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Material-related printing errors are frequently encountered in 3D printers based on the Fused Filament Fabrication (FFF) technology. When the materials used are investigated, materials such as PLA, ABS, and Nylon are generally preferred at affordable prices. However, printing problems may occur due to the mechanical characteristics difference between the materials. Among the main reasons for this are parameters such as material characters, melting temperature, and extrusion speed. These errors cause problems such as filament breaking, nozzle clogging, and filament falling out of the holder. As a result, the filament flow is interrupted, and the printing process continues without extrusion of the filament because the 3D printer cannot detect this flow. 3D printers on the market do not have a system to detect possible printing errors. Therefore, in this study, a system design measures the filament weight via a load sensor and informs the user has been performed. 3D printers could stop the printing process since they can detect possible errors in advance with the developed system and prevent electricity consumption and time loss. In addition, the amount of used and remaining filaments, which are not yet common in 3D printers, can be followed by the user.

Motor Imaginary (MI) electroencephalography (EEG) signals are generated with the recording of brain activities when a participant imagines a movement without physically performing it. The correct decoding of MI signals have been became an important task due to the application of these signals in the rehabilitation process of paralyzed patients in recent studies. However, the decoding of the these signals is still an evolving challenge in the design of a brain-computer interface (BCI) system. In this study, a machine learning based approach using Poincare measurements from non-linear measurements of MI EEG signals is proposed for classification of four-class MI tasks. The m-lagged Poincare plots were used to extract non-linear features and m is set to be values from 1 to 10. The performances of feature vectors which are extracted from 10 lag values and feature vector which is the combinations of these vectors were investigated separately in experimental evaluation section. The 24 different typical classification algorithms were tested in differentiating MI tasks using 5-fold cross-validation. Each of the these algorithms tested 10 times to analyzed the repeatability of the experimental results. The highest classifier performance of 47.08% among these 11 feature vectors was achieved over the combination feature vector that includes all lag values features using Quadratic Support Vector Machine (SVM). According to average accuracy value of 24 classifiers in 11 feature vector, the most discriminative feature set is 9th vector that consists of features extracted when lag value defined as 9. As a result, the innovative aspect of this study is the application of Poincare plots, one of the nonlinear feature extraction methods, in motor imaginary task classification.

Motor Imaginary (MI) electroencephalography (EEG) signals are obtained when a subject imagines a task without essentially applying it. The accurate decoding of MI EEG signals plays an important role in the design of brain-computer interface (BCI) systems due to the use of these signals in the rehabilitation process of paralyzed patients in recent studies. In this study, two different MI tasks were tried to be differentiated by extracting time-domain and frequency-domain features from 22 channel EEG signals and determining best combination of important and distinctive features based on statistical significance. MI EEG signals were supplied from BCI Competition IV Dataset-IIa. These features were differentiated using 25 different classification algorithms and 5-fold cross-validation method. The repeatability of the results was examined testing each algorithm 10 times. As a result, the highest average accuracy rate of 60.69% was calculated in the Quadratic Support Vector Machine (SVM) using all features and 62.52% in the Ensemble Subspace Discriminant (ESD) algorithm using only the selected features by the independent t-test. The results showed that the independent t-test based feature selection increased the performance in 20 classifiers, and decreased the performance in 5 classifiers. Also, the effectiveness of the feature selection method examined using the paired-sample t-test which is known as repeated measures t-test. The significance value, p-value was found as 0.04. Therefore, the independent t-test based feature selection method is an effective feature selection method and is providing the significant improvement in classifier performance.

Jaundice is a condition that results from an increase in bilirubin level in the blood. Its prevalence in newborns is around 60-70%. When this temporary jaundice becomes pathological and left untreated, significant damages may occur such as brain damage, vision loss, lung and kidney dysfunction, and even death. One of the methods used for the treatment of jaundice is phototherapy. In this study, a design has been made with 3 foldable LED panels to increase the target area. In addition, high-voltage LEDs with blue-green white wavelengths were used. Thus, it was aimed to minimize the risks of nausea and dizziness caused by intense blue light. An automatic system has been achieved by using temperature and light intensity sensors. The system will warn the user at temperatures and light intensity that are harmful to the baby.

Brain Computer Interface (BCI) is a system that enables people to communicate with the outside world and control various electronic devices by interpreting only brain activity (motor movement imagination, emotional state, any focused visual or auditory stimulus, etc.). The visual stimulation based recording is one of the most popular methods among various electroencephalography (EEG) recording methods. Steady-state visual-evoked potentials (SSVEPs) where visual objects are blinking at a fixed frequency play an important role due to their high signal-to-noise ratio and higher information transfer rate in BCI applications. However, the design of multiple (more than 3) commands systems in SSVEPs based BCI systems is limited. The different approaches are recommended to overcome these problems. In this study, an approach based on machine learning is proposed to determine stimulating frequency in SSVEP signals. The data set (AVI SSVEP Dataset) is obtained through open access from the internet for simulations. The dataset includes EEG signals that was recorded when subjects looked at a flickering frequency at seven different frequencies (6-6.5-7-7.5-8.2-9.3-10Hz). In the machine learning-based approach Wigner-Ville Distribution (WVD) is used and features are extracted using Time-Frequency (TF) representations of EEG signals. These features are classified by Decision Tree, Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes, Ensemble Learning classifiers. Simulation results demonstrate that the proposed approach achieved promising accuracy rates for 7 command SSVEP systems. As a consequence, the maximum accuracy is achieved in the Ensemble Learning classifier with 47.60%.

