MSE Values Research Articles

An Efficient Flood Forecasting Model Using an Optimal Deep Belief Network

<p>Floods inflict significant damage globally each year, underscoring the importance of accurate and timely flood prediction to mitigate property loss and life. Precise flood prediction provides governments with crucial preemptive alerts regarding potential flood disasters, enabling timely evacuations and life-saving measures. Although various ML (machine learning) models have demonstrated improved performance compared to traditional statistical models in flood prediction, they often overlook the spatial features crucial to understanding the generation of floods. DL (deep learning) is used in flood prediction to enhance the promptness and efficiency of flood levels predictions. This work presents an optimized DL model to forecast floods using time series data. Initially, the data set was cleaned and normalized by linear interpolation. Then, the DL model ODBN (optimal deep belief network) is utilized for flood forecasting time series prediction. ODBN is the integration of DBN and SCA (Sinh-Cosh algorithm). The experimental analysis is carried out on the real-time dataset and achieved better MSE and RMSE values of 0.75 and 0.94 respectively. The findings suggest that the use of an ODBN is an effective method of accurately forecasting floods.</p>

Comparison Of Single Moving Average And Winter Exponential Smoothing Methods In Predicting The Number Of Divorce Cases At The Religious Court Of Cibinong

Based on data from the Central Bureau of Statistics, the divorce rate in Indonesia shows a tendency to increase from year to year. Similar conditions were experienced by Cibinong district. The forecasting method that will be used in this study is the Single Moving Average and Winter's Exponential Smoothing. The results of forecasting the number of lawsuits in 2023 from July to December with movements with two obtained forecasts in July were 602, August 283, MAD value = 67.29, MSE value = 8.722, MAPE = 11.61, RMSE = 1.46, Accuracy value of 88.39%. Movements with four forecasting periods in July were 620, August 448, September 301, October 141. MAD value = 99.33, MSE value = 14,722, MAPE = 16.59, RMSE = 1.90, Accuracy value of 83.41%. Forecasting with the Winters Exponential Smoothing Method with Alpha: 0.1, Beta: 0.3 and Gamma: 0.5, the forecast results obtained in July were 504.10026, August 491.61306, September 663.18788, October 745.41004, November 732.42766 and December 732.10904. MAE value is 171.65116, MSE value is 686.63361, MAD value is 38.74, MSE value is 2.797 MAPE value is 6.0 and RMSE value is 0.83 and accuracy value is 94.00%. Based on the calculation results above, it is concluded that forecasting the number of divorce filings at Cibinong Religious Court from January 2018 to December 2023 with the Winter Exponential Smoothing method has MAD, MSE, MAPE, RMSE values smaller than values in the Single Moving Average method. Winter Exponential Smoothing method is more appropriate with an accuracy value of 94.00%.

Prediction of daily photovoltaic plant energy output using machine learning

ABSTRACT The energy crisis and global warming are the two significant factors that have led to the accelerated development of PV power plants. Accurate prediction of PV plant power output is crucial to enhance its competitiveness in the renewable energy market, maintain grid stability, and reduce reliance on conventional energy sources. The present study uses nine machine-learning methods to estimate PV plant energy output. The usefulness of the examined models for real-time energy output prediction is assessed to guarantee optimum security and management in the renewable energy sector. The datasets used in this study represent data from 01 Jan 2019 to 30 Nov 2022 and pertain to the solar PV plant located in Bhopal city. The specific application of these methods to the actual plant dataset adds practical relevance, providing insight into their feasibility in a real-world situation. The results were compared using five statistical metrics (MAE, MSE, RMSE, R2, and MAPE) to demonstrate the study’s quality and reproducibility. The results show that MAE, MSE, RMSE, R2, and MAPE values of all algorithms range from 19.7 kWh to 76.6 kWh, from 5408.0 kWh to 14,958.0 kWh, from 73.5 kWh to 122.3 kWh, from 86.5% to 98.4%, from 3.7% to 12.9% respectively. The ANN and the RF models demonstrated superior accuracy compared to other models, with ANN being the top-performing model.

