Hybrid Artificial Intelligence Method Research Articles

Multi-objective optimization of reservoir development strategy with hybrid artificial intelligence method

Optimization of subsurface hydrocarbon production holds paramount importance for decision-makers as it determines crucial development strategies such as optimal well placement and well control parameters (e.g. injection/production rates of injectors and producers). Despite the availability of numerous established optimization methods in this field, traditional reservoir production optimization methods face challenges in simultaneously addressing multiple development objectives and coordinating the interaction of well control parameters in different control steps. In this work, we construct a hybrid artificial intelligence method to jointly optimize well placement and well control parameters, taking into account two development objectives and dynamic optimization. It consists of two stages. First, a reservoir potential map is generated with the production potential formula that considers reservoir pressure, remaining oil saturation and other reservoir properties (permeability, hydrocarbon column height) etc. The reservoir potential map provides guidance for placing well in medium to high potential areas and engineering constraints for the optimization process. Then, a hybrid artificial intelligence method that couples deep learning method (Long and Short Term Memory (LSTM)) and multi-objective optimization algorithm (Non-dominated Sorting Genetic Algorithm II (NSGA-II)) is established to seek a compromise between the two objectives in water-flooding processes. The LSTM neural network is trained as the surrogate model to replace the high-fidelity simulator to achieve high efficiency of overall optimization workflow. The NSGA-II algorithm is employed for handling the joint optimization problem of well placement and well control parameters by maximizing the cumulative oil production and minimizing the water cut. The performance of the proposed method is tested on one benchmark function and two reservoir models. On the 2D synthetic reservoir model the optimized scheme leads to a notable increase of 3 × 104 m3 in cumulative oil production, accompanied by 17 % reduction in water cut when contrasted with the base scheme. Similarly, within the 3D reservoir model, the optimized scheme results in a substantial enhancement, boosting cumulative oil production by 14 × 104 m3 and reducing water cut by 20% compared to the base scheme. Moreover, the proposed method surpasses alternative multi-objective optimization (MOO) algorithms by demonstrating 82% and 95% reduction in optimization time, respectively. The results demonstrate that this method can provide the optimal water-flooding strategies under the premise of different development objectives. The Pareto front (or optimal solutions) generated by the hybrid method offers a variety of diverse water-flooding strategies to assist subsurface engineers in making informed decisions.

Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods

Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods

Application of Artificial Intelligence in Oncology Nursing: A Scoping Review.

Artificial intelligence (AI) has been increasingly used in healthcare during the last decade, and recent applications in oncology nursing have shown great potential in improving care for patients with cancer. It is timely to comprehensively synthesize knowledge about the progress of AI technologies in oncology nursing. The aims of this study were to synthesize and evaluate the existing evidence of AI technologies applied in oncology nursing. A scoping review was conducted based on the methodological framework proposed by Arksey and O'Malley and later improved by the Joanna Briggs Institute. Six English databases and 3 Chinese databases were searched dating from January 2010 to November 2022. A total of 28 articles were included in this review-26 in English and 2 in Chinese. Half of the studies used a descriptive design (level VI). The most widely used AI technologies were hybrid AI methods (28.6%) and machine learning (25.0%), which were primarily used for risk identification/prediction (28.6%). Almost half of the studies (46.4%) explored developmental stages of AI technologies. Ethical concerns were rarely addressed. The applicability and prospect of AI in oncology nursing are promising, although there is a lack of evidence on the efficacy of these technologies in practice. More randomized controlled trials in real-life oncology nursing settings are still needed. This scoping review presents comprehensive findings for consideration of translation into practice and may provide guidance for future AI education, research, and clinical implementation in oncology nursing.

Diagnosis of Breast Cancer with Hybrid Artificial Intelligence Method

According to the data of 2020, it is seen that 1 of every eight cancers diagnosed worldwide and the 5th among cancers that cause death is breast cancer. Cancer can spread to different organs and reach an incurable stage in patients who are not diagnosed and treated at the right time. Therefore, reducing the time taken for breast cancer diagnosis and reducing mortality rates are of great importance for accurate and early diagnosis of the disease. This study aims to improve the accuracy of cancer detection by using various machine learning algorithms and methods for artificial intelligence-based breast cancer diagnosis. By using ultrasonography images taken from 780 people, image information processed with statistical parameters was extracted. Artificial intelligence-based breast cancer detection was performed by applying three different machine learning algorithms and the hybrid machine learning algorithm designed as a combination of these algorithms on the extracted data set. In this way, early detection of cancerous cells will be carried out without creating advanced risks for the individual, and treatment will be possible.

Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA).

In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R2), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R2, and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO2 fixation rate.

Study of the influence of Arabic mother tongue on the English language using a hybrid artificial intelligence method

ABSTRACT Accent recognition refers to the problem of inferring the native language of a speaker from his foreign-accented speech. Differences in accent are due to both articulation and prosodic characteristics. The automatic identification of foreign accents is valuable for different speech systems, such as speech recognition, speaker identification or voice conversion. This paper aims to identify the native languages of non-native English speakers from different countries in the Arabic region: the researchers choose Saudi Arabia to represent the eastern Arabic region in Asia, Egypt to represent the eastern Arabic region in Africa and Tunisia to represent the western Arabic region in Africa. In this research, reinforcement learning (RL), a sub-branch of machine learning, and artificial neural network will be used as an intelligent method to classify and predict the speech. The aim is to train a neural network to automatically detect speech accents. Then the researchers develop a hybrid multi-agent RL algorithm that takes advantage from the multi-agent communication and cooperative agents on the language detection process. Hence, the aim is to help sociolinguists and discourse analysts. As for the Saudi context, this study will be very useful on resolving e-learning issues such as linguistic problems of students.

EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions

Abstract Reliable river streamflow (RSF) forecasting is an important issue due to its impact on planning and operation of the water resources system. In this study, based on Lower Upper Bound Estimation (LUBE), hybrid artificial intelligence methods were used for point and interval prediction of monthly RSF. Two states based on stations' own data and upstream stations' data were considered for RSF modeling of the Housatonic River during the period of 1941–2018. Ensemble Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD) methods were used for enhancing the streamflow point forecasting accuracy. Interval Prediction (PI) was applied for tolerating increased uncertainty. Results showed that in state 1, the error criterion value for the superior model decreased from 0.155 to 0.082 and 0.09 for the EEMD- and VMD-based models, respectively. Generally, hybrid models increased the modeling accuracy between 20% and 40%. Via the integrated approaches, the upstream stations' data was successfully used for streamflow prediction of stations without data. In this state, the PI coverage probability values for the VMD-based model were approximately 12% higher than the single model. Generally, the VMD-based model led to more desirable results due to having higher PI coverage probability and lower mean PI width values.

Groundwater Potential Mapping Using GIS-Based Hybrid Artificial Intelligence Methods.

Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC=0.742) model outperformed RF-FT (AUC=0.736), BA-FT (AUC=0.714), and single FT (AUC=0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.

Using a hybrid artificial intelligence method for estimating the compressive strength of recycled aggregate self-compacting concrete

Nowadays, because of human activities, the earth's environment is in danger. So, all industries including construction and building industries should be concerned about the environment, and their activities have to be in this direction. Using waste materials in construction is one of the solutions for decreasing environmental damage, for example; using recycled aggregate in various types of concrete including self-compacting concrete (SCC). Moreover, the usage of a non-destructive method for determining the mechanical properties of concrete can improve the environmental situation. To do so, in this study the radial basis function neural network (RBFNN) assisted by firefly algorithm (FA) is proposed for predicting the compressive strength of recycled aggregate self-compacting concrete (RASCC). In these regards, the information of 310 samples of RASCC has been collected from previous studies. In this model, the water to binder ratio, age, the ratio of recycled coarse aggregate, coarse aggregate, fine aggregate, and superplasticizer have been considered as input variables, and compressive strength as the output variable. Also, an ANN model has been utilized to conduct comparisons. The performance of the models has been evaluated based on statistical parameters, and by comparing the results of the model with experimental results. The values of statistical parameters of RBFNN model (for all data: R-values:0.97 & RMSE:3.3) show that the correlation between results of the model and experimental results are high and the error of the model’s results are acceptable. Moreover, results of the models indicate that both models have good ability and acceptable accuracy for predicting the compressive strength of RASCC.

