Recurrent Artificial Neural Network Research Articles

The Efficiency of Artificial Recurrent Neural Network (RNN) in Predicting Academic Performance for Students

The Efficiency of Artificial Recurrent Neural Network (RNN) in Predicting Academic Performance for Students

Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2.

A genome, composed of a precisely ordered sequence of four nucleotides (ATCG), encompasses a multitude of specific genome features like AAA motif. Mutations occurring within a genome disrupt the sequential order and composition of these features, thereby influencing the evolutionary trajectories and yielding variants. The evolutionary relatedness between a variant and its ancestor can be estimated by assessing evolutionary distances across a spectrum of genome features. This study develops a novel, alignment-free algorithm that considers both the sequential order and composition of genome features, enabling computation of the Fréchet distance (Fr) across multiple genome features to quantify the evolutionary status of a variant. Integrating this algorithm with an artificial recurrent neural network (RNN) reveals the quantitative evolutionary trajectory and origin of SARS-CoV-2, a puzzle unsolved by alignment-based phylogenetics. The RNN generates the evolutionary trajectory from Fr data at two levels: genome sequence mutations and organism variants. At the genome sequence level, SARS-CoV-2 evolutionarily shortens its genome to enhance its infectious capacity. Mutating signature features, such as TTA and GCT, increases its infectious potential and drives its evolution. At the organism level, variants mutating a single biomarker possess low infectious potential. However, mutating multiple markers dramatically increases their infectious capacity, propelling the COVID-19 pandemic. SARS-CoV-2 likely originates from mink coronavirus variants, with its origin trajectory traced as follows: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Together, mutating multiple signature features and biomarkers delineates the evolutionary trajectory of mink-origin SARS-CoV-2, leading to the COVID-19 pandemic.

Enhancement of optical surface quality based on real-time compensation of temperature-driven thermal errors in diamond turning

In diamond turning, thermal error compensation is critical for improving machining accuracy. In the ultra-precision diamond turning of optical devices, fluctuations in room temperature cause periodic waviness on the machined surface, which degrades the optical performance. Surface finishing processes, such as polishing, are generally required to remove surface waviness. However, the materials used in the polishing process can pollute the environment. Moreover, the polishing process produces a large amount of waste, including contaminated alcohol and tissue. This study presents a new method for compensating temperature-driven thermal errors during single-point diamond turning (SPDT) using machine learning. Long short-term memory (LSTM), an artificial recurrent neural network architecture, was used to estimate the thermal error from the temperatures observed at several locations around the SPDT machine. The performance of the proposed method was evaluated by comparing its estimation accuracy with those of other machine learning methods (multiple linear regression and support vector machine). The thermal error was compensated for by controlling the tool position according to the estimated thermal error. To evaluate the results of the thermal error compensation, the intensities of the laser beam profiles reflected from two flat aluminum mirrors with diameters of 100 mm were observed at several propagation distances. The waviness formed on the uncompensated surface resulted in a dramatic increase in intensity owing to the constructive interference caused by Bragg scattering, whereas the intensity of the reflection from the compensated surface did not vary as the waviness was removed.

Identification of structured nonlinear state–space models for hysteretic systems using neural network hysteresis operators

Hysteretic system behavior is ubiquitous in science and engineering fields including measurement systems and applications. In this paper, we put forth a nonlinear state–space system identification method that combines the state–space equations to capture the system dynamics with a compact and exact artificial neural network (ANN) representation of the classical Prandtl–Ishlinskii (PI) hysteresis. These ANN representations called PI hysteresis operator neurons employ recurrent ANNs with classical activation functions, and thus can be trained with classical neural network learning algorithms. The structured nonlinear state–space model class proposed in this paper, for the first time, offers a flexible interconnection of PI hysteresis operators with a linear state–space model through a linear fractional representation. This results in a comprehensive and flexible model structure. The performance is validated both on numerical simulation and on measurement data.

Architecture of the brain's visual system enhances network stability and performance through layers, delays, and feedback.

