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Öğe 2 Stages-region-based P300 Speller in Brain-Computer Interface(Taylor & Francis Ltd, 2019) Oralhan, ZekiBrain-computer interface (BCI) applications present communication model without using peripheral nerves and neuromuscular systems. The P300 waves are used in BCI applications. Signal classification accuracy is a significant parameter for P300 BCI application. In this study, our goal is to investigate P300 speller structure for higher classification accuracy. There are a lot studies about P300 speller variations of stimulus model. This models mostly includes about row-column-based visual or audio stimuli model P300 spellers. But there is not enough study about region-based P300 spellers. This study contributes to region-based P300 spellers researches. Our new paradigm 2-stages region-based P300 speller has different amount of regions and stimulus position. During the experiment also, row-column-based P300 speller was used for comparing accuracy rates with our unique design 2-stages region-based P300 speller. The subject focused on the desired character stimulus. We used the stepwise linear discriminant analysis (SWLDA) method for classification that either included the desired P300 signal or not. According to SWLDA, the maximum mean classification accuracy value of the experiment was 83.33% with 2-stages region-based P300 speller. With this new paradigm, the classification accuracy was improved by 23.89% according to the most commonly used row-column-based P300 speller.Öğe 3D Input Convolutional Neural Networks for P300 Signal Detection(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Oralhan, ZekiP300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants A and B for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset.Öğe A new Approach for Hybrid BCI speller based on P300 and SSVEP(2019) Oralhan, ZekiP300 and steady state visual evoked potential (SSVEP) are type of electroencephalography (EEG) phenomena that widely used in braincomputer interface (BCI) systems since both of them have high signal response and signal noise ratio. Classification accuracy rate ofsignal, and signal detection time affect overall performance of BCI systems. These both values are used for calculation informationtransfer rate (ITR) that is a key performance indicator for a BCI system. A P300 based BCI or a SSVEP based BCI have higher ITRvalues than other type of BCI systems. Thus, in this study our aim was to use together these both P300 and SSVEP phenomena in a BCIspeller. We proposed a hybrid BCI speller based on P300 and SSVEP. Moreover, our proposed BCI speller interface allows to use onlyP300 stimuli, only SSVEP stimuli, or hybrid stimuli. In this BCI speller, there are numbers in 3 × 3 matrix form for elicitind P300 signaland also 9 white square flickering objects were placed near numbers for eliciting SSVEP. In this research, experiments were performedin two stage (training and online stages) with three sessions (only SSVEP stimuli session, only P300 stimuli session, and hybrid session).Five subjects participated experiments. We used support vector machine method for detection of P300 signal and SSVEP. According toexperiment results, average classification accuracy values were 83.78%, 84.67%, and 90.89% with using only SSVEP stimuli, onlyP300 stimuli, and hybrid stimuli, respectively. Furhermore, average information transfer rate values were 6.81, 6.97, and, 8.19 bit/minwith using only SSVEP stimuli, only P300 stimuli, and hybrid stimuli, respectively. Results showed that the proposed hybrid BCI spellerbased on P300 and SSVEP reached higher classification accuracy and ITR values than using only SSVEP stimuli or only P300 stimulibased BCI spellers.Öğe A New Paradigm for Region-Based P300 Speller in Brain Computer Interface(Ieee-Inst Electrical Electronics Engineers Inc, 2019) Oralhan, ZekiElectroencephalography-based brain computer interface systems could provide alternative communication methods for severely disabled people who cannot use their neuromuscular systems. The P300 signal is one of the event related potentials that are used for brain computer interface systems. The most important performance parameter of a P300 based brain computer interface system is information transfer rate that is calculated by using classification accuracy and P300 signal detection time. Moreover, P300 speller has a very critical role for classification accuracy and information transfer rate in a P300 based brain computer interface. Although most of studies are about row column based P300 speller in literature, region based P300 speller proved that has higher classification accuracy than row column based one. There are very few studies about region based P300 speller. This study aims to investigate methods for obtaining higher classification accuracy