Paroxysmal atrial fibrillation (PAF) is the initial stage of atrial fibrillation, one of the most common arrhythmia types. PAF worsens with time and affects the patient's life quality negatively. In this study, we aimed to diagnose PAF early, so patients can start taking precautions before this disease gets worse. We used the atrial fibrillation prediction database, an open data from Physionet and constructed our approach using convolutional neural networks. Heart rate variability (HRV) features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations (fast Fourier transform, Lomb-Scargle, and Welch periodogram), time-frequency-domain measures using wavelet transform, and nonlinear Poincare plot measures. We also normalized these features using min-max normalization and z-score normalization methods. In addition, we also applied alternatively the heart rate normalization (HRN), which gave promising results in a few HRV-based research, before calculating these features. Thus, HRV data, HRN data, and HRV features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural networks classifier. We tuned the classifiers using 90% of samples and tested the classifiers' performances using 10% of data. The proposed approach resulted in 95.92% accuracy, 100% precision, 91.84% recall, and 95.74% f1-score in HRV with z-score feature normalization. When the heart rate normalization was also applied, the proposed approach reached 100% accuracy, 100% precision, 100% recall, and 100% f1-score in HRV with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. In addition, although deep learning models offer no use of separate feature extraction processes, this study reveals that using HRV-specific feature extraction techniques may improve the performance of deep learning algorithms in HRV-based studies. Comparing the existing studies, we concluded that our approach provides a much better tool to diagnose PAF patients. WOS:000709712800008 2-s2.0-85117118593

COVID-19 virüsü özellikle yaşlı bireyleri ve kronik rahatsızlığı bulunan hastaları ciddi bir şekilde etkileyen ve ölümlere sebep olmaktadır. Hızlı ve doğru bir erken teşhis ölüm oranını düşürmede ve bu salgının ekonomik maliyetini azaltmada anahtar bir role sahiptir. Bu amaçla, teşhis kitleri, teşhis aletleri ve tıbbi görüntüleme kullanılarak teşhis gibi yöntemler geliştirilmiştir. Her ne kadar bunlar arasında bilgisayarlı tomografi ile elde edilen göğüs görüntüleri altın bir standart olarak kabul edilse de, bu cihaza erişimde genellikle sorun yaşanmaktadır. Bu nedenle, röntgen cihazı gibi daha kolay ulaşılabilen cihazlar yardımıyla teşhis konulması oldukça önemlidir. Kaggle tarafından sunulan ve göğüs röntgen görüntülerinden oluşan “COVID-19 radiography database” veri tabanı bu çalışmada kullanılmıştır. Üç farklı ResNet modeli (ResNet 50, ResNet 101 ve ResNet 152) (a) COVID-19 hastalarının sağlıklı bireylerden ayırt edilmesi, (b) COVID-19 hastalarının zatürre hastalarından ayırt edilmesi ve (c) COVID-19 hastalarının zatürre hastaları ve sağlıklı bireylerden ayırt edilmesi için denenmiştir. Bu modeller arasında en yüksek başarılı sonuçları ResNet 50 modeli vermiştir. Elde edilen sonuçlara göre, COVID-19 hastalarının sağlıklı bireylerden ayırt edilmesinde %99,3 başarıya, COVID-19 hastalarının zatürre hastalarından ayırt edilmesinde %99,2 başarıya ve COVID-19 hastalarının hem normal bireylerden hem de zatürre hastalarından ayırt edilmesinde %97,3 başarıya ulaştık. Bu sonuçlar bildiğimiz kadarıyla sadece röntgen görüntüleri kullanılarak COVID-19 teşhisinde elde edilen en yüksek sınıflandırıcı başarımlarıdır. Sonuç olarak, önceden eğitilmiş ResNet 50 modeli COVID-19 hastalarının sadece göğüs röntgen görüntülerinden hızlı ve doğru bir şekilde tespit edilmesinde büyük bir potansiyele sahiptir. Röntgen cihazları sağlık kuruluşlarında diğerlerine kıyasla nispeten daha kolay erişilebilir cihazlar olduğundan, bu çalışmada kullanılan modelin bu salgını yenme konusunda yardımcı olacağına inanıyoruz., The COVID-19 virus has affected seriously and caused death especially for older people and patients with chronic diseases. Rapid and accurate early diagnosis has a key role to reduce the mortality and to decrease the economic cost of this pandemic. For this purpose, diagnostic kits, diagnostic aids, and diagnosis using medical imaging methods have been investigated. Although the chest imaging using Computed Tomography (CT) has been accepted as a golden standard among them, there is big challenge to reach this equipment in general. Hence, the diagnosis using more accessible devices like X-rays is very crucial. Kaggle’s chest X-ray images called the “COVID-19 radiography database” were used in this study. Three different ResNet models (ResNet 50, ResNet 101, and ResNet 152) were investigated (a) to discriminate patients with COVID-19 from normal subjects, (b) to discriminate patients with COVID-19 from patients with Pneumonia, and (c) to discriminate patients with COVID-19, patients with Pneumonia, and normal subjects. ResNet 50 model gave the highest performances among these three models. As a result, we achieved the accuracy of 99.3% to discriminate COVID-19 and Normal, the accuracy of 99.2% to discriminate COVID-19 and Pneumonia, and the accuracy of 97.3% to discriminate COVID-19, Normal, and Pneumonia. In our knowledge, these results are the highest classification accuracies in the literature in diagnosing COVID-19 using x-ray images only. In conclusion, the pre-trained ResNet 50 model has a big potential to detect the patients with COVID-19 quickly and accurately using chest X-Ray images only. Since X-ray devices are relatively more accessible devices in health organizations, we believe that the model used in this study may help defeating this pandemic.

