Electric bus arrival and charging station placement assessment using machine learning techniques

This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

Performance evaluation of machine learning techniques in predicting cumulative absolute velocity

In the construction of ultra-high voltage (UHV) transformation substations, mass concrete is highly susceptible to temperature-induced cracking due to thermal gradients arising from the disparity between internal hydration heat and external environmental conditions. Such cracks can severely compromise the structural integrity and load-bearing capacity of foundations, making accurate temperature prediction and effective thermal control critical challenges in engineering practice. To address these challenges and enable real-time monitoring and dynamic regulation of temperature evolution, this study proposes a novel hybrid forecasting model named CPO-VMD-SSA-Transformer-GRU for predicting temperature behavior in mass concrete. First, sine wave simulations with varying sample sizes were conducted using three models: Transformer-GRU, VMD-Transformer-GRU, and CPO-VMD-SSA-Transformer-GRU. The results demonstrate that the proposed CPO-VMD-SSA-Transformer-GRU model achieves superior predictive accuracy and exhibits faster convergence toward theoretical values. Subsequently, four performance metrics were evaluated: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The model was then applied to predict temperature variations in mass concrete under laboratory conditions. For the univariate time series at Checkpoint 1, the evaluation metrics were MAE: 0.033736, MSE: 0.0018812, RMSE: 0.036127, and R2: 0.98832; at Checkpoint 2, the values were MAE: 0.016725, MSE: 0.00091304, RMSE: 0.019114, and R2: 0.96773. In addition, the proposed model was used to predict the temperature in the rising stage, indicating high reliability in capturing nonlinear and high-dimensional thermal dynamics in the whole construction process. Furthermore, the model was extended to multivariate time series to enhance its practical applicability in real-world concrete construction. At Checkpoint 1, the corresponding metrics were MAE: 0.56293, MSE: 0.34035, RMSE: 0.58339, and R2: 0.95414; at Checkpoint 2, they were MAE: 0.85052, MSE: 0.78779, RMSE: 0.88757, and R2: 0.91385. These results indicate significantly improved predictive performance compared to the univariate configuration, thereby further validating the accuracy, stability, and robustness of the multivariate CPO-VMD-SSA-Transformer-GRU framework. The model effectively captures complex temperature fluctuation patterns under dynamic environmental and operational conditions, enabling precise, reliable, and adaptive temperature forecasting. This comprehensive analysis establishes a robust methodological foundation for advanced temperature prediction and optimized thermal management strategies in real-world civil engineering applications.

Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

Accurate prediction of solar irradiation, particularly Diffuse Horizontal Irradiance (DHI), is essential for optimizing solar energy systems. This paper presents a hybrid framework that integrates satellite imagery and satellite-derived data to improve the precision of DHI forecasts. Two key datasets were employed: ground-based Solar Irradiation Measurement (SRM) and satellite-based Solar Irradiation (SSR). The proposed methodology utilizes machine learning models along two paths. The first path processes satellite imagery using advanced techniques, including pixel imputation with a modified Random Forest (RF) and Generative Adversarial Network (GAN), noisy region detection with Self-Organizing Maps (SOM), and noise removal through a Latent Diffusion Model (LDM). The second path handles tabular data through a diffusion probabilistic model designed for missing data imputation. These features are then merged and fed into a Long Short-Term Memory (LSTM) network, enhanced to capture seasonality and trends for accurate DHI predictions. The paper makes significant contributions by introducing a robust hybrid framework that leverages both satellite imagery and tabular data, incorporating novel preprocessing methods such as GAN-based pixel imputation and diffusion-based noise removal. The LSTM model is further adapted to handle seasonal and trend components, resulting in enhanced DHI forecasting accuracy. The proposed model demonstrates superior performance, achieving a Mean Squared Error (MSE) of 8.170 W/m2, Root Mean Squared Error (RMSE) of 1.749 W/m2, Mean Absolute Error (MAE) of 0.693 W/m2, and an R-squared value of 2.574, significantly outperforming conventional methods such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), and traditional LSTM models. Additionally, the model records a low Mean Absolute Percentage Error (MAPE) of 0.8183% and a computation time of 35.4 s, highlighting its efficiency. These results contribute to more accurate and reliable solar energy resource management by offering enhanced DHI forecasts across various spatial and temporal scales.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Air temperature (T, °C), relative humidity (RH, %), and global solar radiation (G, MJ.m-2 ) have been measured in the meteorological station at South Valley University (SVU) at Qena, Egypt, from 2000 to 2013, while the total column ozone (TCO, DU) is downloaded from Giovanni's website. T, RH, and TCO are important meteorological parameters, and they are useful to estimate the missing data of the global solar radiation (G), as global solar radiation is desirable for electricity generation applications and for agriculture. Qena is a subtropical region in Upper Egypt, as it's characterized by clear weather most days of the year, and it's very hot in the summer and cold in the winter. The meteorological station at South Valley University (SVU) stopped measuring the G. The linear regression equation and the most important statistical indices are used in this paper such as, the determination coefficient (R2 ), the mean absolute error (MAE), the mean absolute bias error (MABE), the mean square error (MSE), the root mean square error (RMSE), the mean percentage error (MPE), the mean bias error (MBE), the model efficiency (ME), and the agreement index (d). For verification of the empirical models' efficiency, the data of a new period has used, 2013, and the results of the models were excellent and valid for estimating the missing data, as R2 was more than 0.92 in all models but it was near one in models 1 and 4. MAE was close to zero for all models. MBE, MPE, and MABE were close to zero for all models except model 3. Model 1 was the best one, as, R2 , MAE, MABE, MSE, RMSE, MAPE, d, ME, and MPE were 0.9883, 0.0193, 0.383, 0.2303, 0.4799, 1.927, 0.9979, 0.992, and 0.2932, respectively

