Teknik Bilimler Meslek Yüksekokulu / Vocational School of Technical Sciences

Permanent URI for this collectionhttps://hdl.handle.net/11727/2031

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An Android Application for Geolocation Based Health Monitoring, Consultancy And Alarm System
(2018) Tartan, Emre Oner; Ciflikli, Cebrail
In the last decade significant progress have been made in smart phone technology as well as in wireless wide area network technologies. Today among a wide population smart phones and mobile applications are considered as indispensable part of daily life. A field that mobile applications have great potential is health monitoring. Health monitoring covers various physiological signals. One of these signals is heart rate which is related to cardiovascular state of the body. Recently producers offer heart rate monitoring with the on board or wearable heart rate sensor. Although the main trend is for individual usage especially in sports, heart rate monitoring can also be benefited in an emergency alarm system for people who have potential risks while doing sports or elderly people. Such a distant monitoring system can be helpful to deliver first aid in emergency cases. Moreover an health expert can monitor states of the patients in real time. In this study we benefit the facilities provided by mobile technology and propose a geolocation-based heart rate monitoring system. The developed mobile application can send alarm message through notification, sms, mail and allows messaging with the health expert for consultancy. Hence if anomalies are observed in heart rate variability during the outdoor activities, emergency information can be delivered in the shortest time and the delays which have crucial affects can be prevented. The same framework can be extended to a more general system including different sensors for monitoring various physiological signals.
An Approach to the Classification of Environmental Sounds by LSTM Based Transfer Learning Method
(2020) Ankishan, Haydar
This electronic Effective frequency extraction from acoustic environmental sounds in frequency and time axis increases the importance of voice recognition, sound detection, environmental classification in recently. For this purpose, there are many studies in the literature on the discrimination of acoustic environmental sounds. These studies generally perform these operations with the help of machine learning and deep learning algorithms. In this study, a new artificial intelligence architecture using two long short term memory networks (LSTM) is designed. The structure, which uses both raw data and the proposed feature vector at its inputs, is reinforced by the transfer learning approach. The obtained classification results were fused at the decision level. As a result of experimental studies, five different environmental acoustic sounds were subdivided into 97.15% test accuracy. In environmental studies conducted in pairs, it is seen that the environmental sounds have reached 100% accuracy. Experimental results have shown that the proposed artificial intelligence architecture with fusion support at decision level is capable of discriminating acoustic environmental sounds.
Blood pressure prediction from speech recordings
(2020) Ankishan, Haydar
The aim of this study is to extract new features to show the relationship between speech recordings and blood pressure (BP). For this purpose, a database consisting of / a / vowels with different BP values under the same room and environment conditions is presented to the literature. Convolutional Neural Networks- Regression (CNN-R), Support Vector Machines- Regression (SVMs-R) and Multi Linear Regression (MLR) are used in this study to predict BP with extracted features. From the experiments, the highest accuracy rates of BP prediction from / a / vowel have been obtained based on Systolic BP values with CNNR. In the study, 89.43 % for MLR, 92.15 % for SVM-R and 93.65 % for CNN-R are obtained when ReliefF has been used. When the root mean square errors (RMSE) are considered, the lowest error value is obtained with CNN-R as RMSE = 0.2355. In conclusion, it can be observed that the proposed feature vector (FVx) shows a relationship between BP and the human voices, and in this direction, it can be used as an FVx in a system that will be developed in order to follow the tension of individuals. (C) 2020 Elsevier Ltd. All rights reserved.
Brain Tumor Prediction with Deep Learning and Tumor Volume Calculation
(2021) Karayegen, Gokay; Aksahin, Mehmet Feyzi
Classification of acoustic signals with new feature: Fibonacci space (FSp)
(2019) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019
In this study, a new feature and feature space (FSp) are introduced by using the approach of Fibonacci series formation. The results are presented as two experimental studies. The nine groups of acoustic signals and pathological human voices are investigated in the first and second experiments, respectively. Convolutional Neural Network (CNN) and Multi-Class Support Vector Machines (M-SVMs) are used to figure out the effect of the proposed feature and its FSp on the classification accuracy. It is observed that the proposed feature and its formed space yield significant results for the discrimination of those signals. Experimental studies show that the classification accuracy of test data is increased by 5.3% when the proposed feature is used with CNN and M-SVMs. In addition, each acoustic group is significantly discriminated in both experimental studies. It is concluded that the proposed feature and its space can be used as a temporal feature for different purposes such as automatic speech recognition, pattern recognition, and emotional voice discrimination etc. (C) 2018 Elsevier Ltd. All rights reserved.