Anteroposterior (AP) lag screw, posteroanterior (PA) lag screw, or posterior buttress plate are usually performed for posterior malleolar fixation, but the biomechanically strongest technique is unclear. The aim of our study was to biomechanically compare 3 different fixation methods for posterior malleolar fractures; AP lag screw, PA lag screw, and closed-loop double endobutton. Fracture models were created using a thin blade power saw after drawing the fracture line. The resultant fracture involved 30% of the joint on the distal tibial joint surface and extends with an angulation of approximately 50 degrees using 15 tibia composite bone samples. After anatomical reduction, fixation was achieved with 3.5 mm cortical screw in PA direction and in AP direction for group PA and AP, respectively. In Group DL, fixation was achieved with a closed-loop double endobutton (double lift loop, Orthomed, Turkey). The highest compression force to generate all displacement amounts was required for the double loop group (Group DL). The strongest fixation against compression was a double loop. The PA group was the second strongest fixation, and the AP group was the biomechanically weakest among these 3 fixation techniques. The closed-loop double endobutton technique was found biomechanically superior to anterior to posterior or posterior to anterior screw fixation techniques for posterior malleolar fracture.

Dementia is one of the most common diseases in the world. The number of patients with dementia is increasing rapidly day by day. Patients with dementia are commonly older than 65 years, and they already use additional drugs for other diseases. It becomes a challenge for individuals in this age group to keep track of their drug timings themselves for dementia patients especially. This study aims to design a device that allows the following dosage and usage regularity of the pills for this purpose. There are five different slices for the pills. This device also has five distinct alarm hours to remind to take pills. When the clock hits a scheduled time, an alarm starts to beep, and an LED flashes to warn the patient. The user can take the pill by pressing the green button within two minutes after the alarm. In this case, servo motors connected pill sections to the "shelf" part of the device. By pulling the shelf, the patient can reach the pills. Otherwise, this device sends an e-mail to the preset e-mail address (and a short message to the phone number) of the person who is responsible for the patient.

Brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) have been acceleratingly used in different application areas from entertainment to rehabilitation, like clinical neuroscience, cognitive, and use of engineering researches. Of various electroencephalography paradigms, SSVEP-based BCI systems enable apoplectic people to communicate with outside world easily, due to their simple system structure, short or no training time, high temporal resolution, high information transfer rate, and affordable by comparing to other methods. SSVEP-based BCIs use multiple visual stimuli flickering at different frequencies to generate distinct commands. In this paper, we compared the classifier performances of combinations of binary commands flickering at seven different frequencies to determine which frequency pair gives the highest performance using temporal and spectral methods. For SSVEP frequency recognition, in total 25 temporal change characteristics of the signals and 15 frequency-based feature vectors extracted from the SSVEP signal. These feature vectors were applied to the input of seven well-known machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbour, and Ensemble Learning). In conclusion, we achieved 100% accuracy in 7.5 - 10 frequency pairs among these 2,520 distinct runs and we found that the most successful classifier is the Ensemble Learning classifier. The combination of these methods leads to an appropriate detailed and comparative analysis that represents the robustness and effectiveness of classical approaches.

