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Full-Text Articles in Computer Engineering
Comparison Of Deep Learning And Regression-Based Mppt Algorithms In Pv Systems, Murat Sali̇m Karabi̇naoğlu, Beki̇r Çakir, Mustafa Engi̇n Başoğlu, Abdülvehhab Kazdaloğlu, Azi̇z Güneroğlu
Comparison Of Deep Learning And Regression-Based Mppt Algorithms In Pv Systems, Murat Sali̇m Karabi̇naoğlu, Beki̇r Çakir, Mustafa Engi̇n Başoğlu, Abdülvehhab Kazdaloğlu, Azi̇z Güneroğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance …
Forecasting Tv Ratings Of Turkish Television Series Using A Two-Level Machinelearning Framework, Büşranur Akgül, Tayfun Küçükyilmaz
Forecasting Tv Ratings Of Turkish Television Series Using A Two-Level Machinelearning Framework, Büşranur Akgül, Tayfun Küçükyilmaz
Turkish Journal of Electrical Engineering and Computer Sciences
TV rating is a numeric estimate of the popularity of television programs. Forecasting TV ratings is considered an important asset for investment planning of media due to its potential of reducing the risks of future ventures. The aim of this study is to develop a machine learning model capable of efficiently forecasting the TV ratings of Turkish TV series in a practical manner. To this end, two prediction models were proposed for forecasting the TV ratings of television series, facilitating an extensive set of features. A contribution of this study is the inclusion of social media-based features using search trends …
3d Skull Surface Completion Method Based On Multi-Exemplars, Reziwanguli Xiamixiding, Guohua Geng, Gulisong Nasierding, Qingqiong Deng, Dilinuer Keyimu, Zulipiya Maimaitiming, Wanrong Zhao, Zheng Lei
3d Skull Surface Completion Method Based On Multi-Exemplars, Reziwanguli Xiamixiding, Guohua Geng, Gulisong Nasierding, Qingqiong Deng, Dilinuer Keyimu, Zulipiya Maimaitiming, Wanrong Zhao, Zheng Lei
Journal of System Simulation
Abstract: In order to repair the damaged skulls, a skull completion method based on multiple exemplars was proposed. A 3D skull model database was constructed, and all the exemplars within the database were registered with a standard skull model. Each exemplar was then divided into a missing part and a remaining part according to the given damaged skull. After that, the relationship between the missing part and the remaining part was obtained by a regression algorithm. This relationship was used to calculate the missing part of the given skull, and a complete skull could be obtained by merging the two …
Modeling Compaction Parameters Using Support Vector And Decision Treeregression Algorithms, Abdurrahman Özbeyaz, Mehmet Söylemez
Modeling Compaction Parameters Using Support Vector And Decision Treeregression Algorithms, Abdurrahman Özbeyaz, Mehmet Söylemez
Turkish Journal of Electrical Engineering and Computer Sciences
Shortening the periods of compaction tests can be possible by analyzing the data obtained from previous laboratory tests with regression methods. The regression analysis applied to current data reduces the cost of experiments, saves time, and gives estimated outputs. In this study, the MLS-SVR, KB-SVR, and DTR algorithms were employed for the first time for the estimation of soil compaction parameters. The performances of these regression algorithms in estimating maximum dry unit weight (MDD) and optimum water content (OMC) were compared. Furthermore, the soil properties (fine-grained soil, sand, gravel, specific gravity, liquid limit, and plastic limit) were employed as inputs …
Automated Elimination Of Eog Artifacts In Sleep Eeg Using Regression Method, Mehmet Dursun, Seral Özşen, Sali̇h Güneş, Bayram Akdemi̇r, Şebnem Yosunkaya
Automated Elimination Of Eog Artifacts In Sleep Eeg Using Regression Method, Mehmet Dursun, Seral Özşen, Sali̇h Güneş, Bayram Akdemi̇r, Şebnem Yosunkaya
Turkish Journal of Electrical Engineering and Computer Sciences
Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine …
Prediction Of Gross Calorific Value Of Coal Based On Proximate Analysis Using Multiple Linear Regression And Artificial Neural Networks, Mustafa Açikkar, Osman Si̇vri̇kaya
Prediction Of Gross Calorific Value Of Coal Based On Proximate Analysis Using Multiple Linear Regression And Artificial Neural Networks, Mustafa Açikkar, Osman Si̇vri̇kaya
Turkish Journal of Electrical Engineering and Computer Sciences
Gross calorific value (GCV) of coal was predicted by using as-received basis proximate analysis data. Two main objectives of the study were to develop prediction models for GCV using proximate analysis variables and to reveal the distinct predictors of GCV. Multiple linear regression (MLR) and artifcial neural network (ANN) (multilayer perceptron MLP, general regression neural network GRNN, and radial basis function neural network RBFNN) methods were applied to the developed 11 models created by different combinations of the predictor variables. By conducting 10-fold cross-validation, the prediction accuracy of the models has been tested by using $ R^2 $, $ RMSE …
A Comparative Review Of Regression Ensembles On Drug Design Datasets, Mehmet Fati̇h Amasyali, Kadri̇ Okan Ersoy
A Comparative Review Of Regression Ensembles On Drug Design Datasets, Mehmet Fati̇h Amasyali, Kadri̇ Okan Ersoy
Turkish Journal of Electrical Engineering and Computer Sciences
Drug design datasets are usually known as hard-modeled, having a large number of features and a small number of samples. Regression types of problems are common in the drug design area. Committee machines (ensembles) have become popular in machine learning because of their good performance. In this study, the dynamics of ensembles used in regression-related drug design problems are investigated with a drug design dataset collection. The study tries to determine the most successful ensemble algorithm, the base algorithm--ensemble pair having the best/worst results, the best successful single algorithm, and the similarities of algorithms according to their performances. We also …