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Öğe Application of ABM to Spectral Features for Emotion Recognition(MEHRAN UNIV ENGINEERING & TECHNOLOGY, 2018) Demircan, Semiye; Kahramanli, HumarER (Emotion Recognition) from speech signals has been among the attractive subjects lately. As known feature extraction and feature selection are most important process steps in ER from speech signals. The aim of present study is to select the most relevant spectral feature subset. The proposed method is based on feature selection with optimization algorithm among the features obtained from speech signals. Firstly, MFCC (Mel-Frequency Cepstrum Coefficients) were extracted from the EmoDB. Several statistical values as maximum, minimum, mean, standard deviation, skewness, kurtosis and median were obtained from MFCC. The next process of study was feature selection which was performed in two stages: In the first stage ABM (Agent-Based Modelling) that is hardly applied to this area was applied to actual features. In the second stageOpt-aiNET optimization algorithm was applied in order to choose the agent group giving the best classification success. The last process of the study is classification. ANN (Artificial Neural Network) and 10 cross-validations were used for classification and evaluation. A narrow comprehension with three emotions was performed in the application. As a result, it was seen that the classification accuracy was rising after applying proposed method. The method was shown promising performance with spectral features.Öğe Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech(SPRINGER, 2018) Demircan, Semiye; Kahramanli, HumarIn the present study, emotion recognition from speech signals was performed by using the fuzzy C-means algorithm. Spectral features obtained from speech signals were used as features. The spectral features used were Mel frequency cepstral coefficients and linear prediction coefficients. Certain statistical features were extracted from the spectral features obtained in the study. After the selection of the extracted features, cluster centers were identified by using type-1 fuzzy C-means (FCM) algorithm and used as input to the classifier. Supervised classifiers such as ANN, NB, kNN, and SVM were used for classification. In the study, all seven emotions of the EmoDB database were used. Of the features obtained, FCM clustering was applied to Mel coefficients and obtained clusters centers were used as input for classification. The results showed that using FCM for preprocessing aim increased the success rate. The comparison of the classification methods showed that the maximum success rate was obtained as 92.86% using the SVM classifier.Öğe Çoklu etmen sistemleri kullanılarak enerji nakil hattı güzergâh optimizasyonu(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2009) Demircan, Semiye; Aydın, MusaGünümüzde hızla artan elektrik enerjisi ihtiyacıyla birlikte enerji nakil hattı güzergâhlarının belirlenmesine verilen önem de artmaktadır. Amaçlanan, güzergah seçiminde dikkat edilmesi gereken kriterler göz önüne alınarak daha az maliyetli ve optimum yolun bulunmasıdır. Bu çalışmada coğrafi bilgi sistemleri tabanlı enerji nakil hattı güzergâh optimizasyonu gerçekleştirilmiştir. Dağıtık Yapay Zekânın bir alt dalı olan Çoklu Etmen Sistemleri (ÇES) kullanılarak gerçekleştirilen bu optimizasyonda enerji nakil hattı güzergâhını etkileyen kriterler tek tek incelenmiştir. Uygulama Selçuk Üniversitesi Kampüs alanı üzerinde gerçekleştirilmiştir. Bunun için öncelikle kampüs alanının söz konusu kriterleri içeren sayısal haritası oluşturulmuştur. Matlab ortamında, Çoklu Etmen Sistemlerinin Q-Öğrenme Algoritması kullanılarak optimum güzergah tespit edilmiştir.Öğe Emotion Recognition via Agent-Based Modelling(IEEE, 2017) Demircan, Semiye; Kahramanli, HumarEmotion recognition is one of the most popular research areas in recent times. Emotion recognition is also made from facial expressions and sound signals, as can be done biomedical signals. Especially when face-to face communication is not possible, emotion can he recognized from the sound data. In this study, emotion recognition was performed from the sound data. One of the most important steps in feeling recognition is feature selection. Feature selection can be done in many different ways. In this study, a new agent-based approach to emotion recognition is presented. The agent-based modeling features were then selected by opt-ainet optimization method. The goal is automatic selection of features that give the best classification accuracy.Öğe Finding optimum route of electrical energy transmission line using multi-criteria with Q-learning(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Demircan, Semiye; Aydin, Musa; Durduran, S. SavasDue to an increasing energy requirement the consideration of route determination is becoming important. The aim of this project is to find an optimum result considering its important criteria. Finding an optimum route is a complex problem. It does not mean the shortest path to the problem. It is important to find the best way under the criterion that is determined by experts. Because of this we did not use the classical shortest path algorithm and we applied one of algorithms of the Artificial Intelligence. In this work, Geographic Information System (GIS)-based energy transmission route planning had been performed. In this optimization, using Multiagent Systems (MAS) which is a subdirectory of Distributed Artificial Intelligence the multi-criteria affecting energy transmission line (ETL) had been severally analyzed. The application had been actualized on the Selcuk University Campus Area. Therefore, the digital map of the campus area particularly had been composed containing of relevant criteria. Using Q- learning Algorithm of Multiagent System the optimum route had been determined. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Route Optimization with Q-learning(WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC, 2008) Demircan, Semiye; Aydin, Musa; Durduran, S. SavasDue to increasing energy requirement the consideration of route determination is becoming important. The aim of this project is to find optimum result considering its important criteria. In this work, Geographic Information System (GIS) based energy transmission route optimization had been performed. In this optimization, using Multiagent Systems which is subdirectory of Distributed Artificial Intelligence the criteria affecting energy transmission line had been severally analyzed. The application had been actualized on the Selcuk University Campus Area. Therefore the digital map of the campus area particularly had been composed containing of relevant criteria. Using Q-learning Algorithm of Multiagent System the optimum route had been determined.Öğe Water content classification on cucurbitaceae family fruits from VIS/NIR spectroscopic data(Selçuk Üniversitesi, 2024) Ürgen, Nurullah; Demircan, SemiyeVisible and Near-Infrared (Vis-NIR) spectroscopy is a technique used to determine the chemical and physical properties of matter by analyzing electromagnetic radiation across a broad wavelength range, specifically from 400 to 2500 nm. In this application, the aim is to assess the quality attributes of six Cucurbitaceae family fruits, namely: zucchini, bitter melon, ridge gourd, melon, chayote, and cucumber, using a single classification model for all fruits rather than individual models. This classification model predicts whether it exceeds 90% according to fruits based on water content. Samples with water content above 90% are labeled as high-water content, while those below are categorized as low-water content. For preprocessing, Standard Normal Variate (SNV) and Neighborhood Components Analysis (NCA) methods were employed to optimize the feature space. The model was trained using a Support Vector Machine (SVM) classifier. Without feature extraction, the accuracy ranged from 90% to 92.5%; however, with feature extraction, the accuracy increased to 95%-97.5%. This classification model successfully predicts high water content, an essential indicator of product quality and productivity, across the dataset with high precision. By integrating comprehensive data processing and machine learning techniques, this study demonstrates a reliable method for assessing product quality, contributing significantly to the field of agricultural and food industry quality control.