In the visual system of primates, image information propagates across successive cortical areas, and there is also local feedback within an area and long-range feedback across areas. Recent findings suggest that the resulting temporal dynamics of neural activity are crucial in several vision tasks. In contrast, artificial neural network models of vision are typically feedforward and do not capitalize on the benefits of temporal dynamics, partly due to concerns about stability and computational costs. In this study, we focus on recurrent networks with feedback connections for visual tasks with static input corresponding to a single fixation. We demonstrate mathematically that a network's dynamics can be stabilized by four key features of biological networks: layer-ordered structure, temporal delays between layers, longer distance feedback across layers, and nonlinear neuronal responses. Conversely, when feedback has a fixed distance, one can omit delays in feedforward connections to achieve more efficient artificial implementations. We also evaluated the effect of feedback connections on object detection and classification performance using standard benchmarks, specifically the COCO and CIFAR10 datasets. Our findings indicate that feedback connections improved the detection of small objects, and classification performance became more robust to noise. We found that performance increased with the temporal dynamics, not unlike what is observed in core vision of primates. These results suggest that delays and layered organization are crucial features for stability and performance in both biological and artificial recurrent neural networks.

Systematic integration of information big data and physics teaching based on deep learning algorithms

Abstract This paper first investigates the main performance of the shackles of traditional physics teaching on students and its negative consequences and then designs a physics teaching process model based on the teaching environment of information big data and constructs a framework of the physics teaching system by utilizing data mining technology. Then, the physics teaching system is modeled based on the artificial neuron model and recurrent neural network, and the attention mechanism is used to improve the overall generalization performance of the model. Finally, the application effect of the physics teaching system is compared through empirical evidence. The results show that there is a deviation in the mean value of the two classes; the average score of the experimental class is 75.552 points, and the average score of the control class is 65.910 points, and there is a difference of 9.642 points between the two classes’ mean values. The data show that applying a physics teaching system can improve the learning status quo of students’ mechanical imitation, help students better master knowledge, enhance students’ ability to use information technology and achieve better teaching results than traditional teaching.

Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks

Long-term dependence is an essential feature for the predictability of time series. Estimating the parameter that describes long memory is essential to describing the behavior of time series models. However, most long memory estimation methods assume that this parameter has a constant value throughout the time series, and do not consider that the parameter may change over time. In this work, we propose an automated methodology that combines the estimation methodologies of the fractional differentiation parameter (and/or Hurst parameter) with its application to Recurrent Neural Networks (RNNs) in order for said networks to learn and predict long memory dependencies from information obtained in nonlinear time series. The proposal combines three methods that allow for better approximation in the prediction of the values of the parameters for each one of the windows obtained, using Recurrent Neural Networks as an adaptive method to learn and predict the dependencies of long memory in Time Series. For the RNNs, we have evaluated four different architectures: the Simple RNN, LSTM, the BiLSTM, and the GRU. These models are built from blocks with gates controlling the cell state and memory. We have evaluated the proposed approach using both synthetic and real-world data sets. We have simulated ARFIMA models for the synthetic data to generate several time series by varying the fractional differentiation parameter. We have evaluated the proposed approach using synthetic and real datasets using Whittle’s estimates of the Hurst parameter classically obtained in each window. We have simulated ARFIMA models in such a way that the synthetic data generate several time series by varying the fractional differentiation parameter. The real-world IPSA stock option index and Tree Ringtime series datasets were evaluated. All of the results show that the proposed approach can predict the Hurst exponent with good performance by selecting the optimal window size and overlap change.

Wind Speed Forecasting Using Wavelet Analysis and Recurrent Artificial Neural Networks Based on Local Measurements in Singida Region, Tanzania

High accuracy wind speed forecasting is essential for wind energy harvest and plays a significant role in wind farm management and grid integration. Wind speed is intermittent in nature, which makes the forecasting to be a big challenge. In the present study, three hybrid single-step wind speed forecasting techniques are proposed and tested by local measurement data in Singida region, Tanzania. The three techniques are based on Wavelet Analysis (WA), Back Propagation (BP) optimization algorithm, and Recurrent Neural Network (RNN). They are referred to as WA-RNN, BP-RNN, and WA-BP-RNN. The model results showed that WA-BP-RNN outperforms the other two proposed techniques, with minimum statistical errors of 0.56 m/s (BIAS), 6.89% (MAPE) and 0.53 m/s (RMSE). Furthermore, the WA-BP-RNN technique has shown highest correlation value of 0.95, which indicates that, the strength of a linear association between the observed and forecasted dataset of the wind speed. In addition, the deployment of the BP optimization algorithm in the proposed technique showed improvements of the model results.

