Root Mean Square Error Values Research Articles

Machine Learning-Based Software for Predicting Pseudomonas spp. Growth Dynamics in Culture Media

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R2adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R2adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, Bf: 0.998 to 1.047; accuracy factor, Af: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology.

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

AbstractThis study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

NH4 Modelling with ARIMA and LSTM

AI-Mining is a prototype designed to detect various environmental gases, including CO2, NH3, NH4, and hydrogen, alongside temperature, pressure, and humidity. This study emphasizes the importance of modeling NH4 time series data due to its critical role in environmental and health monitoring. Accurate NH4 predictions facilitate early pollution detection and timely greenhouse gas control interventions. The study investigates the effectiveness of AI-Mining in detecting and predicting gas levels, focusing on data collection and analysis. Initial data analysis employed the Autoregressive Moving Average (ARIMA) model, specifically ARIMA (1,1,1), described by the equation yt = 0.0311 - 0.0750yt-1 + 0.3842εt-1. Despite its use, ARIMA's Root Mean Square Error (RMSE) performance was found lacking compared to more advanced methods. Given the classification of the obtained data as big data and time series, the Long Short-Term Memory (LSTM) method was also applied. The LSTM model initially used two layers with tanh and relu activation functions, and its performance was further explored by adding a third layer and varying the number of neurons (64, 128, and 256). The Adam optimizer was consistently used across all LSTM variations. Results indicated that increasing layers and neurons did not significantly impact LSTM's performance, with RMSE values around 0.023. However, LSTM consistently outperformed ARIMA in prediction accuracy, highlighting its robustness and reliability. Consequently, the study recommends using LSTM for predicting other recorded data in AI-Mining, underscoring its superiority in handling complex environmental datasets.

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Accuracy of recent intraocular lens power calculation methods in post-myopic LASIK eyes

This retrospective study compared postoperative prediction errors of recent formulas using standard- or total keratometry (K or TK) for intraocular lens (IOL) power calculation in post-myopic LASIK patients. It included 56 eyes of 56 patients who underwent uncomplicated cataract surgery, with at least 1-month follow-up at Keio University Hospital in Tokyo or Hayashi Eye Hospital in Yokohama, Japan. Prediction errors, absolute errors, and percentage of eyes with prediction errors within ± 0.25 D, ± 0.50 D, and ± 1.00 D were calculated using nine formulas: Barrett True-K, Barrett True-K TK, Haigis-L, Haigis TK, Pearl-DGS, Hoffer QST, Hoffer QST PK, EVO K, and EVO PK. Statistical comparisons utilized Friedman test, Conover’s all-pairs post-hoc, Cochran’s Q, and McNemar post-hoc testing. Root-Mean-Square Error (RMSE) was compared with heteroscedastic testing. Barrett True-K TK had the lowest median predicted refractive error (-0.01). EVO PK had the smallest median absolute error (0.20). EVO PK had the highest percentage of eyes within ± 0.25 D of the predicted value (58.9%), significantly better than Haigis-L (p = 0.047). EVO PK had the lowest mean RMSE value (0.499). The EVO PK formula yielded the most accurate IOL power calculation in post-myopic LASIK eyes, with TK/PK values enhancing accuracy.

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities.Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Biofilm characterization and dynamic simulation of advanced rope media reactor for the treatment of primary effluent.

Biofilm modeling is inherently complex, often requiring multiple assumptions and simplifications. In biofilm modeling, default or literature-based values in biofilm systems are usually used to estimate biofilm parameters, including boundary layer, biofilm density, thickness, attachment, and detachment rates. This study aimed to characterize and model the biofilm of a specific rope-type fixed media system, removing carbon and total inorganic nitrogen, coupled with sensitivity analysis. Among the five model parameters, the sensitivity analysis of this study showed that boundary layer thickness is the most influential parameter for predicting effluent ammonia and nitrate concentrations, and biofilm density is most sensitive with respect to effluent chemical oxygen demand (COD). The least sensitive parameter is the detachment rate. Based on the calculated mean absolute error (MAE) and root mean squared error (RMSE), the calibrated BioCord fixed-film reactor (BFFR) model accurately predicted effluent ammonium and dissolved oxygen (DO) in the continuously aerated bench-scale reactor (R1) and failed to predict well in the intermittently aerated bench-scale reactor (R2). RMSE values calculated for NH3-N and DO in R1 are 0.95 and 0.53 mg/L, respectively. In the BioCord pilot plant's case, ammonium-N predicted by the model fit the measured values well, while it overpredicted DO concentrations. PRACTITIONER POINTS: Fixed biofilm BioCord reactors were studied for primary effluent treatment. A methodology was developed to characterize biofilms. Boundary layer thickness is the most influential parameter for predicting effluent ammonia and nitrate concentrations. Biofilm density is the most sensitive parameter with respect to effluent COD. The calibrated BFFR model can predict effluent ammonium, nitrite, and nitrate-nitrogen.

