In recent years, the use of artificial intelligence algorithms in geotechnical engineering has increased, and successful results have been obtained in geotechnical engineering using artificial intelligence algorithms. The objective of this study is to estimate the shear strength of glass fiber reinforced clay soil using ANFIS. For this purpose, specimens with different water contents (13%, 15% and 17%) and different glass fiber addition ratios (0%, 1%, 1.5% and 2%) were prepared. The ANFIS models were created using the shear strength (τ) data obtained by direct shear tests on the prepared specimens. To create the best fitting ANFIS model in the current study, 75%, 77%, 80%, and 83% of the data for training and 25%, 23%, 20%, and 17% of the data for testing were used, respectively. However, to estimate the shear strength in each ANFIS model, the normal stress (σ), glass fiber content (Fc), and water content (ω) are considered as input parameters. Statistical parameters such as root mean square error (RMSE), regression coefficient (R2), root square error (RSE), and mean absolute error (MAE) were also calculated to determine the success rates of the ANFIS models. Examination of the statistical parameters revealed that the data used 80% for training and 20% for testing provided the best results in estimating the shear strength of the ANFIS model.

/pdf/cam-fiberle-guclendirilmis-killi-zeminin-kayma-mukavemetinin-3mg6mdzj.pdf

Cam Fiberle Güçlendirilmiş Killi Zeminin Kayma Mukavemetinin Uyarlamalı Ağ Tabanlı Bulanık Çıkarım Sistemi (ANFIS) ile Tahmini

Unmanned Aerial Vehicles (UAV) has an increasingly application for military and civilian fields. Currently, UAVs can perform many tasks such as search-rescue, surveillance by safely. UAVs are specifically designed for their using purpose. The designs of UAVs are an important and long process. It is necessary to evaluate many parameters in the thrust system design. Because of thrust system is the most important system of UAVs. Traditional thrust system design is a trial and error method that is costly and ineffective. In this study, we examined that uav which use brushless motor in thrust system. The force generated by the thrust system has been estimated by using the Adaptive Neuro-Fuzzy Inference System (ANFIS). In the ANFIS model, the thrust force estimation was made by using propeller and motor information. RCbenchmark 1580 model dynamometer was used to measure the accuracy of the ANFIS estimates. Mean square error (MSE) was used to compare test data and ANFIS model. Low MSE ratio shows that ANFIS model is near to real data.

/pdf/anfis-based-thrust-estimation-of-a-small-rotary-wing-drone-4bdziom4o3.pdf

Anfis Based Thrust Estimation of a Small Rotary Wing Drone

Estimating product drying kinetics is critical to obtain the best drying process without compromising product quality and necessitates the development of numerical drying models. This research aims to compare the prediction models developed using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS), two popular machine learning approaches in the recent years. Zucchini slices were chosen as samples and dried in a solar-assisted microwave belt dryer at 0.245 m/min belt speed and microwave powers of 0.7, 1, and 1.4 kW. On the data set obtained by computing the moisture content and drying rate values, prediction models were developed using ANN and ANFIS approaches. These models were evaluated using the coefficient of determination, mean absolute percent error, and root mean square error data. The ANFIS-based prediction model outperformed the ANN model in terms of drying rate performance, but the ANN model outperformed the ANFIS model in terms of moisture content values. Results showed that both methods established can be utilized to estimate zucchini slices.

Modeling the Moisture Content and Drying Rate of Zucchini (Cucurbita pepo L.) in a Solar Hybrid Dryer Using ANN and ANFIS Methods

Son yillarda ulkelerin ekonomik buyume ve gelismesinde rol oynayan en onemli unsurlardan biri girisimciliktir. Girisimcilik ile ulkelerin rekabet gucu artmakta, ekonomik ve sosyal sorunlara daha hizli cozum bulunabilmektedir. Bu nedenle ulkeler sahip olduklari insan kaynaginin girisimcilik potansiyellerini surekli analiz etmeli ve gelistirmelidirler. Bu calismada girisimciligin en onemli belirleyicilerinden biri olan kisilik ozellikleri ile girisimcilik potansiyeli arasindaki iliskiler Adaptif Ag Tabanli Bulanik Cikarim Sistemi (ANFIS) ile modellenmistir. ANFIS ile hem girisimcilik potansiyelini etkileyen kisilik ozelliklerin belirlenmesi hem de kisilik ozelliklere bagli olarak bir kisinin girisimcilik potansiyelinin tahminlenmesi amaclanmistir. Arastirma sonuclarina gore; basarma ihtiyaci, belirsizlik toleransi ve yenilikciligin girisimcilik potansiyelini en fazla etkileyen, risk alma, kontrol odagi ve kendine guvenin en az etkileyen kisilik ozellikler oldugu ve ANFIS’in tahminleme performansinin % 90,47 oldugu sonucuna ulasilmistir.

