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Online dead body detection experiment with an unmanned underwater vehicle [Bir insansiz sualti araci ile çevirimiçi ceset tanilama deneyi]

Berik M. | Kartal S.K.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, real-time online body detection under water was conducted using an unmanned underwater observation tool. According to the underwater position of the vehicle, data from the vehicle camera is provided to identify different body parts of a body in a real-time video stream. Here we present an approach for underwater human body detection based on the use of highly educated classifiers. The algorithm's performance in real time video shooting of the car is optimized to reduce the false positive rate by aiming to identify a corpse part of each picture frame. According to the results obtained, it was ensured that the corpse pa . . .rts were successfully detected under changing conditions with incorrect positive perception. Algorithms were developed using the Pyhton program. © 2018 IEEE Daha fazlası Daha az

Classification of power quality events signals with pattern recognition methods by using Hilbert transform and genetic algorithms [Güç kalitesi bozulma sinyallerinin hilbert dönüşümü ve genetik algoritmalar kullanilarak örüntü tanima yöntemleri ile siniflandirilmasi]

Karasu S. | Sarac Z.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, instantaneous envelope, phase and frequency series are obtained by Hilbert transform for Power Quality (PQ-Power Quality) disturbances signals. Rms, Thd, energy, entropy and statistical properties are applied to these series. With the wrapper feature selection approach, a set of features is obtained that has a small number of feature subset and a high performance from 36 features. Genetic Algorithm (GA) is used as a search algorithm and the classifier algorithm is K nearest neighborhood (KNN). Support Vector Machines (SVM) for selected features are also used in the classification step. The learning algorithm is obtain . . .ed as KNN, the model performance that classifies PQ classes with 99.07%. The number of feature sets is 8. In addition, performance under noisy data is also tested to show that the generated model has a generalized structure. © 2018 IEEE Daha fazlası Daha az

Computer-assisted diagnosis of vertebral column diseases by adaptive neuro-fuzzy inference system

Uzun, Rukiye | İşler, Yalçın | Erkaymaz, Okan | Kocadayı, Yasemin

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, a clustering algorithm based on adaptive neural fuzzy inference system (ANFIS) was used for computer-assisted diagnosis of the vertebral column disorder from machine learning databases of UCI (University of California Irvine). Features of pelvic incidence, pelvic tilt and lumbar lordosis angle given in this dataset was applied to the inputs of the algorithm. The performance of algorithm was evaluated using mean square error and regression coefficient criteria to discriminate patients with vertebral column disease from healthy subjects. As a result, the classification performance of 90.82% was obtained by using the gen . . .erated model of ANFIS. © 2018 IEEE Daha fazlası Daha az

Vibrational resonance in a Hodgkin-Huxley neuron under electromagnetic induction

Baysal, Veli | Yılmaz, Ergin

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this paper, effects of electromagnetic induction on vibrational resonance phenomenon in a Hodgkin-Huxley neuron are investigated. By stimulating Hodgkin-Huxley neuron with both high-frequency signal and low-frequency weak signal, its weak signal detection capacity have been investigated under electromagnetic induction effect. Obtained results show that electromagnetic induction causes decreasing of the amplitude of vibrational resonance effect emerging depending on the amplitude of high frequency signal. Also, vibrational resonance phenomenon occurs at smaller amplitudes of high frequency signal in Hodgkin-Huxley neuron which is . . .under electromagnetic induction effect. Finally, it is found that the best detection of the weak signal in a Hodgkin-Huxley neuron under electromagnetic induction effect is realized under an optimal electromagnetic current intensity. © 2018 IEEE Daha fazlası Daha az

The usage of artificial neural network as post processing algorithm in digital holography [Sayisal holografide yapay sinir aglarinin son işlem algoritmasi olarak kullanilmasi]

Kaya G.U. | Sarac Z.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

The purpose of this study is to train the reconstructed sound waves, which is obtained from recording holograms via digital holography, and reduce the noise from this sound wave without using noise Altering techniques. The noise reduction is achieved by approximating the reconstructed sound wave trained by YSA to the actaul recording sound wave. A network topology is created for training and the system was tested. The performance of the system is given by showing how closely the sound wave trained by YSA approaches the actual sound wave. © 2018 IEEE.

Effects of autapse on weak signal detection in FFL network motifs

Baysal, Veli | Yılmaz, Ergin

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this paper, effects of autapse on signal detection capacity of Izhikevich neuron in feed-forward-loop network motifs are investigated. Obtained results showed that autapse significantly enchances singal detection of Izhikevich neuron at proper autaptic time delay values compared without autapse. Also, it is seen that feed-forward-loop motifs have significant effects on signal detection ability of Izhikevich neuron. It is obtained that signal detection of Izhikevich neuron are best in T1 feed-forward-loop motif. © 2018 IEEE.