Comparison of prediction fuzzy modeling towards high-risk symptoms of lung cancer

Lung cancer constituted 12.2% of newly diagnosed cancer cases globally in 2020. The high fatality rate of the condition is attributed to delayed diagnosis and inadequate symptom recognition. In Malaysia, the incidence of lung cancer is estimated to be 1 in 60 males and 1 in 138 females, with a median age of 70 years or above. Most lung cancer cases were detected during advanced stages, specifically stages III and IV, with a prevalence exceeding 90% for both genders. In Malaysia, most patients are diagnosed in stages III and IV, which are associated with a lower likelihood of long-term survival. Many cases are identified at a late stage, characterized by significant tumor expansion or the spread of cancer cells to areas that cannot be treated surgically. Malaysians are unaware of cancer symptoms; hence the situation is common. To improve survival and reduce mortality, Malaysians must recognize the symptoms of lung cancer. Fuzzy linear regression and multiple linear regression models have been compared to predict high-risk lung cancer symptoms in Malaysia. The fuzzy linear regression model analyses secondary data, eliminates irrelevant information and enhances precision in the results. Lung cancer patients at Al-Sultan Abdullah Hospital (UiTM Hospital) in Selangor provided data for this study. Data from 124 lung cancer patients were analyzed using Microsoft Excel, SPSS, and MATLAB. To improve data accuracy, the study used cross-validation measurement error (MSE and RMSE). According to data analysis, hemoptysis and chest pain are high-risk symptoms with MSE and RMSE values of 1.549 and 1.245, respectively.

An instance-based transfer learning model with attention mechanism for freight train travel time prediction in the China–Europe railway express

Since the inception of the China–Europe Railway Express (CRE), rail transportation has emerged as a predominant means of transporting goods across international borders. The quality and reliability of railway transport services are constrained by the precision of train travel times. However, the real-time data available for the CRE trains is rather limited due to the volume and duration of operations, which increases the difficulty of forecasting CRE train travel times. In order to enhance the accuracy of predictions, a novel two-stage transfer learning model with the Long Short-Term Memory attention (T.R2_LSTM_A) is introduced in this study. By incorporating attention mechanisms, the Long Short-Term Memory networks (LSTM) module, the two-stage TrAdaBoost.R2 algorithm, and transfer learning (TL), the model captures the inherent characteristics of time series data and attains a high level of accuracy in predictions. This approach effectively mitigates the limitations of current transfer learning methods, which are primarily caused by insufficient sample data. In the model, an instance-based TL method is utilized to address the shortcomings of insufficient data, and an attention-based LSTM is selected as a base learner to overcome the thorny issues of gradient disappearance and explosion. Additionally, LSTM Attention (LSTM_A), a novel attention mechanism, is introduced to improve the ability of the proposed model to capture complicated characteristics. The T.R2_LSTM_A model is systemically evaluated and shown to be superior to Random Forest (RF), Support Vector Regression (SVR), LSTM, convolution-based LSTM (CLSTM), AdaBoost_LSTM, LSTM with attention mechanism (LSTM_Attention), and TrAdaBoost.R2_RF (T.R2_RF) for predicting train travel time, using a real-life case study of CRE. The MSE, RMSE, MAPE, and MAE values for the T.R2_LSTM_A model are equal to 5.572, 2.361, 2.578, and 1.767, respectively. These values surpass those of other widely used prediction methods, indicating that the T.R2_LSTM_A model is well-suited for predicting train travel times in the CRE.

