Basis Of Root Mean Square Error Research Articles

Perovskite lattice constant prediction framework using optimized artificial neural network and fuzzy logic models by metaheuristic algorithms

Perovskites have gained significant attention in recent years due to their unique and versatile material properties. The lattice parameters of the perovskite compounds play a crucial role in the engineering of layers and substrates for heteroepitaxial thin films. As an essential parameter in the cubic perovskite structure, the lattice constant, plays a significant role in the development of materials for specific technological applications and serves as a distinctive identifier of the crystal structure of the material. In the field of materials science, advanced Computational Intelligence (CI)-based techniques have become increasingly important for simulating the relationship between the physicochemical parameters of chemical elements and the physical properties of materials and compounds. Hence, this paper presents efficient techniques based on artificial neural network (ANN) and fuzzy logic to predict the lattice constants of pseudo-cubic and cubic perovskites. The identification of optimized parameters for the ANN and fuzzy logic models is accomplished using innovative metaheuristic algorithms such as, Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO) and Imperialist Competitive Algorithm (ICA). In the first part, the study assessed, the effectiveness of various metaheuristic algorithms (PSO-IWO-ICA) in tuning the parameters of the ANN prediction structure in order to get the optimal parameter of the ANN. Whereas in the second part, once we extracted the best optimization algorithm, we combined it with the fuzzy logic technique and then we compared the effectiveness of the two techniques, ANN and Fuzzy logic. On the basis of root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R2), the proposed PSO-ANN and PSO-Fuzzy based models are compared with existing and recent models such as Ubic, Sidey, and Owolabi. The proposed PSO-Fuzzy model performs better than our PSO-ANN model, the Ubic, Sidey, and Owolabi models, with performance improvement of 70.90%, 90.36%, 89.74% 84.46%, respectively on the basis of RMSE. Similarly, it attains performance improvement of 71.26%, 90.31%, 89.58%, and 85.02% on the basis of MAE. Furthermore, the developed PSO-ANN based model outperforms the Ubic, Sidey and Owolabi models with performance improvement of 66.86%, 64.74% and 46.60% respectively, on the basis of RMSE and percentage enhancement of 66.27%, 63.75%, and 47.90% when compared on the basis of MAE. Although the PSO-Fuzzy model has the best performance of all the compared models, the developed PSO-ANN based model possesses the advantage of easy implementation in addition to its moderate performance.

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method.

Structural transformation and magnetic ordering interplays for emergence as well as suppression of superconductivity in 122-iron-based superconducting materials. Electron and hole doping play a vital role in structural transition and magnetism suppression and ultimately enhance the room pressure superconducting critical temperature of the compound. This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. The developed PS-SVR-RAD model, which utilizes ionic radii (RAD) and the concentrations of dopants as descriptors, shows better performance over the developed PS-SVR-LAT model that employs lattice parameters emanated from structural transformation as descriptors. Using the root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error as performance measuring criteria, the developed PS-SVR-RAD model performs better than the PS-SVR-LAT model with performance improvement of 15.28, 7.62 and 72.12%, on the basis of RMSE, CC and Mean Absolute Error (MAE), respectively. Among the merits of the developed PS-SVR-RAD model over the PS-SVR-LAT model is the possibility of electrons and holes doping from four different dopants, better performance and ease of model development at relatively low cost since the descriptors are easily fetched ionic radii. The developed intelligent models in this work would definitely facilitate quick and precise determination of critical transition temperature of 122-iron-based superconductor for desired applications at low cost with experimental stress circumvention.

Challenging handheld NIR spectrometers with moisture analysis in plant matrices: Performance of PLSR vs. GPR vs. ANN modelling

