Statistical Performance Indices Research Articles

Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree

Several recent studies have used various data mining techniques to obtain accurate electrical energy demand forecasts in power supply systems. This paper, for the first time, compares the efficiency of the decision tree (DT) and classic genetic programming (GP) data mining models developed for electrical energy demand forecasting in Nicosia, Northern Cyprus. The models were trained and tested using daily electricity consumptions measured during the period 2011-2016 and were compared in terms of three statistical performance indices including coefficient of determination, mean absolute percentage error and concordance coefficient. The prediction results showed that the proposed models can be effectively applied to forecasts of electrical energy demand. The results also indicated that the GP is slightly superior to DT in terms of the performance indices.

New stochastic modeling strategy on the prediction enhancement of pier scour depth in cohesive bed materials

AbstractScouring around the piers, especially in cohesive bed materials, is a fully stochastic phenomenon and a reliable prediction of scour depth is still a challenging concern for bridge designers. This study introduces a new stochastic model based on the integration of Group Method of Data Handling (GMDH) and Generalized Likelihood Uncertainty Estimation (GLUE) to predict scour depth around piers in cohesive soils. The GLUE approach is developed to estimate the related parameters whereas the GMDH model is used for the prediction target. To assess the adequacy of the GMDH-GLUE model, the conventional GMDH and genetic programming (GP) models are also developed for evaluation. Several statistical performance indicators are computed over both the training and testing phases for the prediction accuracy validation. Based on the attained numerical indicators, the proposed GMDH-GLUE model revealed better predictability performance of pier scour depth against the benchmark models as well as several gathered literature studies. To provide an informative comparison among the proposed techniques (i.e. GMDH-GLUE, GMDH, and GP models), an improvement index () is employed. Results indicated that the GMDH-GLUE model achieved = 6% and = 3%, demonstrating satisfying performance improvement in comparison with the previously proposed GMDH model.

Wind power prediction using a three stage genetic ensemble and auxiliary predictor

This paper presents a novel method for accurate wind power prediction by applying computational intelligence approaches while exploiting Auxiliary Predictor (AxP) and Genetic Programming (GP) based ensemble of Neural Networks (AxP-GPNN). The inherent fluctuations in the power generated by wind mills may affect their optimal integration in the electric grid and therefore, accurate prediction is highly desired. To cater these fluctuations and highly nonlinear mapping, we present an ensemble approach, where the auxiliary predictor is constructed with Radial Basis Function (RBF) network and Relevance Vector Machine (RVM) and various neural networks are then employed as base regressors. Use of RVM is based on its established advantages for robust prediction on unseen data to address the overfitting issue in training phase. AxP is used for suitable weight initialization to base predictors and provides initial decision space to base learners. Further, an ensemble of neural networks based on GP is developed which utilizes the base predictions of neural networks as well as the auxiliary information generated by AxP. The GP ensemble based forecasting engine is thus robust to minor variations in the data as compared to the individual base regressors. We also employ information-theoretic feature selection on physical measurements of the wind mills. Results have been extracted in the form of statistical performance indices including mean absolute error, standard deviation error and mean square error. These error measures are compared with the other existing wind power prediction techniques. These results present better wind power estimates and reduced prediction error. Paired t-test for the proposed model with other machine learning based models is carried out for further evaluation. Overall, these comparisons validate the importance of auxiliary predictor in ensemble model of GP and ANNs.

Scale-up effect analysis and modeling of liquid–solid circulating fluidized bed risers using multigene genetic programming

Understanding scale-up effects on the hydrodynamics of a liquid‒solid circulating fluidized bed (LSCFB) unit requires both experimental and theoretical analysis. We implement multigene genetic programming (MGGP) to investigate the solid holdup and distribution in three LSCFB systems with different heights. In addition to data obtained here, we also use a portion of data sets of LSCFB systems developed by Zheng (1999) and Liang et al. (1996). Model predictions are in good agreement with the experimental data in both radial and axial directions and at different normalized superficial liquid and solid velocities. The radial profiles of the solid holdup are approximately identical at a fixed average cross-sectional solid holdup for the three LSCFB systems studied. Statistical performance indicators including the mean absolute percentage error (6.19%) and correlation coefficient (0.985) are within an acceptable range. The results suggest that a MGGP modeling approach is suitable for predicting the solid holdup and distribution of a scaled-up LSCFB system.