Combustion Characteristic Prediction of Dual Direct Injection Fuel (Diesel-Propane) on RCEM Based on an Artificial Neural Network Approach

Studies from around the world show that engines using biofuel, LPG, and CNG emit fewer pollutants than those using conventional fuels. Experimental research has focused on a rapid compression and expansion machine (RCEM) that resembles a compression ignition (CI) engine. It uses dual direct injection fuel, diesel and propane (DP), with propane injection timing varying from 0 to 40 before top dead center (BTDC) and diesel injection timing remaining at 10 BTDC. The compression ratio was changed at points 17 and 19 by adjusting the RCEM connecting rod. A converge simulation program was used to run the simulation model, which was used to examine how the fire and inflow inside the chamber developed. The ANN method was used to predict pressure, temperature, power, TKE, and ITE data output based on propane energy fraction, compression ratio, and SOI of propane as input data parameters. It was noticed that the ANN prediction on experimental data has a higher accuracy compared to the simulation prediction. The R and MSE values were used to identify the accuracy of the prediction on output parameter data. ANN generalization capability is comparatively high when trained with large enough databases. The highest accuracy of prediction was produced on TKE, which had an MSE of 0.003715 and R value of 0.99981 from 287900 sample data. This shows that the ANN model is quite accurate in forecasting output experimental data.

Research on Apple’s Stock Price Trend Forecasting

As the world's largest company by market capitalization, Apple has attracted the attention of many investors. Many investors have developed a strong interest in Apple stocks. However, it is not easy to study the trend of Apple stocks. Because there are many factors affecting Apple's stock price changes, it is very complicated and difficult to review the details of all these factors. Predicting and analyzing stock prices can provide investors with practical tools to raise funds and reduce investment risks. This paper starts with the time series research method of stock prices, which has proved to be a relatively effective method. Through this method, this paper can have an in-depth understanding of the fluctuation of stock prices, so as to better grasp market trends and make more informed investment decisions. By using CNN and LSTM models for prediction, the author finds that it rose by an average of 4% per trading day, and its MSE value was basically below 10, The MSE of other stocks has been optimized from more than 1900 shares predicted in less than 10 a share. This paper splits and analyzes the adjusted data to obtain more reasonable and accurate prediction results. Finally, this article aims to provide valuable reference for those who are interested in stock forecasting or studying the trend of Apple's stock price.

Inversion of Soil Moisture Content in Cotton Fields Using GBR-RF Algorithm Combined with Sentinel-2 Satellite Spectral Data

Soil moisture content plays a vital role in agricultural production, significantly influencing crop growth, development, and yield. Thoroughly understanding the specific soil moisture content in cotton fields is crucial for enhancing agricultural efficiency and driving sustainable agricultural development. This study utilized the gradient-boosting regression–random forest (GBR-RF) algorithm and the GBR and RF algorithms separately, in conjunction with Sentinel-2 satellite images, to estimate cotton soil moisture content, focusing on the B1–B8 bands and in particular the sensitive B6, B7, and B8 bands. The soil data in the jujube orchard of the study area were collected using soil augers at a depth of 30 cm, with soil data collected from a depth of 20 to 30 cm. The findings revealed that the integrated learning algorithm GBR-RF demonstrated high accuracy, with R2, MAE, and MSE results of 0.8838, 1.0121, and 1.6168, respectively. In comparison, the results using just the GBR algorithm yielded R2, MAE, and MSE values of 0.8158, 1.1327, and 1.9645, respectively, while those obtained from the RF algorithm were 0.8415, 1.0680, and 1.8331, respectively. These results indicate that the algorithms exhibited strong generalization, robustness, and accuracy, with GBR-RF outperforming GBR and RF by 8.34% and 5.03%, respectively, in combination with using the B1–B8 bands for inversion. Furthermore, utilizing the full-band data resulted in R2 values that were up to 24.27% higher than those of the individual bands, affirming the efficacy of band combinations for improved accuracy. This study’s demonstration of the positive impact of integrated learning algorithms on estimating cotton soil moisture content underscores the advantages of multi-band data combinations over single-band data, highlighting their ability to enhance accuracy without significantly impacting errors. Importantly, this study’s findings, while not limited to a single experimental field, have broad applicability in cotton precision agriculture, offering valuable insights for research on yield enhancement and agricultural efficiency.