Evaluation of fused multisource data of air temperature based on dropsonde and satellite observation

Continuous vertical air temperature (AT) from in-situ observation is of crucial importance for understanding the atmospheric environment, but the satellite data that have complete spatial coverage lacked vertical in-situ observation data, and the vertical dropsonde data from in-situ observations only were single-point observations. Therefore, this article introduced machine learning algorithms for fusing in-situ observation and multi-satellite data to achieve spatial continuity of vertical data on a large scale. Specially, random forest (RF), support vector regression (SVR), artificial neural network (ANN) and recurrent neural network (RNN) were employed to capture the non-linear relationships between the variables and estimated AT. The pre-training process and fine-tuning process ensured the prediction of AT spatiotemporal distribution. The four models were implemented for three-dimensional AT estimating across China. Additionally, we used the radiosonde observation data to evaluate the accuracy of estimated AT data under conventional weather and typhoon conditions. Our results revealed that the RF model performed the best with the R of 0.9992, the MAE of 0.70 °C, and the RMSE of 1.04 °C at the national scale, followed by the SVR and ANN models. The RNN model exhibited promising results under typhoon conditions, which will be valuable insights for further research on the applicability of machine learning models under different weather conditions. Besides, having a larger sample size does not necessarily result in reduced errors. For instance, the MAE value for SVR in the pressure height range of 100–200 hPa was larger than that in the pressure height range of 300–400 hPa, but the former sample size was 16,324, which was 7433 higher than the latter.

Path loss modeling based on neural networks and ensemble method for future wireless networks

In light of the technological advancements that require faster data speeds, there has been an increasing demand for higher frequency bands. Consequently, numerous path loss prediction models have been developed for 5G and beyond communication networks, particularly in the millimeter-wave and subterahertz frequency ranges. Despite these efforts, there is a pressing need for more sophisticated models that offer greater flexibility and accuracy, particularly in challenging environments. These advanced models will help in deploying wireless networks with the guarantee of covering communication environments with optimum quality of service. This paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ANN), artificial recurrent neural network (RNN) based on long short-term memory (LSTM), shortly known as RNN-LSTM, and convolutional neural network (CNN). Moreover, an ensemble-method-based neural network path loss model is proposed in this paper. Finally, an extensive performance analysis of the four models is provided regarding prediction accuracy, stability, the contribution of input features, and the time needed to run the model. The data used for training and testing in this study were obtained from measurement campaigns conducted in an indoor corridor setting, covering both line-of-sight and non-line-of-sight communication scenarios. The main result of this study demonstrates that the ensemble-method-based model outperforms the other models (ANN, RNN-LSTM, and CNN) in terms of efficiency and high prediction accuracy, and could be trusted as a promising model for path loss in complex environments at high-frequency bands.

A deep learning approach to predict and optimise energy in fish processing industries

The fish processing sector is experiencing increased pressure to reduce its energy consumption and carbon footprint as a response to (a) an increasingly stringent energy regulatory landscape, (b) rising fuel prices, and (c) the incentives to improve social and environmental performance. In this paper, a standalone forecasting computational platform is developed to optimise energy usage and reduce energy costs. This short-term forecasting model is achieved using an artificial neural network (ANN) to predict power and temperature at thirty-minute intervals in two cold rooms of a fish processing plant. The proposed ANN function is optimised by genetic algorithms (GA) with simulated annealing algorithms (SA) to model the relationships between future temperature and power and the system variables affecting them. To assess the accuracy of the proposed method, extensive experiments were conducted using real-world data sets. The results of the experiments indicate that the proposed ANN model performs with higher accuracy than (a) the long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture, (b) peephole-LSTM, and (c) the gated recurrent unit (GRU). This paper finds that using GA & SA algorithms; ANN parameters can be optimised. The RMSE obtained by the ANN compared with the second-ranked method GRU was consequently 16% and 4% less for the predicted temperature and power. The results in one particular site demonstrate energy cost savings in the range of 15%–18% after applying the forecast-optimiser approach. The proposed prediction model is used in a fish processing plant for energy management and is scalable to other sites.

Motion prediction using brain waves based on artificial intelligence deep learning recurrent neural network.