Evolutionary optimization of neural network to predict sediment transport without sedimentation

Sedimentation in open channels occurs frequently and is relative to system inflow. The long-term retention of sediments on channel beds can increase the possibility of variations in deposits and their eventual consolidation. This study compares three hybrid artificial intelligence methods in estimating sediment transport without sedimentation (STWS). We employed the Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) methods in combination with the Artificial Neural Network (ANN) to overcome the weakness of ANN training with conventional algorithms. We used the ICA, GA and PSO methods to optimize the weights of the ANN layers. Using dimensional analysis, we placed the effective parameters in predicting sediment transport into five non-dimensional groups. Six models are proposed and run using three hybrid methods (18 models in total). As the comparisons demonstrate, the proposed combined models are more accurate than ANN and existing equations in estimating the densimetric Froude number (Fr). However, we found the ICA–ANN superior to GA–ANN and PSO–ANN, as it produces explicit solutions to the problem. The ICA–ANN has the lowest prediction uncertainty band for Fr of all developed models. Moreover, the variation trend of the Fr for all input variables (except overall friction factor of sediment) is a second-order polynomial.

Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study.

Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)-non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R2 had superb performance in comparison with all other individual models. According to DF-NSGAII, the highest ER and SEN can be obtained from the medium containing 14.27mM NH4+, 38.92mM NO3-, 22.79mMK+, 5.08mM Cl-, 3.34mM Ca2+, 1.67mMMg2+, 2.17mM SO42-, and 1.44mM H2PO4-. Based on the DF-GA model, the maximum SEN can be obtained from a medium containing 0.61μM thiamine, 5.93μM nicotinic acid, 0.25μM biotin, and 0.26μM riboflavin. The efficiency of the established-optimized medium was experimentally compared to Murashige and Skoog medium (MS) for embryogenesis of five chrysanthemum cultivars, and results indicated the efficiency of optimized medium over MS medium.Key points• MLP, SVR, and ANFIS were fused by a bagging method to develop a data fusion model.• NSGA-II and GA were linked to the data fusion model for establishing and optimizing a new embryogenesis medium.• The new culture medium (HNT) had better efficiency than MS medium.

Smart frost measurement for anti-disaster intelligent control in greenhouses via embedding IoT and hybrid AI methods

Abstract A novel Agro-industrial IoT (AIIoT) technology and architecture for intelligent frost forecasting in greenhouses via hybrid Artificial Intelligence (AI), is reported. The Internet of Things (IoT) allows the objects interconnection on the physical world using sensors and actuators via the Internet. The smart system was designed and implemented through a climatological station equipped with Artificial Neural Networks (ANN) and a fuzzy associative memory (FAM) for ecological control of the anti-frost disaster irrigation. The ANN forecasts the inside temperature of the greenhouses and the fuzzy control predicts the cropland temperatures for the activation of five output levels of the water pump. The results were compared to a Fourier-statistical analysis of hourly data, showing that the ANN models provide a temperature prediction with effectiveness higher than 90%, as compared to monthly data model. Moreover, results of this process were validated through the determination of the coefficient of variance analysis method ( R 2 ).

Hybridizing the Machine Learning Techniques for Prediction of Sediment Yield

Rivers are an integral part of the hydrologic cycle and are the major dynamic geologic agents that play major role for transformation of sediments from land to the oceans. Sedimentation is the biggest problem. Evaluation of suspended sediment yield is an essential parameter under the assessment on Dam filling, protecting of aquatic organism and wildlife habitats, understanding the flood capacity and hydroelectric equipment in hydro-electric power. The assurance of sediment yield through different traditional way isn’t exactly correct because of the participation of different complex processes.There are many limitations of traditional methods but it can be overcome by artificial intelligence techniques. So, in this study, the MOGA-ANN (Multi-objective genetic algorithm based artificial neural network) hybrid artificial intelligence method is used to estimate the sediment yield in Krishna river basin, India. The research done for evaluation of the suspended sediment load by taking 20 years of data from Vijayawada, gauging station which is the downward station in Krishna river. The proposed MOGA-ANN model provided low root mean square error (0.03354) and high correlation coefficient (0.9214) during test phase. It exhibited satisfactory performance.

Neural network modeling based double-population chaotic accelerated particle swarm optimization and diffusion theory for solubility prediction

Solubility is as a key chemical and physical property. Solubility prediction methods are applied in diverse fields including preparation synthesis and modifications of materials. To overcome the shortcomings of existing solubility prediction methods, taking the mass transfer of two-phase system as an example, a solubility prediction model based on the diffusion theory and hybrid artificial intelligence method was proposed in this paper. An improved double-population chaotic accelerated particle swarm optimization (APSO) algorithm combined diffusion theory was developed according to the particle evolution utilizing diffusion energy. The developed algorithm was applied in the training of parameters of the radial basis function artificial neural network and then a model for predicting solubility was developed. The experimental results of supercritical carbon dioxide solubility in 8 polymers were consistent with the predicted values by the model, indicating the high prediction accuracy. The average relative deviation, squared correlation coefficient, and root mean square error were respectively 0.0036, 0.9970, and 0.0152, displaying its higher comprehensive performance. The model may also be applied in other physicochemical fields.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