Comparison of SVM and ANFIS for Snore Related Sounds Classification by Using the Largest Lyapunov Exponent and Entropy
(,2013, 2013) Ankışhan, Haydar; Yılmaz, Derya
Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS.
Correlations between problem and solution domain measures of open source software
(2017) Ayyidiz, Tulin Ercelebi; Kocyigit, Altan; 0000-0002-7372-0223; AAE-1726-2021
Software size measurement and effort estimation methodologies in use today usually take the detailed requirements of software to be developed as the primary input and a certain amount of time and expertise is needed for size measurement. This paper analyzes the open source projects' correlations between the problem domain measures (the number of nouns and verbs) and solution domain measures (the number of software classes and methods). In this paper, 27 open source software projects are analyzed. Linear regression and cross validation techniques are applied to investigate the relation between the sizes of problem domain (i.e., conceptual) and solution domain (i.e., design) measures. The results reveal a strong correlation between the problem domain measures and the solution domain measures constituting the corresponding software. The results suggest that it is possible to use problem domain descriptions in the early stages of software development projects to make plausible predictions for the size and effort of the software.
Deep Learning Based Multi Modal Approach for Pathological Sounds Classification
(2020) Ankishan, Haydar; Kocoglu, Arif
Automatic detection of voice disorders is very important because it makes the diagnosis process simpler, cheaper and less time consuming. In the literature, there are many studies available on the analysis of voice disorders based on the characteristics of the voice and subdividing the result of this analysis. In general, these studies have been carried out in order to subdivide the sound into pathological - normally sub - groups by means of certain classifiers as a result of subtraction of the features on frequency, time or hybrid axis. In contrast to existing approaches, in this study, a multiple- deep learning model using feature level fusion is proposed to distinguish pathological-normal sounds from each other. First, a feature vector (HOV) on the hybrid axis was obtained from the raw sound data. Then two CNN models were used. The first model has used raw audio data and the second model has used HOV as an input. Feature data in both model SoftMax layers were obtained as a matrix, and canonical correlation analysis (Canonical Correlation Analysis (CCA) was applied at feature level fusion. The new obtained feature vector was used as an input for multiple support vector machines (M-SVMs), Decision Tree (DTC) and naive bayes (NBC) classifiers. When the experimental results are examined, it is seen that the new multi-model based deep learning architecture provides superior success in classifying pathological sound data. With the results of the study, it will be possible to automatically detect and classify the pathology of these patients according to the proposed system.
Detecting COVID-19 from Respiratory Sound Recordings with Transformers
(2022) Aytekin, Idil; Dalmaz, Onat; Ankishan, Haydar; Saritas, Emine U.; Bagci, Ulas; Cukur, Tolga; Celik, Haydar; Drukker, K; Iftekharuddin, KM
Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.
Discovery of a glitch in the accretion- powered pulsar SXP 1062
(2017) Imam, Sıtkı Cagdas; Serim, M.M.; Sahiner, S.; Cerri-Serim, D.; Baykal, A.; 0000-0003-0820-9186
We present timing analysis of the accretion-powered pulsar SXP 1062, based on the observations of Swift, XMM-Newton and Chandra satellites covering a time span of about 2 yr. We obtain a phase coherent timing solution that shows that SXP 1062 has been steadily spinning down with a rate-4.29(7) x10(-14) Hz s(-1) leading to a surface magnetic field estimate of about 1.5 x 10(14) G. We also resolve the binary orbital motion of the system from X-ray data that confirms an orbital period of 656(2) d. On MJD 56834.5, a sudden change in pulse frequency occurs with Delta v = 1.28(5) x 10(-6) Hz, which indicates a glitch event. The fractional size of the glitch is Delta v/v similar to 1.37(6) x 10(-3) and SXP 1062 continues to spin-down with a steady rate after the glitch. A short X-ray outburst 25 d prior to the glitch does not alter the spin-down of the source; therefore, the glitch should be associated with the internal structure of the neutron star. While glitch events are common for isolated pulsars, the glitch of SXP 1062 is the first confirmation of the observability of this type of events among accretion-powered pulsars. Furthermore, the value of the fractional change of pulse frequency ensures that we discover the largest glitch reported up to now.
