklı sinir ağlarının performansının skalogram ve spektogram görüntüleri ile eğitildiğinde nasıl performans gösterdiği belirlenmiştir. Bu karşılaştırmaya ek olarak öznitelik çıkartma ve makine öğrenmesi modelleri ile gerçekleştirilen ritim sınıflandırma performansının derin öğrenme modelleri kullanılarak gerçekleştirilen sınıflandırma performansı karşılaştırılmıştır. Kalp hızı değişkenliği analizinde iki dakikalık sinyal uzunluğu ile gerçekleştirilen aritmi sınıflandırmasında en yüksek doğruluk değeri %87,91 ile topluluk öğrenme modeli ile elde edilmiştir. Beş dakikalık sinyal uzunluğunda ise destek vektör makinesi ile %90,48 doğruluğa ulaşılmıştır. Spektogram çıktıları kullanılarak gerçekleştirilen sınıflandırmada maksimum doğruluk AlexNet ile %91,90'dır. Skalogram çıktıları ile gerçekleştirilen sınıflandırmada ise maksimum doğruluk GoogleNet modeli ile %92,85 olarak elde edilmiştir. En yüksek sınıflandırma performansı skalogram görüntüleri ve evrişimsel sinir ağları kullanılarak elde edilmiştir. Gözlemlenen bulgular, değerlendirilen bu modellerin EKG sinyallerinde aritmilerin sınıflandırılmasında yüksek performans verdiğini göstermektedir
Growth Hormone-Releasing Pituitary Microadenoma Overshaded by a Macroadenoma: a Case of Double Pituitary Adenomas and Review of the Literature
No full textSenol Akbulut, Ozde/0000-0002-6024-1899; Kuzucu, Pelin/0000-0003-0484-3753Objectives: Double pituitary adenoma is a rare entity that can pose a significant challenge. The incidence of double or multiple pituitary adenomas is similar to 1% in autopsy cases and 0.4-1.3% in surgical series. Its definition varies, including 'double adenomas' in the literature in contrast to 'multiple adenomas', which is more specific and suitable. While some authors require separating topographically unique tumours, others have used a looser definition of separate immunohistochemistry. Case presentation: We presented the case of a 26-year-old patient with recurrent carpal tunnel syndrome symptoms, with double pituitary adenomas secreting growth hormone (GH) and thyroid-stimulating hormone (TSH). To date, 89 patients have been reported in the literature with symptomatic carpal tunnel syndrome, but only five had GH-TSH secretion. Conclusions: Double adenoma resection is of great importance for ensuring successful biochemical treatment. To ensure a successful operation, a careful preoperative 3T MRI examination is of great importance.Preparation for publication of this article is partly supported by the Turkish Neurosurgical Society.Turkish Neurosurgical Societ
Leveraging Deep Learning and Image Conversion of Executable Files for Effective Malware Detection: a Static Malware Analysis Approach
No full textThe escalating sophistication of malware poses a formidable security challenge, as it evades traditional protective measures. Static analysis, an initial step in malware investigation, involves code scrutiny without actual execution. One static analysis approach employs the conversion of executable files into image representations, harnessing the potency of deep learning models. Convolutional neural networks (CNNs), particularly adept at image classification, have potential for malware detection. However, their inclination towards structured data requires a preprocessing phase to convert software into image -like formats. This paper outlines a methodology for malware detection that involves applying deep learning models to image -converted executable files. Experimental evaluations have been performed by using CNN models, autoencoder-based models, and pre -trained counterparts, all of which have exhibited commendable performance. Consequently, employing deep learning for imageconverted executable analysis emerges as a fitting strategy for the static analysis of software. This research is significant because it utilized the largest dataset to date and encompassed a wide range of deep learning models, many of which have not previously been tested together