Elektroensefalografi (EEG) beyin aktivitelerinin ölçümünü sağlayan ve girişimsel olmayan bir yöntemdir. Elektrofizyolojik kaynağına göre yedi farklı EEG kayıt türü mevcuttur. Bunlar arasında görsel uyaranlar eşliğinde alınan EEG de mevcuttur. Son zamanlarda görsel uyaranların belli bir sıklıkla gösterildiği durağan durum görsel uyaran potansiller (DDGUP) sıklıkla beyin bilgisayar arayüzlerinde (BBA) kullanılmaya başlanmıştır. Literatürde DDGUP sinyallerinden deneklerin odaklandıkları objelerin tespiti üzerine odaklanılmıştır. Oysa ki, sadece odaklanılan nesne bilgisi değil aynı zamanda uyaranın gösterilme sıklığı DDGUP sinyalinin içine girmektedir. Üstelik DDGUP sinyalleri durağan olmadığından öznitelik çıkarımı için dalgacık dönüşümü etkili bir yöntemdir. Ancak dalgacık dönüşümü öncesi kullanılacak ana dalgacık türünün belirlenmesi gerekir. Bu çalışmada, DDGUP sinyalinden uyartım sıklığının belirlenmesi için farklı ana dalgacık türlerinden çıkarılan temel öznitelikler farklı sınıflandırıcılara uygulanmıştır. Sekiz farklı uyaran frekansında kayıt edilen DDGUP sinyalinden iyi bilinen EEG frekans bantlarında yer alan enerji, varyans ve entropi gibi öznitelikler çıkarılmıştır. Altı farklı dalgacık fonksiyonu (Haar, Daubechies, Symlet, Coiflet, Biorthogonal, Reverse Biorthogonal) bu amaçla kullanılmıştır. Ayrıca, çok sayıdaki özniteliklerden etkili olanların tespit edilmesi için varyans analizi (ANOVA) de gerçekleştirilmiştir. Altı temel sınıflandırıcı ile DDGUP sinyalleri sınıflandırılmış ve sınıflandırıcı performansları birbirleri ile karşılaştırılmıştır. Elde edilen sonuçlara göre ANOVA ile öznitelik seçiminin sınıflandırıcı performanslarını düşürdüğü görülmüştür. Ayrıca denekler tek olarak dikkate alındığında her denek için en yüksek başarıma farklı sınıflandırıcı ve ana dalgacık türünde ulaşıldığı görülmüştür. Tüm denekler birlikte değerlendirildiğinde ise, topluluk sınıflandırma (Ensemble learner) algoritması ile Reverse Biorthogonal ana dalgacığı kullanılarak elde edilen öznitelikler birlikte kullanıldığında, uyaran frekansı en yüksek doğrulukla tespit edilebilmiştir. Sonuç olarak, uyartım sıklık bilgisinin SSVEP içerisinden çıkarılması halinde daha yüksek doğrulukla çalışan BBA cihazlarının tasarlanabileceği düşünülmektedir.

The progress in technological and scientific fields in the 20th century has transformed and shaped the lives of people remarkably. In healthcare, technology can be described as the application of the methods, systems, medicines, devices, vaccines and organized information to improve the quality of life by providing solutions for the needs of both professionals and patients. Besides its potential of reducing fragmentation and costs while increasing the safety, utilization of technology can be considered as the key to achieving a safe, sustainable, and person-centered healthcare environment. Virtual Reality (VR) is a computer-generated three-dimensional interactive environment that stimulates multiple sensory methods. Considering the fact that VR is one of the crucial technological trends of this century, it has the potential of affecting the health industry until 2025 extremely. It is a real-time graphic simulation that includes visual, auditory, and tactile experiences. In the field of health, VR is used for multiple purposes as training of medical students for critical surgeries, education of nursing candidates, and treatment of some diseases. As essential members of the healthcare team, nurses should be a strict follower of the rapidly evolving technology to provide efficient and quality care. The utilization of VR in nursing education has the advantage of decreasing faulty interventions, which enhances patient safety as well as the quality of the healthcare services in the future.

Cardiovascular diseases, which involve heart and blood vessel dysfunctions, cause a higher number of deaths than any other disease in the world. Throughout history, many approaches have been developed to analyze cardiovascular health by diagnosing such conditions. One of the methodologies is recording and analyzing heart sounds to distinguish normal and abnormal functioning of the heart, which is called Phonocardiography. With the emergence of machine learning applications in healthcare, this process can be automated via the extraction of various features from phonocardiography signals and performing classification with several machine learning algorithms. Many studies have been conducted to extract time and frequency domain features from the phonocardiography signals by segmenting them first into individual heart cycles, and then by classifying them using different machine learning and deep learning approaches. In this study, various time and frequency domain features have been extracted using the complete signal rather than just segments of it. Random Forest algorithm was found to be the most successful algorithm in terms of accuracy as well as recall and precision.

Discovery of X-Rays is the beginning point of the medical imaging which developed and diversified in years. Since early days of X-Ray discovery they are used in also for imaging of teeth, in 1896, Dr. Otto Walkhoff imaged his mouth with X-Ray exposure. X-Rays helped the dentists to diagnose tooth decays and bone loss, examine dental structures and identify abnormalities of these structures. Today developments in technology resulted in different imaging techniques, X-Rays are used for Projectional Radiography and Computed Tomography, besides there are Nuclear Imaging, Magnetic Resonance Imaging and Ultrasound Imaging that widely used. In this review, imaging techniques for dental applications with the extension of artificial intelligence is examined to provide a brief information.

In this study, the intensive care unit patient survival is predicted by machine learning algorithms according to the examinations performed in the first 24 hours. The data of intensive care patients collected from approximately two hundred hospitals over a period of one year were used. Algorithms are run in Python environment. Machine learning models were compared with the Cross-Validation method, and the random forest algorithm is used. The model made the prediction with 92,53% accuracy rate.

Surface EEG measurements that can be performed in hospitals and laboratories have reached a wearable and portable level with the development of today's technologies. Artificial intelligence-assisted brain-computer interface (BCI) systems play an important role in individuals with disabilities to process EEG signals and interact with the outside world. In particular, the research is becoming widespread to meet the basic needs of individuals in need of home care with an increasing population. In this study, it is aimed to design the BCI system that will detect the hunger and satiety status of the people on the computer platform through EEG measurements. In this context, a database was created by recording EEG signals with eyes open and eyes closed by 20 healthy participants in the first stage of the study. The noise of the EEG signal is eliminated by using a low pass, high pass, and notch filters. In the classification, using Wavelet Packet Transform (WPT) with Coiflet 1 and Daubechies 4 wavelets, 77.50% accuracy was achieved in eyes closed measurement, and 81% in eyes open measurement.