The global demand for natural products grows rapidly, intensifying the request for the development of high-throughput, fast, non-invasive tools for quality control applicable on-site. Moisture content is one of the most important quality parameters of natural products. It determines their market suitability, stability and shelf life and should preferably be constantly monitored. Miniaturized near-infrared (NIR) spectroscopy is a powerful method for on-site analysis, potentially fulfilling this requirement. Here, a feasibility study for applicability and analytical performance of three miniaturized NIR spectrometers and two benchtop instruments was evaluated in that scenario. The case study involved 192 dried plant extracts composed of five different plants harvested in different countries at various times within two years. The reference analysis by Karl Fischer titration determined the water content in this sample set between 1.36% and 6.47%. For the spectroscopic analysis half of the samples were laced with a drying agent to comply with the industry standard. The performance of various calibration models for NIR analysis was evaluated on the basis of root-mean square error of prediction (RMSEP) determined for an independent test set. Partial least squares regression (PLSR), Gaussian process regression (GPR) and artificial neural network (ANN) models were constructed for the spectral sets from each instrument. GPR and ANN models performed superior for all samples measured by handheld spectrometers and for native ones analyzed by benchtop instruments. Moreover, the accuracy penalty when analyzing native samples was lower for GPR and ANN prediction as well. With GPR or ANN calibration, miniaturized spectrometers offered the prediction performance at the level of the benchtop instruments. Therefore, in this analytical application miniaturized spectrometers can be used on-site with no penalty to the performance vs. laboratory-based NIR analysis.

Extreme learning machine and swarm-based support vector regression methods for predicting crystal lattice parameters of pseudo-cubic/cubic perovskites

Lattice parameters of perovskite compounds play crucial roles in engineering of buffer layers and substrates for heteroepitaxial films. As a result, predictive models that can effectively estimate these lattice parameters are highly desired. Therefore, this work presents elegant methods of predicting lattice parameters of pseudo-cubic/cubic perovskite through the development of the extreme learning machine (ELM) based model and hybridization of the particle swarm optimization (PSO) technique with the support vector regression algorithm (SVRA). The generalization and predictive strengths of the proposed SVRA-PSO and ELM-based models are compared with existing methods such as the Ubic model and the recently developed Sidey model on the basis of root mean square error (RMSE), mean absolute error, mean absolute percentage error (MAPE), and correlation coefficient. The developed SVRA-PSO model performs better than the ELM-based model, the Ubic model, and the Sidey model, with performance improvement of 20.99%, 29.29%, and 33.39%, respectively, on the basis of MAPE. Similarly, the SVRA-PSO, respectively, attains performance improvement of 24.74%, 34%, and 37.89% on the basis of RMSE. Furthermore, the developed ELM-based model outperforms the Ubic and Sidey models with performance improvement of 15.70% and 10.50%, respectively, on the basis of MAPE and percentage enhancement of 17.48% and 12.31% when compared on the basis of RMSE. Although the SVRA-PSO model has the best performance of all the compared models, the developed ELM-based model possesses the advantage of easy implementation in addition to its moderate performance.

Ensemble-Based Support Vector Regression with Gravitational Search Algorithm Optimization for Estimating Magnetic Relative Cooling Power of Manganite Refrigerant in Magnetic Refrigeration Application

Magnetic refrigeration technology (MRT) is considered an energy-efficient and environmental-friendly system of refrigeration that has a considerable potential of replacing the classical gas-compression expansion method of refrigeration. Inclusion of manganite-based material (MBM) in MRT as a magnetic refrigerant has attracted significant attention recently due to cost effectiveness of the refrigerant as well as better resistance to oxidation and corrosion as compared to the commonly used metal gadolinium refrigerant. Relative cooling power (RCP) is one of the most important parameters to be considered while assessing the usefulness of MBM. Its value can be altered through doping with external materials and accurate estimation of the dopant influence is required to achieve the right amount of RCP. This present research work proposes support vector regression (SVR) ensemble models with gravitational search algorithm (GSA) hyper-parameters optimization, for estimating RCP of MBM and to determine the influence of dopants on RCP using ionic radii and dopant concentrations as descriptors. GSA-SVR ensemble model (GSE) is developed by employing the outputs of five different SVR models as descriptors while GSA-SVR ensemble model with averaging (GSEA) uses the average of the five different SVR models as its descriptor. The novel ensemble models outperform other SVR models, specifically; GSE performs better than GSA-SVR model and the conventional SVR model with performance improvement of 269.14% and 283.61%, respectively on the basis of root mean square error (RMSE). Furthermore, GSEA outperforms GSE, GSA-SVR model and conventional SVR with performance improvement of 27.51%, 370.70%, and 389.14%, respectively on the basis of RMSE. The developed GSE and GSEA also perform better than the existing RCP model in the literature with performance improvement of 11.53% and 42.21%, respectively. The results of this research work will not only serve to circumvent the experimental challenges of RCP measurement without loss of experimental precision but also further promotes environment-friendly system of refrigeration.

