• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1779-1786
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• 2014

Artificial Intelligence-based techniques have been applied to problems where mathematical relations can not be presented due to complicatedness of the physical process. A representative example in hydraulics is the local scour around bridge piers. This study presents a GEP model for predicting the local scour around bridge piers. The model is trained by 64 laboratory data to build the regression equation, and the constructed model is verified against 33 laboratory data. Comparisons between the models with dimensional and normalized variables reveals that the GEP model with dimensional variables predicts better. The proposed model is now applied to two field datasets. It is found that the MAPE of the scour depths predicted by the GEP model increases compared with the predictions of local scours in laboratory scale. In addition, the model performance increases significantly when the model is trained by the field dataset rather than the laboratory dataset. The findings suggest that apart from the ANN model, GEP model is a sound and reliable model for predicting local scour depth.

• Computers and Concrete
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• v.17 no.4
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• pp.489-498
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• 2016

In this paper, the flexural strength ($f_{fs}$) and splitting tensile strength ($f_{sts}$) of concrete containing different proportions of fly ash have been modeled by using gene expression programming (GEP). Two GEP models called GEP-I and GEP-II are constituted to predict the $f_{fs}$ and $f_{sts}$ values, respectively. In these models, the age of specimen, cement, water, sand, aggregate, superplasticizer and fly ash are used as independent input parameters. GEP-I model is constructed by 292 experimental data and trisected into 170, 86 and 36 data for training, testing and validating sets, respectively. Similarly, GEP-II model is constructed by 278 experimental data and trisected into 142, 70 and 66 data for training, testing and validating sets, respectively. The experimental data used in the validating set of these models are independent from the training and testing sets. The results of the statistical parameters obtained from the models indicate that the proposed empirical models have good prediction and generalization capability.

• Computers and Concrete
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• v.19 no.6
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• pp.717-724
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• 2017

In this paper, four formulas are proposed via gene expression programming (GEP)-based models and regression analysis (RA) to predict the flexural strength ($f_s$) values of mortars containing different mineral admixtures that are ground granulated blast-furnace slag (GGBFS), silica fume (SF) and fly ash (FA) at different ages. Three formulas obtained from the GEP-I, GEP-II and GEP-III models are constituted to predict the $f_s$ values from the age of specimen, water-binder ratio and compressive strength. Besides, one formula obtained from the RA is constituted to predict the $f_s$ values from the compressive strength. To achieve these formulas in the GEP and RA models, 972 data of the experimental studies presented with mortar mixtures were gathered from the literatures. 734 data of the experimental studies are divided without pre-planned for these formulas achieved from the training and testing sets of GEP and RA models. Beside, these formulas are validated with 238 data of experimental studies un-employed in training and testing sets. The $f_s$ results obtained from the training, testing and validation sets of these formulas are compared with the results obtained from the experimental studies and the formulas given in the literature for concrete. These comparisons show that the results of the formulas obtained from the GEP and RA models appear to well compatible with the experimental results and find to be very credible according to the results of other formulas.

• Structural Engineering and Mechanics
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• v.47 no.3
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• pp.401-419
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• 2013

In this study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the effects of infill walls on base reactions and roof drift of reinforced concrete frames were investigated. Current standards generally consider weight and fundamental period of structures in predicting base reactions and roof drift of structures by neglecting numbers of floors, bays, shear walls and infilled bays. Number of stories, number of bays in x and y directions, ratio of shear wall areas to the floor area, ratio of bays with infilled walls to total number bays and existence of open story were selected as parameters in GEP and ANFIS modeling. GEP and ANFIS have been widely used as alternative approaches to model complex systems. The effects of these parameters on base reactions and roof drift of RC frames were studied using 3D finite element method on 216 building models. Results obtained from 3D FEM models were used to in training and testing ANFIS and GEP models. In ANFIS and GEP models, number of floors, number of bays, ratio of shear walls and ratio of infilled bays were selected as input parameters, and base reactions and roof drifts were selected as output parameters. Results showed that the ANFIS and GEP models are capable of accurately predicting the base reactions and roof drifts of RC frames used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9367-9373
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• 2014

In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are requentlyused lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2679-2681
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• 2016