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN>CN>DN.AN), Hata model (DN>BN>CN>AN), Cos-231 Hata model (CN>AN>BN>DN), and Egli model (AN>BN>DN>CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Machine learning prediction of microhardness and corrosion current density of Ni-Co alloys produced by scanning jet electrodeposition

ABSTRACT Scanning jet electrodeposition (SJE) is a promising method for electrodeposition. Integrated with automation and machine learning, intelligent electrodeposition can be achieved. In this study, a machine learning method was used to develop the relationship between the process parameters and properties of Ni-Co alloys produced by SJE. To evaluate the various models, the coefficient of determination (R 2), mean absolute percentage error (MAPE), and root mean square error (RMSE) were calculated as performance metrics. The multivariate nonlinear regression (MNR) model shows a higher R 2, lower MAPE and RMSE values, and better performance in both the training and testing sets, indicating superior approximation and prediction accuracies for microhardness. A back-propagation artificial neural network (BP-ANN) shows stable R 2 value of closest to 1, and the lowest MAPE and RMSE for corrosion current density and presents an exceptional generalisation ability. Finally, under the present experimental conditions, the highest microhardness and corresponding process parameters were determined using the MNR model, and the lowest corrosion current density was optimised using the BP-ANN model.

Data protection enhancement in smart grid communication: An efficient multi-layer encrypting approach based on chaotic techniques and steganography

In this study, a multi-layer encryption system is proposed through which sensitive and important data in smart grids can be secured from unauthorized persons as well as cyber threats. The suggested system is analyzed using fundamental performance assessment metrics say as average processing times across ten runs, peak signal-to-noise ratio (PSNR), and root mean square error (RMSE). The encryption process involves hiding the data encrypted by a digital dictionary and the advanced encryption standard (AES) algorithm in an image. In this way, AES encryption suggests rapid processing with a short average time per each block; similarly, the decryption technique, which includes the dictionary and AES decryption also renders the results in a justified computational time nominating it as a practical approach. It is worth noting that the small key era and dictionary construction instances provoke innovations in the general efficiency of the encryption mechanism. The conducted study, based on recorded values for PSNR, RMSE, UACI, and NPCR, demonstrates that the proposed multi-layer encryption system, which incorporates dictionary encryption and AES, is a robust and a fast method to shield important information and attaining a balance between protection and computational time.

Enhancing Soil Texture and Bulk Density Mapping Using Soil Grids and Machine Learning: A Comparative Analysis with Observed Data

Digital soil mapping plays a crucial role in understanding soil variability and informing sustainable land management practices. This study focuses on the Kurdistan Region of Iraq (KRI), evaluating the accuracy of SoilGrids, a global-scale soil mapping initiative, and exploring the efficacy of machine learning algorithms in refining soil properties estimations. The aim of this research was to assess and represent the physical parameters of soils effectively by comparing ground truth soil sampling data with data obtained from SoilGrids regarding clay, silt, and sand fractions and bulk density. Comparative analyses were conducted between ground truth soil sampling data and SoilGrids predictions, revealing significant differences across soil mineral fractions including clay, silt, sand fractions, and bulk density. The results showed that the mean clay fraction in the ground truth dataset differed notably from SoilGrids estimation, with a Mean Absolute Deviation (MAD) of 124.0 g kg-1 and Root Mean Square Error (RMSE) of 152.5. However, the integration of machine learning algorithms, particularly the Extreme Gradient Boosting (XG Boost) algorithm, showed promising results in improving accuracy. The XG Boost algorithm exhibited a relatively low MAD of 97.9 g kg-1 for clay fractions, indicating a better approximation of observed values compared to SoilGrids. Significant percent improvements in RMSE and Mean Absolute Percentage Error (MAPE) values were observed across soil fractions and bulk density measurements, ranging from approximately 15% for clay to 35% for sand fractions and 20% for bulk density. These findings highlight the importance of integrating advanced mapping techniques and machine learning algorithms to enhance soil mapping methodologies. Moving forward, efforts to expand ground truth datasets through targeted soil sampling campaigns and develop international collaboration initiatives will be crucial for improving the accuracy and reliability of soil mapping products in the KRI. By incorporating advanced mapping approaches, we can better support sustainable land management practices and environmental conservation efforts in the region.