Estimating the average diffuse solar radiation based on multiple parameters: a case study of arid climate zone of Pakistan

In this study, 50 empirical models from 2 categories, diffuse fraction and diffusion coefficient, were defined with 4 subcategories to estimate monthly mean diffuse solar radiation over the arid climate zone selecting Karachi as a case study. Commonly used statistical performance indicators are analysed to check the accuracy of models. For diffuse fraction models, MAD, MARE, RMSE, RRMSE and ranged from 0.0552 to 0.1789 kWh/m2-day, 0.0272 to 0.0807, 0.0702 to 0.2238 kWh/m2-day, 0.0026 to 0.0081 and 0.90 to 0.99, respectively. Similarly, for diffusion coefficient models, MAD, MARE, RMSE, RRMSE and ranged from 0.0719 to 0.1618 kWh/m2-day, 0.0336 to 0.0724, 0.0847 to 0.2145 kWh/m2-day, 0.0031 to 0.0078 and 0.92 to 0.98, respectively. Results displayed that (1) diffuse fraction models have higher performance than diffusion coefficient models. (2) Sunshine hour fraction models outperformed clearness index, correlated models. (3) Combined models are more accurate than single variable-based models. (4) The proposed models showed better performance with models available in the literature.

Prediction of Later‐Age Concrete Compressive Strength Using Feedforward Neural Network

Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time‐consuming experiments. Notably, the determination of the later‐age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later‐age concrete is crucial in specific applications. In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later‐age concrete. The proposed model was fully evaluated in terms of performance and prediction capability over statistical results of 1000 simulations under a random sampling effect. The results showed that the proposed algorithm was an excellent predictor and might be useful for engineers to avoid time‐consuming experiments with the statistical performance indicators, namely, the Pearson correlation coefficient (R), root‐mean‐squared error (RMSE), and mean squared error (MAE) for the training and testing parts of 0.9861, 2.1501, 1.5650 and 0.9792, 2.8510, 2.1361, respectively. The results also indicated that the FNN model was superior to classical machine learning algorithms such as random forest and Gaussian process regression, as well as empirical formulations proposed in the literature.

Assessment of the quality of cause-of-death data in Greenland, 2006-2015.

Background: While the system of registration of mortality and cause of death (COD) in Greenland was established several decades ago, reporting procedures follow a complicated administrative process. Timely and reliable reporting on mortality and COD is of high importance for the usability of the collected data for research, health planning and decision making. Methods: COD data collected by the Chief Medical Office in Greenland from 2006 to 2015 (4490 registered deaths) were analysed with the software Analysis of National Causes of Death for Action (ANACONDA) v4.0. Unusable or insufficiently specified ICD codes are identified. The Vital Statistics Performance Index for Quality (VSPI(Q)) is estimated for the overall quality conclusions of the register's usability. Results: Sixty-eight per cent of the input data for Greenland was coded with a usable underlying COD, 24% with an unusable cause and 8% of deaths with an insufficiently specified cause. Almost 700 deaths were coded to an unusable code of 'very high impact'. The most prevalent unusable underlying causes were other ill-defined and unspecified causes, including no death certificate available, followed by senility, heart failure, sepsis and shock and cardiac arrest. The VSPI(Q) score was 66%, representing medium quality. Conclusions: In the 10 years' worth of data analysed, the true underlying COD in many cases was unknown. Several likely explanations for this include lack of systematic COD training for physicians, logistic and capacity challenges in Greenland that potentially could reduce the quality of the collected data and its usability in providing essential information about the true pattern of mortality in Greenland.

An ANFIS‐based model for solar energy forecasting and its smart grid application

In the present work, a model underlying the principle of adaptive neural‐fuzzy inference system (ANFIS) architecture is proposed. This research investigates the ANFIS‐based model performance to forecast global solar energy under different weather conditions, namely, clear sky, hazy sky, hazy and cloudy sky, and cloudy sky; and for distinct climate zones using salient meteorological parameters. Power generation from solar photovoltaic (PV) may be affected by different factors, such as cloud cover, topographical locations, time, and seasonal variations. Therefore, accurate forecasting of PV power is an important factor for system reliability and robustness. In this work, the proposed model is implemented for smart grid applications in forecasting short‐term PV power in a composite climate zone. Finally, the accuracy of the model is compared with other approaches, namely, support vector machine, feedforward neural network, multivariate adaptive regression splines, generalized regression neural network, linear neural network, fuzzy logic‐based model, and empirical models based on multiple regression analysis using statistical performance indicators. The overall analysis reveals that the ANFIS‐based model outperforms the other models in terms of accuracy.

