Hybrid Artificial Intelligence Approach Research Articles

A hybrid artificial intelligence approach for modeling the carbonation depth of sustainable concrete containing fly ash

One of the major challenges in the civil engineering sector is the durability of reinforced concrete structures against carbonation during the physico-chemical process of interaction of hydrated cementitious composites with carbon dioxide. This aggressive process causes carbon penetration into the reinforcement part, which affects the behavior of the structure during its lifetime due to corrosion risk. A countermeasure is using alternative cementitious materials to improve concrete texture and resist increased carbonation depth (CD). Considering that the CD test requires a long time and a skilled technician, this study strives to provide an alternative approach by moving from traditional laboratory-based methods towards artificial intelligence (AI) techniques for modeling the CD of sustainable concrete containing fly ash (CCFA). Despite the development of single AI models so far, it is undeniable that utilizing metaheuristic optimization techniques in the form of hybrid models can improve their performance. To this end, a new hybrid model from the integration of biogeography-based optimization (BBO) technique with artificial neural network (ANN) is developed for the first time to estimate the CD of CCFA. The error distribution results revealed that 59% of the ANN predictions had errors within the range of (− 1 mm, 1 mm], while the corresponding percentage for the ANN-BBO predictions was 70%, indicating an 11% reduction in the prediction errors by the proposed hybrid model. Furthermore, A10-index highlighted a performance improvement of 78% for the hybrid model, which met the closeness of the predicted values to the observed ones, so that the value of this index for models of ANN and ANN-BBO was 0.5019 and 0.8947, respectively. Analyzing the cross-validation confirmed the reliability and generalizability of the developed model. Also, the three most influential variables in estimating the CD were exposure time (27%), carbon dioxide concentration (22%), and water/binder (18%), respectively. Finally, the superiority of the ANN-BBO model was verified by comparing it with previous studies’ models.

Hybrid Artificial Intelligence Approach to COVID-19 Diagnosis from CT Images: Deep Networks and Classification Analysis

Using lung images obtained by computed tomography (CT), this study aims to detect coronavirus (Covid-19) disease with deep learning (DL) techniques. The study included 751 lung CT images from 118 Covid-19 patients and 628 lung CT images from 100 healthy individuals. In total, 70% of the 1379 images were used for training and 30% for testing. In the study, two different methods were proposed on the same dataset. In the first method, the images were trained on AlexNet, VGG-16, VGG-19, GoogleNet and a proposed network. The performance metrics obtained from the five networks were compared and it was observed that the proposed network achieved the highest accuracy value with 95.61%. In the second method, the images were trained on VGG-16, VGG-19, DenseNet-121, ResNet-50 and MobileNet networks. Among the image features obtained from each of these networks, the best 1000 features were selected by Principal Component Analysis (PCA). The best 1000 features were classified with Random Forest (RF) and Support Vector Machines (SVM). According to the classification results, the best 1000 features selected from the features extracted by the VGG-16 and MobileNet networks were obtained with the highest accuracy rate of 93.94% using SVM. It is thought that this study can be a helpful tool in the diagnosis of Covid-19 disease while reducing time and labor costs with the use of artificial intelligence (AI).

An Intelligent Network Method for Analyzing Corporate Consumer Repurchase Behavior Using Deep Learning Neural Networks

Earth system models (ESMs) are our key tools for analyzing the planet's existing state and predicting its evolution in the next continuing human-caused events. However, the use of artificial intelligence (AI) approaches to augment or even replace conventional ESM functions has expanded in recent years, raising hopes that AI will be able to overcome some of the major difficulties in climate research. We address the advantages and disadvantages of neural ESM neurons, as well as the unsolved question of whether AI will eventually replace ESMs. Dynamic geophysical events are the foundation of Earth and environmental studies. Given the widespread acceptance of AI and the growing amount of Earth data, the geoscientific community may wish to seriously explore using artificial intelligence (AI) approaches at a much deeper level. Although it is a tall ambition to integrate hybrid physics and AI approaches from a fresh perspective, geology has yet to figure out how to make such methods feasible. This research is an important step towards realising the concept of combining physics and artificial intelligence to address problems with the Earth's system.

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes.