Observation of Four-Top Production in the Multilepton Final State With the Atlas Detector (vol 83, 496, 2023)
No full text[No Abstract Available
Capacitated Mobile Facility Location Problem With Mobile Demand: Efficient Relief Aid Provision To En Route Refugees
No full textAs a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugees to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions. © 2024 Elsevier Lt
Perceived Parenting Attitudes and Obsessive Compulsive Symptoms: Investigating the Mediating Role of Difficulties in Emotion Regulation
No full textBu çalışma, algılanan ebeveyn tutumları ile obsesif kompulsif semptomlar (OKS) arasındaki ilişkide duygu düzenlemede yaşanan güçlüklerin (DDG) aracı rolünü araştırmıştır. Buna bağlı olarak, babanın ve annenin duygusal sıcaklığının, aşırı koruyuculuğunun, reddediciliğinin ve duygu düzenlemede yaşanan güçlüklerin yordayıcı rolleri incelenmiştir. Ayrıca, DDG'nin aracılık rolü de incelenmiştir. Araştırmanın örneklemi, Türkiye'de yaşayan ve 18 yaş ve üzeri 344 bireyden oluşmuştur. Katılımcılara, uygun örnekleme yöntemi kullanılarak sosyal medya gönderileri ve e-postalar aracılığıyla ulaşılmıştır. Veri toplama araçları Demografik Bilgi Formu, Kısaltılmış Algılanan Ebeveyn Tutumları Ölçeği-Çocuk Formu (KAET-Ç), Duygu Düzenlemede Zorluklar Ölçeği (DDZÖ) ve Maudsley Obsesif Kompulsif Soru Listesi (MOKSL)'dir. Toplanan veriler, hiyerarşik regresyon analizleri ve aracılık analizleri yapılarak analiz edilmiştir. Bulgular, babanın aşırı koruyuculuğunun, annenin aşırı koruyuculuğunun ve duygu düzenlemede yaşanan güçlüklerin obsesif kompulsif semptomları anlamlı olarak yordadığını göstermiştir. Aynı zamanda, duygu düzenlemede yaşanan güçlüklerin, annenin aşırı koruyuculuğu ve obsesif kompulsif semptomlar arasındaki ilişkide kısmi bir aracı rol oynadığı tespit edilmiştir. Bulgular, mevcut literatür ışığında tartışılmıştır.The current study investigated the mediating role of the difficulties in emotion regulation (DER) in the relationship between perceived parenting attitudes and obsessive compulsive symptoms (OCS). Specifically, the predictor roles of paternal and maternal emotional warmth, overprotection, rejection, and emotion regulation difficulties were examined. The mediating role of the DER was also examined. The sample consisted of 344 individuals living in Türkiye, aged 18 years and older. Convenience sampling method was used to reach the participants through social media posts and e-mails. Data collection instruments were the Demographic Information Form, the short form of EMBU ("My Memories of Upbringing") (s-EMBU), Difficulties in Emotion Regulation Scale (DERS), and Maudsley Obsessional-Compulsive Inventory (MOCI). The collected data was analyzed by conducting hierarchical regression analyses and mediation analyses. The findings demonstrated that paternal overprotection, maternal overprotection, and difficulties in emotion regulation significantly predicted obsessive compulsive symptoms. Moreover, DER partially mediated the relationship between maternal overprotection and obsessive compulsive symptoms. The findings were discussed in light of the existing literature
Improving the Antimicrobial Properties of Polypropylene Meshes Used in Hernia Surgeries with Amine-Functional Polymer Coatings