Does a Partial Rotator Cuff Tear Affect Pitching Ability? Results From an MRI Study.

Background:Numerous studies have examined changes in the athletic performance of baseball pitchers after rotator cuff surgery. However, only a few studies have evaluated changes in athletic performance caused by partial rotator cuff tears that are not treated surgically.Purpose:To examine the course of partial-thickness rotator cuff tears and its possible effect on the athletic performance of professional pitchers.Study Design:Case series; Level of evidence, 4.Method:Of 191 professional pitchers who attended our clinic between January 2009 and October 2018, 52 individuals had partial-thickness tears with at least 2 years of follow-up magnetic resonance imaging (MRI) scans and were included in this study. All initial MRI examinations were performed when a season was finished or during the off-season for regular medical check-up purposes. Hence, any abnormal finding on MRI, which suggests damage to the rotator cuff tendon, was assumed to have occurred during the previous season. The mean follow-up MRI period was 40.8 months (range, 24.4-100.9 months). We defined the year before an athlete’s first MRI at our clinic as the year of damage, and we evaluated athletic performance during the season before the damage (pre-damage year 1), the season of the damage (damage year), and 1 and 2 seasons after the damage (post-damage years 1 and 2). We evaluated the changes in 5 statistical performance indicators: earned run average (ERA), fielding independent pitching (FIP), walks plus hits divided by innings pitched (WHIP), winning percentage (WPCT), and innings pitched (IP).Results:The partial-thickness tears progressed in 39 of 52 (75%) patients. Of these 39 patients, 34 (87%) were grade 1 in severity and 5 (12.8%) were grade 2 or higher. The ERA of the pitchers did not increase significantly immediately after damage or at post-damage years 1 and 2. WPCT increased significantly compared with pre-damage year 1 (P < .001), and IP decreased significantly during the follow-up period (P < .001). Although no significant decrease in pitching ability was noted based on these 3 indices, significant increases were observed for FIP and WHIP.Conclusion:A partial-thickness rotator cuff tear does not have significant influence on the athletic performance of professional baseball pitchers in the short term based on conventional performance indicators. Our findings suggest that WHIP and IP decline significantly at 2 years after damage is noticed.

Model Prediction and Optimization of Waste Lube Oil Treated with Natural Clay

In this work, used lube oil was treated using natural acid-free clay. Clay was added at different amounts (5, 10, and 20 g) to 100 mL of waste engine oil at various temperatures (250, 350, 400, and 450 °C) and mixed at a speed of 800 rpm for 30 min. After settling and separation, the treated oil was diluted with kerosene before being examined using a Ultraviolet–visible (UV) spectrophotometer. In order to achieve cost-effective recycling, this process is modeled using the response surface method (RSM). Five regression models (linear, quadratic, Two Factor Interactions (2FI), cubic, and reduced-order quadratic model) were developed, then tested, and examined by calculating the statistical performance indicators (R2, R2adj, Akaike’s Information Criterion corrected (AICc), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE)). The results obtained reveal that the modified quadratic model outperforms the rest of the models in terms of the low value of RMSE, the lowest AICc, lowest BIC, and the highest R2 and R2adj. The developed modified quadratic model is optimized successfully to predict optimum operation conditions. Results show that optimum operation conditions are at the minimum area under the curve for UV absorption at 223.358; this can be achieved with a process temperature of 266.246 °C and clay quantity of 5.331 g. This model agreed with experimental data regardless of the effectiveness of red clay in the therapy of lube oil.