Machine learning predictions for carbon monoxide levels in urban environments

The increasing carbon emissions in Malaysia necessitate accurate methods to track and control pollution levels. This study focuses on predicting carbon monoxide (CO) concentrations in Petaling Jaya using various machine learning models, and two important parameters, CO concentration and time, were considered in the analysis. Six distinct machine learning models were assessed to gauge their predictive capabilities. These models encompassed a range of regression modeling techniques namely; Linear, Tree, Gaussian Process, Ensemble of Trees, Support Vector, and Artificial Neural Networks (ANN) modelling. The Matern 5/2 Gaussian Process Regression (GPR) model consistently outperformed other models across all scenarios, demonstrating high R2 values and low RMSE, MSE, and MAE values. Specifically, in scenarios 1, 2, 3, and 4, the Matern 5/2 model exhibited the lowest RMSE (0.084–0.088) and highest R2 (0.97), highlighting its reliability and robustness in predicting CO concentrations. Additionally, the Rational Quadratic model achieved an R2 of 0.97 with an RMSE of 0.088 in scenario 1, while the Quadratic SVM excelled in scenario 3 with an R2 of 0.965 and low RMSE, MSE, and MAE values (0.085, 0.007, and 0.066). These findings provide valuable insights for environmental protection, renewable energy transition, energy efficiency, sustainable land use planning, and public awareness. However, acknowledging the study's single-area focus and potential limitations in representing diverse regions, further research is warranted to explore carbon emissions across varied areas and enhance the generalizability of the findings.

Astrophysics-based transit search optimization heuristics for parameter estimation of multi-frequency sinusoidal signals

This study investigates an astrophysics-based Transit search optimization algorithm (TSOA) for solving a challenging engineering problem. This innovative strategy uses the fundamentals of physics to improve the accuracy and efficacy of problem solving techniques. Harmonic distortions in power systems have become a massive challenge because of the nonlinear loads associated with the electrical power distribution system. Overheating of the equipment, motor failure, capacitor failure, and improper power metering are all issues caused by harmonic distortion. A new examination of the causes and consequences of these issues, as well as the status of hardware and software available for harmonic evaluation, is necessary in light of the unprecedented advancements in power electronic devices and their integration at all levels in the power and energy system. In order to estimate phase and amplitude simultaneously with a specified frequency, an objective function of power system harmonics is created. Keeping in mind the adverse effects of harmonics, parameter estimation is carried out under various conditions by taking different particle sizes and signal-to-noise ratios. TSOA proved its efficacy for both phase and amplitude parameters under different situations and precisely estimated the harmonics signal up to an accuracy of 1.1648E–15. Two harmonic signals were taken in this research work, and the best MSE values achieved are 9.748E–4, 8.287E–07, 6.157E–10, and 1.165E–15 for 30, 60, 90, and 150 dB noise, respectively, under case study 1 while varying the particle size from 50 to 450. The results for case study 2 proved to be best up to 7.373E–16, and no significant change occurred by increasing the generations above 500. The proposed study would be a step further in developing a more accurate and robust computing platform for robust estimation of harmonics arising in power and energy systems.

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

The biogas has the potential to serve as a substitute as fossil derived fuel. The present study investigates anaerobic co-digestion of organic waste for predictive modelling. Several co-digestion studies were performed with different pH, solid concentration, temperature, and co-digestion ratios. A water displacement apparatus was used to test biogas yield, and data was collected meticulously. Linear regression (LR) and Random Forest (RF) based models were built with Python-based tools and tested using statistical measures for the prediction of biogas yield. The LR showed a strong linear association, with R and R2 values of 0.9892 and 0.9785, respectively. However, RF surpassed LR, with higher R and R2 values of 0.9919 and 0.9826, respectively. Furthermore, RF had lower MSE and MAE values, indicating higher prediction accuracy and precision. RF consistently scored well in tests, demonstrating its ability to capture complicated relationships while minimizing prediction mistakes. Taylor's diagrams further demonstrated RF's excellent performance during both the training and testing periods. Overall, RF emerges as the optimum model for reliably estimating biogas output in anaerobic co-digestion systems, with important implications for waste-to-energy processes.

