EKG işaretlerinden YSA ve korelasyon matrislerine dayalı tıkayıcı uyku apnesi teşhisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Tıkayıcı uyku apnesi (TUA) sendromu, uyku sırasında aralıklı üst solunum yolu tıkanıklıklarına neden olan, kalp ve sinir aktivitelerini etkileyerek uyku desenini bozan ciddi bir hastalıktır. Şu anda, TUA’nin tanısında polisomnografi (PSG) kullanılmaktadır. PSG, çok sayıda elektrot bağlantısına ihtiyaç duyan, genellikle gece uyku esnasında gerçekleştirilen, pahalı, zaman alıcı bir test yöntemidir. Literatürde çok sayıda bilimsel çalışma, sadece elektrokardiagram (EKG) işaretlerinin kalp hızı değişkenliği (KHD) analizine dayalı yöntemler ile TUA tanısının koyulabileceğini kabul etmektedir. Bu şekilde daha pratik, ucuz ve girişimsel olmayan bir yol ile son derece doğru sonuçlar elde edilebilen alternatif bir çözüm sunulmaktadır. Bu şekilde hastalık sınıflandırmada yüksek doğruluğa ulaşılmasına karşın hangi özellik parametrelerinin bu sınıflandırmada daha etkili olduğu ve parametre seçimi konusunda en uygun KHD analiz yöntemi için ortak bir bakış açısı bulunmamaktadır. Bu çalışma, öncelikle TUA hastalarına ait tek-kanal EKG işaretlerindeki KHD’ni zaman, frekans ve doğrusal olmayan yöntemleri kullanarak kapsamlı bir şekilde analiz eder. Daha sonra KHD’nden elde edilen bu özellikleri kullanarak yeni bir sınıflandırma şeması sunar. Ayrıca, korelasyon matrisleri (KM)’ne dayalı yeni bir özellik seçim metodu önerir. Elde edilen sonuçlar, KM’nin hastalık sınıflandırma işlemlerinde özellik kümelerinin seçim ve sınırlandırılması, hedef hastalığı hangi parametrelerin daha iyi ayırt edebildiğini sayısal olarak belirlemesi ve yapay sinir ağları (YSA) sınıflandırma başarımını artırması bakımından değerli bulunmuştur.Obstructive sleep apnea (OSA) syndrome, which causes intermittent upper airway occlusion during sleep, affecting the heart and nervous activity that disrupts sleep patterns, is a serious disease. At present, polysomnography (PSG) is used for the diagnosis of OSA. PSG, requiring a large number of electrodes’ connection, is usually carried out during night sleep, and therefore an expensive, time-consuming test method. Many articles that appeared in the literature agreed upon the diagnosis of OSA can be achieved only through the analysis of heart rate variability (HRV) of ECG signals. In this way, highly accurate results can be obtained. Also, it offers an alternative solution that is more practical, inexpensive and non-invasive as well. Although high accuracies have been achieved in the classification of disease, there has not been a consensus on the matter of which feature parameters are more effective in this classification and the selection of the most appropriate method of HRV analysis. This study, initially, presents a new classification scheme for OSA by using common features belonging to time, frequency and non-linear domains of the HRV analysis of single-channel ECG in a comprehensive manner. In addition, it proposes a new method of feature selection based on the correlation matrices (CM). The results obtained in the classification of disease with using CM were found valuable in terms of selecting and limiting of feature sets, determining which parameters numerically better identify the target disease and increasing the performance of ANN

A new fuzzy logic based career guidance system: WEB-CGS

Izbor zanimanja na mnogo načina uvelike utječe na društveni život pojedinaca. Ipak, izbor odgovarajuće karijere postaje sve teži kad se uzme u obzir postojanje sve većeg broja zanimanja i mogućnosti usmjeravanja. Shodno tome, sve je veća važnost profesionalnog usmjeravanja. U ovom se radu razvija sustav pomoću kojega se automatski može ponuditi profesionalno usmjeravanje. To je WEB-CGS (web-based carrier guidance system) koji funkcionira kao web usluga koja se zasniva na neizrazitoj (fuzzy) logici. Cilj je olakšati pojedincu izbor odgovarajućeg zanimanja. U tom sustavu integrirani su prethodni uspjesi u obrazovanju učenika i mišljenja nastavnika te je omogućeno prepoznavanje profesionalnih interesa i mogućnosti učenika. Sustav predviđa interes učenika za usmjeravanje u području informacijske tehnologije, elektrike-elektronike, računovodstva i industrije automobila. Postignuti su obećavajući rezultati usmjeravanja za 300 neopredijeljenih studenata 9. razreda u strukovnoj srednjoj školi.Choosing a career affects individuals’ social life deeply in terms of many dimensions. However, choosing the right career is becoming increasingly difficult given the existence of an increasing number of professions and training opportunities. Consequently, the importance of career orientation increases. In this study, a system that can automatically offer vocational guidance has been developed. This new system is referred to as WEB-CGS (web-based carrier guidance system) and works as a fuzzy logic based web service. The aim is that it will make it easier for an individual to choose the right profession. In this system, students’ prior educational successes and teachers’ views were integrated in a manner which made it possible to identify the students’ professional interests and capacities. The system forecasts vocational school students’ interest with regard to Information Technology, Electrics-Electronics, Accounting, and Automotive. Promising results were obtained with regard to 300 unbiased 9th grade students in terms of orienting them towards an appropriate profession