Assessment and validation of global horizontal radiation: a case study in Mauritius

ABSTRACTValidated solar maps are very crucial for a holistic site selection, design and financial evaluation of photovoltaic (PV) projects. During this research, the temporal and spatial variations of global horizontal solar radiation are investigated. The performance of PV systems has been analyzed. A solar map has been developed and validated against performance of PV systems for Mauritius. For the purpose, initially global horizontal irradiance was measured at intervals of 30 s across five sites in the country since 2013. Measurement data showed that yearly averaged daily insolation varied between 3.25 kWh/m2 and 5.04 kWh/m2 across the country. Angstrom Prescott (AP) coefficients were then calibrated in order to model daily radiation at the measurement sites. AP coefficient, a, varied between 0.132 and 0.278 while, a + b varied between 0.662 and 0.831. With the aid of sunshine hour data obtained from Mauritius Meteorological Services, monthly averaged and yearly averaged insolation are computed for additional six sites in order to have a wide coverage of the country. Statistical performance indicators were computed and results showed good agreement between measurement and the linear univariate AP model. Yearly averaged daily global horizontal insolation for eleven sites were used to generate a solar map. The average annual insolation levels have been estimated to vary between 1237 kWh/m2 and 1901 kWh/m2 over the island. The latter values represent the average capacity utilization factor of photovoltaic plants, which are estimated to vary between 14.1% and 21.7%. The results add up to highly valuable information on solar exposure for adoption of off-grid and grid tied photovoltaic technologies.

Modeling Construction labour productivity using Evolutionary Polynomial Regression

Construction projects are labour-intensive and labour costs are a substantial percentage of total budget. Impaired labour productivity causes an increase in construction project schedule. Labour productivity is one of the most frequently discussed topics in the construction industry, and modelling labour productivity by utilising different techniques has been getting more attention. It is a challenging task as it requires identifying the influencing factors as well as considering the associated interdependencies. This paper investigates the application of evolutionary polynomial regression (EPR) for modelling labour productivity in formwork installation. EPR is a data-driven hybrid modelling technique based on evolutionary computing and has been successfully applied to solving civil engineering problems. Results obtained from the EPR model were compared with the outcomes of three other methods: best subset, stepwise, and general regression neural network (GRNN). Results demonstrate the predictive superiority of the developed EPR model for nonlinear problems based on statistical performance indicators.

A version of Organization of Multithread Processing of Confidential Data on the Basis of Cellular Automata

Рurpose of research is to develop a multithread processing system based on an encryption algorithm using cellular automata and to study statistical performance indicators depending on the hardware components and the size of the input block, and to develop recommendations for improving the cryptostrength of the method. Methods . A mathematical model of the encryption method using a floating window based on cellular automata was considered [3]. To study the speed of confidential data processing, there was developed a variant of the organization of the structure of the software module with an extended block of setting parameters that determine the dimension of the matrix, the line of activation of the bit neighborhood of the processed elements, the number of parallel computations (threads) and the rule of expansion of the boundary elements of the matrix. A method for the development of the dependence curve of the processing time and inital parameters that can be applied both to process individual files and continuous network subscriber data flow, is proposed. Results. The cryptographic module implementing the encryption method on the basis of cellular automata, which specific feature is a multithread mode of operation and dynamic control of the block of initial parameters, was developed. Recommendations for setting the neighborhood of the active elements of the matrix and the number of threads taking into account the architecture of the CPU were formulated. Experimental studies were conducted to confirm the completeness and correctness of the proposed solutions. The expediency of using high-speed hard disk drives and saving the results of encryption in asynchronous segmented mode with working thread-bind results was revealed. Conclusion. The proposed version of the organization of the confidential data processing system in the form of a software module, taking into account the features of the hardware, allows optimization of the processing speed, and the compliance with the recommendations for the expansion of the neighborhood in the block transformation can improve the cryptographic algorithm based on cellular automata with a floating window.

A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant and complicated problem in pile analysis and design. The aim of this research is to develop a new AI predictive models for predicting pile bearing capacity. The first predictive model was developed based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) structure optimized by particle swarm optimization (PSO) algorithm called as ANFIS–GMDH–PSO model; the second model introduced as fuzzy polynomial neural network type group method of data handling (FPNN–GMDH) model. A database consists of different piles property and soil characteristics, collected from literature including CPT and pile loading test results which applied for training and testing process of developed models. Also a common artificial neural network (ANN) model was applied as a reference model for comparing and verifying among hybrid developed models for prediction. The modelling results indicated that improved ANFIS–GMDH model achieved relatively higher performance compared to ANN and FPNN–GMDH models in terms of accuracy and reliability level based on standard statistical performance indices such as coefficient of correlation (R), mean square error, root mean square error and error standard deviation values.