Towards enhanced surface roughness modeling in machining: an analysis of data transformation techniques

Data transformation methods are utilized to convert datasets into non-integer formats, potentially altering their distribution patterns. This implies that the variance and standard deviation of the dataset may be altered after the dataset undergoes data transformation operations. Improving model accuracy is a primary application of these methods. This study compares the efficacy of three data transformation techniques: square root transformation, logarithmic transformation, and inverse transformation. The comparison is conducted within the context of developing a surface roughness model for a turning process. Eighteen experiments are performed using the Box-Behnken method, with surface roughness chosen as the response variable. The surface roughness dataset undergoes transformation using the mentioned methods. Four surface roughness regression models are then built: one without transformation, one with square root transformation, one with logarithmic transformation, and one with inverse transformation. Evaluation metrics include coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), Mean Absolute Error (%MAE), and Mean Squared Error (%MSE). Results indicate logarithmic transformation as the most effective, followed by square root transformation, in enhancing model accuracy. The surface roughness model utilizing data transformation exhibits high R-Sq and R-Sq(adj) values, at 0.8792 and 0.7434 respectively. On the other hand, this model has %MAE and %MSE values of only 10.33 and 2.05 respectively. Conversely, inverse transformation exhibits the least effectiveness among the three methods

Exploring effects of carbon, nitrogen, and phosphorus on greywater treatment by polyculture microalgae using response surface methodology and machine learning

The microalgae-based wastewater treatment is a promising technique that contribute to achieving sustainable development goals (SDGs), such as SDG-6, “Clean Water and Sanitation”. However, it is strongly influenced by the initial composition of wastewater. In this study, the impact of initial organics and nutrient concentration on the removal of total organic carbon (TOC), total carbon (TC), ammonium (NH4+), total nitrogen (TN), and phosphate (PO43−) from greywater using native polyculture microalgae was explored. Response surface methodology was employed along with two machine learning approaches, AdaBoost and XGBoost, to evaluate the interactions among three main factors: TOC, NH4+, and PO43−, and their effects on treatment efficiency. The C/N ratios for achieving maximum TOC and TC removal efficiency of 99.2% and 97.7% were determined to be 10.3, and 65.4–73.6, respectively. Notably, the N/P ratio did not significantly affect their removal. The highest NH4+ removal efficiency, reaching 96.2%, was attained at C/N ratios of 4.3, 24.0, 38.2, and 212.9, coupled with N/P ratios of 0.3, 2.6, and 23.4. Highest TN removal efficiency of 77.2% was achieved at C/N and N/P ratios of 12.2 and 2.0, respectively. Highest PO43− removal of 78.8% was obtained at N/P ratio 12.8. However, C/N ratio did not affect the removal efficiency. Maintaining these specified C/N and N/P ratios in the influent greywater would ensure that the treated greywater meets the required standards for various reuse applications, including flushing, groundwater recharge, and surface water discharge. The integration of RSM with AdaBoost and XGBoost provided accurate predictions of removal efficiencies. For all the models, XGBoost had the highest R2, and lowest MAE and MSE values. The cross validation of RSM models with AdaBoost and XGBoost further reinforced the reliability of these models in predicting treatment outcomes.

Chinese Ancient Paintings Inpainting Based on Edge Guidance and Multi-Scale Residual Blocks

Chinese paintings have great cultural and artistic significance and are known for their delicate lines and rich textures. Unfortunately, many ancient paintings have been damaged due to historical and natural factors. The deep learning methods that are successful in restoring natural images cannot be applied to the inpainting of ancient paintings. Thus, we propose a model named Edge-MSGAN for inpainting Chinese ancient paintings based on edge guidance and multi-scale residual blocks. The Edge-MSGAN utilizes edge images to direct the completion network in order to generate entire ancient paintings. It then applies the multi-branch color correction network to adjust the colors. Furthermore, the model uses multi-scale channel attention residual blocks to learn the semantic features of ancient paintings at various levels. At the same time, by using polarized self-attention, the model can improve its concentration on significant structures, edges, and details, which leads to paintings that possess clear lines and intricate details. Finally, we have created a dataset for ancient paintings inpainting, and have conducted experiments in order to evaluate the model’s performance. After comparing the proposed model with state-of-the-art models from qualitative and quantitative aspects, it was found that our model is better at inpainting the texture, edge, and color of ancient paintings. Therefore, our model achieved maximum PSNR and SSIM values of 34.7127 and 0.9280 respectively, and minimum MSE and LPIPS values of 0.0006 and 0.0495, respectively.

