Hybrid Computational Intelligence Approach Research Articles

A hybrid computational intelligence approach for bioremediation of amoxicillin based on fungus activities from soil resources and aflatoxin B1 controls

Nowadays, releasing the Emerging Pollutants (EPs) in the nature is one of the main reasons for many health and environmental disasters. Amoxicillin as an antibiotic is one of the EPs and categorized as the Endocrine Disrupting Compounds (EDCs) in hazardous materials. Accumulation of amoxicillin in the soil bulk increases the cancer risk, drug resistances and other epidemiological diseases. Hence, the soil bioremediation of antibiotics can be a solution for this problem which is more environmental-friendly system. This study technically creates a bio-engine setup in soil bulk for remediation of amoxicillin based on Aspergillus Flavus (AF) activities and Removal Percentage (RP) of amoxicillin with Aflatoxin B1 Generation (AG) controls. The main novelty is to propose a hybrid computational intelligence approach to do optimization for mechanical and biological aspects and to predict the behavior of bio-engine's effective mechanical and biological features in an intelligent way. The optimization model is formulated by the Central Composite Design (CCD) which is set by the Response Surface Methodology (RSM). The prediction model is formulated by the Random Forest (RF), Adaptive Neuro Fuzzy Inference System (ANFIS) and Random Tree (RT) algorithms. According to the experimental practices from real soil samples in different times and places, concentration of amoxicillin and Aflatoxin B1 are set equal to 25 mg/L (ppm) and 15 μg/L (ppb). Likewise, the outcomes of experiments in CCD-RSM computations are evaluated by curve fitting comparisons between linear, 2FI, quadratic and cubic polynomial equations with considering to regression coefficient and predicted regression coefficient values, ANOVA and optimization by sequential differentiation. Based on the results of CCD-RSM, the RP performance in the optimum conditions is measured around 86% and in 25 days after runtime, the RP and AG are balanced in the safe mode. The proposed hybrid model achieves the 0.99 accuracy. The applicability of the research is done using real field evaluations from drug industrial park in Mashhad city in Iran. Finally, a broad analysis is done and managerial insights are concluded. The main findings of the present research are: (I) with application of bioremediation from fungus activities, amoxicillin amounts can be control in soil resources with minimum AG, (II) ANFIS model has the best accuracy for smart monitoring of amoxicillin bioremediation in soil environments and (III) based on the statistical assessments Aeration Intensity and AF/Biological Waste ratio are most effective on the amoxicillin removal percentage.

A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks

Finite element (FE) based structural health monitoring (SHM) algorithms seek to update structural damage indices through solving an optimisation problem in which the difference between the response of the real structure and a corresponding FE model to some excitation force is minimised. These techniques, therefore, exploit advanced optimisation algorithms to alleviate errors stemming from the lack of information or the use of highly noisy measured responses. This study proposes an effective approach for damage detection by using a recently developed novel swarm intelligence algorithm, i.e. the marine predator algorithm (MPA). In the proposed approach, optimal foraging strategy and marine memory are employed to improve the learning ability of feedforward neural networks. After training, the hybrid feedforward neural networks and marine predator algorithm, MPAFNN, produces the best combination of connection weights and biases. These weights and biases then are re-input to the networks for prediction. Firstly, the classification capability of the proposed algorithm is investigated in comparison with some well-known optimization algorithms such as particle swarm optimization (PSO), gravitational search algorithm (GSA), hybrid particle swarm optimization-gravitational search algorithm (PSOGSA), and grey wolf optimizer (GWO) via four classification benchmark problems. The superior and stable performance of MPAFNN proves its effectiveness. Then, the proposed method is applied for damage identification of three numerical models, i.e. a simply supported beam, a two-span continuous beam, and a laboratory free-free beam by using modal flexibility indices. The obtained results reveal the feasibility of the proposed approach in damage identification not only for different structures with single damage and multiple damage, but also considering noise effect.

Metaheuristic least squares support vector machine-based lateral strength modelling of reinforced concrete columns subjected to earthquake loads

Existing physics-based methods for estimating the lateral strength of reinforced concrete (RC) columns lack generalization capabilities, where the methods for predicting the shear strength of shear- and flexure-shear-critical columns cannot be used to predict the flexural strength of flexure-critical columns, and vice versa. This paper proposes a nature-inspired, metaheuristic, least squares-support vector machine for regression (LS-SVMR) model for enhancing the generalization of lateral strength prediction of RC columns. The metaheuristic LS-SVMR model is a hybrid computational intelligence approach, which integrates LS-SVMR to elucidate the nonlinear mapping between influential factors and the lateral strength with a nature-inspired metaheuristic optimization algorithm, called coupled simulated annealing (CSA), to efficiently tune the hyper-parameters and facilitate the training process. The generalization performance is validated by comparison with several machine learning (ML) models using cross-validation strategies and with commonly used physics-based approaches as well as by successfully modeling the physical processes associated with variation of lateral strength of flexure-, shear-, and flexure-shear-critical columns when input predictors vary.

Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach

Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density prediction model of landslide displacement and quantify the associated predictive uncertainties. The hybrid computational intelligence approach consists of two steps: first, the input variables are selected through copula analysis; second, kernel-based support vector machine quantile regression (KSVMQR) is employed to perform density prediction. The copula-KSVMQR approach is demonstrated through a complex landslide in the Three Gorges Reservoir Area (TGRA), China. The experimental study suggests that the copula-KSVMQR approach is capable of construction density prediction by providing full probability density distributions of the prediction with perfect performance. In addition, different types of predictions, including interval prediction and point prediction, can be derived from the obtained density predictions with excellent performance. The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. Given the satisfactory performance, the presented copula-KSVMQR approach shows a great ability to predict landslide displacement.

