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Abstract:In this study, the premise and consequent parameters of ANFIS are optimized using Genetic Algorithm (GA) based on a population algorithm. The proposed approach is applied to the nonlinear dynamic system identification problem. The simulation results of the method are compared with the Backpropagation (BP) algorithm and the results of other methods that are available in the literature. With this study it was observed that the optimisation of ANFIS parameters using GA is more successful than the other methods.

River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kılayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazıköy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. To validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method’s performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model.

In the medical field, successful classification of microarray gene expression data is of major importance for cancer diagnosis. However, due to the profusion of genes number, the performance of classifying DNA microarray gene expression data using statistical algorithms is often limited. Recently, there has been an important increase in the studies on the utilization of artificial intelligence methods, for the purpose of classifying large-scale data. In this context, a hybrid approach based on the adaptive neuro-fuzzy inference system (ANFIS), the fuzzy c-means clustering (FCM), and the simulated annealing (SA) algorithm is proposed in this study. The proposed method is applied to classify five different cancer datasets (i.e., lung cancer, central nervous system cancer, brain cancer, endometrial cancer, and prostate cancer). The backpropagation algorithm, hybrid algorithm, genetic algorithm, and the other statistical methods such as Bayesian network, support vector machine, and J48 decision tree are used to compare the proposed approach's performance to other algorithms. The results show that the performance of training FCM-based ANFIS using SA algorithm for classifying all the cancer datasets becomes more successful with the average accuracy rate of 96.28% and the results of the other methods are also satisfactory. The proposed method gives more effective results than the others for classifying DNA microarray cancer gene expression data.

Automated Heritage Building Information Modelling (HBIM) from the point cloud data has been researched in the last decade as HBIM can be the integrated data model to bring together diverse sources of complex cultural content relating to heritage buildings. However, HBIM modelling from the scan data of heritage buildings is mainly manual and image processing techniques are insufficient for the segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Artificial Intelligence (AI) based deep learning methods such as PointNet are introduced in the literature for point cloud segmentation. Yet, their use is mainly for manufactured and clear geometric shapes and components. To what extent PointNet based segmentation is applicable for heritage buildings and how PointNet can be used for point cloud segmentation with the best possible accuracy (ACC) are tested and analysed in this paper. In this study, classification and segmentation processes are performed on the 3D point cloud data of heritage buildings in Gaziantep, Turkey. Accordingly, it proposes a novel approach of activity workflow for point cloud segmentation with deep learning using PointNet for the heritage buildings. Twenty-eight case study heritage buildings are used, and AI training is performed using five feature labelling for segmentation namely, walls, roofs, floors, doors, and windows for each of these 28 heritage buildings. The dataset is divided into clusters with 80% training dataset and 20% prediction test dataset. PointNet algorithm was unable to provide sufficient accuracy in segmenting the point clouds due to deformation and deterioration on the existing conditions of the heritage case study buildings. However, if PointNet algorithm is trained with the restitution-based heritage data, which is called synthetic data in the research, PointNet algorithm provides high accuracy. Thus, the proposed approach can build the baseline for the accurate classification and segmentation of the heritage buildings.

Prostate cancer is one of the most common types of cancer among males as well as causing the most deaths. Early diagnosis of prostate cancer plays an important role in the treatment of the disease. Therefore, microarray technology is widely used in the diagnosis of inherited diseases such as prostate cancer. With this technology, it is possible to obtain more knowledge about cancer by analyzing thousands of gene expressions. However, it is quite difficult to analyze complex relationships among thousands of genes in microarray data. For this reason, high performance artificial intelligence-based classification methods are needed in recent years. In this study, a hybrid method has been proposed for optimizing the parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) with Genetic Algorithm (GA) in order to classify prostate cancer gene expression profiles. The performance of the proposed method is compared with those of ANFIS models trained by different learning algorithms. According to obtained results, the proposed method is more successful than the other methods, with the accuracy of 90.32%.

ÖZSınıflandırma, verilerin analiz edilmesi için önemli bir veri madenciliği tekniği olup tıp, genetik ve biyomedikal mühendisliği başta olmak üzere birçok alanda kullanılmaktadır. Özellikle tıp alanında DNA mikrodizi gen ekspresyon verilerini sınıflandırmaya yönelik yapılan çalışmalarda artış görülmektedir. Ancak, mikrodizi gen ekspresyon (ifade) verilerinde bulunan gen sayılarının çokluğu ve bu veriler arasında doğrusal olmayan bağıntılar bulunması gibi problemlerden dolayı geleneksel sınıflandırma algoritmalarının başarımları sınırlı kalabilmektedir. Bu sebeplerden dolayı son yıllarda sınıflandırma probleminin çözümü için yapay zekâ tekniklerine dayalı sınıflandırma yöntemlerine olan ilgi giderek artmaya başlamıştır. Bu çalışmada, karaciğer mikrodizi kanser veri setinin sınıflandırılması için Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi (ANFIS) ve Genetik Algoritmaya (GA) dayalı hibrid bir yaklaşım önerilmiştir. Simülasyon sonuçları, diğer bazı yöntemlere ait sonuçlarla karşılaştırılmıştır. Elde edilen sonuçlardan, önerilen yöntemin diğer yöntemlere göre daha başarılı olduğu görülmüştür.Anahtar Kelimeler: Neuro-fuzzy, ANFIS, genetik algoritma, sınıflandırma, mikrodizi gen ifade Training ANFIS structure using genetic algorithm for liver cancer classification based on microarray gene expression data ABSTRACT Classification is an important data mining technique, which is used in many fields mostly exemplified as medicine, genetics and biomedical engineering. The number of studies about classification of the datum on DNA microarray gene expression is specifically increased in recent years. However, because of the reasons as the abundance of gene numbers in the datum as microarray gene expressions and the nonlinear relations mostly across those datum, the success of conventional classification algorithms can be limited. Because of these reasons, the interest on classification methods which are based on artificial intelligence to solve the problem on classification has been gradually increased in recent times. In this study, a hybrid approach which is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) are suggested in order to classify liver microarray cancer data set. Simulation results are compared with the results of other methods. According to the results obtained, it is seen that the recommended method is better than the other methods.

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