Erman Çakıt

Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques

We investigate the relationship between adverse events and infrastructure development investments in an active war theater.We develop soft computing techniques (ANN, FIS, and ANFIS) for estimating the number of adverse events based on the occurrence of economic improvement projects.The performance of each model was investigated and compared to all other models using the calculated mean absolute percentage error (MAPE) values.When the model accuracy was calculated based on the MAPE for each of the models, ANN had better predictive accuracy than FIS and ANFIS models, as demonstrated by experimental results.The sensitivity analysis results show that the importance of economic development projects varied based on the specific regions and time period. The purpose of this paper is to investigate the relationship between adverse events and infrastructure development investments in an active war theater by using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) where the accuracy of the predictions is directly beneficial from an economic and humanistic point of view. Fourteen developmental and economic improvement projects were selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded or hijacked, and the total number of adverse events has been estimated.The results obtained from analysis and testing demonstrate that ANN, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic project data. When the model accuracy was calculated based on the mean absolute percentage error (MAPE) for each of the models, ANN had better predictive accuracy than FIS and ANFIS models, as demonstrated by experimental results. For the purpose of allocating resources and developing regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater, with emphasis on predicting the occurrence of events. We conclude that the importance of infrastructure development projects varied based on the specific regions and time period.

Fuzzy Inference Modeling with the Help of Fuzzy Clustering for Predicting the Occurrence of Adverse Events in an Active Theater of War

This study investigated the relationship between adverse events and infrastructure development projects in an active theater of war using fuzzy inference systems (FIS) with the help of fuzzy clustering that directly benefits from its prediction accuracy. Fourteen developmental and economic improvement projects were selected as independent variables. These were based on allocated budgets and included a number of projects from different time periods, urban and rural population density, and total number of adverse events during the previous month. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, or hijacked and the total number of adverse events has been estimated. The performance of each model was investigated and compared to all other models with calculated mean absolute error (MAE) values. Prediction accuracy was also tested within ±1 (difference between actual and predicted value) with values around 90%. Based on the results, it was concluded that FIS is a useful modeling technique for predicting the number of adverse events based on historical development or economic project data.

Assessing the Relationship Between Hand Dimensions and Manual Dexterity Performance for Turkish Dental Students

The objectives of this study included: (i) a determination of whether there is a difference in manual dexterity as a function of gender and dentistry curriculum and (ii) an assessment of hand anthropometric characteristics on manual dexterity test performance. In total, 155 dental students (86 males and 69 females) in their first, second, third, fourth, and fifth years of a five-year undergraduate program took part in the study that involved a simple manual dexterity test. We used a paired sample t-test to compare differences between males and females and among students of different years. Pearson’s correlation coefficients were computed as a measure of association between parameters. The results demonstrate that anthropometric data of both hands have small but significant effects on test performance, and that small hands are associated with better test performance.

Ergonomic assessment of airport shuttle driver tasks using an ergonomic analysis toolset

This study aimed to (a) evaluate strength requirements and lower back stresses during lifting and baggage handling tasks with the 3D Static Strength Prediction Program (3DSSPP) and (b) provide additional analyses using rapid entire body assessment (REBA) and the NASA task load index (TLX) to assess the risks associated with the tasks. Four healthy female shuttle drivers of good health aged between 55 and 60 years were observed and interviewed in an effort to determine the tasks required of their occupations. The results indicated that lifting bags and placing them in a shuttle were high risk for injury and possible changes should be further investigated. The study concluded there was a potential for injury associated with baggage storing and retrieval tasks of a shuttle driver.

Understanding the Social and Economic Factors Affecting Adverse Events in an Active Theater of War: A Neural Network Approach

This study focused on the application of artificial neural networks (ANNs) to model the effect of infrastructure development projects on terrorism security events in Afghanistan. The dataset include adverse events and infrastructure aid activity in Afghanistan from 2001 to 2010. Several ANN models were generated and investigated for Afghanistan and its seven regions. In addition to a soft-computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANN approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, the developed ANN model provided better prediction accuracy with respect to the MLR approach. The results obtained from this analysis demonstrate that ANNs can predict the occurrence of adverse events according to economic infrastructure aid activity data.