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Öğe A fuzzy overlay model for mapping adverse event risk in an active war theatre(Taylor & Francis Ltd, 2018) Çakıt, Erman; Karwowski, WaldemarThis study discusses a series of fuzzy overlay analysis performed within a Geographic Information System (GIS) on recent adverse events throughout the war in Afghanistan. Three types of input variables are considered in terms of number of people killed, wounded and hijacked over the period 2004-2010 in order to identify the risk level in Afghanistan using fuzzy GIS approach. To conclude, most risky areas are accumulated in the eastern region of the country and major population centres. The proposed approach could enable military decision-makers to obtain a better understanding of the socio-spatial dynamic of incidents in Middle East.Öğe Application of soft computing techniques for estimating emotional states expressed in Twitter ® time series data(Springer London, 2020) Çakıt, Erman; Karwowski, Waldemar; Servi, LesBecause the emotional states of selected social groups may constitute a complex phenomenon, a suitable methodology is needed to analyze Twitter ® text data that can reflect social emotions. Understanding the nature of social barometer data in terms of its underlying dynamics is critical for predicting the future states or behaviors of large social groups. This study investigated the use of the supervised soft computing techniques (1) fuzzy time series (FTS), (2) artificial neural network (ANN)-based FTS, and (3) adaptive neuro-fuzzy inference systems (ANFIS) for predicting the emotional states expressed in Twitter ® data. The examined dataset contained 25,952 data points reflecting more than 380,000 Twitter ® messages recorded hourly. The model prediction accuracy was performed using the root-mean-square error. The ANFIS approach resulted in the most accurate prediction among the three examined soft computing approaches. The findings of the study showed that the FTS, ANN-based FTS, and ANFIS models could be used to predict the emotional states of a large social group based on historical data. Such a modeling approach can support the development of real-time social and emotional awareness for practical decision-making, as well as rapid socio-cultural assessment and training. © 2019, Springer-Verlag London Ltd., part of Springer Nature.Öğe Detecting adverse events in an active theater of war using advanced computational intelligence techniques(Sprınger Internatıonal Publıshıng Ag, 2019) Zurada, Jozef; Shi, Donghui; Karwowski, Waldemar; Guan, Jian; Çakıt, Erman; Aliev, RA; Kacprzyk, J; Pedrycz, W; Jamshidi, M; Sadikoglu, FMThis study investigates the effectiveness of advanced computational intelligence techniques in detecting adverse events in Afghanistan. The study first applies feature reduction techniques to identify significant variables. Then it uses five cost-sensitive classification methods. Finally, the study reports the resulting classification accuracy rates and areas under the receiver operating characteristics charts for adverse events for each method for the entire country and its seven regions. It appears that when analysis is performed for the entire country, there is little correlation between adverse events and project types and the number of projects. However, the same type of analysis performed for each of its seven regions shows a connection between adverse events and the infrastructure budget and the number of projects allocated for the specific regions and times. Among the five classifiers, the C4.5 decision tree and k-nearest neighbor seem to be the best in terms of global performance.Öğe Estimating electromyography responses using an adaptive neuro-fuzzy inference system with subtractive clustering(Wiley, 2017) Çakıt, Erman; Karwowski, WaldemarThis study aimed to develop an adaptive neuro-fuzzy inference system (ANFIS) approach to estimate the normalized electromyography (NEMG) responses, where the independent variables are demographic variables including population, gender, ethnicity, age, height, weight, posture, and muscle groups. The study groups comprised 75 US-based (54 males and 21 females) and 10 Japan-based (all males) automobile assembly workers. A total of 65 inputs and 1 output reflecting the NEMG values were considered at the beginning. After correlating analysis results, a total of 35 significant predictors were considered for both ANFIS and regression models. The data were partitioned into two datasets, one for training (70% of all data) and one for validation (30% of all data). In addition to a soft-computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANFIS approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, ANFIS had better predictive accuracy than MLR, as demonstrated by the experimental results. Overall, this study demonstrates that the ANFIS approach can predict normalized EMG responses according to subjects' demographic variables, posture, and muscle groups.Öğe Fuzzy ınference modeling with the help of fuzzy clustering for predicting the occurrence of adverse events in an active theater of war(Taylor and Francis Inc., 2015) Çakıt, Erman; Karwowski, WaldemarThis 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. © 2015 Taylor & Francis Group, LLC.