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

Çakıt, Erman; Karwowski, Waldemar; Bozkurt, Halil; Ahram, Tareq Z.; Thompson, William; Mikusiński, Piotr; Lee, Gene

doi:10.1016/j.asoc.2014.09.028

Cited by 20 publications

(12 citation statements)

References 45 publications

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“…On the other hand, FIS systems cannot learn but there is a rule‐based knowledge. Thus, ANFIS overcomes the disadvantages of both ANN and FIS systems (Çakıt et al., ).…”

Section: Methodsmentioning

confidence: 99%

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

Çakıt

Karwowski

2017

Hum Ftrs & Erg Mfg Svc

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This 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.

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“…On the other hand, FIS systems cannot learn but there is a rule‐based knowledge. Thus, ANFIS overcomes the disadvantages of both ANN and FIS systems (Çakıt et al., ).…”

Section: Methodsmentioning

confidence: 99%

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

Çakıt

Karwowski

2017

Hum Ftrs & Erg Mfg Svc

Self Cite

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“…Sensitivity analysis was performed based on trained ANNs and implemented as an approach to determine the cause and effect relationship between the independent and dependent variables [7]. In sensitivity analysis, a matrix of values was created containing information for each input/output combination computed as a percentage such that the sum of all sensitivity values for a particular output totals 100% [31].…”

Section: Sensitivity Analysismentioning

confidence: 99%

A Neural Network Approach for Assessing the Relationship Between Grip Strength and Hand Anthropometry

Çakıt¹,

Durgun²,

Cetik³

2015

NNW

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This study aimed to determine grip strength data for Turkish dentistry students and developed prediction models that allow: i) investigation of the relationship between grip strength and hand anthropometry using artificial neural networks (ANNs) and stepwise regression analysis, ii) prediction of the grip strength of Turkish dentistry students, and iii) assessment of the potential impact of hand anthropometric variables on grip strength. The study included 153 right-handed dentistry students, consisting of 81 males and 72 females. From 44 anthropometric and biomechanical measurements obtained from the right hands of the participants; five anthropometric measurements were selected for ANN and regression modeling using stepwise regression analysis. We included stepwise regression analysis results to assess the predictive power of the neural network approach, in comparison to a classical statistical approach. When the model accuracy was calculated based on the coefficient of determination (R 2), the root mean squared error (RMSE) and the mean absolute error (MAE) values for each of the models, ANN showed greater predictive accuracy than regression analysis, as demonstrated by experimental results. For the best performing ANN model, the testing values of the models correlated well with actual values, with a coefficient of determination (R 2) of 0.858. Using the best performing ANN model, sensitivity analysis was applied to determine the effects of hand dimensions on grip strength and to rank these dimensions in order of importance. The results suggest that the three most sensitive input variables are the forearm length, the hand breadth and the finger circumference at the first joint of digit 5 and that the ANNs are promising techniques for predicting hand grip strength based on hand breadth, finger breadth, hand length, finger circumference and forearm length.

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“…More recent studies used linear regression, neural networks, fuzzy inference systems (FISs), adaptive neuro-fuzzy inference systems (ANFISs), fuzzy overlay models, fuzzy C-means with subtractive clustering, and data streaming methods to predict and detect four types of events: The number of people killed, wounded, and hijacked, as well as other events based on infrastructure development spending and other variables in a war theater in Afghanistan [9,[31][32][33][34][35][36][37]. These four categories of events are collectively called "adverse events," which is the term that will be used throughout this paper.…”

Section: Introductionmentioning

confidence: 99%

“…A unique CA model that incorporates GIS and is capable of evaluating the impact of infrastructure development projects on adverse events, in terms of the location, time, and impact of these incidents, was developed. Using the developed cellular automata, the user can create "what-if" scenarios to forecast adverse events in comparison to previous models [9,[31][32][33][34][35][36][37]. The proposed cellular automata modeling approach is capable of identifying the location, time, and impact of future adverse events.…”

Section: Introductionmentioning

confidence: 99%

A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater

et al. 2019

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This study presents a cellular automata (CA) model to assist decision-makers in understanding the effects of infrastructure development projects on adverse events in an active war theater. The adverse events are caused by terrorist activities that primarily target the civilian population in countries such as Afghanistan. In the CA-based model, cells in the same neighborhood synchronously interact with one another to determine their next states, and small changes in iteration yield to complex formations of adverse event risks. The results demonstrate that the proposed model can help in the evaluation of infrastructure development projects in relation to changes in the reported adverse events, as well as in the identification of the geographical locations, times, and impacts of such developments. The results also show that infrastructure development projects have different impacts on the reported adverse events. The CA modeling approach can be used to support decision-makers in allocating infrastructure development funds to stabilize active war regions with higher adverse event risks. Such models can also improve the understanding of the complex interactions between infrastructure development projects and adverse events.

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

Abstract: false2016-03-16T23:02:09

Cited by 20 publications

References 45 publications

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

A Neural Network Approach for Assessing the Relationship Between Grip Strength and Hand Anthropometry

A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater

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