The rapid evolution of Internet of Things (IoT) applications demands advancements in wireless communication technologies to handle increasing data rates and connectivity requirements. This article presents our novel research on utilizing machine learning techniques to enhance the efficiency of MIMO antennas for Wireless Communication and IoT applications in the Terahertz (THz) frequency band. Our research assesses antenna performance using various methodologies, including simulation and RLC equivalent circuit models. The proposed design operates at 6.51 THz, 7.48 THz, and 8.46 THz, with bandwidths of 0.7 THz, 0.69 THz, and 0.89 THz, respectively. It features a maximum gain of 13.53 dBi and compact dimensions of 160 × 75 μm2. Additionally, it demonstrates excellent isolation, exceeding −32 dB, −44 dB, and −45 dB across these bands, with over 96.5% efficiency in all operating bands. By designing a similar RLC circuit in ADS and simulating it, we validated the results obtained from CST. Both CST and ADS simulators produced comparable reflection coefficients. Furthermore, several machine learning algorithms were applied to test the design. Various metrics, including variance score, R-squared, mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE), were used to evaluate the machine learning models. Among the five models analyzed, the Gradient Boosting Regression model exhibited the lowest error rates (4.94% MAE, 6.60% MSE, and 4.13% RMSE) and achieved the highest accuracy, exceeding 98% in predicting isolation. Considering all these factors, it is evident that this antenna is an excellent choice for the THz band in 6G wireless communication.

Increased industrialization, urbanization, and population rise have caused considerably poor air quality, resulting in serious health complaints and environmental damage. Precise forecasting of Air Quality Index (AQI) is crucial in order to address problems and facilitate proactive health responses. This research seeks to compare and assess the predictive capability of four models – Levenberg-Marquardt artificial neural network (LM-ANN), Bayesian regularization artificial neural network (BR-ANN), multiple linear regression (MLR), and convolutional neural network (CNN). Air quality observations from five Bhubaneswar monitoring stations, recorded between January 2020 and December 2023, were compared against the chosen models. The performance of the models was evaluated through mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2 ) and t-statistics analysis. The CNN model performed the best with a predictive accuracy of MSE 42.81, RMSE 6.54, MAE 2.93, and R 2 0.97. The t-statistic test also proved that CNN had the best proximity with actual values (t=0.212) and BR-ANN had the maximum deviation (t=0.983). The research validates that CNN performs better than conventional models in AQI forecasting because it can retain spatial information and learn hierarchical features.

The interlayer bonding condition in asphalt pavement significantly affects pavement performance. This study employed machine learning techniques to predict interlayer shear strength (ISS). Feed-forward artificial neural networks (ANN) and random forest (RF) models were developed and compared with traditional multiple linear regression (MLR). Utilizing 156 datasets, divided into 70% training and 30% testing, model performance was assessed using R-squared, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) was utilized for model interpretation. The results indicated that the ANN and RF models outperformed MLR, explaining over 95% of experimental data. RF exhibited superior performance with lowest MSE, RMSE, and MAE (0.0029, 0.0538, and 0.0376 MPa). SHAP analysis highlighted the significance of temperature, normal stress, shear deformation rate, and curing time as influential variables in ISS prediction. Elevated temperature adversely influenced ISS, while normal stress, shear deformation rate, and curing time positively contributed to ISS.

Background:Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control.Methods:Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error).Results:The optimum ES model was Brown’s linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively.Conclusion:The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short‐term memory (FOLSTM) neural network for long‐term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K‐means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell‐shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi‐Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state‐of‐the‐art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with the help of the backward elimination and forward selection method for modeling. After analyzing and evaluating the variables in Eviews software, the four variables of debt ratio (CL. E), size (SIZE), net profit to sales ratio (NETP. S), and interest rate coverage ratio (ICR) are selected in the backward elimination method. In the forward selection method, in addition to the above variables, operating profit margin (OPM) is also chosen. The estimated model of these variables in both methods shows a low ratio of R2 coefficient that is approximately 7%. In the test case, the model of forward selection method has less error in all four criteria of root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Tile coefficient (TIC) compared to the backward elimination method.