Designing an Assistant System Encouraging Ergonomic Computer Usage

Today, people of almost every age group are users of computers and computer aided systems. Technology makes our life easier, but it can also threaten our health. In recent years, one of the main causes of the proliferation of diseases such as lower back pain, neck pain or hernia, Arthritis, visual disturbances and obesity is wrong computer usage. The widespread use of computers also increases these findings. The purpose of this study is to direct computer users to use computers more carefully in terms of ergonomics. The user-interactive system developed for this purpose controls distance of the user to the screen and calculates the look angle and the time spent looking at the screen and provides audio or text format warning when necessary. It is thought that this system will reduce the health problems caused by the frequency of computer usage by encouraging individuals to use computers ergonomically

Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach

Background: Identifying and validating biomarkers' scores of polymorphic bands are important for studies related to the molecular diversity of pathogens. Although these validations provide more relevant results, the experiments are very complex and time-consuming. Besides rapid identification of plant pathogens causing disease, assessing genetic diversity and pathotype formation using automated soft computing methods are advantageous in terms of following genetic variation of pathogens on plants. In the present study, artificial neural network (ANN) as a soft computing method was applied to classify plant pathogen types and fungicide susceptibilities using the presence/absence of certain sequence markers as predictive features. Results: A plant pathogen, causing downy mildew disease on cucurbits was considered as a model microorganism. Significant accuracy was achieved with particle swarm optimization (PSO) trained ANNs. Conclusions: This pioneer study for estimation of pathogen properties using molecular markers demonstrates that neural networks achieve good performance for the proposed application

Monkeypox Detection Using CNN with Transfer Learning

Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with transfer learning tools and hyperparameter optimization was provided. In the CNN structure, a hybrid function learning model was developed by customizing the transfer learning model together with hyperparameters. Implemented on the custom model MobileNetV3-s, EfficientNetV2, ResNET50, Vgg19, DenseNet121, and Xception models. In our study, AUC, accuracy, recall, loss, and F1-score metrics were used for evaluation and comparison. The optimized hybrid MobileNetV3-s model achieved the best score, with an average F1-score of 0.98, AUC of 0.99, accuracy of 0.96, and recall of 0.97. In this study, convolutional neural networks were used in conjunction with optimization of hyperparameters and a customized hybrid function transfer learning model to achieve striking results when a custom CNN model was developed. The custom CNN model design we have proposed is proof of how successfully and quickly the deep learning methods can achieve results in classification and discrimination

Monkeypox Detection Using CNN with Transfer Learning

Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with transfer learning tools and hyperparameter optimization was provided. In the CNN structure, a hybrid function learning model was developed by customizing the transfer learning model together with hyperparameters. Implemented on the custom model MobileNetV3-s, EfficientNetV2, ResNET50, Vgg19, DenseNet121, and Xception models. In our study, AUC, accuracy, recall, loss, and F1-score metrics were used for evaluation and comparison. The optimized hybrid MobileNetV3-s model achieved the best score, with an average F1-score of 0.98, AUC of 0.99, accuracy of 0.96, and recall of 0.97. In this study, convolutional neural networks were used in conjunction with optimization of hyperparameters and a customized hybrid function transfer learning model to achieve striking results when a custom CNN model was developed. The custom CNN model design we have proposed is proof of how successfully and quickly the deep learning methods can achieve results in classification and discrimination

An Efficient and Reliable Algorithm for Wireless Sensor Network

In wireless sensor networks (WSN), flooding increases the reliability in terms of successful transmission of a packet with higher overhead. The flooding consumes the resources of the network quickly, especially in sensor networks, mobile ad-hoc networks, and vehicular ad-hoc networks in terms of the lifetime of the node, lifetime of the network, and battery lifetime, etc. This paper aims to develop an efficient and reliable protocol by using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Unicasting is used when the desired destination is at a minimum distance to avoid an extra overhead and increases the efficiency of the network in terms of overhead and energy because unicasting is favorable where the distance is minimum. Similarly, multicasting is used when the desired destination is at maximum distance and increases the network’s reliability in terms of throughput. The results are implemented in the Department of Computer Science, Bacha Khan University Charsadda (BKUC), Pakistan, as well as in the Network Simulator-2 (NS-2). The results are compared with benchmark schemes such as PUMA and ERASCA, and based on the results, the performance of the proposed approach is improved in terms of overhead, throughput, and packet delivery fraction by avoiding flooding

Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images

Brain MR images are the most suitable method for detecting chronic nerve diseases such as brain tumors, strokes, dementia, and multiple sclerosis. They are also used as the most sensitive method in evaluating diseases of the pituitary gland, brain vessels, eye, and inner ear organs. Many medical image analysis methods based on deep learning techniques have been proposed for health monitoring and diagnosis from brain MRI images. CNNs (Convolutional Neural Networks) are a sub-branch of deep learning and are often used to analyze visual information. Common uses include image and video recognition, suggestive systems, image classification, medical image analysis, and natural language processing. In this study, a new modular deep learning model was created to retain the existing advantages of known transfer learning methods (DenseNet, VGG16, and basic CNN architectures) in the classification process of MR images and eliminate their disadvantages. Open-source brain tumor images taken from the Kaggle database were used. For the training of the model, two types of splitting were utilized. First, 80% of the MRI image dataset was used in the training phase and 20% in the testing phase. Secondly, 10-fold cross-validation was used. When the proposed deep learning model and other known transfer learning methods were tested on the same MRI dataset, an improvement in classification performance was obtained, but an increase in processing time was observed