Electroencephalogram (EEG) research has gained widespread use in various research domains due to its valuable insights into human body movements. In this study, we investigated the optimization of motion discrimination prediction by employing an artificial intelligence deep learning recurrent neural network (gated recurrent unit, GRU) on unique EEG data generated from specific movement types among EEG signals. The experiment involved participants categorized into five difficulty levels of postural control, targeting gymnasts in their twenties and college students majoring in physical education (n=10). Machine learning techniques were applied to extract brain-motor patterns from the collected EEG data, which consisted of 32 channels. The EEG data underwent spectrum analysis using fast Fourier transform conversion, and the GRU model network was utilized for machine learning on each EEG frequency domain, thereby improving the performance index of the learning operation process. Through the development of the GRU network algorithm, the performance index achieved up to a 15.92% improvement compared to the accuracy of existing models, resulting in motion recognition accuracy ranging from a minimum of 94.67% to a maximum of 99.15% between actual and predicted values. These optimization outcomes are attributed to the enhanced accuracy and cost function of the GRU network algorithm's hidden layers. By implementing motion identification optimization based on artificial intelligence machine learning results from EEG signals, this study contributes to the emerging field of exercise rehabilitation, presenting an innovative paradigm that reveals the interconnectedness between the brain and the science of exercise.

Enhanced artificial transgender longicorn algorithm & recurrent neural network based enhanced DC-DC converter for torque ripple minimization of BLDC motor

This paper proposes an enhanced DC-DC converter with hybrid control method for torque ripple minimization of BLDC motor. Initially, a BLDC motor is controlled with an enhanced Cuk converter. The application of a switched inductor is used to update the Cuk converter operation. In this method, the control mechanism incorporates two control loops, namely, the speed control loop and torque control loop, which are utilized to recover the execution of BLDC. Thus, the proposed system is the combined performance of the Enhanced Artificial Transgender Longicorn Algorithm (EATLA) and Recurrent Neural Network (RNN) to improve control loop operations. In the Artificial Transgender Longicorn Algorithm (ATLA), the crossover and mutation approach are used as part of the scattering process to build the accuracy search process. In this article, the EATLA-RNN algorithm for limiting speed and torque error of BLDC motor is explored. However, the proposed method output is subject to input of the speed and torque controllers. The proposed topology with the controller is executed on MATLAB/Simulink workstation, and torque ripple minimization is analyzed toother existing approaches such as particle swarm optimization (PSO) and bacterial foraging (BF) algorithm.

Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling

This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem. The uncertainty is modeled directly as part of recurrent artificial neural networks, but using two fundamentally different approaches: one based on Deep Quantile Regression and the other on Bayesian neural network. Both approaches use a recurrent neural network to predict multiple time steps into the future, but handle the time-dependent uncertainty estimation differently. We present a novel sampling technique in order to aggregate quantile estimates for link level travel time to yield the multi-link travel time distribution needed for a vehicle to travel from its current position to a specific downstream stop point or transfer site.To motivate the relevance of uncertainty-aware models in the domain, we focus on the connection protection application as a case study: An expert system to determine whether a bus driver should hold and wait for a connecting service, thus ensuring the connection, or break the connection and reduce its own delay. Our results show that the proposed quantile sampling method performs overall best for the 80%, 90% and 95% prediction intervals, both for a 15 min time horizon into the future (t+1), but also for the 30 and 45 min time horizon (t+2 and t+3), with a constant, but very small underestimation of the uncertainty interval (1–4 pp.). However, we also show, that the Bayesian model still can outperform the DQR for specific cases. Lastly, we demonstrate how a simple decision support system can take advantage of our uncertainty-aware travel time models to prioritize the difference in travel time uncertainty for bus holding at strategic points, thus reducing the introduced delay for the connection protection application.

Recurrent neural network architecture for forecasting banana prices in Gujarat, India.

