Computational Intelligence Models Research Articles

Microwave-based and convective drying of cabbage (Brassica oleracea L. var capitata L.): Computational intelligence modeling, thermophysical properties, quality and mid-infrared spectrometry

This study assessed and compared the impact of hot air oven drying (HAD) at 50, 60 and 70 °C and microwave drying (MWD) at 195, 307 and 521 W on the kinetics of thermophysical properties and quality of dried cabbage. The thermophysical properties were computed using established model equations. The proximate composition, bioactive compounds, antioxidant capacity (AOC), color and functional groups were analyzed using Association of official analytical chemists (AOAC) test protocol, High performance liquid chromatography (HPLC), Ultraviolet-visible (UV–Vis) spectrometry and Attenuated total reflectance coupled to Fourier transformed infrared spectroscopy (ATR-FTIR), respectively. The moisture ratio was modeled using probabilistic computational intelligence modeling approaches. The results revealed that only density, thermal diffusivity and thermal effusivity appeared to be kinetically controlled by moisture content. Stepwise linear regression (SLR) model and Gaussian process regression (GPR) model were more accurate in simulating the moisture ratio. Increasing HAD temperature preserved all of the proximate compositions except carbohydrates. Similarly, increasing MWD power preserved all the proximate compositions except crude fiber. Increasing HAD temperature from 50 to 60 °C and 60–70 °C decreased the total flavonoid content (TFC) and total phenolic content (TPC) by 21.31 % and 13.60 %, respectively but increased the AOC from 27.2 to 39.6 %. Increasing MWD power levels from 195 to 307 W and 307–521 W decreased TFC by 13.2 % and 0.7 %, respectively but increased the TPC by 9.72 % and 13.14 %, thus AOC increased from 35.2 to 45.7 %. MWD presented higher AOC relative to HAD. Drying at 50 °C and 307 W gave the highest whitening index in HAD and MWD. ATR-FTIR analysis revealed the formation of new compounds by thermal effect. SLR and GPR were more accurate in modeling the drying kinetics. HAD and MWD affected the quality parameters of cabbage differently, thus the choice of the dryer type will depend on the parameters of interest.

Computational intelligence modelling of methylene blue adsorption by metal-organic frameworks

ABSTRACT Computational intelligence models for wastewater treatment utilise mathematical algorithms to simulate and optimise the processes involved in the removal of pollutants from wastewater, aiding in the design and operation of the wastewater treatment plant. The aim of this work was to compare the efficiency of computational models in modelling the dynamic adsorption of dyes. Metal-organic frameworks (MOFs) are considered as adsorbents because of their excellent properties. Further, the dataset used for this study was sourced from literature pertaining to the adsorption of methylene blue (MB), which is one of the commonly found dyes in wastewater. Adsorption parameters considered for the study are initial concentration, bed height, flow rate, pH and time, for the continuous adsorption process. The efficacy of the selected computational models was compared by employing various statistical metrics. Moreover, the results show the efficiency of artificial neural network and radial basis function neural network models is superior to other models based on R2 and mean square error, which were in the range of 0.95–0.999 and 10–3–10–5, respectively. In summary, the computational intelligence models serve as the best tools for the prediction of the adsorption performance of MOFs for MB.

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration.

Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of moisture during the dehydration process of glutinous rice. The hyperparameters of the models were optimized with three strategies: Bayesian optimization, grid search and random search. To understand the parameters that facilitate intelligence model adaptation to the dehydration process, global sensitivity analysis (GSA) was used to compute the impact of the input variables on the model output. The result shows that the optimum computational intelligence techniques include the 3-9-1 topology trained with Bayesian regulation function for ANN, Gaussian kernel function for SVM, Matérn covariance function combined with zero mean function for GPR, boosting method for EoT and 4 minimum leaf size for DT. GPR has the highest performance with R2 of 100% and 99.71% during calibration and testing of the model, respectively. GSA reveals that all the models significantly rely on the variation in time as the main factor that affects the model outputs. Therefore, the computational intelligence models, especially GPR, can be applied for an effective description of moisture evolution during small-scale and industrial dehydration of glutinous rice. © 2024 Society of Chemical Industry.

Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review

Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models.

