Yazar "Haznedar, Bulent" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    The Effect of Adaptive Neuro-fuzzy Inference System (ANFIS) on Determining the Leadership Perceptions of Construction Employees
    (Springer Int Publ Ag, 2023) Keles, Abdullah Emre; Haznedar, Bulent; Keles, Muemine Kaya; Arslan, Mustafa Turan
    In the construction industry, which is Turkey's locomotive and the strategic sector, determining the kind of leadership that impacts employees' productivity is directly related to the success of the business. The identification of leadership types that will motivate and support employees has great importance in terms of construction businesses where the human element is at the forefront. From the point of view of the site chiefs, it is thought that it will benefit all the stakeholders in the construction sector to determine which leader type will motivate which employees. In this study, the productivity relations between the engineers working in construction companies constructing buildings in Adana Province and the employees who are the hierarchically lower-level employees of these persons were investigated using bi-directional surveys. The impact of leadership types on the employees' productivity has been investigated using machine learning. The effects of ANFIS method and the use of genetic algorithm (GA) on the training of ANFIS for the classification are investigated. The data set, which was prepared within the scope of the study, was classified by ANFIS-genetic algorithm (ANFIS-GA), ANFIS-backpropagation algorithm (ANFIS-BP), and ANFIS-hybrid algorithm (ANFIS-HB) algorithms after the required preprocesses. The 10-fold cross-validation technique is used to test the performance of the classification methods. According to the obtained results, the highest accuracy rate of 82.18% is obtained when ANFIS-GA algorithm is used as a classifier. As a result of the study, it is concluded that for this data set, ANFIS, an artificial neural network-based algorithm, is more successful in determining the leadership perceptions of construction employees when it is trained by GA.
  • Küçük Resim
    Öğe
    Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data
    (Springer Heidelberg, 2021) Haznedar, Bulent; Arslan, Mustafa Turan; Kalinli, Adem
    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.