No full textIX. Polymer Science and Technology Congress with International Participation 16-18 September 2024, Ankara METU Culture and Convention CenterPolypropylene (PP) meshes utilized in hernia surgeries are prone to infection-related complications due to their inherent susceptibility to bacterial colonization. This study focuses on developing a method to functionalize PP meshes with specific amine-functional polymers (Poly(2-(dimethylamino)ethyl methacrylate) and poly[(dimethylaminoethyl methacrylate)-co-(ethyleneglycol dimethacrylate)]) to enhance their antimicrobial properties. The aim is to introduce positively charged amino groups onto the mesh surface, facilitating interaction with bacterial cell membranes, leading to bacterial cell death, and imparting antibacterial characteristics. The modified PP meshes were characterized using Fourier Transform Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), and Water Contact Angle (WCA) measurements. Antimicrobial activity of poly- DMAEMA and poly-DMAEMA-EGDMA (quaternary amine) coated PP meshes will be assessed using the agar diffusion method against both Gram-positive (Escherichia coli) and Gram-negative (Staphylococcus aureus) bacteria. This approach offers a promising solution to enhance postoperative healing by augmenting the antimicrobial properties of meshes employed in hernioplasty, thereby minimizing the risk of postoperative infections
Desing Of A Lattice-Core Sandwich Panel For Additive Manufacturing
No full textAdditive Manufacturing Conference (AMC) Türkiye 2024 April 25 – 27, 2024In the present work, the finite element analysis (FEA) of AlSi10Mg body-centered-cubic lattice-core sandwiches manufactured by laser powder bed fusion additive manufacturing technique were performed using solid, beam and homogenized solid and shell models. Vibration experiments were performed for three different additively manufactured lattice sandwich specimens. The natural frequencies of the modal FEA showed good agreements with those of the experiments. It was further shown that the beam element model was the most efficient modeling method for both static and modal FEA by considering the computational cost.The studies were carried out within the scope of 20AG008 project under the Scientific and Technological Research Council of Turkey (TÜBİTAK)-1004-Center of Excellence Support Program. Authors gratefully acknowledge the TÜBİTAK for their valuable support
Layered Conjugated Porous Fused Aromatic Network Structures of Two-Dimensional Carbon Nitride: a First-Principles Calculation of Optoelectronic Properties
No full textVery recently, the two-dimensional (2D) structure of poly-benzimidazobenzophenanthroline (C5N) has been effectively synthesized [Javeed Mahmood et al., Adv. Mater. 2021, 33, 2,004,707]. Inspired by interesting experimental findings on 2D layered C5N structures, we employ DFT study to examine the electronic, structural, and optical features of C5N in bulk, bilayer, and monolayer honeycomb crystal configurations. The obtained results demonstrate that all configurations of the C5N structures have a strong bond network with cohesive energies comparable to graphene. In the ground state, the C5N bulk, bilayer and monolayer honeycomb crystal structures are a semiconductor. It is found that the bandgap of the C5N structures slightly increases with the decrease in the number of layers. The optical properties indicate the bulk structure possesses a greater capacity to absorb a broad range of visible light compared to the monolayer and bilayer.National Research Foundation of Korea (NRF) [NRF-2015M2B2A4033123]; National Research Foundation of Korea (NRF) - Korea government (MSIT)This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2015M2B2A4033123)
Accuracy Versus Precision in Boosted Top Tagging With the Atlas Detector