Stock price forecasting by supervised learning methods

Predicting stock prices has already long been a popular but challenging topic due to unavoidable uncertainties in various factors. With continuous exploration and development of machine learning algorithms over the years, they have become more and more popular in forecasting stock value changes. This paper aiming to predict future stock prices using returns from the past by applying three different supervised learning methods to the seemingly unpredictable dataset, namely Long Short-Term Memory (LSTM), Linear regression and Fully connected Neural Networks (FNN). R-squared and MSE were used as statistical indicators of the performance of each model. The calculated R-squared values for linear regression, LSTM and FNN were 0.876975, 0.898741 and 0.929504 respectively. In addition, the MSE of the corresponding models were 5.7786893, 0.0007542 and 0.0005237. As a conclusion, FNN performed the best in the sense that it put out the highest R-squared and lowest MSE value. As a result, FNN may take priority to the other manners in future predictions.

Artificial neural network (ANN) analysis on thermophysical properties of magnetohydrodynamics flow with radiation in an arc-shaped enclosure with a rotating cylinder

The objective of this research is to examine the thermophysical features of magnetic parameter (Ha) and time step (τ) in a lid-driven cavity using a water-based Al2O3 nanofluid and the efficacy of ANN models in accurately predicting the average heat transfer rate. The Galerkin weighted residual approach is used to solve a set of dimensionless nonlinear governing equations. The Levenberg-Marquardt back propagation technique is used for training ANN using sparse simulated data. The findings of the investigation about the flow and thermal fields are shown. Furthermore, a comparative study and prediction have been conducted on the impact of manipulating factors on the average Nusselt number derived from the numerical heat transfer analysis. The findings of the research indicate that, in the absence of magnetohydrodynamics, a rise in the Hartmann number resulted in a drop in both the fluid velocity profile and magnitude. Conversely, it was observed that the temperature and Nusselt number exhibited an increase under these conditions. The mean temperature of the fluid rises as the Hartmann number drops, reaching a peak value of 0.114 when Ha = 0. The scenario where Ha = 0, representing the lack of magnetohydrodynamics, shows the highest average Nusselt number, whereas the instance with Ha = 45 presents the lowest Nusselt number. The ANN model has a high level of accuracy, as seen by an MSE value of 0.00069 and a MAE value of 0.0175, resulting in a 99% accuracy rate.

ANN approach for the prediction of pyrolytic behaviour of pigeon pea stalk and insight into thermo-kinetics of pseudo components using RSM

Artificial neural network (ANN) is the widely used technique for prediction and modeling of complex processes. In this study, ANN was applied for predicting the complex pyrolysis behaviour by employing critical process parameters like heating rate (°C/min) and temperature (°C). Two anticipated variables in input layer were used to predict the derivative weight loss (%/°C) by propagating in twelve neurons. The model's reliability and adequacy were validated by the best training performance data recorded at 36 training epochs with an MSE value of 2.34 × 10−5. In order to estimate the thermo-kinetics of the Pigeon pea stalks (PPS), thermogravimetric analysis was done at four heating rates (10–40 °C/min). The TG-spectra were resolved into their pseudo components using deconvolution technique. The activation energy and pre-exponential factor were estimated by Friedman isoconversional method. In continuation, response surface method (RSM) was employed to explore the effect of process parameter on thermodynamic parameters ΔG, ΔH, and ΔS. The recorded statistical values illustrated the adequacy and robustness of the fitted model with anticipated variables. Pyrolytic reaction order was also determined for each pseudo components through master plot method.