The forecasting of horticulture commodity prices, such as bananas, has wide-ranging impacts on farmers, traders and end-users. The considerable volatility in horticultural commodities pricing estimates has allowed farmers to exploit various local marketplaces for profitable sales of their farm produce. Despite the demonstrated efficacy of machine learning models as a suitable substitute for conventional statistical approaches, their application for price forecasting in the context of Indian horticulture remains an area of contention. Past attempts to forecast agricultural commodity prices have relied on a wide variety of statistical models, each of which comes with its own set of limitations. Although machine learning models have emerged as formidable alternatives to more conventional statistical methods, there is still reluctance to use them for the purpose of predicting prices in India. In the present investigation, we have analysed and compared the efficacy of a variety of statistical and machine learning models in order to get accurate price forecast. Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average model (SARIMA), Autoregressive Conditional Heteroscedasticity model (ARCH), Generalized Autoregressive Conditional Heteroscedasticity model (GARCH), Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) were fitted to generate reliable predictions of prices of banana in Gujarat, India from January 2009 to December 2019. Empirical comparisons have been made between the predictive accuracy of different machine learning (ML) models and the typical stochastic model and it is observed that ML approaches, especially RNN, surpassed all other models in the majority of situations. Mean Absolute Percent Error (MAPE), Root Mean Square Error (RMSE), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error (MASE) and mean directional accuracy (MDA) are used to illustrate the superiority of the models and RNN resulted least in terms of all error accuracy measures. RNN outperforms other models in this study for predicting accurate prices when compared to various statistical and machine learning techniques. The accuracy of other methodologies like ARIMA, SARIMA, ARCH GARCH, and ANN falls short of expectations.

Deep learning techniques for prediction of pneumonia from lung CT images

Pneumonia disease is caused by viruses and bacteria which affect one or both lungs. It is the most dangerous disease that causes huge cancer death worldwide. Early detection of Pneumonia is the only way to improve a patient’s chance for survival. We can detect this disease from X-ray or computed tomography (CT) lung images using deep learning techniques. This research paper provides a solution to medical practitioners in predicting the impact of virus as high-risk, low-risk and medium-risk among the population being tested through various deep learning techniques such as convolutional neural networks, artificial neural network (ANN) and recurrent neural networks using long short-term memory cells. We observed 3000 CT images of Pneumonia confirmed patients and achieved the accuracy resulting 98–99%. The performance of the classifiers is evaluated using parameters such as confusion matrix, accuracy, F-measure, precision and recall. The results prove that deep learning affords a fitting tool for fast screening of Pneumonia and discovering high-risk patients and preventing them by providing suitable medical remedies.

Deep learning based on connected vehicles for icing pavement detection

Slippery road conditions, such as snowy, icy or slushy pavements, are one of the major threats to road safety in winter. The U.S. Department of Transportation (USDOT) spends over 20% of its maintenance budget on pavement maintenance in winter. However, despite extensive research, it remains a challenging task to monitor pavement conditions and detect slippery roadways in real time. Most existing studies have mainly explored indirect estimates based on pavement images and weather forecasts. The emerging connected vehicle (CV) technology offers the opportunity to map slippery road conditions in real time. This study proposes a CV-based slippery detection system that uses vehicles to acquire data and implements deep learning algorithms to predict pavements' slippery conditions. The system classifies pavement conditions into three major categories: dry, snowy and icy. Different pavement conditions reflect different levels of slipperiness: dry surface corresponds to the least slippery condition, and icy surface to the most slippery condition. In practice, more attention should be paid to the detected icy and snowy pavements when driving or implementing pavement maintenance and road operation in winter. The classification algorithm adopted in this study is Long Short-Term Memory (LSTM), which is an artificial Recurrent Neural Network (RNN). The LSTM model is trained with simulated CV data in VISSIM and optimized with a Bayesian algorithm. The system can achieve 100%, 99.06% and 98.02% prediction accuracy for dry pavement, snowy pavement and icy pavement, respectively. In addition, it is observed that potential accidents can be reduced by more than 90% if CVs can adjust their driving speed and maintain a greater distance from the leading vehicle after receiving a warning signal. Simulation results indicate that the proposed slippery detection system and the information sharing function based on the CV technology and deep learning algorithm (i.e., the LSTM network implemented in this study) are expected to deliver real-time detection of slippery pavement conditions, thus significantly eliminating the potential risk of accidents.