and information transfer rate with using region based P300 speller that constituted audio and visual stimulus. This is the first research that using audio and visual stimulus for a region based P300 speller in literature. Previous studies about region based P300 spellers focused on spellers with only visual stimulus types. Our new paradigm presents region based P300 spellers with only audio, only visual, and audio-visual stimuli. Audio-visual P300 speller structure is the newest model for region based spellers. The subject focused on the desired character stimulus. We used the stepwise linear discriminant analysis method for classification that either included the desired P300 signal or not. According to stepwise linear discriminant analysis, the mean classification accuracy value of the experiment was 90.31% with the audio-visual region based P300 speller. With this new paradigm, classification accuracy in the audio-visual P300 speller was improved 15.69% and 66,99% according to the visual only and audio only P300 speller that we used in the experiments, respectively.Öğe Campaign and Loyalty Management in B2B Field with Deep Learning Methods(Orclever Science&Resarch Group, 2023) Oralhan, Zeki; Oralhan, BurcuThe paper presents a deep learning model for improving customer loyalty management in the business-to-business (B2B) field. In industries where technology is continuously evolving and competition is fierce, it is critical to maintain client loyalty and improve customer satisfaction. To generate a competitive advantage, the project aims to construct a deep learning-supported model to meet these objectives. The research is covered methodologies involving artificial intelligence algorithms such as deep learning to analyze customer behavior and preferences. Customer data was obtained from ERP systems. Afterwards, deep learning models CNN, RNN and LSTM architectures were applied for modelling. The developed B2B-DL model has achieved high success in predicting customer behavior and offering customized offers. Improvements in customer loyalty management will bring great benefits to companies by causing customer satisfaction rates to increase significantly and customer loss to be reduced. Therefore, the study is showed that the use of deep learning methods in the B2B industry can play an important role in customer loyalty management. In the study, LSTM architecure was achieved the best performance with the accurate valuse as %86Öğe Customer Satisfaction Using Data Mining Approach(İsmail SARITAŞ, 2016) Oralhan, Burcu; Uyar, Kumru; Oralhan, ZekiCustomers and products are the main assets for every business. Companies make their best to satisfy customers because of coming back to their companies. After sales service related to different steps that make customers are satisfied with the company service and products. After sales service covers different many activities to investigate whether the customer is satisfied with the service, products or not? Hence, after sales service is acting very crucial role for customer satisfaction, retention and loyalty. If the after sales service customer and services data is saved by companies, this data is the key for growing companies. Companies can add value their brand value with the managing of this data. In this study, we aim to investigate effect of 6 factors on customer churn prediction via data mining methods. After sale service software database is the source of our data. Our data source variables are Customer Type, Usage Type, Churn Reason, Subscriber Period and Tariff The data is examined by data mining program. Data are compared 8 classification algorithm and clustered by simple K means method. We will determine the most effective variables on customer churn prediction. As a result of this research we can extract knowledge from international firms marketing data.Öğe EEG Based Identification System Design via LSTM(Osman SAĞDIÇ, 2020) Balcı, Furkan; Oralhan, ZekiIdentification systems are designed using highly reliable personal data. Accuracy rate and reliability are the most basic parameters of these systems. Electroencephalography (EEG) signal varies depending on time, internal and environmental factors. As a result of the studies, the usability of the EEG signal in identification systems has been confirmed. It is understood that the signals produced by the body are personalized signals when the environmental effects are minimized. Successful results are known in the time series of the Long-Short Term Memory (LSTM) method. In this study, an identification system was designed by using LSTM method, which is one of the deep learning techniques. Before the LSTM is used, the EEG is subdivided into frequency subcomponents through some operations. It was decided to use the delta wave with correlation analysis of these separated frequency subcomponents. The prepared system was examined under different conditions. A total of 200 tests were performed on 3 different training series. The highest accuracy rate is 89.5%. The average accuracy rate is 86,292%. The prepared system is designed to operate under different conditions. The system is open to development using various optimization algorithms.