Optimizing task scheduling in cloud computing: a hybrid artificial intelligence approach

Optimizing task scheduling in cloud computing: a hybrid artificial intelligence approach

An innovative word embedded and optimization based hybrid artificial intelligence approach for aspect-based sentiment analysis of app and cellphone reviews

An innovative word embedded and optimization based hybrid artificial intelligence approach for aspect-based sentiment analysis of app and cellphone reviews

Enhancing patient information performance in internet of things-based smart healthcare system: Hybrid artificial intelligence and optimization approaches

Utilizing cutting-edge technology in the healthcare industry could provide advanced methods for managing medical records for patients’ treatment. Integrating essential technologies like artificial intelligence (AI) can enhance healthcare services. The intersection of the Internet of Things (IoT) and AI presents various prospects within the healthcare industry. This research paper presents a novel model for enhancing the standard of treatment in smart healthcare systems (SHS) based on the convergence of AI and IoT. We investigate optimizing patient data processing performance in smart healthcare centers. The upgraded particle swarm optimization-long short-term memory (PSO-LSTM) algorithm is introduced to optimize the IoT-based SHS model. In order to perform a more suitable classification of the patient medical data, PSO is determined to be compared with PSO-LSTM to tune several metrics and benchmarks to achieve the highest value on patient data process performance. The proposed outcomes discovered that in terms of the test sets, the predicted values of patient health risks, and just six of them are varied, with an accuracy of 92.5%. This accuracy indicates the PSO-LSTM algorithm has more satisfactory performance and higher efficiency and provides a more secure, reliable, and improved overall patient satisfaction experience.

A Smart Diseases Diagnosis and Classification Strategy of Electronic Healthcare Application Using Novel Hybrid Artificial Intelligence Approaches

In today’s world, the healthcare industry faces difficulties like a scarcity of healthcare professionals, ageing, and rising healthcare costs. Also the classification and decision making process using the data generated via electronic health sensors is of major concern. In the fields of research and medical services, artificial intelligence (AI) is widely employed. However, correct estimate for various illnesses is a significant issue. The implementation of a new hybrid artificial intelligence (AI)-based classifier for helping prediction diagnosis in patients with chronic cancer conditions is examined in this work. Unknown qualities are predicted and given using the Hierarchical Red deer optimization (HRDO) based feature extraction, which is based on realworld cases. The Self-Systemized Generative Fuzzy Algorithm (SSGFA), which finds irregularities in patient data and predicts sickness, is used to create the hybrid classification design. This study’s simulation analysis included datasets for colon, lung, and brain cancer illnesses. The new combination of classifiers’ better performance resulted in total classification with increased accuracy, precision, recall, and F-measure, respectively. In terms of performance indicators, the suggested strategy is also compared to traditional methods. This demonstrates the suggested classification model’s ability to appropriately categorize various illnesses information for categorization.

A new hybrid Artificial Intelligence (AI) approach for hydro energy sites selection and integration

The increase of energy demand in this era leads exploration of new renewable energy sites. Renewable energy offers multiple benefits; hence it is suitable to be harnessed to meet power needs. In Sarawak, exploitation of hydro energy is a very feasible potential due to the abundant river flows and high rainfall volume. Thus, in this paper, 155 potential Hydro Energy Sites (HES) are identified and divided into six districts using a raw and unprocessed data provided by Sarawak Energy Berhad (SEB). Since there are no similar researches previously done for identification and integration of hydro energy sources, in this paper, two stage complex data management was built using 155 HES locations in Sarawak. New spatial mapping technique were used for the first stage. From the new spatial mapping technique, the mapped data were categorized into groups, analysed and created new accurate mapping locations on the Sarawak map in terms of the districts using GIS Spatial tools. Their exact geographical locations were identified, and their coordinate systems have been retrieved as complete final data with geo-referencing technique in QGIS with ID numbers. Moreover, the power capacity of each location of all the 155 HES was quantified. By employing this data, the identified locations have been integrated into the already created 155 HES sites. For the second stage, a new two-part AI hybrid approach has been proposed and applied to improve optimal transmission line routing for each district to locate transmission line paths. The first part of hybrid AI implemented in this paper was TSP-GA and second part implemented in this paper was based on improved fuzzy logic with TSP-GA together. To ensure the optimal results are reliably achieved, both first part of TSP-GA and second part of improved fuzzy TSP-GA are utilized to generate the transmission line routing. These two approaches are required to obtain the minimal values of total distance and total elevation difference of each HES. Based on the benchmarking results, fuzzy TSP-GA successfully improved 12.99% for Song district, 7.52% for Kapit district, 3.71% for Belaga district, 1.54% for Marudi district, 18.01% for Limbang district, 11.00% for Lawas district when comparing against the ordinary TSP-GA approach.