No full textVincter, Manuella/0000-0002-5338-8972; Tzovara, Eftychia/0000-0002-0410-0055; Winklmeier, Frank/0000-0001-8290-3200; Ahmadov, Faig/0000-0003-3644-540X; Mete, Alaettin Serhan/0000-0002-5508-530X; Rieger, Oliver/0009-0008-3521-1920; Terzo, Stefano/0000-0003-3388-3906; Coccaro, Andrea/0000-0003-2368-4559; Antel, Claire/0000-0001-9683-0890; Vergis, Christos/0000-0002-3228-6715; Suchy, Daniel/0009-0007-2966-1063; Thompson, Emily Anne/0000-0001-7050-8203; Sawada, Ryu/0000-0002-2226-9874; Geanta, Andrei-Alexandru/0000-0003-2781-2933; Landon, Murrough/0000-0001-6828-9769; Akesson, Torsten/0000-0003-4141-5408; Zhang, Zhicai/0000-0002-1630-0986; Kowalewski, Robert/0000-0002-7314-0990; Usman, Muhammad/0000-0003-1950-0307; Fisher, Wade/0000-0003-3043-3045; Liu, Yanlin/0000-0001-9190-4547; Redlinger, George/0000-0002-6437-9991; Sammel, Dirk/0000-0003-4484-1410; Cooper-Sarkar, Amanda/0000-0002-7107-5902; Laurier, Alexandre/0000-0002-2575-0743; Golling, Tobias/0000-0001-8535-6687; Gwilliam, Carl/0000-0002-9401-5304; cerri, alessandro/0000-0002-1904-6661; Calafiura, Paolo/0000-0002-1692-1678; Martinez-Agullo, Pablo/0000-0001-8925-9518; Lester, Christopher Gorham/0000-0001-5770-4883; Sadrozinski, Hartmut/0000-0003-0019-5410; Ripellino, Giulia/0000-0002-4053-5144; Balasubramanian, Rahul/0000-0001-5840-1788; Andrean, Stefio Yosse/0000-0002-9766-2670; Degens, Jordy/0000-0002-6966-4935; Vetterli, Michel/0000-0002-7223-2965; Duckeck, Guenter/0000-0002-7756-7801; Shah, Aashaq/0000-0002-6157-2016; Gwenlan, Claire/0000-0002-3518-0617; Maleev, Victor/0000-0003-1028-8602; Pilkington, Andrew/0000-0001-8007-0778; Ran, Kunlin/0000-0003-3119-9924; jia, Jiangyong/0000-0002-5725-3397; Gray, Heather/0000-0002-5293-4716; Unal, Guillaume/0000-0001-8130-7423; Nairz, Armin/0000-0003-3561-0880; Cranmer, Kyle/0000-0002-5769-7094; Dittus, Fridolin/0000-0002-1760-8237; Barberio, Elisabetta/0000-0002-3111-0910; Hanagaki, Kazunori/0000-0003-0676-0441; Artoni, Giacomo/0000-0002-3477-4499; Evans, Harold/0000-0003-2183-3127; Hugging, Fabian/0000-0002-7472-3151; D'Uffizi, Matteo/0000-0003-2499-1649; Han, Kunlin/0000-0002-1627-4810; Ravina, Baptiste/0000-0002-1622-6640; Canonero, Enzo/0000-0002-7180-4562; Mildner, Hannes/0000-0002-0384-6955; Masubuchi, Tatsuya/0000-0001-9984-8009; Padilla, Cristobal/0000-0001-7951-0166; Burlayenko, Oleksandr/0000-0001-8283-935X; Yorita, Kohei/0000-0003-1988-8401; Orestano, Domizia/0000-0001-5103-5527; Schoeffel, Laurent/0000-0002-8081-2353; Vazquez Schroeder, Tamara/0000-0002-9780-099X; Santra, Arka/0000-0003-4644-2579; Charlton, David/0000-0003-0211-2041; Pereira Sanchez, Laura/0000-0001-7913-3313; White, Martin/0000-0001-5474-4580; Ellinghaus, Frank/0000-0003-3596-5331; Gonella, Laura/0000-0002-4919-0808; Novak, Tadej/0000-0002-3053-0913; Majersky, Oliver/0000-0001-8857-5770; Oide, Hideyuki/0000-0002-2173-3233; franklin, melissa/0000-0002-6595-883X; Mincer, Allen/0000-0002-6307-1418; Escalier, Marc/0000-0003-4270-2775; Yang, Hongtao/0000-0003-3554-7113; Gessinger, Paul/0000-0002-3056-7417; Jones, Eleanor/0000-0001-6289-2292; Mogg, Philipp/0000-0003-2688-234X; Straessner, Arno/0000-0003-2460-6659; Abramowicz, Halina/0000-0001-5329-6640; Vranjes, Nenad/0000-0001-5415-5225; Pizzini, Alessio/0000-0001-8891-1842; Vermeulen, Jos/0000-0003-4378-5736; Heim, Sarah/0000-0002-2639-6571; Loch, Peter/0000-0002-2005-671X; Sciandra, Andrea/0000-0001-7163-501X; Hoppesch, Matthew/0000-0002-7773-3654; Sfyrla, Anna/0000-0002-3003-9905; Bosman, Martine/0000-0002-7290-643X; Rotaru, Marina/0000-0003-4088-6275; Weber, Michele/0000-0002-2770-9031; Herde, Hannah/0000-0001-8926-6734; Stupak III, John/0000-0001-9610-0783; Leney, Katharine/0000-0002-1525-2695; David, Claire/0000-0002-1794-1443; Villa, Mauro/0000-0002-9181-8048; Hrynevich, Aliaksei/0000-0002-5411-114X; BALLABENE, ERIC/0000-0001-9700-2587; Kretzschmar, Jan/0000-0002-8515-1355; Ellis, Nicolas/0000-0002-1920-4930; TERRON, JUAN/0000-0003-0132-5723; Terashi, Koji/0000-0001-6520-8070; Turtuvshin, Tulgaa/0000-0001-9471-8627; Ventura-Gonzalez, Salvador/0000-0001-5246-0779; Yu, Yi/0000-0003-4762-8201; 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