Predicting food consumption using contextual factors: An application of machine learning models

Abstract Background Improving diet quality relies on making manageable adjustments to eating behaviours. Personalised nutrition interventions hold promise for modifying behaviour. Machine learning (ML) offers a novel approach to examining dietary behaviours in personalised nutrition by leveraging data on past behaviours and environmental contexts. This study aims to investigate whether contextual factors at eating occasions (EO) can predict food consumption to enhance diet quality. Methods Cross sectional data from the Measuring Eating in Everyday Life Study (MEALS) were analysed (n = 675, 18-35y). A smartphone food diary app recorded dietary intakes at EO for 3-4 non-consecutive days, also capturing social-environmental (e.g., activity) and physical-environmental factors (e.g., consumption location). Participant characteristics were collected via an online survey. Food groups intake (servings per EO) followed Australian Dietary Guidelines. This study benchmarked two established models, gradient boost decision tree and random forest, which have previously shown high performance in similar tasks. Performance was evaluated using 10-fold cross-validation, measuring mean absolute error (MEA), root mean square error (RMSE), and R squared. Feature importance analysis identified key variables for predicting food consumption. Results ML predicts most food groups at EO using contextual factors, with slight differences between actual and predicted consumption (<1 serving per EO). For fruits, dairy, and meat, MEA values were 0.35, 0.34, and 0.56 servings, respectively (RMSE values: 0.61, 0.50, and 0.80 servings). Self-efficacy, age and consumption location were influential in most ML models. Conclusions ML offers insights into contextual factors and food consumption, suggesting directions for precision nutrition interventions. Future research should identify positive influences of contextual factors on dietary behaviours and incorporate these insights into interventions. Key messages • Machine learning can effectively predict food consumption based on contextual factors at eating occasions. • Understanding the influence of contextual factors such as consumption location on food consumption can inform the development of precision nutrition interventions aimed at improving diet quality.

COMPARISON OF ARIMA AND LSTM METHODS IN PREDICTING JAKARTA SEA LEVEL

As a coastal city, Jakarta faces enormous risks from sea level rise brought on by climate change, and it is critical to create efficient plans to anticipate and minimize any potential negative effects. Predictive modeling is essential in addressing this challenge in order to anticipate and mitigate any potential negative effects of sea level rise. Therefore, research was conducted with the aim of comparing the performance of two prediction methods, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). Sea level was predicted using both techniques up to the end of 2023. Performance indicators, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), were employed to assess the quality of both prediction models. The result shows that the ARIMA (1,1,4) model is more effective in predicting sea level than the LSTM. The MAE, MAPE, and RMSE values for ARIMA (1,1,4) are 7.19, 4.86%, and 10.35, respectively. In the meantime, the sea level in Jakarta is predicted to remain reasonably steady, according to the forecasted findings from both models. This study is expected to make a significant contribution to understanding and mitigating the potential impacts of sea level rise in Jakarta as a result of climate change.

Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models

Study regionThe Kaidu River Basin originates from the southern slope of the Tienshan Mountains in the Xinjiang Uygur Autonomous Region, China. Study focusAccurate runoff simulation and prediction significantly affect flood control, drought resilience, and water resource allocation decisions. This study establishes the GR4J-6 model (modèle du Génie Rural à 4 paramètres Journalier-6, including a snowmelt module) and integrates it with the LSTM (Long Short-Term Memory) model to construct the hybrid GR4J-6-LSTM model and enhance the simulation accuracy of snowmelt runoff. A case study is conducted in the Kaidu River Basin to demonstrate the applicability of these models in cold and arid regions. The accuracy of the GR4J-6, LSTM, and GR4J-6-LSTM models is evaluated using Nash-Sutcliffe efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Squared Error (RMSE) metrics. In addition, the contributions of each feature variable in the models are analyzed using the SHapley Additive exPlanations (SHAP) method to enhance the reliability of the results. New hydrological insights for the regionThe GR4J-6 model demonstrated good applicability in the Kaidu River Basin, with NSE, KGE, and RMSE values of 0.69, 0.79, and 39.39 m3/s during the validation period, respectively. The hybrid model GR4J-6-LSTM exhibited the highest comprehensive accuracy among all the models, with NSE, KGE, and RMSE values of 0.84, 0.87, and 28.79 m3/s, respectively. In the LSTM model, temperature and precipitation were found to significantly influence the simulated runoff, indicating that higher temperature and precipitation lead to increased runoff. In the GR4J-6-LSTM model, Tmin (minimum temperature) and the hydrological feature variable Qsim exhibited a strong positive correlation with simulated runoff, as Tmin and Qsim increased, they promoted stronger flow production. This study provides a framework for runoff simulation in snowmelt river basins, offering a reference for projecting extreme hydrological events under climate change.