Progressive expansion: Cost-efficient medical image analysis model with reversed once-for-all network training paradigm

Low computational cost artificial intelligence (AI) models are vital in promoting the accessibility of real-time medical services in underdeveloped areas. The recent Once-For-All (OFA) network (without retraining) can directly produce a set of sub-network designs with Progressive Shrinking (PS) algorithm; however, the training resource and time inefficiency downfalls are apparent in this method. In this paper, we propose a new OFA training algorithm, namely the Progressive Expansion (ProX) to train the medical image analysis model. It is a reversed paradigm to PS, where technically we train the OFA network from the minimum configuration and gradually expand the training to support larger configurations. Empirical results showed that the proposed paradigm could reduce training time up to 68%; while still being able to produce sub-networks that have either similar or better accuracy compared to those trained with OFA-PS on ROCT (classification), BRATS and Hippocampus (3D-segmentation) public medical datasets. The code implementation for this paper is accessible at: https://github.com/shin-wl/ProX-OFA.

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.

Real-time Health Monitoring and Speedy Personal Alert Support System using Medical IoT-Cloud Infrastructures

Health care monitoring technology is evolving day by day with the help of computational techniques and intelligent diagnosis models. Compared to conventional approaches and clinical observations, recent health care methodologies are heavily depending on more accurate computational platforms to diagnose various diseases. On the stream, computational principles and intelligent diagnosis model are proposed for monitoring and classifying the biosensor signal data. The existing diagnosis procedures that are using crucial computational models have been trained to observe the medical symptoms of particular disease. The contributions of recent medical support systems and computational models are closely made of Internet of Medical Things (IoMT) environment. However, the existing techniques are applied to obtain accurate decisions only rather than speedy response properties. Against the issue, the proposed Real-time Gated Recurrent Unit (GRU) based Health Monitoring (RGHM) system has been initiated to build more reliable, fast and accurate health assistance solutions. Most of the recent works are taking more complex data processing techniques that are producing higher delay and resources. The proposed RGHM works against these issues and attains effective results compared to current techniques such as Smart Artificial Intelligence based Monitoring Technique (SAIMT), Patient Monitoring with IoT model (PM-IoT) and Patient Centric Cloud Health Monitoring system (PCCHM). The performance of proposed RGHM is illustrated in result section that shows the RGHM has attained 11% to 14% of better performance than existing techniques.

Constructing the Bounds for Neural Network Training Using Grammatical Evolution

Artificial neural networks are widely established models of computational intelligence that have been tested for their effectiveness in a variety of real-world applications. These models require a set of parameters to be fitted through the use of an optimization technique. However, an issue that researchers often face is finding an efficient range of values for the parameters of the artificial neural network. This paper proposes an innovative technique for generating a promising range of values for the parameters of the artificial neural network. Finding the value field is conducted by a series of rules for partitioning the original set of values or expanding it, the rules of which are generated using grammatical evolution. After finding a promising interval of values, any optimization technique such as a genetic algorithm can be used to train the artificial neural network on that interval of values. The new technique was tested on a wide range of problems from the relevant literature and the results were extremely promising.

Effects of Proximate Analysis on Coal Ash Fusion Temperatures: An Application of Artificial Neural Network.

The temperature at which coal ash melts has a significant impact on the operation of a coal-fired boiler. The coal ash fusion temperature (AFT) is determined by its chemical composition, although the relationship between the two varies. Therefore, it is important to have mathematical models that can reliably predict the coal AFTs when designing coal-based processes based on their coal ash chemistry and proximate analysis. A computational intelligence model based on the interrelationships between coal properties and AFTs was used to predict the AFTs of the coal investigated. A model that integrates the ash, volatile matter, fixed carbon contents, and ash chemistry as input and the AFT [softening temperature, deformation temperature, hemispherical temperature, and flow temperature] as an output provided the best indicators to predict AFTs. The findings from the models indicate (a) a method for determining the AFTs from the coal properties; (b) a reliable technique to calculate the AFTs by varying the proximate analysis; and (c) a better understanding of the impact, significance, and interactions of coal properties regarding the thermal properties of coal ash. This study creates a predictive model that is easy to use, computer-efficient, and highly accurate in predicting coal AFTs based on their ash chemistry and proximate analysis data.