The Effects of Illumination on the Current Conduction Mechanisms of the Au/C20H12/N-Si Schottky Barrier Diode (SBD)
(2023) Bengi, Seda; 0000-0002-3348-0712; HPH-9613-2023
Using the I-V characteristics both in the dark and under varied illumination-intensities (50-250 mW.cm(-2)) by 50 mW.cm(-2) steps in the wide range bias-voltage (+/- 5 V), specific fundamental electrical and photo effects parameters of the Au/C20H12/n-Si SBD were examined. Due to the creation of electron-hole pairs and their drift in opposite directions under the influence of an electric field, the value of photocurrent in the reverse bias (I-ph) increases when illumination density is increased. The barrier height (Phi(B0)) tended to decrease due to the increase in photocurrent, while the diode's ideality factor (n) increased with increasing illumination intensity. Also, the Schottky structure's open-circuit voltage (V-oc), short circuit current (I-sc), filling factor (FF), and efficiency (eta) were determined to be 0.358 V, 95.5 mu A, 33%, and 0.028% under 50 mW.cm(-2), respectively. The R-s and R-sh values found using Ohm's law are a vital function of illumination and voltage which decrease with increasing illumination intensity. Additionally, using the forward bias I-V data and accounting for the bias dependent of the effective barrier height (Phi(e)) and ideality factor n(V) for different illumination intensities, the energy density distribution profile of surface states (N-ss) was obtained. According to these results, the organic interlayer consisting of C20H12 is light-sensitive and suitable for optoelectronic devices such as photodetectors and photodiodes.
Elevator Parking Approach in Nearest Car Method
(2018) Ciflikli, Cebrail; Tartan, Emre Oner
A fundamental factor that determines the system efficiency and the quality of service in elevator group control systems is the used elevator dispatching algorithm. Along with the elevator dispatching algorithm, using an elevator parking algorithm can provide improvements in the performance of an elevator group control system. In this study considering a system that uses Nearest Car Method as the elevator dispatching algorithm, average passenger waiting time is investigated under different traffic conditions using three parking algorithms and when no parking algorithm is used. For a more efficient elevator control system an adaptive park algorithm which is changing according to varying traffic conditions is proposed.
Estimation of heartbeat rate from speech recording with hybrid feature vector (HFV)
(2019) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019
This paper introduces a new hybrid feature vector for revealing the relationship between human voice and heartbeat rate (HBR). Various samples of the sustained vowel /a/ for different HBR have been extracted from a database which is created for this study. A convolutional neural network (CNN)-Regression (R), support vector machines (SVMs)-Regression (R), and multiple linear regression (MLR) are used as regression models. The experimental results show that the percentage of predictions within an acceptable error margin has been obtained as 98.92% for CNN-R, 98.70% for SVMs-R and 96.88% for MLR when Forward Sequential is used as a feature selection algorithm. The results also reveal that the CNN-R (root mean square error (RMSE) =0.3909) has produced better prediction values in estimating HBR than those produced by SVMs-R (RMSE=0.4277) and MLR (RMSE =0.4449). As a result, it is seen that the extracted hybrid feature vector provides a novel relationship between human voice and HBR. (C) 2019 Elsevier Ltd. All rights reserved.
A hybrid measure for the discrimination of the acoustic signals: Feature matrix (FMx)
(2019) Anskishan, Haydar; Inam, Sitki Cagdas; 0000-0002-6240-2545; 0000-0003-0820-9186; AAH-4421-2019
We introduce a new feature matrix (FMx) to discriminate the acoustic signals with the help of their hybrid characteristics. The FMx has hybrid domain characteristics consisting of feature values such as distributional area (polygonal area), maximum values of the histogram and fundamental frequency of the difference-difference (d2d) vector. To show the performance of the FMx, three different datasets are used together with quadratic discriminant analysis (QDA), multiclass support vector machines (M-SVMs) and convolutional neural networks (CNN). The simulation results show that FMx provides effective and useful information for the discrimination of the signals into subclasses with the help of ReliefF and forward sequential algorithms. In simulations, the test accuracies with QDA, M-SVMs and CNN were obtained as 94.20%, 100% and 100% respectively. So, the results of the simulations support the effectiveness of the FMx for the acoustic signal classification with three different datasets compared to the previous studies. (C) 2019 Elsevier Ltd. All rights reserved.