Modeling and optimization of phycoremediation of heavy metals from simulated ash pond water through robust hybrid artificial intelligence approach

AbstractIn thermal power plants, ash pond water is being polluted with heavy metals such as Lead (II), Nickel (II), Manganese (II), Cadmium (II), Chromium (VI), and so on, through leaching from coal ash and this can eventually contaminate the groundwater. In the present study, a consortium consisting of a cyanobacterium Synechococcus sp. and green algae Chlorella sp. was used for phycoremediation of such heavy metals from simulated ash pond water. The concentrations of metals, pH and days were varied during experimentation. An accurate data‐driven artificial neural network (ANN) model was developed with the experimental data to find a relation between the input variables of the phycoremediation process with the percentage removal of the pollutants and amount of biomass produced. The optimum ANN design and ANN algorithm were evaluated using an automatic exhaustive search technique. To maximize metal removal and biomass production, a hybrid artificial neural network‐simulated annealing (ANN‐SA) optimization technique was applied to determine optimal values of input parameters. The established modeling and optimization technique is generic and can be applied to any other experimental study.

Coal permeability alteration prediction during CO2 geological sequestration in coal seams: a novel hybrid artificial intelligence approach

Coal permeability alteration prediction during CO2 geological sequestration in coal seams: a novel hybrid artificial intelligence approach

A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System

Energy has become an integral part of our society and global economic development in the twenty-first century. Despite tremendous technological advancements, fossil fuels (coal, natural gas, and oil) continue to be the world’s primary source of energy. Global energy scenarios indicate a change in coal consumption trends in the future, which in turn will have commercial, geopolitical, and environmental consequences. We investigated coal consumption up to 2030 using a new hybrid method of WOANFIS (whale optimization algorithm and adaptive neuro-fuzzy inference system). The WOANFIS method’s performance was assessed by the MSE (Mean Squared Error), MAE (Mean Absolute Error), STD (error standard deviation), RMSE (Root Mean Squared Error), and coefficient of correlation (R2) among the real dataset and the WOANFIS result. For the prediction of global coal consumption, the proposed WOANFIS had the best MAE, RMSE, and correlation coefficient (R2) values, which were 0.00113, 0.0047, and 0.98, respectively. Lastly, future global coal consumption was predicted up to 2030 by WOANFIS. Following 150 years of coal dominance, the results demonstrate that WOANFIS is a suitable method for estimating worldwide coal consumption, which makes it possible to plan for the transition away from coal.

A Novel Parameters’ Identification Procedure for Aortic Walls Based on Hybrid Artificial Intelligence Approaches

It is of great significance to have research on the deformation characteristics and stress distribution of aortic wall. Reliable prediction of constitutive parameters requires an inverse process, which possesses challenges. This work proposes an inverse procedure to identify the constitutive parameters of aortic walls, which integrates nonlinear finite element method (FEM), random forest (RF) model and hybrid Random Search (RS) and Grid Search (GS) algorithm. FEM models are first established to simulate nonlinear deformation of aortic walls subjected to uniaxial tension tests. A dataset of nonlinear relationship between the engineering stress and main stretch of aortic walls is created using FEM models and the nonlinear relationship is learned through RF model. The hybrid RS&GS algorithms are used to adjust the major hyperparameters in RF. Then the optimized RF is utilized to predict constitutive parameters of aortic walls with the help of uniaxial tension tests. The prediction results show that the RF optimized by hybrid RS&GS (RF-RS&GS) approach is an effective and accurate approach to identify the constitutive parameters of aortic walls. The present RF-RS&GS model can be further extended for the predictions of constitutive parameters of other types of nonlinear soft materials. Additionally, the relative importance of constitutive parameters of aortic walls in Gasser–Ogden–Holzapfel (GOH) strain energy function is investigated. It is found that the parameters [Formula: see text] and [Formula: see text]in GOH are most intensive to the engineering stress of aortic walls.

A New Hybrid Artificial Intelligence (AI) Approach for Hydro Energy Sites Selection and Integration

A New Hybrid Artificial Intelligence (AI) Approach for Hydro Energy Sites Selection and Integration

A Hybrid Artificial Intelligence Approach to Portfolio Management

The tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These 4 steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.

Social media users and cybersecurity awareness: predicting self-disclosure using a hybrid artificial intelligence approach

PurposeThis study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information.Design/methodology/approachThis study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach.FindingsThe results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70.Practical implicationsThe findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.Originality/valueThis study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).

Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables

AbstractStreamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prime (WM5P) techniques, as two robust artificial intelligence (AI) models, were applied for forecasting the monthly streamflow in Khoshkroud and Polroud Rivers located in two basins with the same names. Results of hybrid AI techniques were compared with those achieved by two stand-alone models of GEP and M5P. Seven combinations of hydrological (H) and meteorological (M) variables were considered to investigate the effect of climatic variables on the performance of the proposed techniques. Moreover, the performance of both stand-alone and hybrid models were evaluated by statistical criteria of correlation of coefficient, root-mean-square error, index of agreement, the Nash–Sutcliffe model efficiency coefficient, and relative improvement. The statistical results revealed that there is a dependency between ‘the M5P and GEP performance’ and ‘the geometric properties of basins (e.g., area, shape, slope, and river network density)’. It was found that a preprocessed technique could increase the performance of M5P and GEP models. Compared to the stand-alone techniques, the hybrid AI models resulted in higher performance. For both basins, the performance of the WM5P model was higher than the WGEP model, especially for extreme events. Overall, the results demonstrated that the proposed hybrid AI approaches are reliable tools for forecasting the monthly streamflow, while the meteorological and hydrometric variables are taken into account.

Correction to: Using hybrid artificial intelligence approach based on a neuro‑fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran

Landslide susceptibility analysis is beneficial information for a wide range of applications, including land use management plans. The present attempt has shed light on an efficient landslide susceptibility mapping framework that involves an adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods including grey wolf optimization (GWO), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA) in the East Azerbaijan of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 766 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. In the next phase, three data mining techniques were united with the ANFIS model, comprising ANFIS-grey wolf algorithm (ANFIS-GW), ANFIS-particle swarm optimization (ANFIS-PSO), and ANFIS-shuffled frog leaping algorithm (ANFIS-SFLA), were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were utilized with the aim of validating and contrasting the predictive capability of the models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the ANFIS-PSO model had high performance on both the training (RMSE = 0.288, MAE = 0.069, AUC = 0.89) and validation dataset (RMSE = 0.309, MAE = 0.097, AUC = 0.89), after which, the ANFIS-GWO model and the ANFIS-SFLA model demonstrated the second and third rates. Besides, the study revealed that benefiting the optimization algorithm with the proper selection of the techniques could facilitate landslide susceptibility modeling.

Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Peak Shear Strength Along Soil-Geocomposite Drainage Layer Interfaces

Peak shear strength of soil-Geocomposite Drain Layer (GDL) interfaces is an important parameter in the designing and operating related engineering structures. In this paper, a database compiled from 316 large direct shear tests on soil-GDL interfaces has been established. Based on this database, five different machine learning models: Back Propagation Artificial Neural Network (BPANN) and Support Vector Machine (SVM), with hyperparameters optimised by Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA), respectively, and Extreme Learning Machine (ELM) optimised by Exhaustive Method, were adopt to assess the peak shear strength of soil-GDL interfaces. Then, a comprehensive investigation and comparison of the predictive performance for the models was conducted. Also, based on the selected optimal machine learning model, sensitivity analysis was conducted, and an empirical equation developed based on it. The research indicated that GA and PSO could significantly increase forecasting precision in a small number of iterations. The BPANN model optimised by PSO has the highest forecasting precision based on the statistics criteria: Root-Mean-Square Error, Correlation Coefficient, Coefficient of Determination, Wilmot’s Index of Agreement, and Mean Absolute Percentage Error. The normal stress has the biggest impact on the peak shear strength, followed by drainage core type, moisture saturation of the soil layer, shearing surface, soil type, consolidation condition, geotextile specification, soil density and drainage core thickness, and the ranking is affected partly by the data distribution of input parameters in the database based on mechanism analysis. An empirical equation developed from the optimal model was proposed to estimate the peak shear strength, which provides convenience for geotechnical engineering personnel with limited knowledge of machine learning technique.

Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs

In this study, the SCVW (a novel type of Sharp-Crested V-notch weirs) is employed for more experimentally and theoretically investigations. The length of the hydraulic jump at downstream of the SCVW (LjSCVW) is measured via new supplementary experimental datasets via most popular vertex angles θ (128° and 60°). The experiments are conducted under aerated, steady, and free overflow conditions in an open channel for large physical models. To assess the variations of LjSCVW versus the θ, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (Nst) in the SCVW, and various tailgate angles (ɸ). Via dimensional analysis and pre-processing method, most effectual parameters on the LjSCVW were obtained as Fr1 (approaching Froude number), Re1 (incoming Reynolds number), and (relative sequent depths). Based on the experimental results, by increasing the values of Fr1 and Re1, the values of LjSCVW noticeably increased. Three types of data-driven models (DDMs), namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) are developed to estimate the LjSCVW. The suggested hybrid model is a coupled form of SVR with ant colony optimization (ACO) algorithm, which is used to enhance the estimation precision of the LjSCVW. According to the attained statistical indices (determination coefficient (R2) = 0.98, root mean square error (RMSE) = 0.191, and value of total grade (TG) = 19.78) and scatter plots, the hybrid SVR-ACO model was determined as the most superior method to estimate the LjSCVW with high accuracy.