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In this paper we classified 4 skin lesions (Melanoma, Melanocytic Nevus, Basal Cell Carcinoma, Benign keratosis) from ISIC 2019 dataset which was published by International Skin Imaging Collabration in 2019. We used InceptionV3 convolutional neural network model for classification. We applied two preprocessing methods: High Dimensional Model Representation (HDMR) and Hilbert Transform. In conclusion we obtained 89% accuracy on classification of Basal Cell Carcinoma using Hilbert Transform. Moreover, we obtained 78% accuracy on classification of Melanoma using Contrast Enhancement High Dimensional Model Representation (HDMR). [ABSTRACT FROM AUTHOR]

Design codes aim to prevent shear failure of reinforced concrete (RC) beams since it is a brittle failure. An accurate prediction of shear strength is important for a proper design of an RC beam. There exist various equations for predicting the shear strength of RC beams. With increasing computational power, it is possible to develop numerical models delivering more accurate predictions than those equations do. In this paper, an artificial neural network (ANN) model developed for predicting the shear strength of RC slender beams without shear reinforcement is presented. The comparisons of the model with five design code equations and fourteen equations proposed by various researchers are given. The model has a better performance than the considered equations do in predicting the shear strength of the beams considered in this study. A parametric study conducted for investigating the effects of various parameters on the shear strength of RC slender beams without shear reinforcement by using the ANN model is also presented. A significant size effect on the shear strength of RC slender beams without shear reinforcement is observed through the results of the parametric study. [ABSTRACT FROM AUTHOR]

Main focus of this work is to realize image searching within another image in an efficient way. Image searching within another image is accomplished through the integrated use of image matching techniques and searching algorithms. Artificial neural networks along with various image features such as average color value, color standard deviation, correlation and edge parameters are used for image matching whereas genetic algorithms were used for image searching. In the work presented in this paper, an integrated method based on smart searching algorithms, quick image matching methods and parallel programming techniques were proposed and implemented. Proposed method was tested on several low and highresolution reference and template images. Results revealed that the proposed method can successfully match images and significantly reduce the total search time. [ABSTRACT FROM AUTHOR]

Highly accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In recent years, a variety of modeling approaches and methodologies have been applied to predict HPC's compressive strength from a wide range of variables, with different ratios of success. In this study, an appropriate machine learning method, using different mixing ratios for the prediction of compressive strength of HPC, is investigated. In recent years, rather developing machine learning methods; Artificial Neural Networks (ANN) and Support Vector Machines (SVM) 's applicabilities for the prediction, handled in this study, are being investigated and extremely high results were obtained. In this paper, it's obtained that prediction success of SVM has been found more satisfactory than ANN's. It is concluded that the SVM's can be used effectively as an alternative method by research labs and the concrete firms for predicting the strength. [ABSTRACT FROM AUTHOR]

Forecasting studies are extremely important in the technical, social and economic research. Generally, we know it is very difficult to forecast with higher accurate about a system by using recent values. In the scientific literature, the forecasting studies of energy, personnel planning, production planning, climate changes, sales and marketing and economics etc. are frequently found. In this paper, for an emergency calls center in Isparta province of Turkey an artificial neural network (ANN) forecasting model was developed to determine the number of calls for as health, fire and security services on a pilot implementation of the emergency calls center on a single number 112. In the developed model, the gradient descent with adaptive learning and momentum (GDX) algorithm is selected as the training algorithm with feed-forward back-propagation by using 80% of input data and the 20% of input data is used for testing set data from last month. After the testing, the mean absolute percentage error (MAPE) rate is obtained as 4.5% and it is useful to test. In addition, the forecasting results of the next month are shown that the MAPE values are 2.65%, 6.40% and 5.24% with ANN, trend analysis and ARIMA (1 1 1) models respectively and, the number of calls are found separately on the types of calls in daily. Consequently, the developed model by using ANN to forecast the number of calls in an emergency call center is more accurate than the trend analysis and ARIMA models. [ABSTRACT FROM AUTHOR]

In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggregates images were taken with 45° and 90° by a 10 Mp (Sony DSC-R1) and 7.1 Mp (Canon EOS 350D) camera. Aggregates images were processed and analyzed by using MATLAB Image Processing and Neural Network Toolbox. Classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. Results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods. [ABSTRACT FROM AUTHOR]

In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through Artificial Neural Networks. For classification, it was a used the feature vector which was calculated by using digital image processing techniques. Of the five different type coarse aggregates images were taken with 45° and 90° by a 10 Mp (Sony DSC-R1) and 7.1 Mp (Canon EOS 350D) camera. Aggregates images were processed and analyzed by using MATLAB Image Processing and Neural Network Toolbox. Classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. Results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods. [ABSTRACT FROM AUTHOR]

Main focus of this work is to realize image searching within another image in an efficient way. Image searching within another image is accomplished through the integrated use of image matching techniques and searching algorithms. Artificial neural networks along with various image features such as average color value, color standard deviation, correlation and edge parameters are used for image matching whereas genetic algorithms were used for image searching. In the work presented in this paper, an integrated method based on smart searching algorithms, quick image matching methods and parallel programming techniques were proposed and implemented. Proposed method was tested on several low and high-resolution reference and template images. Results revealed that the proposed method can successfully match images and significantly reduce the total search time.

Highly accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In recent years, a variety of modeling approaches and methodologies have been applied to predict HPC’s compressive strength from a wide range of variables, with different ratios of success. In this study, an appropriate machine learning method, using different mixing ratios for the prediction of compressive strength of HPC, is investigated. In recent years, rather developing machine learning methods; Artificial Neural Networks (ANN) and Support Vector Machines (SVM)’s applicabilities for the prediction, handled in this study, are being investigated and extremely high results were obtained. In this paper, it’s obtained that prediction success of SVM has been found more satisfactory than ANN's. It is concluded that the SVM’s can be used effectively as an alternative method by research labs and the concrete firms for predicting the strength.

In this study, effect of the marble cutting parameters on energy consumption was estimated by using Artificial Neural Network and ANFIS. In both of these estimation methods, marble's hardness, speed of circular saw rotation and cutting speed parameters were used as inputs, specific energy factor, cutting energy in per unit volume was taken as output. The data for training networks were taken from a PC based block cutter machine (S/T) with circular diamond saw blade at Afyon Kocatepe University Labs. These data are cutting parameters of "Denizli Travertine" and "Bilecik Beige" type marbles. Some of these data were used for training and then all data were used for testing stage of the networks. To show the effectiveness of the study, estimated data were compared with experimental data. As conclusion, it was reported that estimated data were quite close to experimental result. [ABSTRACT FROM AUTHOR]