Estimating cause-specific mortality in Madagascar: an evaluation of death notification data from the capital city

BackgroundTrends in cause-specific mortality in most African countries are currently estimated from epidemiological models because the coverage of the civil registration system is low and national statistics on causes of death are unreliable at the national level. We aim to evaluate the performance of the death notification system in Antananarivo, the capital city of Madagascar, to inform cause-of-death statistics.MethodsInformation on the sex of the deceased, dates of birth and death, and underlying cause of death were transcribed from death registers maintained in Antananarivo. Causes of death were coded in ICD-9 and mapped to cause categories from the Global Burden of Disease 2016 Study (GBD). The performance of the notification system was assessed based on the Vital Statistics Performance Index, including six dimensions: completeness of death registration, quality of cause of death reporting, quality of age and sex reporting, internal consistency, level of cause-specific detail, and data availability and timeliness. We redistributed garbage codes and compared cause-specific mortality fractions in death records and estimates from the GBD with concordance correlation coefficients.ResultsThe death notification system in Antananarivo performed well on most dimensions, although 31% of all deaths registered over the period 1976–2015 were assigned to ICD codes considered as “major garbage codes” in the GBD 2016. The completeness of death notification, estimated with indirect demographic techniques, was higher than 90% in the period 1975–1993, and recent under-five mortality rates were consistent with estimates from Demographic and Health Surveys referring to the capital city. After redistributing garbage codes, cause-specific mortality fractions derived from death notification data were consistent with GBD 2016 for the whole country in the 1990s, with concordance correlation coefficients higher than 90%. There were larger deviations in recent years, with concordance correlation coefficients in 2015 at 0.74 (95% CI 0.66–0.81) for men and 0.81 (95% CI 0.74–0.86) for women.ConclusionsDeath notification in Antananarivo is a low-cost data source allowing real-time mortality monitoring, with a potential to improve disease burden estimates. Further efforts should be directed towards evaluating data quality in urban centers in Madagascar and other African countries to fill important data gaps on causes of death.

Efficacy of hybrid neural networks in statistical downscaling of precipitation of the Bagmati River basin

Abstract This study investigates and analyses the present and future senarios of precipitation using statistical downscaling techniques at selected sites of the Bagmati River basin. Statistical downscaling is achieved by feed forward neural network (FFNN) and wavelet neural network (WNN) models. Potential predictors for the model development are selected based on the performances of Pearson product moment correlation and factor analysis. Different training algorithms are compared and the traincgb training algorithm is selected for development of FFNN and WNN models. The visual comparison and the statistical performance indices were calculated between observed and predicted precipitation. From the analysis of results, it is evident that WNN models were well capable of (training: RMSE 1.61–1.67 mm, R 0.94–0.952; testing: RMSE 1.68–1.78 mm, R 0.93–0.95) predicting precipitation followed by FFNN model for all the selected sites. Hence, the projected precipitation (2014–2036) is found by WNN model only with inputs as different GCMs data. The projected precipitation results are analysed for the period 2014–2036 and find that there is a decrease in precipitation with respect to base period data (1981–2013) by 66.62 to 84.21% at Benibad, 4.53 to 21.74% at Dhenge and 6.40 to 22.27% at Kamtaul, respectively.

Sediment transport modeling in rigid boundary open channels using generalize structure of group method of data handling