Effect of channel noise and dynamic synapse structure on latency dynamics in neural system

Çalım, Ali | Özer, Mahmut | Uzuntarla, Muhammet

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

Experimental and theoretical studies in recent years suggest that the first spike latency is an effective information carrier and contains more neural information than other spikes. Noise Delayed Decay (NDD) phenomenon emerges when the first spike latency of the neuron exposed to the periodic driving is maximum at a certain noise intensity interval. In this study, the latency dynamics of a single Hodgkin-Huxley neuron is investigated under periodic driving, background activity through dynamic synapses, and channel noise. The system response with first spike latency is investigated as a function of the presynaptic firing rate, the pa . . .rameter with an appropriate biophysical reality to control the level of activity in the nervous system. First, NDD behavior is investigated under suprathreshold stimulation in the presence of synapses at different levels of depression and channel noise. It is then desired to observe the NDD phenomenon under subthreshold stimulation with the same strategy. Our results have shown that the background activity occurring in the presence of dynamic synapses and the channel noise are significant system dynamics in observing the NDD behavior. © 2018 IEEE Daha fazlası Daha az

Classification of cervical cancer data and the effect of random subspace algorithms on classification performance

Erkaymaz, Okan | Palabaş, Tuğba

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

Computer assisted automatic diagnostic systems are used for the purpose of speeding up diagnosis and treatment and helping to make the right decision. In this study, cervical cancer is identified using four basic classifiers: Naive Bayes (NB), k-Nearest Neighbor (kNN), Multilayer Perceptron (MLP) and Decision Trees (KA-C4.5) algorithms and random subspaces ensemble algorithm. Gain Ratio Attribute Evaluation (GRAE) feature extraction algorithm is applied to contribute to classification performance. The classification results obtained with all datasets and reduced datasets are compared with respect to performance criteria such as accu . . .racy, Root Mean Square Error (RMSE), Sensitivity, Specificity performance criteria. According to the obtained performance analysis, it is seen that the classification performance with the random subspace ensemble algorithm using the kNN basic classifier on the reduced data set is the highest (%95.51). © 2018 IEEE Daha fazlası Daha az

Prediction of Bitcoin prices with machine learning methods using time series data [Zaman serisi verilerini kullanarak makine ögrenmesi yöntemleri ile bitcoin fiyat tahmini]

Karasu S. | Altan A. | Sarac Z. | Hacioglu R.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012-2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training ph . . .ase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model. © 2018 IEEE Daha fazlası Daha az

Hammerstein model performance of three axes gimbal system on Unmanned Aerial Vehicle (UAV) for route tracking [Rota takibinde insansiz hava araci üzerinde bulunan üç eksenli yalpa sisteminin hammerstein model başarimi]

Altan A. | Hacioglu R.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, focuses on the non-linear Hammerstein model under external disturbance with white Gaussian noise based on the experimental input (motor velocities) and output (end effector position) data of the three axes gimbal system on the Unmanned Aerial Vehicle (UAV), which is autonomously moving for route tracking. The performance of UAV in reaching the target point on a planned route in sinusoidal form in avoiding obstacles, depends on the route tracking performance of the three axes gimbal system on the UAV. In intelligence activities such as exploration and surveillance, Hammerstein and Nonlinear AutoRegressive and Moving Av . . .erage (NARMA) models of the gimbal system with great importance for UAV that real time image transmission and in the tasks of leaving payloads to targets with unknown coordinates with the least mistake are obtained. The parameters of the obtained models are estimated by Recursive Least Squares (RLS) algorithm and the model performances are compared. © 2018 IEEE Daha fazlası Daha az

Choose of wart treatment method using Naive Bayes and k-nearest neighbors classifiers [Naive Bayes ve En Yakin k Komsu Siniflandiricilari ile Sigil Tedavi Yöntemi Seçimi]

Uzun R. | Isler Y. | Toksan M.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, the success of cyrotheraphy and immunotherapy methods on common warts and plantar warts were predicted among 180 patients using machine learning methods. As a classifier, Naive Bayes and k-nearest neighbors with different neighborhood values of k were experimented. Data sets that are online available via Internet were used in the study. As a result, whether the treatment method by considering given features will give positive result could be estimated with the accuracy of 80% by using k-nearest neighbors classifier with the neighborhood value of k=7. © 2018 IEEE.

Occupancy detection from temperature, humidity, light, CO2 and humidity ratio measurements using machine learning techniques [Makine Ögrenmesi Teknikleri ile Sicaklik, Nem, Aydinlik Seviyesi, CO2 ve Nem Orani Ölçümlerinden Varlik Tespiti]

Palabas T. | Eroglu K.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

Order to save energy and to use energy resources efficiently, automatic occupancy determination based on sensor information is performed and energy is adjusted according to the demand in a closed area. In this study is used records consisting of T (temperature), H (humidity), L (light level), CO2 (carbon dioxide) and R (humidity ratio) sensor data. Occupancy analysis based on sensor data has been performed with REPTree (Reduced Error Pruning tree), NB (Naive Bayes), SVM (Support Vector Machine) and KNN (K Nearest Neighbor) classification algorithms. The highest classification success (97.98%) was obtained with the REPTree classifica . . .tion algorithm. Then the importance of the attributes is determined by the CFS (Correlation-based Feature Selection) algorithm to be taken into account in reducing costs in the data collection step and the effect of the attributes on the classification performance is examined. Finally, ensemble algorithms Adaboost, Bagging, RandomSubSpace are used to increase classification success and achieve more stable results. The performance evaluation criteria are shown that the ensemble algorithms improve the classification success. The highest classification success (98.28%) was obtained by using the Adaboost community algorithm together with the ADTree classifier. According to the sensor values in the dataset, the use of office room was determined with high success rate. © 2018 IEEE Daha fazlası Daha az

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