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Öğe An approach to estimate occupational accidents using least-squares support vector machines(Academic Publication Council, 2017) Ceylan, Huseyin; Parlakyildiz, SakirLeast-squares support vector machines represent an emerging technique that has been adopted to estimate accidents. In this study, occupational accident estimation models were developed using the least-squares support vector machine method for the Republic of Turkey. In addition, linear regression analysis, nonlinear regression analysis, and artificial neural network models were considered. During the development phase of the models, statistical data from 1970 to 2012 were used to consider parameters such as insured workers, workplaces, occupational accidents, deaths, and permanent incapacities. Using these models, the numbers of accidents, deaths, and permanent incapacities resulting from occupational accidents were estimated for three different scenarios in the Republic of Turkey through the end of 2025. The performances of the developed models were evaluated considering the mean absolute percent errors and the mean absolute errors. In addition, we compared the least-squares support vector machine, linear regression analysis, nonlinear regression analysis, and artificial neural network methods in terms of their estimation performances. Our simulation results demonstrate that the proposed least-squares support vector machine model outperforms other techniques in terms of accuracy and has a rapid convergence capability when estimating occupational accidents.Öğe An Artificial Neural Networks Approach to Estimate Occupational Accident: A National Perspective for Turkey(Hindawi Ltd, 2014) Ceylan, HuseyinOccupational accident estimation models were developed by using artificial neural networks (ANNs) for Turkey. Using these models the number of occupational accidents and death and permanent incapacity numbers resulting from occupational accidents were estimated for Turkey until the year of 2025 by the three different scenarios. In the development of the models, insured workers, workplace, occupational accident, death, and permanent incapacity values were used as model parameters with data between 1970 and 2012. 2-5-1 neural network architecture was selected as the best network architecture. Sigmoid was used in hidden layers and linear function was used at output layer. The feed forward back propagation algorithm was used to train the network. In order to obtain a useful model, the network was trained between 1970 and 1999 to estimate the values of 2000 to 2012. The result was compared with the real values and it was seen that it is applicable for this aim. The performances of all developed models were evaluated using mean absolute percent errors (MAPE), mean absolute errors (MAE), and root mean square errors (RMSE).Öğe Due Date Single Machine Scheduling Problems with Nonlinear Deterioration and Learning Effects and Past Sequence Dependent Setup Times(Hindawi Ltd, 2014) Ceylan, HuseyinWe present some problems against due dates with nonlinear learning and deterioration effects and past sequence dependent setup times. In this study, two effects (learning and deterioration) are used for the same processing time. The processing time of a job is shorter if it is scheduled later, rather than in the sequence. This phenomenon is known in the literature as a "learning effect." On the other hand, in many realistic scheduling settings, a job processed later consumes more time than the same job processed earlier-this is known as scheduling with deteriorating jobs. In the past sequence dependent setup times approach, the setup time of a job is proportionate to the sum of processing times of the jobs already scheduled. In this study, we demonstrated that some problems with due dates remain polynomially solvable. However, for some other problems, we concentrated on finding polynomially solves under their special cases.