A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.

A hybrid computational intelligence approach to predict spectral acceleration

In this study, a new explicit method has been suggested to predict the spectral acceleration characteristic of strong ground-motions based on hybridizing genetic algorithm (GA), multilayer perceptron neural network (MLPNN), and regression analysis (RA), called GA-NN-RA. The predictor variables encompass a period of vibration, magnitude, closest distance co-seismic rupture, shear wave velocity averaged over the top 30 m and flag for reverse faulting earthquakes. To develop the model, a data set of strong ground-motion records gathered by Pacific Earthquake Engineering Research Center has been employed. For confirmation and efficiency of proposed model, an additional set of test that is not involved in the modeling has been applied. The obtained results using GA-NN-RA show good accuracy in comparison with other ground motion models. Also, the proposed model is capable of evaluating the spectral acceleration for any records without restriction in a period of vibration.

A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)

This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO–SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO–SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination (R2) = 0.888. Hence, the PSO–SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects.

Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases

During the last few decades, climate change has increased air pollutant concentrations with a direct and serious effect on population health in urban areas. This research introduces a hybrid computational intelligence approach, employing unsupervised machine learning (UML), in an effort to model the impact of extreme air pollutants on cardiovascular and respiratory diseases of citizens. The system is entitled Air Pollution Climate Change Cardiovascular and Respiratory (APCCCR) and it combines the fuzzy chi square test (FUCS) with the UML self organizing maps algorithm. A major innovation of the system is the determination of the direct impact of air pollution (or of the indirect impact of climate change) to the health of the people, in a comprehensive manner with the use of fuzzy linguistics. The system has been applied and tested thoroughly with spatiotemporal data for the Thessaloniki urban area for the period 2004-2013.

A hybrid computational intelligence framework in modelling of coal-oil agglomeration phenomenon

The phenomenon of Coal–Oil agglomeration for recovering the coal fines by agitating the coal-water slurries in oil is often practised by coal industry to ensure a safe and healthy environment. Experimental procedure for implementing this phenomenon is complex which involves three main mechanisms: crushing, ultimate and proximate analysis. Past studies have often focused on studying this phenomenon by the application of statistical modelling based on response surface designs. The response surface designs hold an assumption of pre-definition of the model structure, which may introduce uncertainty in the predictive ability of the model. Alternatively, the computational intelligence approach of Genetic programming (GP) that evolves the explicit models automatically can be used. However, the effective functioning of GP is often affected by its nature of producing the models of complex size. Therefore, this work develops a hybrid computational intelligence approach namely, Support vector regression-GP (SVR-GP) to study the coal-oil agglomeration phenomenon. Experimental studies based on five inputs, namely, oil dosage, agitation speed, agglomeration time, temperature, and pH are used to measure the organic matter recovery (OMR (%)) from the coal water slurries. A hybrid computational intelligence approach of SVR-GP is proposed in formulating the relationship between OMR (%) and the five inputs. The performance comparison and validation of the SVR-GP model is done based on the coefficient of determination, root mean square error and mean absolute percentage error. 2-D and 3-D surface analysis based on parametric and sensitivity approach is then conducted on the proposed model to find the relevant relationships between OMR (%) and inputs. The findings suggest that the pH of coal slurry has a significant effect on the OMR (%) and hence is important for reducing coal waste generation and promoting a cleaner environment.

MODELING RESERVOIR WATER RELEASE DECISION USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Reservoir water release decision is one of the critical actions in determining the quantity of water to be retained or released from the reservoir. Typically, the decision is influenced by the reservoir inflow that can be estimated based on the rainfall recorded at the reservoir’s upstream areas. Since the rainfall is recorded at several different locations, the use of temporal pattern alone may not be appropriate. Hence, in this study a spatial temporal pattern was used to retain the spatial information of the rainfall’s location. In addition, rainfall recorded at different locations may cause fuzziness in the data representation. Therefore, a hybrid computational intelligence approach, namely the Adaptive Neuro Fuzzy Inference System (ANFIS), was used to develop a reservoir water release decision model. ANFIS integrates both the neural network and fuzzy logic principles in order to deal with the fuzziness and complexity of the spatial temporal pattern of rainfall. In this study, the Timah Tasoh reservoir and rainfall from five upstream gauging stations were used as a case study. Two ANFIS models were developed and their performances were compared based on the lowest square error achieved from the simulation conducted. Both models utilized the spatial temporal pattern of the rainfall as input. The first model considered the current reservoir water level as an additional input, while the second model retained the existing input. The result indicated that the application of ANFIS could be used successfully for modeling reservoir water release decision. The first model with the additional input showed better performance with the lowest square error compared to the second model.

Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming

Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

Breast tumor malignancy modelling using evolutionary neural logic networks

The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff. Data were properly encoded in a computer database and accordingly, various alternative modelling techniques were applied on them, in an attempt to form diagnostic models. Previous methods included standard optimisation techniques, as well as artificial intelligence approaches, in a way that a variety of related publications exists in modern literature on the subject. In this report, a hybrid computational intelligence approach is suggested, which effectively combines modern mathematical logic principles, neural computation and genetic programming in an effective manner. The approach proves promising either in terms of diagnostic accuracy and generalization capabilities, or in terms of comprehensibility and practical importance for the related medical staff.