Machine learning-based prediction and mathematical optimization of Capecitabine solubility through the supercritical CO2 system

This work represents development of computational intelligence models for assessment of estimating solubility/bioavailability of orally-taken drugs in SCF (supercritical fluid). The SCF considered in this work is CO2. This approach can provide an appropriate opportunity for the development of sustainable and environmentally-friendly technology with valuable advantages in the drug delivery industry. We modeled the solubility of Capecitabine in CO2SCF using multiple machine learning models. The inputs are Pressure and Temperature in the used dataset and the single output (Y) is the solubility of the drug. Three well-known supervised learning models of KNN (K-Nearest Neighbor), MLP (Multilayer Perceptron), and SVR (Support Vector Regression) were selected for modeling in this research. The hyper-parameters of them were optimized using Cuckoo search algorithm (CS) and then final models assessed using multiple metrics. In terms of coefficient of determination, KNN, MLP, and SVR models have score values of 0.959, 0.996, and 0.952, respectively. Also, they have error rates of 13.78, 1.54, and 19.23 with MSE metric. These facts introduce MLP as the best model obviously and so we used this model for final analysis in this study due to its superior architecture.

Optimized Computational Intelligence Model for Estimating the Flexural Behavior of Composite Shear Walls

This article presents a novel approach to estimate the flexural capacity of reinforced concrete-filled composite plate shear walls using an optimized computational intelligence model. The proposed model was developed and validated based on 47 laboratory data points and the Transit Search (TS) optimization algorithm. Using 80% of the experimental dataset, the optimized model was selected by determining the unknown coefficients of the network-based computational structure. The remaining 20% of the data was used to evaluate the accuracy of the model, and the best-performing structure was selected. Furthermore, the final neural network details were subjected to statistical analysis to extract a user-friendly formula, making it easier to apply in practice. The proposed ANN model and the proposed user-friendly formula were then compared with the AISC 341-16 and experimental results and demonstrated their efficacy in predicting the flexural behavior of composite shear walls. Overall, the proposed approach provides a more reliable and efficient framework for estimating the flexural behavior of composite shear walls.

Soft Computing to Predict Earthquake-Induced Soil Liquefaction via CPT Results

Earthquake-induced soil liquefaction (EISL) can cause significant damage to structures, facilities, and vital urban arteries. Thus, the accurate prediction of EISL is a challenge for geotechnical engineers in mitigating irreparable loss to buildings and human lives. This research aims to propose a binary classification model based on the hybrid method of a wavelet neural network (WNN) and particle swarm optimization (PSO) to predict EISL based on cone penetration test (CPT) results. To this end, a well-known dataset consisting of 109 datapoints has been used. The developed WNN-PSO model can predict liquefaction with an overall accuracy of 99.09% based on seven input variables, including total vertical stress (σv), effective vertical stress (σv′), mean grain size (D50), normalized peak horizontal acceleration at ground surface (αmax), cone resistance (qc), cyclic stress ratio (CSR), and earthquake magnitude (Mw). The results show that the proposed WNN-PSO model has superior performance against other computational intelligence models. The results of sensitivity analysis using the neighborhood component analysis (NCA) method reveal that among the seven input variables, qc has the highest degree of importance and Mw has the lowest degree of importance in predicting EISL.

Computational intelligence modeling using Artificial Intelligence and optimization of processing of small-molecule API solubility in supercritical solvent

Preparation of small-molecule API (Active Pharmaceutical Ingredient) at submicron size would be of great benefit for pharmaceutical engineering, as the drug particles at submicron size possess higher solubility in water. Indeed, the drug bioavailability can be enhanced when the nanomedicine is prepared. In this study, the solubility of the drug desoxycorticosterone acetate (DA) is being examined to assess its viability of nanonization using supercritical operation. Two inputs are temperature and pressure which were considered for machine learning modeling in this study. The drug's solubility is the only output to be estimated by the optimized models. This dataset has 45 rows of data that were gathered at 5 different pressure and temperature levels. Support vector machine (SVM) is used as the core of the models built in this research. Epsilon-SVR and nu-SVR are models based on this concept, which together with two different polynomial and RBF kernels form the four models built in this research for estimation of DA drug solubility. The models are also optimized with the help of a new TLCO method. All four final models have an R2 score higher than 0.9, and among them, the Epsilon-SVR model with RBF kernel has the best performance with 0.967.