ABSTRACTThe main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the RF-NBT model. The spatial database consisted of eighteen conditioning factors that were selected using the information gain ratio (IGR) method. The model was evaluated using quantitative statistical criteria, including the sensitivity, specificity, accuracy, root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUC). Furthermore, the new model was compared with the NBT, functional tree (FT), logistic model tree (LMT) and reduced-error pruning tree (REPTree) soft computing benchmark models. The findings indicated that the RF-NBT model showed an increased prediction accuracy relative to the NBT model using both the training and validation datasets, and the RF-NBT model exhibited a greater capability for landslide susceptibility mapping. The new RF-NBT model also showed the most preferable results compared with the FT, LMT and REPTree models. Finally, an analysis of the landslide density (LD) using the RF-NBT model demonstrated that the very high susceptibility (VHS) class had the highest LD (3.552) among the landslide susceptibility maps. These results can be used for the planning and management of areas vulnerable to landslides in order to prevent damages caused by such natural disasters.

Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory

A solubility prediction model based on a hybrid artificial intelligence method integrated with diffusion theory is proposed.

Measurement and optimization of atmospheric plasma sprayed CoMoCrSi coatings parameters on Ti-6Al-4V substrates affecting microstructural and properties using hybrid abductor induction mechanism

Abstract A hybrid artificial intelligence method (AIM) is used to optimize the atmospheric plasma spraying (APS) processing parameters for CoMoCrSi coatings deposited on Ti-6Al-4V substrates. In the proposed approach, the Taguchi design method is used to obtain an initial solution for the optimal set of APS parameters. The true optimal values of the spray distance, chamber pressure, current, argon gas flow rate and hydrogen gas flow rate are then obtained using an artificial neural network (ANN) and genetic algorithm (GA). The coatings deposited using the processing parameters determined using the Taguchi design method alone have a porosity of 8.5%. However, following the ANN/GA optimization procedure, the porosity reduces to just 5.6%. The XRD analysis results show that the as-sprayed coatings contain Cr 3 Si phase as a result of the high-temperature plasma flame. In addition, it is shown that the structure of the Ti phase in the coating transforms from a α-Ti crystal structure (i.e., HCP) to a β-Ti crystal structure (i.e., BCC) during thermal treatment at temperatures to 1200 °C. Finally, it is shown that the hardness of the annealed coatings increases with both an increasing annealing time and an increasing annealing temperature. The higher hardness under higher temperature conditions is attributed to the precipitation of Cr 3 Si phase. Overall, the coatings obtained in the present study using the optimal spray-coating parameters have a low porosity, a high hardness and a good thermal stability at high temperatures.

Developing energy forecasting model using hybrid artificial intelligence method

An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder

Classification of psychiatric disorders is becoming one of the major focuses of research using artificial intelligence approach. The combination of feature selection and classification methods generates satisfactory outcomes using biological biomarkers. The use of quantitative electroencephalography (EEG) cordance has enhanced the clinical utility of the EEG in psychiatric and neurological subjects. Trichotillomania (TTM), a kind of body focused repetitive behavior, is defined as a disorder characterized by repetitive hair pulling that results in noticeable hair loss. Phenomenological observations underline similarities between hair-pulling behaviors and compulsions seen in obsessive-compulsive disorder (OCD). Despite the recognized similarities between OCD and TTM, there is evidence of important differences between these two disorders. In order to dichotomize the subjects of each disorder, artificial intelligence approach was employed using quantitative EEG (QEEG) cordance values with 19 electrodes from 10 brain regions (prefrontal, frontocentral, central, left temporal, right temporal, left parietal, occipital, midline, left frontal and right frontal) in 4 frequency bands (delta, theta, alpha and beta). Machine learning methods, artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (k-NN) and Naïve Bayes (NB), were used in order to classify 39 TTM and 40 OCD patients. SVM, with its relatively better performance, was then combined with an improved ant colony optimization (IACO) approach in order to select more informative features with less iterations. The noteworthy performance of the hybrid approach underline that it is possible to discriminate OCD and TTM subjects with 81.04% overall accuracy.