Background: Gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NEN) are relatively rare tumors, not equally distributed in gastro-intestinal system. In 2010, a revised version of the WHO classification of GEP-NENs was published. This study reports for the first time the distribution and characteristics of GEP-NEN in a Lebanese population. Materials and Methods: This descriptive retrospective study concerns all the digestive neuroendocrine tumors with their characteristics diagnosed in $H\hat{o}tel$ Dieu de France in Beirut, Lebanon from 2001 to 2012, all the pathology reports being reanalyzed according to the latest WHO 2010 classification. The characteristics and features of GEP-NEN analyzed in this study were age, gender, grade and site. Results: A total of 89 GEP-NENs were diagnosed, representing 28.2% of all neuroendocrine tumors. The mean age of GEP-NEN patients was 58.7 years and the M/F sex ratio was 1.2. The primary localization was as follows: 21.3%(19) pancreatic, 18% (16) gastric, 15.7% (14) duodenal, 11.2% (10) appendix, 10.1% (9) intestinal, 10.1% (9) colorectal (7.9% colonic and 2.2% rectal), 5.6% (4) hepatic, 2.2% (2) ampulla, 1.1% (1) esophageal and 7.9%(5) NOS digestive (metastatic with unknown primary). Of the 89 patients with GEP-NEN, 56.2% (50) were diagnosed as grade I, 11.2% (10) as grade II, 20.2% (18) as grade III and 12.4% (11) were considered as mixed adeno-neuroendocrine carcinomas (MANEC). Conclusions: This study, one of the rare examples based on the 2010 WHO classification of neuroendocrine tumors in the literature, indicates that in the Lebanese population, all duodenal and appendicular tumors are G1 and the majority of MANEC tumors are gastric and pancreatic tumors. Moreover, more duodenal tumors and fewer rectal tumors were encountered in our study compared to European reports.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.686-693
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• 2013

Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Opposition-based Differential Evolution (ODE) algorithms have been applied to the GEP problem. The original GEP problem has been modified by incorporating Virtual Mapping Procedure (VMP). The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units have been considered. The results have been compared with Dynamic Programming (DP) method. The ODE performs well and converges faster than DE.

• Food Science of Animal Resources
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• v.38 no.6
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• pp.1160-1167
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• 2018

The objective of this study was to evaluate the composition, pH, titratable activity, microbial properties, and antioxidant effect of yogurt using ginseng extract powder (GEP), Lactobacillus plantarum NK181, and Streptococcus thermophilus as the starter culture. Different concentration of GEP (0%, 0.5%, 1%, 1.5%, and 2% (w/v)) were used in the yogurt. During yogurt fermentation, pH was decreased; however, titratable acidity and viable cell counts were increased. The addition of GEP to yogurt led to a decrease in moisture content and an increase in the fat, ash, and total solids content. The antioxidant effect using 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging, ${\beta}$-carotene bleaching, and ferric reducing antioxidant power (FRAP) assay gradually increased with added GEP. Overall, yogurt fermented with 1% GEP was acceptable in terms of cell viability and antioxidant effect. These results might provide information regarding development of ginseng dairy products with enhanced antioxidant activities and probiotic properties.

• Computers and Concrete
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• v.32 no.1
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• pp.1-13
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• 2023

This paper discusses a framework for predicting the flexural strength of prestressed and non-prestressed FRP reinforced T-shaped concrete beams using soft computing techniques. An analysis of 83 tests performed on T-beams of varying widths has been conducted for this purpose with different widths of compressive face, beam depth, compressive strength of concrete, area of prestressed and non-prestressed FRP bars, elasticity modulus of prestressed and non-prestressed FRP bars, and the ultimate tensile strength of prestressed and non-prestressed FRP bars. By analyzing the data using two soft computing techniques, named artificial neural networks (ANN) and gene expression programming (GEP), the fundamental parameters affecting the flexural performance of prestressed and non-prestressed FRP reinforced T-shaped beams were identified. The results showed that although the proposed ANN model outperformed the GEP model with higher values of R and lower error values, the closed-form equation of the GEP model can provide a simple way to predict the effect of input parameters on flexural strength as the output. The sensitivity analysis results revealed the most influential input parameters in ANN and GEP models are respectively the beam depth and elasticity modulus of FRP bars.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.45-51
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• 2007

본 연구는 공수방식이나 기능점수(Funtion Point)방식에 의존해 온 기존의 게임콘텐츠 원가산정 방식의 문제점을 인식하고 이를 대체할 새로운 게임요소점수(Game Element Point) 접근방식 하에서 구체적인 원가산정 방법론을 개발 제시하였다. 게임콘텐츠 원가산정 모델은 게임콘텐츠에 고유한 게임요소를 도출하여 이를 게임요소점수(GEP; Game Element Point)로 환산하고 여기에 게임요소당 단가를 곱하여 원가를 산정하는 형태를 가지고 있다. 또한 제시된 방법론의 실무적용 가능성을 타진하기 위해 게임 개발업체들의 실제원가 자료를 입수하여 실제 개발원가와 GEP방식으로 계산된 원가를 비교 검증하였다. 탐색적 수준의 사례 비교를 통한 검증결과 GEP방식의 적용이 가능하고 또한 기존방식을 대체할 좀 더 실무적이고 정확한 게임 컨텐츠 원가산정 방식으로 사용할 수 있는 것으로 판명되었다.