Computational intelligence modeling of hyoscine drug solubility and solvent density in supercritical processing: gradient boosting, extra trees, and random forest models

This work presents the results of using tree-based models, including Gradient Boosting, Extra Trees, and Random Forest, to model the solubility of hyoscine drug and solvent density based on pressure and temperature as inputs. The models were trained on a dataset of hyoscine drug with known solubility and density values, optimized with WCA algorithm, and their accuracy was evaluated using R2, MSE, MAPE, and Max Error metrics. The results showed that Gradient Boosting and Extra Trees models had high accuracy, with R2 values above 0.96 and low MAPE and Max Error values for both solubility and density output. The Random Forest model was less accurate than the other two models. These findings demonstrate the effectiveness of tree-based models for predicting the solubility and density of chemical compounds and have potential applications in determination of drug solubility prior to process design by correlation of solubility and density to input parameters including pressure and temperature.

Echo State Networks: Novel reservoir selection and hyperparameter optimization model for time series forecasting

The use of computational intelligence models for multi-step time series forecasting tasks has presented satisfactory results in such a way that they are considered models with an excellent future for this type of problem. From the point of view of computational cost, the current alternatives combined with classical models are generating hybrid models that present even better results. Within the AutoML category, the optimization of hyperparameters and the selection of network topologies has become a challenge. Reservoir Computing, which is within the area of ​​Recurrent Neural Networks (RNN), proposes a particular model called Echo State Networks. which has been tested in different applications with excellent results; however, the difficulty in specifying the hyperparameters has been the subject of continuous study given the random nature of the set of neurons called Reservoir. Based on the Separation Ratio Graph (SRG) model for performance analysis, this paper proposes a new model, called Echo State Network - Genetic Algorithm - Separation Ratio Graph (ESN-GA-SRG), which optimizes network hyperparameters and at the same time selects the best topology for the Reservoir using the SRG coefficient, to find the reservoir that offers the most suitable dynamic behavior. The performance of this new model is evaluated on forecasting two sets of time series benchmarks with different characteristics of sampling periodicity, skewness, and stationarity. The results obtained show that the ESN-GA-SRG model was superior in predicting these time series in most cases, with statistical significance, when compared to other models that have been presented for this type of problem in the literature.

Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility

Determination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learning nature. These parameters are molded based on temperature and pressure input features due to the significant variability of drug solubility with the temperature and pressure. The experimental data have been collected and inputted the models to train them and used the data for testing the machine learning models. The results are useful for production of nanomedicine with enhanced solubility in solvents. Decision Tree (DT), Extra Trees (ET), and Gradient Boosting (GB) models are used and optimized using SCA algorithm to obtain more robust models for prediction of the drug solubility in the solvent. So, the developed models are called SCA-DT, SCA-ET, and SCA-GB in this study and have R2-scores of 0.932, 0.951, and 0.997, respectively. The SCA-DT model has an RMSE error rate of 0.0948, this rate is 0.0822 for SCA-ET, and 0.0203 for SCA-GB. So, the SCA-GB is introduced as the best model of this research for prediction of Lenalidomide solubility in the solvent.

K-nearest neighbor based computational intelligence and RSM predictive models for extraction of Cadmium from contaminated soil

• CI predictive models’ application on soil washing are scare in literature. • Predictive models faced dwindling accuracy when few sample data are available. • Performance of Cd ions soil extraction prediction using KNN were investigated. • KNN performance was better over traditional RSM for Cd soil extraction. • KNN are suitable for soil washing predictions under RSM few samples data. Computational intelligence (CI) predictive models based on k -Nearest Neighbor (KNN) algorithms were developed for Cd ions removal from contaminated soil using environmentally friendly chelating-agent polyaspartate. Based on extracted Cd ions into the chelating-agent, residual Cd ions in treated soil and Cd removal efficiency, the performances of the KNN models were compared with response surface methodology (RSM) models using whole data set (KNN1) and split data (KNN2) scenarios using correlation coefficient (R 2 ) and root mean square error (RMSE). Optimal performances of the developed KNN based models were found to be significantly influenced by the nearest neighbor’s k -parameter attributed to the disparity in the two approaches. The KNN1 demonstrated better performances characterized by higher R 2 = 0.984–0.999 and lower RSME of 0.399–6 against the RSM models’ R 2 = 0.7882–0.990 and RSME 2.08–20.36, respectively. For the KNN2 models, even though lower performances were obtained, yet the soil remediation efficiency models, demonstrated enhanced performance over the RSM models.