Designing optimal architecture of recurrent neural network (LSTM) with particle swarm optimization technique specifically for educational dataset

Designing an optimal neural network architecture plays an important role in the performance of a neural network model. In the past few years, various bio-inspired optimization techniques have been applied to find the optimal architecture of a neural network model. In this paper particle swarm optimization (PSO) technique has been applied with recurrent neural network long short-term memory (LSTM) algorithm to find an optimal architecture for feed forward neural network. To optimize the architecture of neural network model Parameters considered are hidden neurons, learning rate and activation function. Fitness function applied for the selection of the optimal combination of the parameters is root mean square error (RMSE). Due to privatization of education number of private institutes and universities are increasing rapidly every year. This increase has resulted in huge number of data (NAAC reports) regarding the assessment and accreditation of higher education institutions. Dataset of 500 educational institutes has been collected from the official site of National Assessment and Accreditation Council (NAAC). Hybrid model of PSO with LSTM algorithm has been applied to the educational dataset. Selection of the optimal architecture is done on the basis of RMSE, Accuracy and other performance parameters.

Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement

Accurate estimation of reference crop evapotranspiration (ET0) is important due to its crucial role in determining crop water requirement in irrigated agriculture. Though a great number of models have been developed, their rigorous evaluation with measurements is still lacking, leading to confusion and arbitrariness in model selection. In this paper daily estimates of 16 ET0 models, including five combination-, six radiation and five temperature-based ones, were compared with weighing lysimeter measurements during crop growing season (April through October) in 2012 at a semiarid site in China. Daily ET0 was measured by two weighing lysimeters (area 1.3m×1.3m, depth 2.3m) located in a fescue grass (Festuca arundinacea Schreb) plot (100m×100m) surrounded by a 167ha crop, winter wheat rotated with summer maize. We found the models were ranked decreasingly as: FAO-ppp-17 Penman>1963 Penman>FAO-24 Blaney-Criddle (BC)>1996 Kimberly Penman>FAO-24 radiation>FAO-56 Penman-Monteith (PM)>FAO-24 Penman>Turc>DeBruin-Keijman>Jensen-Haise>Priestley-Taylor>Hargreaves>Makkink>Hamon>Blaney- Criddle>Mcloud on basis of RMSE (root mean square error). Overall, the combination models performed best with RMSE ranging from 0.93 to 1.32mmd−1, followed by the radiation models with RMSE from 1.28 to 1.79mmd−1, and the temperature models with RMSE from 1.09 to 2.48mmd−1. The best combination model (FAO-ppp-17 Penman) was respectively 29% and 17% more accurate than the best radiation (FAO-24 radiation) and temperature (FAO-24 BC) models. Better performance of the combination and radiation models resulted because they explicitly contain the dominant factors influencing ET0. All models tended to overestimate under low evaporative demand while underestimating the measured values under high demand, but on average the combination and radiation methods underestimated by 0.46mmd−1 and 0.60mmd−1, respectively, whereas the temperature method overestimated by 0.21mmd−1. All combination and radiation models, and the Hargreaves and FAO-24 BC in temperature method showed robust structure. To improve them future efforts should be on local calibration, but for temperature models showing structure failure focus should be on its optimization. The coefficients of commonly used models were calibrated and related to meteorological variables. Particularly, those of the Priestley-Taylor, Makkink, Turc and the Hamon were enhanced, while those of the Hargreaves and BC were decreased. In climate similar to the current site in China we suggest continued use of the older Penman equations for combination method and the FAO-24 radiation or Turc for radiation method. Meanwhile, two questions need to be addressed in future studies: i) adoption of the FAO-56 PM equation as the sole standard for computing ET0 and the proper value for surface resistance; and ii) the effectiveness of the later modifications to the original wind function in the Penman equation.

Bond rates and causality

Everyone has their explanation of interest rates. The efficient market model has at least three forms for predicting next period's interest rate. We use the intuition of a successful threshold model for predicting turning points in the Aaa bond rates to formulate a test of the semistrong form of the efficient markets hypothesis. A Granger causality test is used to demonstrate the inadequacy of the semistrong form model. Out of sample forecasts, our model outperformed the weak form model for Aaa rates and AAaa rates on the basis of root mean-squared error.