Öğe Gaining insight by applying geographical modeling(CRC Press, 2016) Çakıt, Erman; Karwowski, WaldemarThis chapter is a review of adverse events throughout the war in Afghanistan by representing the mapping of these events. Three types of adverse events were considered in terms of the number of people killed, wounded, and hijacked and their total number in the active war theater of Afghanistan over the period of 2004-2010. For the purpose of understanding the patterns of adverse events, the results can be summarized by visualizing the occurrence of incidents by region; the emphasis was on analyzing the number of people killed, wounded, and hijacked to determine the risk of different parts of Afghanistan. © 2011 by Taylor & Francis Group, LLC.Öğe Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques(Elsevier, 2014) Çakıt, Erman; Karwowski, Waldemar; Bozkurt, Halil; Ahram, Tareq; Thompson, William; Mikusinski, Piotr; Lee, GeneThe 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. (C) 2014 Elsevier B.V. All rights reserved.Öğe Potential applications of soft-computing techniques for human socio-cultural behavior modeling(Academic Publications, 2017) Çakıt, Erman; Karwowski, WaldemarHuman socio-cultural behavior (HSCB) modeling has received much attention in attempts to successfully understand the effects of social and cultural factors on human behavior. The principal aim of HSCB modeling is to better organize and control the human terrain during nonconventional warfare and other missions. The intent of this study is to review and classify the literature based on the applications of different kinds of data for modeling HSCB and highlight the soft-computing methodologies suitable for HSCB event/incident data that have been applied successfully to crime data.Öğe Predicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection(Springer, 2017) Çakıt, Erman; Karwowski, WaldemarThis study presents an adaptive neuro-fuzzy inference system (ANFIS) approach performed to estimate the number of adverse events where the dependent variables are adverse events leading to four types of variables: number of people killed, wounded, hijacked and total number of adverse events. Fourteen infrastructure development projects were selected based on allocated budgets values at different time periods, population density, and previous month adverse event numbers selected as independent variables. Firstly, number of independent variables was reduced by using ANFIS input selection approach. Then, several ANFIS models were performed and investigated for Afghanistan and the whole country divided into seven regions for analysis purposes. Performances of models were assessed and compared based on the mean absolute errors. The difference between observed and estimated value was also calculated within range with values around 90 %. We included multiple linear regression (MLR) model results to assess the predictive power of the ANFIS approach, in comparison to a traditional statistical approach. When the model accuracy was calculated according to the performance metrics, ANFIS showed greater predictive accuracy than MLR analysis, as indicated by experimental results. As a result of this study, we conclude that ANFIS is able to estimate the occurrence of adverse events according to economical infrastructure development project data.Öğe Understanding patterns of infrastructure development in the active war theater of Afghanistan over the period 2002-2010(ELSEVIER SCIENCE BV, 2015) Çakıt, Erman; Karwowski, Waldemar; Ahram, T; Karwowski, W; Schmorrow, DThis study aims to review economic development throughout the war in Afghanistan by representing the total number of economic improvement projects and their respective total budgets within the active war theater of Afghanistan, over the period of 2002-2010. Data used for this study includes eleven infrastructure development projects that took place between 2002 and 2010 in Afghanistan. The total number of economic improvement projects, as well as the total budget of those projects, have been used for understanding patterns of infrastructure development in Afghanistan. The country was divided into seven regions for pattern analysis, where each region consists of various numbers of provinces, districts, and records. Based on the results obtained, it was concluded that the total number of economic improvement projects were at their lowest in 2002 and at their highest in 2007. When the regions were compared, the central region had the highest number of economic improvement projects, whereas the south-western region had the lowest number of economic improvement projects. Project-type analyses revealed that water supply and sanitation projects were most numerous, while agriculture projects were least numerous. Considering the total budget of economic improvement projects, the north-eastern region had the highest total budget ($177.5 million) and the south-eastern region had the lowest total budget ($95.6 million). The combined total budget of transport-type projects had the highest total value ($366.2 million) amongst project types, while agriculture projects had the lowest combined total budget ($2.6 million). Understanding these patterns may provide useful information about strategies and tactics to be employed for similar scenarios in an active war theater in the future. (Öğe Understanding the social and economic factors affecting adverse events in an active theater of war: A neural network approach(Springer, 2018) Çakıt, Erman; Karwowski, Waldemar; Hoffman, MThis 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.