Öğe Evaluation of Ski Centers' Performance Using Multiple-Criteria Decision-Making Methods(Sciendo, 2022) Oralhan, Burcu; Oralhan, Zeki; Kirdök, NurIntroduction. This study aimed to determine the criteria for the choice of nine different leading ski centers that serve actively in the ski tourism sector of Turkey, to calculate the criteria weights, and to measure the performance of these centers. Material and methods. In this context, the data were defuzzified using the CFCS method, and the fuzzy DEMATEL method was used to determine the criteria affecting the choice of ski centers. Then, the TOPSIS method was applied to measure the performance of ski centers by using the criteria weights obtained with the fuzzy DEMATEL method. Results. As a result of the analysis, the weights of the main criteria were found as follows: facility amenities, price, accessibility, accommodation, alternative tourism, and visitors' rating scores. Consequently, the top three ski resorts according to their scores are SC4, SC1, and SC9, respectively. The ski center which is coded SC9 is ranked at the bottom. Conclusions. The study examined the ski centers that actively operate in Turkey. This could be considered as a spatial decision-making problem. This study could be a road map for the performance evaluation in ski tourism. Moreover, the results will be beneficial for the ski centers to identify their deficiencies and carry out improvement works in attracting the increasing demand for skiing to their centers. © 2022 Burcu Oralhan et al., published by Sciendo.Öğe P300 Tabanlı Beyin Bilgisayar Arayüzü Sistemlerinde UyaranlarArası Sürenin ve Uyaran Yapısının Performansa Etkisi(2019) Oralhan, ZekiElektroensefalografi temelli beyin bilgisayar arayüzü sistemleri kas sistemini kullanamayan hastalar için dış dünyaile iletişimini mümkün kılmaktadır. EEG temelli beyin bilgisayar arayüzü sistemleri için çeşitli beyin sinyalaktiviteleri kullanılmaktadır. Olay odaklı potansiyellerden bir tanesi olan P300 sinyali beyin bilgisayar arayüzüsistemleri için elverişli bir beyin sinyalidir. P300 tabanlı bir beyin bilgsayar arayüzü için en önemli performansparametrelerinden birisi sınıflandırma doğruluk oranıdır. Bu çalışmada satır sütun temelli P300 heceleticiyapısındaki değişiklikle daha yüksek doğruluk oranı ile elde edilmesi hedeflenmiştir. P300 heceleticisi matrisyapısında ve uyaranların aralık süreleri üzerinde değişiklikler yapılmıştır. Bundan önceki bir çok çalışma P300heceletici yapısındaki uyaran renk ve biçimlerindeki değişiklikler ile yapılmıştır. Uyaran yapısı ve uyaran aralıksürelerindeki değişikleri kıyaslayıcı P300 heceleticileri ile ilgili çalışmalar yeterli seviyede değildir. Bu çalışmadadört farklı yapıdaki satır sütun bazlı P300 heceletici kullanılarak deneyler yapılmıştır. Deneyler ile toplanan EEGkayıtları ön işlemden geçirildikten sonra adımsal doğrusal ayrışım analizi ile sınıflandırılmıştır. Sınıflandırmaneticesinde bu çalışmada karşılaştırılan heceleticilerden; 4x4 satır sütın bazlı P300 heceleticinin 150 ms uyaranaralık süresine sahip olan yapıdaki formu, ortalama doğruluk oranı %84,76 ile en yüksek olarak tespit edilmiştir.En düşük performans ise; 6x6 satır sütın bazlı P300 heceleticinin 300 ms uyaranlar arası geçiş süresine sahipmodunda %50,48 olarak gözlenmiştir. Bu çalışma, satır sütun bazlı P300 heceleticisinin uyaran matris yapısındakideğişikliği ve farklı uyaran aralık sürelerinde yapılan deneylerle yüksek doğruluk oranı ile elde edilebileceğinigöstermiştir.Öğe Pet Şişe Şişirme Makinelerinde Fırın Optimizasyonu(2020) Bayam, Burcu; Oralhan, ZekiBu çalışma, Pet Şişe Makinesi üreticilerinin ve son kullanıcılarının bilgilerine danışılarak; Türkiye’de sınırlı sayıda olan Pet ŞişeŞişirme makinalarında yer alan fırınlar üzerine yapılmıştır. Preformun, makineye girmesinden itibaren kendi ekseni etrafında hareketetmesi ile fırın bölümlerinden geçirilerek yeterli ısıya ulaşmasından sonra, şişirme ünitesinde 40 bar yüksek basıncın verilmesiyleşişeler elde edilmektedir. Elde edilen şişelerin kaliteli üretilmesi oldukça önemlidir. Bunun sebebi, Pet Şişe Şişirme makinelerinindevamında, etiketleme, dolum ve paletleme makinelerinin de yer alıyor olmasıdır. Şişelerin kalitesiz olması durumunda hattın tamamıdurmaktadır. Şişelerin sağlam olarak üretilmesini sağlamanın en önemli yolu ise, şişenin hammaddesi olan preformların verimliısıtılmasıdır. Söz konusu çalışma, prefomların ısıl işlemlerinin daha verimli şekilde uygulanmasını sağlamıştır. Preformun ısılişlemlerinde, fırın