EARTHQUAKE FREQUENCY DATA MODELING IN MENTAWAI USING FUZZY TIME SERIES LEE AND FUZZY TIME SERIES TSAUR

The Fuzzy Time Series (FTS) was first studied by Song and Chissom based on the theory of fuzzy sets and the concept of linguistic variables and their applications discovered by Zadeh. FTS has several models, namely FTS Lee, FTS Tsaur, and so on. In this study, we will model earthquake frequency data in Mentawai using FTS Lee and FTS Tsaur. The seismicity data used in this study is earthquake frequency data in the Mentawai which are calculated from 1960 to 2022. Additionally, the seismicity data source is taken from the U.S. Geological Survey catalog. Based on MAPE and MSE, the results obtained on the FTS Lee and FTS Tsaur models are MAPE values of 37,511% and 27,051%. And the MSE values obtained were 27,073 and 11,671. Thus, the best model used in modeling data on the frequency of earthquake occurrences in the Mentawai Islands is the Ruey Chyn Tsaur Fuzzy Time Series model.

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections

This research focuses on a comprehensive comparative analysis of shear strength prediction in slab–column connections, integrating machine learning, design codes, and finite element analysis (FEA). The existing empirical models lack the influencing parameters that decrease their prediction accuracy. In this paper, current design codes of American Concrete Institute 318-19 (ACI 318-19) and Eurocode 2 (EC2), as well as innovative approaches like the compressive force path method and machine learning models are employed to predict the punching shear strength using a comprehensive database of 610 samples. The database consists of seven key parameters including slab depth (ds), column dimension (cs), shear span ratio (av/d), yield strength of longitudinal steel (fy), longitudinal reinforcement ratio (ρl), ultimate load-carrying capacity (Vu), and concrete compressive strength (fc). Compared with the design codes and other machine learning models, the particle swarm optimization-based feedforward neural network (PSOFNN) performed the best predictions. PSOFNN predicted the punching shear of flat slab with maximum accuracy with R2 value of 99.37% and least MSE and MAE values of 0.0275% and 1.214%, respectively. The findings of the study are validated through FEA of slabs to confirm experimental results and machine learning predictions that showed excellent agreement with PSOFNN predictions. The research also provides insight into the application of metaheuristic models along with ANN, revealing that not all metaheuristic models can outperform ANN as usually perceived. The study also highlights superior predictive capabilities of EC2 over ACI 318-19 for punching shear values.

Optimal design of heat pipes for city gate station heaters by applying genetic and Bayesian optimization algorithms to an artificial neural network model

This study contains a novel idea to improve the performance of city gate station heaters, using pulsating heat pipes with optimized structure. In this research, a hybrid method including Bayesian and genetic algorithms is implemented to optimize the architecture of an artificial neural network (ANN) model and find the optimal operating point for heat pipes. In the previous study, the authors have proposed a neural network-based model to predict the thermal performance of heat pipes, based on a wide range of their design parameters, including dimensions of heat pipe, number of turns, working fluid, inclination angle, filling ratio, and heat input. In the present study, the prediction model architecture is optimized using Bayesian optimization algorithm. The best performance is achieved when iterations of the employed Bayesian optimization is set to 300 iterations or samples, which 150 samples are produced to train the Bayesian model and other 150 iterations are utilized in order to find the optimal ANN structure and hyper-parameter tuning of the model. The optimal ANN model with structure of 9-97-21-68-100-1 has evaluation parameters of 0.980 and 0.014 for R2 and MSE of total dataset, respectively. The performance metrics indicate an improvement in accuracy of the ANN model with a 53.33% reduction in MSE value and a 3.15% increase in R2 score. Then, the genetic algorithm is applied to the optimal model to find the optimum operating parameters of the heat pipe. In this regard, the design parameters of the best heat pipe in order to use in the city gate station heaters, are obtained by setting the genetic algorithm population equal to 300. Finally, as a practical case study, a real heater with capacity of 10,000 SCMH which is located in Iran is selected. Considering constraints and limitations of this heater, the proposed model indicates that a vertical pulsating heat pipe with four turns which is filled 55% with water as the working fluid, has the optimal thermal resistance of 0.104 K/W. Utilizing the optimal heat pipe in the mentioned heater is a promising solution for efficiency improvement and fuel consumption and CO2 emission reduction, which decreases the operating costs of the heater as well.