The LOFT mission concept - A status update
(2016) Inam, S.C.
The Large Observatory For x-ray Timing (LOFT) is a mission concept which was proposed to ESA as M3 and M4 candidate in the framework of the Cosmic Vision 2015-2025 program. Thanks to the unprecedented combination of effective area and spectral resolution of its main instrument and the uniquely large field of view of its wide field monitor, LOFT will be able to study the behaviour of matter in extreme conditions such as the strong gravitational field in the innermost regions close to black holes and neutron stars and the supra-nuclear densities in the interiors of neutron stars. The science payload is based on a Large Area Detector (LAD, > 8m(2) effective area, 2-30 keV, 240 eV spectral resolution, 1 degree collimated field of view) and a Wide Field Monitor (WFM, 2-50 keV, 4 steradian field of view, 1 arcmin source location accuracy, 300 eV spectral resolution). The WFM is equipped with an on-board system for bright events (e. g., GRB) localization. The trigger time and position of these events are broadcast to the ground within 30 s from discovery. In this paper we present the current technical and programmatic status of the mission.
Max-Min Space Approach for Acoustic Signal Analysis
(2017) Ankishan, Haydar; Baysal, Ugur; 0000-0002-6240-2545; AAH-4421-2019; AAJ-5711-2020
Acoustic signals having pathological problem are difficult to discriminate from each other. Despite the presence of many features, the difficulties arise from the chaotic and nonlinear nature of these voices. Unlike the existing features, a new feature and feature space are emphasized in this study. Considering the maximum and minimum values of acoustic signals at certain time intervals, the relation between them is revealed and Max-Min space is created. Experimental studies have shown that the space distribution between pathological and normal sounds is completely separated from each other and that the space-scattering field sizes are different from each other. As a result of the studies, a time-based feature is introduced which allows the separation of chaotic and nonlinear acoustic signals in the literature.
A model for the visualization and analysis of elevator traffic
(2019) Ciflikli, Cebrail; Tartan, Emre Oner
Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.
A New Approach for Discriminating the Acoustic Signals: Largest Area Parameter (LAP)
(2018) Ankishan, Haydar; Inam, S. Cagdas
Feature extraction of sound signals is essential for the performance of applications such as pattern and voice recognition etc. In this study, a method based on a novel feature is proposed to separate pathological human voice signals from healthy ones as well as to separate subgroups of pathological voices from each other. The voices are examined in time-frequency domain. Their differences obtained from the results of the proposed method are investigated and the mechanism of the method is demonstrated using experimental cases. It is concluded that the method succeeds to discriminate the voices marked "healthy" and "pathological".
A New Approach for Estimation of Heart Beat Rates from Speech Recordings
(2017) Ankishan, Haydar; Baysal, Ugur; 0000-0002-6240-2545; AAH-4421-2019; AAJ-5711-2020
Today, people are able to have information about their mental state, behavior, and health status in some issues from the features of the voices. The study involves calculating the heart rates of people using nonlinear equations with the help of the features of sound recordings. The model proposed for the study consists of the four inputs of the difference equation parameters which change with constant and variable sound features. When the experimental studies were examined, it was observed that the heart rate could be predicted with an accuracy of 89.76% by using 10s sound recordings. With the proposed equation, it is observed that the heart beat rate is related to the speech features, can be calculated these features with minimal error rate and also the nonlinear equation is presented in the literature.
A New Approach for the Acoustic Analysis of the Speech Pathology
(2017) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019
Voice disorders are a common physical problem that can be encountered today and can cause serious problems in the long term. It is necessary to analyze the voice and extract its characteristics correctly so that it can be treated. In some cases, due to their sound characteristics, they do not differ from each other characteristics exactly, and today's systems do not yet have the ability to make correct decisions. This study has taken into account those evident which from voice disturbances and tries to the analysis of these disorders by means of previously unused attributes with the help of classifier (SVMs). In this study, after the sounds are modeled with LPC and MFCC, disorder analysis is performed on the obtained signals. In the results obtained from experimental studies, it has been determined that 100% of the patients with four different diseases can be decomposed together with the used nonlinear features.