bölgesinin optimizasyonu sağlamak için; PLC, HMI, ısı kontrolleri, termal ısı ölçer, termokupl ve termal kamerakullanılmıştır. Termal ısı ölçer, termokupl ve ısı kontrollerinden alınan anlık sıcaklık değerleri, referans olarak PLC’ ye işlenmiş olup,HMI üzerinden gerekli ayarlamalar kullanıcıya sunulmuştur. PLC’ ye alınan referans değerler Codesys tabanlı yazılımda ST dilikullanılarak geliştirilmiştir. Söz konusu çalışmada panel üzerinden anlık değerler gösterilmiştir. Bu veriler kullanılarak, değişken havaşartlarına karşın Pet Şişe Şişirme Makinalarında yer alan infrared lambaların ısı optimizasyonu sağlanmıştır. Şişelerin hammaddesiolan preformun, geliştirilmiş yazılımla ısı optimizasyonu sağlanmış fırından aldığı ısılar, termal kamera ile gözlemlenmiş olup;sonuçlar standart sistemde çalışan fırın ile karşılaştırılmıştır. Elde edilen veriler kapsamında, yazılımın preformu olumlu yöndeetkilediği ve değişken iklim şartlarından etkilenmeyen, daha kaliteli, maliyeti azaltan şişelerin çıkmasına etken olduğugözlemlenmiştir.Öğe Smart City A plication: Internet of Things (IoT) Technologies eased Smart Waste Collection Using Data Mining Approach and Ant Colony Optimization(Zarka Private Univ, 2017) Oralhan, Zeki; Oralhan, Burcu; Yigit, YavuzGlobally today, Living in urban areas is more preferred than in living rural areas. This situation creates many problem for urban living. One of the big problem is waste management in urban areas. Optimizing waste collection has become very important phenomenon for being smart city. In this study, we aimed to optimize waste collection for reduce both cost of collection and pollution effect of environment. We designed a garbage container integrated sensors for measuring fill level of container, temperature, and ratio of carbon dioxide inside the container. We transmitted all information to our waste management software based Internet of Things (IoT) technologies. According to the ant colony algorithm, most efficient waste collection route delivered to garbage truck drivers' cellular enabled smart tablet. We used data mining approach to forecast when garbage container can reach highest level, and the planning of garbage container placement. We applied this smart waste collection management system in a town where is in Kayseri, Turkey. In first step, we applied for 200 Items (garbage containers) in the town that has 548.028 population and urban living ratio is 100%. Before smart waste management system 200 garbage containers was collecting by garbage trucks in a static route. After we had applied smart waste management system, containers were collected by garbage truck in dynamic route. Smart waste management system significantly decreased the trucks' oil cost, carbon emissions, traffic, truck wear, noise pollution, environmental pollution, and work hours. The system presented approximately 30% with in direct cost savings in waste collection.Öğe Smart city application: Internet of Things (IoT) technologies based smart waste collection using data mining approach and ant colony optimization(Zarka Private University, 2017) Oralhan, Zeki; Oralhan, Burcu; Yiğit, YavuzGlobally today, Living in urban areas is more preferred than in living rural areas. This situation creates many problem for urban living. One of the big problem is waste management in urban areas. Optimizing waste collection has become very important phenomenon for being smart city. In this study, we aimed to optimize waste collection for reduce both cost of collection and pollution effect of environment. We designed a garbage container integrated sensors for measuring fill level of container, temperature, and ratio of carbon dioxide inside the container. We transmitted all information to our waste management software based Internet of Things (IoT) technologies. According to the ant colony algorithm, most efficient waste collection route delivered to garbage truck drivers’ cellular enabled smart tablet. We used data mining approach to forecast when garbage container can reach highest level, and the planning of garbage container placement. We applied this smart waste collection management system in a town where is in Kayseri, Turkey. In first step, we applied for 200 Items (garbage containers) in the town that has 548.028 population and urban living ratio is 100%. Before smart waste management system 200 garbage containers was collecting by garbage trucks in a static route. After we had applied smart waste management system, containers were collected by garbage truck in dynamic route. Smart waste management system significantly decreased the trucks’ oil cost, carbon emissions, traffic, truck wear, noise pollution, environmental pollution, and work hours. The system presented approximately 30% with in direct cost savings in waste collection. © 2017, Zarka Private University. All rights reserved.