Nowadays, despite the developing technology lots of patients lost their lives because of wrong and late diagnosis. With early diagnosis, most diseases and negative effects of the diseases for the patient can be prevented. Early diagnosis can also prevent cardiological diseases. Although auscultation of the chest with a stethoscope is an effective and basic method, a stethoscope isn't enough for the diagnosis of some diseases. One example of these diseases is heart valve malfunctions when the valves do not work as desired heart murmurs occur. The main goal of this project is to develop an electronic stethoscope and observing obtained signals as a graphic. The main difficulty while auscultation of chest with a stethoscope is, this procedure needs lots of experience and also even tough physician have enough experience, it's very hard to diagnose grade 1 and 2 heart murmurs. Furthermore, while auscultation tachycardia patients, generally it's very hard to decide where the first heart (S1) sound and second heart sound (S2) begins. In this project, it is planned to demonstrate heart sounds as a graphic. This method provides physicians to diagnose all kinds of chest sounds easily even the sounds which they cannot diagnose or recognize with their ears by stethoscope. Moreover, as the chest sounds are obtained as digital data, these data can be sent as desired. When a physician needs to get someone else's idea, these recordings can be sent to another professional.

In this study, the development of an algorithm for the classification of heart sound phonocardiogram waveforms such as Normal, Murmur, Extrasystole, Artifact. By presenting the approach used for classification from a general machine learning application point of view, the types of classifiers used were detailed by comparing their features and their performance. The Long-Short Term Memory method which supports the classification of each cardiac cycle in sound recordings. In addition to the LSTM-based features, our method incorporates spectral features to summarize the characteristics of the entire sound recording.

Today, many patients are treated in hospital or at home. It was determined that only 74% of the patients in 2014 Şişli Etfal Training and Research Hospital used probe bags. Probe bags are frequently used, very simple to use but also a critical bag. In cases where it is not emptied in time, it can cause infection in patients, and in case of explosion, it can sink around. The infections they cause seriously affect human life. For this reason, it was aimed to develop an Arduino microprocessor based system that would measure the bag's fullness and give a warning. With the help of flex sensors in the project, measurements will be taken from certain parts of the bag and the system will decide which of these is correct. Different warnings will be given at certain occupancy levels with the algorithm aimed to be created within the system. With this study, it was aimed to prevent various infectious diseases, to increase the quality of life of the patient and to reduce the burden on healthcare professionals. According to the results, the studies are promising.

The early diagnosis of epilepsy, which affects the lives of many people worldwide, is the first step of treatment to help patients to continue their lives efficiently. Experts have to spend a lot of time and energy to make this diagnosis as quickly and accuratelyaspossible.The aimofthisstudywasto investigatethe capacity of machine learning algorithms to distinguish epileptic and normal signals to develop a system that can automatically diagnose seizures. LabVIEW was used to obtain the sum of EEG sub-band powers which were used as an attribute for both epileptic and normal records. These attributes were classified with different classifiers using Matlab and as a result of the classification, it was concluded that the sub-band power sum can be used as a meaningful attribute in the classification of epileptic and normal EEG signals.

Epilepsy, also known as Sara in Turkish, is a neurological disorder that is very common in the community. It can have an impact as significant on the quality of life of individuals and cause injury or fatal consequences. The most important symptom of epilepsy is the patient's seizures. In case of seizure, changes in consciousness, behaviour, sensation in motor movements and autonomic functions are observed. As a result, involuntary contractions and movements occur. Epilepsy is a generally unknown disorder in populations with first aid deficiency. Various non-medical interventions are applied during the patient's seizure and these wrong practices adversely affect the life of the patient. Therefore, it should be known by the people around it how the emergency intervention should be done during an epileptic seizure. In this study, a medical device was designed to alert people to epileptic patients during seizures and tell them what to do. With this device, people around the person having an epileptic seizure will be told what should be done with the sound warnings from the device and the patient will be able to survive the seizure in a healthy.

X-rays are a type of electromagnetic energy which widely used in different areas. Since their discovery, x-rays have been used, especially for medical imaging applications. On the other hand, new efficient x-ray generation methods have still been under investigation. Although new imaging modalities arose, xrays have never lost their popularity in many applications like computed tomography and dental imaging. Among these new methods, x-ray generation using pyroelectric crystal materials is one of the promising technologies. Pyroelectric crystals generate an electric field when a temperature change occurs and are affected by various parameters like how many times (count) the crystal’s temperature changed in a second. Hence, this new technology needs more research. In this study, we investigated the relationship between the count number and x-rays’ energy level of pyroelectric materials using linear regression analysis. We used a commercial software package, which is called the Statistical Package for the Social Sciences (SPSS), for this purpose. As a result, we achieved a good fit value of R2 = 0.91.

In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers’ inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.

Paroxysmal Atrial fibrillation is one of the most common complaints of heart disorders that occur as a result of random vibrations of the atria. PAF episode show a serious increase with age, and the next steps are more difficult especially for the elderly. So, diagnosing in the early stages of this disorder is very important for the PAF patients to stop the progression of the disease and to improve the quality of life. For his reason, in this studyitisaimedtobedetectedwhichin5minutesbeforethePAF episodes. The 30-minute data is divided into 10 parts in 5 minutes with 50% overlap. For each part, wavelet transform methods and wavelet entropy are calculated over heart rate variability data. Using these measurements, it is determined whether there is a statistically significant difference between the parts and the early detection performance of PAF was obtained using the k-nearest neighbors classifier. As a result, PAF episode can be statistically distinguished before it occurs and it is determined that the k-nn classifier has about 72% performance 12.5 minutes earlier than a PAF episode.