Multimodal Sensing and Computational Intelligence for Situation Awareness Classification in Autonomous Driving

Maintaining situation awareness (SA) is essential for drivers to deal with the situations that Society of Automotive Engineers (SAE) Level 3 automated vehicle systems are not designed to handle. Although advanced physiological sensors can enable continuous SA assessments, previous single-modality approaches may not be sufficient to capture SA. To address this limitation, the current study demonstrates a multimodal sensing approach for objective SA monitoring. Physiological sensor data from electroencephalogram and eye-tracking were recorded for 30 participants as they performed three secondary tasks during automated driving scenarios that consisted of a pre-takeover (pre-TOR) request segment and a post-TOR segment. The tasks varied in terms of how visual attention was allocated in the pre-TOR segment. In the post-TOR segment, drivers were expected to gather information from the driving environment in preparation for a vehicle-to-driver transition. Participants' ground-truth SA level was measured using the Situation Awareness Global Assessment Techniques (SAGAT) after the post-TOR segment. A total of 23 physiological features were extracted from the post-TOR segment to train computational intelligence models. Results compared the performance of five different classifiers, the ground-truth labeling strategies, and the features included in the model. Overall, the proposed neural network model outperformed other machine learning models and achieved the best classification accuracy (90.6%). A model with 11 features was optimal. In addition, the multi-physiological sensor-model outperformed the single sensing model by comparing prediction performance. Our results suggest that multimodal sensing model can objectively predict SA. The results of this study provide new insight into how physiological features contribute to the SA assessment.

Computational intelligence modeling of nanomedicine preparation using advanced processing: Solubility of fludrocortisone acetate in supercritical carbon dioxide

The method of green technology which is based on supercritical solvent has been studied in this work for analysis of nanomedicine preparation of solid dosage oral medications. Given that the poor drug solubility in aqueous media is a big challenge in pharmaceutical industry, nanomedicines would help improve the drug solubility in aqueous media. The solubility of fludrocortisone acetate in supercritical carbon dioxide is modelled in this research using various machine learning methods because it is a crucial aspect of the expansion of the pharmaceutical business. For this purpose, the accessible data have two input features: a pressure range of 120–300 (bar) and a temperature range of 308–338 (K). MLP, v-SVR and MLR are the basic models used in this research, but not their raw versions. They are improved for modeling drug solubility and coupled with the grey wolf optimization (GWO) in order to optimize the models. The models optimized by GWO showed acceptable results, but among these models, MLP regression has shown better results when coupled with this optimization algorithm. This model has the RMSE error rate of 2.98 × 10−2 and its R2 score is 0.9797 in correlating the solubility data of the medicine.

Использование методов детрендирования для интеллектуальной обработки данных мониторинга строительных процессов

Data processing of monitoring systems of various processes at the stage of construction and operation of buildings requires the development of special tools that belong to the field of artificial intelligence. The trend removal method is one of the ways to preprocess data collected from various sensors and IoT devices that monitor the state of buildings, structures and soil masses during construction and operation. This article analyzes how different approaches to detrending time series affect the performance and accuracy of algorithms for CI computational intelligence models. The analysis compares three approaches: linear detrending, non-linear detrending, and first-order differentiation.
 Five representative methods are used as CI models: DENFIS dynamic evolving fuzzy neural network, GP Gaussian process, MLP multilayer perceptron, OP-ELM optimally trimmed extremal learning machine, and SVM support vector machine. There are three main conclusions from the experiments performed on the four datasets: 1) detrending does not improve overall performance, 2) the empirical mode decomposition method provides better performance than linear detrending, and 3) first-order differentiation in some cases can be effective and in some cases counterproductive for series with common patterns.