Neural Network based wind energy model developed over Anantapur, Andhra Pradesh, India

Wind energy is a prominent renewable energy alternative to the conventional non-renewable energy source with a minimal environmental impact. Even though the fact that wind farms have grown in popularity, each one faces a unique set of challenges. Many sophisticated and new approaches are deployed for forecasting wind power genesis and wind power potential to pinpoint the low wind speed hours and schedule a time for turbine maintenance. This paper connotes wind power forecasting over a region using Artificial Neural Network (ANN) & Recurrent Neural Network (RNN) algorithms to address industry problems. In addition, various case studies were carried out with Meteorological variables includes Pressure, soil moisture, temperature, solar radiation, eastward wind (u10), northward wind (v10), and wind speed to assess the effectiveness of various models, and the best model with the highest degree of accuracy is recommended. This study deals with predicting the hourly wind speed at Anantapur, which is essential knowledge for the subsequent estimate of Wind Power Density (WPD). Short-range with statistical metrics of tiny Mean Absolute Error (MAE), low Root Mean Square Error (RMSE), and high Index Of Agreement (IOA) proving the predictability of the wind speed evolution, predictions made with ANN and RNN models at a lead time of 24 hours have been confirmed to be accurate. This study gives wind speed and wind electricity ability predictions that are useful for enterprises, government agencies, and industry in relation to wind harnessing.

MANAGEMENT OF THE VAULTING SYSTEM BULK MATERIALS BASED ON A NEURAL NETWORK

Background. This article presents the composition and algorithm of the automated control system for the parameters of a pneumatic arch-breaking system installed on bunkers with bulk materials. The relevance and practical significance of the issue considered in the article is determined by the fact that when unloading bulk material from the hopper, various violations of the technological process may occur, manifested in the formation of material freezes inside the container, a decrease in the amount of unloaded material, segregation, up to a complete stop of unloading due to the formation of arches.
 Materials and/or methods. Methods of mathematical modeling, system analysis, comparison, systems theory, as well as architecture and mathematical model of a recurrent neural network with feedback are used.
 Results. The composition of input and output parameters of the control system is substantiated. Methods for solving problems of classifying parameter sets are considered and an artificial recurrent neural network with feedback and with a sigmoidal activation function of neurons of the hidden layer and a linear activation function of the output layer is selected.
 Conclusion. The article analyzes the requirements for the system of automated control of the parameters of the pneumatic arch-breaking system. Based on the analysis of possible states of the system, the composition of the system elements and the sets of input, intermediate and output parameters of the system, as well as the objective function of the system operation, are substantiated. It is shown that the system can be built on the basis of an artificial neural network. The algorithm of the automated control system for the parameters of the pneumatic arch-breaking system has been developed.

Heuristic-Based Ensemble Model Selection Strategy with Parameter Tuning for Optimal Diabetes Mellitus Prediction

Diabetes is a terrible health situation characterized by high-rise blood glucose levels. If it is not predicted at an early stage, then it generates the problems in the human body like kidney failure or premature death, and stroke. Controlling blood glucose levels provides patients with helpful dietary recommendations, which are critical components of diabetes management. In the past decades, diverse conventional approaches have been executed to predict the beginning stages of diabetes mellitus depending on physical and substance tests. Still, developing a new framework that can effectively diagnose diabetes mellitus-affected patients is required. To this end, the major target of this task is to predict diabetes mellitus with an advanced accuracy rate with the help of the Heuristic-based Ensemble Model Selection Strategy (H-EMSS). In the data collection phase, the Pima Indian Diabetes dataset (PID) is taken from the storage area of UCI. The data cleaning is performed in the pre-processing stage, which is the technique of removing or fixing, corrupted, incorrect, duplicate, incomplete data, or incorrectly formatted, inside a dataset. Then, the diabetes prediction is accomplished by the H-EMSS. Here, 10 base learners like Naive Bayes (NB), Convolutional Neural Network (CNN), Linear Regression (LR), Deep Neural Network (DNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Auto Encoder (AE) and Recurrent Neural Network (RNN) are considered. From these, three classifiers are optimally selected by the Modified Scalar Factor-based Elephant Herding Optimization (MSF-EHO), so that the prediction rate will be high. The suggested methodology’s efficacy is also compared and analyzed, with the findings demonstrating the recommended model’s superiority. The overall evaluation is that the Root Mean Square Error (RMSE) of the designed Modified Scalar Factor-based Elephant Herding Optimization-Heuristic-based Ensemble Model Selection Strategy (MSF-EHO-H-EMSS) attains 4.601% and also the Mean Absolute Error (MAE) on the designed method achieves 0.99%. Thus, the given outcomes of the designed method revealed that it achieves elevated performance than the other existing techniques regarding diverse error metrics.