Thanks to developing technology, robots have been integrated into many parts of daily life. Robots are called programmable, multifunctional, work, and timesaving devices. In this study, it is aimed to enhance a robotic arm to become a new tool for transferring an object to the virtual environment by integrating a three-dimensional digitizing property to the robotic arm. The robotic arm is developed using the software of Autodesk Fusion 360 and its physical prototype is implemented using PLA filament in three dimensional printers. A general-purpose microcontroller board of Arduino Mega 2560 model connected to motor drivers is used to control stepper motors that conduct robotic arm actions. Encoders used in the threedimensional digitizer are connected to digital pins of the Arduino board to calculate the X-Y-Z coordinates of the object in the space. The proposed system is controlled via the interface program, which is developed in Python programming language. As a result, a device to use in clinical applications is available in our biomechanics laboratuary, now.

The life quality of the patients with plegia and their caregivers is drastically reduced in daily life. Quadriplegia, which means that four limbs and the body trunk are affected from the plegia, is known as the most severe type of plegia. Since patients with quadriplegia can only move their eyes in their bodies and cannot meet their needs themselves, this limits them both psychologically and physically. In this study, a new system is proposed to use the device control interface in front of the patient via their eye movements and hence to control a device (or devices) whichever they want. Therefore, an improvement in both the patient’s and their caregivers’ life qualities since the patients will be able to conduct some simple activities alone. As a result of the limited experiments, to turn on and turn off the preprogrammed devices are controlled using the proposed system successfully.

This study deals with designing a decentralized multi-input multi-output controller board based on a low-cost microcontroller, which drives both parts of variable-speed scroll compressor and electronic-type expansion valve simultaneously in a chiller system. This study aims to show the applicability of commercial low-cost microcontroller to increase the efficiency of the chiller system, having variable-speed scroll compressor and electronic-type expansion valve with a new electronic card. Moreover, the refrigerant system proposed in this study provides the compactness, mobility, and flexibility, and also a decrease in the controller unit's budget. the study was tested on a chiller system that consists of an air-cooled condenser, a variable-speed scroll compressor, and a stepper driven electronic-type expansion valve. the R134a was used as a refrigerant fluid and its flow was controlled by electronic-type expansion valve in this setup. Both variable-speed scroll compressor and electronic-type expansion valve were driven by the proposed hardware using either proportional integral derivative or fuzzy logic controller, which defines four distinct controller modes. the experimental results show that fuzzy logic controlled electronic-type expansion valve and proportional integral derivative controlled variable-speed scroll compressor mode give more robustness by considering the response time., Turkish National and Scientific Research Council (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [TEYDEB-1505, 5130027], The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by a grant (TEYDEB-1505 No. 5130027) from the Turkish National and Scientific Research Council (TUBITAK).

In this study, a platform to develop remote control appliactions via bluetooth communication by using general purpose microcontroller boards and Adroid-based devices was designed. This system was designed to integrate easily into all kind of devices that was developed by students or manufacturers in especially biomedical area. In addition, it has an Android application. It is possible to transmit control statements wirelessly from a microcontroller board or an Android-based device to another microcontroller board via bluetooth. On the client-side board, there are PWM-driven led illumination control, servo motor control and four on-off relay controls. The implemented system is designed to use easily for educational purposes. Time saving from sofware design applications is aimed in this study.

Medical imaging history has begun with the discovery of X-rays. X-rays that are widely used since their invention in different areas from projectional radiography to computed tomography. Year by year their technology is improved in many aspects especially for their generation of which tubes are changed to get the most efficient rays. Nowadays different mechanisms are studied to obtain X-rays; one of them is pyroelectricity phenomena. Pyroelectricity is a material’s electricity generation from temperature changes. The output spectra of the pyroelectric X-ray generator is quite similar to traditional X-ray tubes, which gives a chance for replacing low-voltage pyroelectric X-ray generators instead of high-voltage conventional X-ray tubes. The results of conducted experiments and continued studies show us that the use of pyroelectricity for X-ray generation has great advantages. More portable X-ray devices may be available in the near future, and these new designs offer safer and easier to operate since they use only 12 Volts instead of kiloVolts. In conclusion, healthcare technologies require high budges in general, this low-cost alternative might make the radiological imaging available for low-income countries. In this paper, the fundamentals of X-ray generation from pyroelectric material is reviewed, a device on the market, COOL-X, is investigated, and both conventional method and pyroelectricity methods are compared.