Sediment transport in open channels has complicated nature and finding the analytical models applicable for channel design in practice is a quite difficult task. To this end, behind theoretical consideration of the open channel sediment transport through incorporating of four fundamental characteristics of fluid, flow, sediment and channel, recently, machine learning techniques are used for modeling of sediment transport in open channels. However, most of the studies in the literature used limited number of data for model development neglecting some effective parameters involved which may affect their performances. Moreover, most of this studies had not provided a comprehensive explicit equation for future use. Accordingly, this study applied four machine learning techniques of Gene Expression Programming (GEP), Extreme Learning Machine (ELM), Generalized Structure Group Method of Data Handling (GS-GMDH) and Fuzzy c-means based Adaptive Neuro-Fuzzy Inference System (FCM-ANFIS) to model sediment transport in open channels. Four existing data sets in the literature with wide ranges of pipe size, sediment size, sediment volumetric concentration, channel bed slope and flow depth are used for the model development. The recommended models are compared with their corresponding conventional regression models taken from the literature in terms of different statistical performance indices. Results indicate superiority of the machine leaning techniques to the conventional multiple non-linear regression models. Although, developed GEP, ELM, GS-GMDH and FCM-ANFIS models have almost same performances, GS-GMDH gives slightly better performance which can be linked to the generalized structure of this approach. A MATLAB code is provided to calculate the sediment transport in open channel for practical engineering.

Applying Bayesian Models to Forecast Rock Mass Modulus

The deformation modulus of a rock mass (Em) is one of the most significant properties used by designers for estimating deformation behavior of rock masses encountered in rock engineering projects (slopes, foundations and tunnels). The Em can only be determined by employing large-scale in situ tests on the rock mass, itself, for example, plate jacking, plate loading, pressuremeter, flat dilatometer, and Goodman jacking. It is sometimes difficult to apply the large scale in situ tests because of time consuming processes and installation required. To overcome this difficulty, the current study aims at predicting the Em on the basis of the rock parameters including the uniaxial compressive strength of intact rock, rock mass rating, Depth and elastic modulus of intact rock (Ei). The Bayesian inference approach is implemented to identify the most appropriate models for estimating the Em among six candidate models that have been proposed. The models were applied to available data given in open source literature. The unknown parameters of the models are considered as random variables. The WinBUGS software which uses Bayesian analysis of complex statistical models and Markov chain Monte Carlo (MCMC) techniques is employed to compute the posterior predictive distributions. The mean values of the model parameters obtained via MCMC simulations are considered for the model prediction performance evaluation. Various statistical performance indexes indexes [mean squared error, root mean squared error, squared correlation coefficient (R2) and mean absolute percentage error] were utilized to compare the performance of estimation models. Overall, the results indicate that the proposed Em model possesses satisfactory predictive performance.

Bayesian prediction of rotational torque to operate horizontal drilling

Horizontal directional drilling is usually used in drilling engineering. In a variety of conditions, it is necessary to predict the torque required for performing the drilling operation. Nevertheless, there is presently not a convenient method available to accomplish this task. In order to overcome this difficulty, the current work aims at predicting the required rotational torque (RT) to operate horizontal directional drilling on the 7 effective parameters including the length of drill string in the borehole (L), axial force on the cutter/bit (P), total angular change of the borehole (KL), radius for the ith reaming operation (Di), rotational speed (rotation per minute) of the bit (N), mud flow rate (W), and mud viscosity (V). In this paper, we propose an approach based on the model selection criteria such as various statistical performance indices mean squared error (MSE), variance account for (VAF), root mean squared error (RMSE), squared correlation coefficient (R2), and mean absolute percentage error (MAPE) to select the most appropriate model among a set of 20 candidate ones to estimate RT, given a set of observed data. Once the most appropriate model is selected, a Bayesian framework is employed to develop the predictive distributions of RT, and to update them with new project-specific data that significantly reduce the associated predictive uncertainty. Overall, the results obtained indicate that the proposed RT model possesses a satisfactory predictive performance.

Integrated intelligent computing paradigm for the dynamics of micropolar fluid flow with heat transfer in a permeable walled channel

In this work, a novel application of bio-inspired computational heuristic paradigm is presented for micropolar fluid flow and heat transfer system in a channel with permeable walls by modeling competency of neural networks, global search of genetic algorithms, and rapid local convergence of sequential quadratic programming. Approximation theory in the mean squared error sense is exploited for the formulation of an objective function to solve the governing nonlinear fluidics system. The designed scheme is employed to study the dynamics of the model in terms of stream function, microrotation, concentration, and temperature profiles for prominent factors based on Reynolds number, Peclet number for diffusion of heat and mass, coupling, spin-gradient viscosity, micro inertia density parameters. The consistency and robustness of the solver are validated through statistical performance indices based on comparison with state of art Adams numerical method for accuracy and complexity measures.