/pdf/kisilik-ozellikleri-ile-girisimcilik-potansiyeli-arasindaki-2m2h1c46zw.pdf

Ki̇şi̇li̇k özelli̇kleri̇ i̇le gi̇ri̇şi̇mci̇li̇k potansi̇yeli̇ arasindaki̇ i̇li̇şki̇ni̇n adapti̇f ağ tabanli bulanik çikarim si̇stemi̇ (anfis) i̇le anali̇zi̇

Numerous artificial intelligence methods have lately been applied in education. This study proposes an Adaptive Neural-network-based Fuzzy Logic (ANFIS) model combining fuzzy logic and artificial neural networks for predicting students' STEM attitudes. The inputs of the research were determined as grade levels and academic achievement scores, and the output was determined as STEM attitudes. The hybrid optimisation method trained the fuzzy inference system (FIS) to recognise the more effective ANFIS model. Afterward, the effects of the input variables on the output variable were examined with statistical techniques. Finally, the students' artificial and real STEM attitude scores were compared. 600 middle school students participated in the study, and data were gathered using a Personal Information Form and STEM Attitude Scale. The study revealed a significant positive correlation (r = 0.298; p = 0.000) between the generated ANFIS scores and real scores, the real and artificial scores didn't indicate a statistically significant difference. Therefore, the results obtained through ANFIS correctly predict students' STEM attitude scores. The grade level and academic achievement variables showed statistically significant differences in STEM attitudes. This study is a concrete example as it shows that it is possible to know some characteristics of students using artificial intelligence. 

Using artificial intelligence to predict students’ STEM attitudes: an adaptive neural-network-based fuzzy logic model

Due to the rapid increase in global competition among organizations and companies, rational approaches in decision making have become indispensable for organizations in today’s world. Establishing a safe and robust path through uncertainties and risks depends on the decision units’ ability of using scientific methods as well as technology. Demand forecasting is known to be one of the most critical problems in organizations.  A company which supports its demand forecasting mechanism with scientific methodologies could increase its productivity and efficiency in all other functions. New methods, such as fuzzy logic and artificial neural networks are frequently being used as a decision-making mechanism in organizations and companies recently.  In this study, it is aimed to solve a critical demand forecasting problem with ANFIS. In the first phase of the study, the factors which impact demand forecasting are determined, and then a database of the model is established using these factors. It has been shown that ANFIS could be used for demand forecasting.

/pdf/uyarlamali-sinirsel-bulanik-cikarim-sisteminin-anfis-talep-5g32hx6umd.pdf

Uyarlamalı Sinirsel Bulanık Çıkarım Sisteminin (ANFIS) Talep Tahmini İçin Kullanımı ve Bir Uygulama

Organizasyonlarda belirli bir surecin herhangi bir noktasinda, onemli bir konuda alinacak kararlar genellikle birden fazla karar vericinin yer aldigi bicimdedir. Genel anlamda birden fazla karar vericinin yer aldigi karar verme isi, grup karar verme olarak adlandirilir. Karar vericilerin verilecek karar konusunda farkli degerlendirmeleri olabilecegi, karar konusundaki degerlendirme kistaslarindaki farkliliklar, oznel nedenler gibi bircok husus, verilecek olan karari etkilemektedir. Bu tip kararlarin daha dogru ve etkin bir bicimde verilebilmesinde karar vericilere destek saglamak icin cok sayida model gelistirilmistir. Bu calismada grup halinde alinan kararlarin daha etkin bir bicime yapilabilmesi icin bir yazilim gelistirilmistir. Bu yazilimda, degerlendirme ile ilgili kriterler, karar vericilere sunulmaktadir. Ayrica, kriterlerin degisik kararlar icin degistirilebilmesine imkan saglandigi gibi kriterlere farkli agirliklar da atanabilmektedir. Calismada GroupDM olarak adlandirilmis yazilim bir tez degerlendirme ornegi uzerinden anlatilmistir. Tez degerlendirme surecleri grup karar verme sureclerine uygun bir ornektir. Yazilim sayesinde bu benzer grup karar verme sureclerinin daha etkin bir bicimde yapilabilecegi ongorulmektedir.

/pdf/grup-karar-verme-yazilimi-groupdm-tez-degerlendirmeleri-icin-23r7rgks0r.pdf

GRUP KARAR VERME YAZILIMI (GroupDM): TEZ DEĞERLENDİRMELERİ İÇİN ÖRNEK BİR UYGULAMA

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Uyarlamalı Sinirsel Bulanık Çıkarım Sisteminin (ANFIS) Talep Tahmini İçin Kullanımı ve Bir Uygulama

GRUP KARAR VERME YAZILIMI (GroupDM): TEZ DEĞERLENDİRMELERİ İÇİN ÖRNEK BİR UYGULAMA