Recently, treatment methods of cryotherapy and immunotherapy have been got off the ground to the patientswith common and plantar warts. On the other hand, there is no proof of which treatment method will besuccessful, yet. In this study, it is tried to predict whether these two methods succeed on wart treatment or notusing the logistic regression and the decision tree algorithms. These algorithms were run on WEKA softwareusing data sets that are freely available from UCI databases in this study. As a result, the success of the selectedwart treatment method was predicted with the accuracy of 85.56% by using the decision tree algorithm., Son zamanlarda yaygın ve ayak taban siğili olan hastalara kriyoterapi ve immünoterapi tedavi yöntemleriuygulanmaya başlanmıştır. Bununla birlikte hangi tedavi yönteminin başarılı olacağına dair bir kanıtbulunmamaktadır. Bu çalışmada, bu iki yöntemin siğil tedavisinde başarılı olup olmayacağı lojistik regresyon vekarar ağacı algoritmaları kullanılarak tahmin edilmeye çalışılmıştır. Çalışmada açık erişime sahip olan UCI veritabanındaki veriler kullanılarak WEKA yazılımı üzerinde algoritmalar koşturulmuştur. Sonuç olarak, seçilensiğil tedavi yönteminin başarısı karar ağacı sınıflandırıcısı ile %85,56 oranında doğru olarak tespit edilmiştir.

Steady-state visual evoked potentials (SSVEPs) have been designated to be appropriate and are in use in many areas such as clinical neuroscience, cognitive science, and engineering. SSVEPs have become popular recently, due to their advantages including high bit rate, simple system structure and short training time. To design SSVEP-based BCI system, signal processing methods appropriate to the signal structure should be applied. One of the most appropriate signal processing methods of these non-stationary signals is the Wavelet Transform. In this study, we investigated both the effect of choosing a mother wavelet function and the most successful combination of classifier algorithm, wavelet features, and frequency pairs assigned to BCI commands. SSVEP signals that were recorded at seven different stimulus frequencies (6–6.5 – 7 – 7.5 – 8.2 – 9.3 – 10 Hz) were used in this study. A total of 115 features were extracted from time, frequency, and time-frequency domains. These features were classified by a total of seven different classification processes. Classification evaluation was presented with the 5-fold cross-validation method and accuracy values. According to the results, (I) the most successful wavelet function was Haar wavelet, (II) the most successful classifier was Ensemble Learning, (III) using the feature vector consisting of energy, entropy, and variance features yielded higher accuracy than using one of these features alone, and (IV) the highest performances were obtained in the frequency pairs with “6–10”, “6.5–10”, “7–10”, and “7.5–10” Hz.

In this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so that patients can take precautions before putting their and others' lives in potentially life-threatening danger. We used the atrial fibrillation prediction database, open data from PhysioNet, and assembled our process based on convolutional neural networks. Conventional heart rate variability features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations, time-frequency-domain measures using wavelet transform, and nonlinear Poincaré plot measures. In addition, we also applied an alternative heart rate normalization, which gave promising results only in a few studies, before calculating these heart rate variability features. We used these features directly and their normalized versions using min-max normalization and z-score normalization methods. Thus, heart rate variability features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural network classifier. We tuned the classifiers' hyperparameters using 90% of feature sets and tested the classifiers' performances using 10% of feature sets. The proposed approach resulted in 87.76% accuracy, 91.30% precision, 80.04% recall, and 87.50% f1-score in heart rate variability with z-score feature normalization. When the heart rate normalization was also utilized, the suggested method gave 100% accuracy, 100% precision, 100% recall, and 100% f1-score in heart rate variability with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. By comparing the existing studies, we conclude that our approach provides a much better tool to determine a near-future paroxysmal atrial fibrillation episode. However, although the achieved benchmarks are impressive, we note that the approach needs to be supported by other studies and on other datasets before clinical trials. © 2021 Author(s). Javna Agencija za Raziskovalno Dejavnost RS, ARRS: J1-2457, P1-0403 M. Perc was supported by the Slovenian Research Agency (Grant Nos. P1-0403 and J1-2457). 2-s2.0-85119059311

Wavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals; however, there exists no evaluation for steady-state visually-evoked potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore, if any, the mother wavelet that suits best to represent SSVEP signals for classification purposes in BCIs. In this study, three common wavelet-based features (variance, energy, and entropy) extracted from SSVEP signals for five distinct EEG frequency bands (delta, theta, alpha, beta, and gamma) were classified to determine three different user commands using six fundamental classifier algorithms. The study was repeated for six different commonly-used mother wavelet functions (haar, daubechies, symlet, coiflet, biorthogonal, and reverse biorthogonal). The best discrimination was obtained with an accuracy of 100% and the average of 75.85%. Besides, ensemble learner gives the highest accuracies for half of the trials. Haar wavelet had the best performance in representing SSVEP signals among other all mother wavelets adopted in this study. Concomitantly, all three features of energy, variance, and entropy should be used together since none of these features had superior classifier performance alone.

High-rise reinforced concrete buildings have technical, economic and environmental advantages for high density development and they have become a distinctive feature for densely populated urban areas around the world. For this purpose, structural design of high-rise reinforced concrete buildings have become forward and particularly serviceability requirements gained more interest. Differential shortening of vertical members is one of the serviceability requirements; however, only a limited number of studies exist. In this study, a practical compensation method was proposed for the differential shortening of columns and shear walls in high-rise reinforced concrete buildings. In the proposed compensation method, vertical members were grouped and the total error was aimed to be minimized by penalizing the higher shortening differences in the groups to simplify the process of building construction. In order to validate the proposed method, a 32-storey high-rise building that was built in Izmir Turkey was investigated considering both the construction sequence and time-dependent effects as shrinkage and creep. Vertical shortening of columns and shear walls in the tower part of the building were calculated. Uniform-grouped compensation method and the proposed penalized errors compensation method with using L1-norm and L2-norm were applied for differential shortenings of columns and shear walls with considering different numbers of member groups. The magnitude of errors for each compensation method was presented and evaluated. Results of the numerical study reveal that the proposed penalized errors compensation method was capable of determining the compensation errors by minimizing the maximum errors efficiently.

Durağan durum görsel uyarılmış potansiyeller (DDGUP), diğer beyin bilgisayar ara yüzü (BBA) tekniklerine oranla oldukça yüksek sinyal-gürültü oranları ve bilgi aktarım hızına sahip oldukları için EEG çalışmalarında sıkça kullanılır. Ayrıca durağan durum paradigmaları, dinamik neokorteks süreçlerinde tercih edilen frekansları karakterize etmek için de kullanılır. Kısa eğitim süresine sahip olan DDGUP’lar, pratik uygulamalarda önemli bir rol oynar. Sinyalleri komuta dönüştürmekte kullanılan, sinyal işleme algoritmaları, BBA sistemlerinin performansını arttırmak için kilit öneme sahiptir. Buna ek olarak, DDGUP sinyallerinin birbirinden farklı yöntemlerle sınıflandırılmasını araştıran çok az çalışma vardır. Bu çalışmada, internetten açık erişim ile alınan veri seti (AVI SSVEP Dataset) üzerinde analizler yapılmıştır. Veri setindeki EEG kayıtları, katılımcılar, rengi siyahtan beyaza hızla değişen yedi farklı frekansta yanıp sönen bir kutuya baktıkları durumda kaydedilmiştir. Oksipital bölgeden kaydedilen DDGUP sinyalleri ilk olarak Hızlı Fourier Dönüşümü uygulanarak, sinyal alt bantlarına (delta, teta, alfa, beta ve gama) ayrılmıştır. Alt bantların her biri için enerji ve varyans öznitelik vektörleri çıkarılmıştır. Öznitelikler altı temel sınıflandırıcı (LDA, k-NN, SVM, Naive Bayes, Topluluk Öğrenmesi, Karar Ağacı) ile sınıflandırılmıştır. Sınıflandırma performansları birbirleri ile karşılaştırılmıştır. Sınıflandırma 5-kat çapraz doğrulama modeli ve hata matrisinden doğruluk değerleri çıkarılarak analiz edilmiştir. Katılımcılar ayrı ayrı göz önüne alındığında %100’e varan sınıflandırma başarımı SVM ve k-NN sınıflandırıcılarında elde edilirken, ortalamalara göre en yüksek başarım Topluluk Öğrenmesi sınıflandırıcısında %79,73 olarak elde edilmiştir.

Background Deteriorated speech understanding is a common complaint in elderly people, and behavioral tests are used for routine clinical assessment of this problem. Cortical auditory evoked potentials (CAEPs) are frequently used for assessing speech detection and discrimination abilities of the elderly, and give promise for differential diagnosis of speech understanding problems. Purpose The aim of the study was to compare the P1, N1, and P2 CAEP latencies and amplitudes in presbycusis with low and high word recognition score (WRS). Research Design A cross-sectional study design was used forthe study. Two groups were formed from the patients with presbycusis based on their scores on the speech recognition test. Study Sample Fifty-seven elderly volunteers participated in the study. The first group composed of 27 participants with high WRS, the other group composed of 30 participants with low WRS. Data Collection and Analysis The CAEP waves were recorded from these participants using speech signals. Latencies and amplitudes of P1 -N1-P2 waves of the two groups were compared with the f-test statistic. Results There were significant prolongation of P1 and N1 latencies in presbycusis with low WRS when compared with presbycusis with a relatively high word score (p Conclusion According to the result of the research, P1 and N1 latencies of presbycusis with low WRS were longer than the participants with high WRS. Factors affecting peripheral auditory system, such as stimulus sensation level, might be responsible for P1 and N1 latency prolongation of the low WRS group.

Paroxysmal atrial fibrillation (PAF) is the mainly encountered type of arrhythmia and there is no validated method to predict a PAF attack before it occurs. In this study, predicting the PAF event was aimed using time-domain heart rate variability (HRV) measures in k- nearest neighbor (k-nn) classifier. Traditional time-domain HRV measures were analyzed in every 5-minute segments from 49 normal subjects, 25 patients with PAF attack and 25 patients with no attack within 45 minutes. All features were investigated whether they showed statistically significance. Significant features were classified by k-nn for odd numbers of neighbors between 1 and 19. This setup was run with two different configurations as study 1 to discriminate patients with PAF attack from normals and patients with no attack, and study 2 to discriminate patients with PAF attack from patients with no attack. SDNN, RMSSD and pNN50 measures were found to show statistically significant differences with p less than 0.05 in segments of 0-5 min, 2.5-7.5 min and 5-10 min intervals only. The maximum classification accuracy was obtained in the time interval of 2.5-7.5 minutes with %79 for Study 1 and just before attack with %80 for Study 2 in the time interval of 0-5 minutes. Results showed that the prediction of PAF events was possible when the classification between normal subjects from PAF patients was accurate. PAF attack can be determined 2.5 minutes earlier by simple classifier algorithms.