A novel hybrid model for predicting the bearing capacity of piles

Due to the uncertainty of soil condition and pile design characteristics, it is always a challenge for geotechnical engineers to accurately determine the bearing capacity of piles. The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. The improved PSO algorithm was used to optimize the LSSVM hyperparameters. The performance of the IPSO-LSSVM model was compared with seven artificial intelligence models, namely adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5MT), multivariate adaptive regression splines (MARS), gene expression programming (GEP), random forest (RF), regression tree (RT) and a stacked ensemble model. Six statistical indices (e.g., coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), BIAS and discrepancy ratio (DR)) were used to evaluate the performance of the models. The R2, MAE, RMSE, RRMSE and BIAS values of the IPSO-LSSVM model were 1, 4.27 kN, 6.164 kN, 0.005 and 0, respectively, for the training datasets and 0.9977, 22 kN, 36.03 kN, 0.0275 and –11, respectively, for the testing datasets. Compared with the ANFIS, MARS, GEP, M5MT, RF, RT and the stacked ensemble models, the proposed IPSO-LSSVM model shows high accuracy and robustness on the test datasets. In addition, the sensitivity, uncertainty, reliability and resilience of the IPSO-LSSVM model were also analyzed in this study. First published online 22 October 2024

Experimental study and mathematical modeling of drying kinetics of mint leaves under forced convection

In this study, we investigated the influence of temperature and air velocity on the drying behavior of Mentha spicata L. leaves using a forced convection dryer. This drying method was chosen for its cost-effectiveness and its ability to facilitate fast and efficient drying while preserving the quality of aromatic herbs. Our aim was to identify the most suitable mathematical model for accurately representing the drying process. We analyzed experimental data on moisture content obtained during thin-layer drying of Mentha spicata L. using various established empirical models from existing literature. Thirteen empirical models were applied to fit the drying curves obtained. Statistical analysis, employing key parameters such as the coefficient of determination (R2), reduced chi-square (χ2), and root mean square error (RMSE), indicated that the Midilli and Kucuk model provided the most accurate fit for the observed drying curves. This was evidenced by the highest R2 values (0.9994, 1.0000), lowest χ2 values (6.07 × 10−6, 6.28 × 10−5), and lowest RMSE values (2.17 × 10−3, 6.86 × 10−3). We observed an increasing trend in moisture diffusivity with temperature variations between 32 °C and 60 °C, while maintaining a constant airflow velocity of 1.2 m/s. Additionally, an increase in mean air velocity from 0.7 to 1.7 m/s correlated with higher moisture diffusivity. The effectiveness of the Arrhenius equation in representing the temperature dependence of diffusivity was confirmed, with an activation energy of 72 kJ/mol, consistent with previous literature findings.

Wind power prediction based on deep learning models: The case of Adama wind farm

Wind is a renewable energy source that is used to generate electricity. Wind power is one of the suitable solutions for global warming since it is free from pollution, doesn't cause greenhouse effects, and it is a natural source of energy. However, Wind power generation highly depends on weather conditions. It is very difficult to easily predict the amount of power generated from wind at a particular instant in time. Adama wind power farm is one of the wind farms in Ethiopia. There is no accurate and reliable forecasting model for the Adama wind farm that enables the forecasting of the power generated from the farm. The main objective of this research is to develop a wind power forecasting model for the Adama wind farm using deep learning techniques. Forecasting of wind power generation capacity involves appropriate modeling techniques that use past wind power generation data. The experiments have been conducted using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). To achieve the highest forecasting accuracy, four years of data (from 2016 to 2019), with 5-min intervals, have been collected with a total of 163,802 rows. For hyperparameter optimization grid search and random search techniques have been utilized. The performances of the proposed deep learning models were investigated error metrics, including Mean Absolute Errors (MAE) and the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R squared (R2). Bi-LSTM outperforms the other two algorithms, scoring 0.644, 0.388, 0.769 and 0.978 MAE, MAPE, RMSE and R2 values respectively. Such wind power forecasting helps energy planners and regional power providers to compute power production and energy generated from other sources.

P10.24.B PREDICTION OF PCFDNA LEVELS, PROGRESSION-FREE SURVIVAL, AND OVERALL SURVIVAL THROUGH MACHINE-LEARNING MODELS BASED ON MRI-DERIVED RADIOMIC FEATURES IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

Abstract BACKGROUND Circulating cell-free DNA (ccfDNA) is a promising tool for monitoring patients with high-grade gliomas (HGGs) in addition to follow-up with conventional (cMRI) and advanced MRI (aMRI), which harbors an enormous amount of quantitative subvisual data that can be used to build models predicting disease behavior. Examining the relationship between ccfDNA levels and quantitative features extracted from cMRI and aMRI gives further insight into this novel biomarker, aiming to combine the biological value of ccfDNA and the spatial resolution of image biomarkers to improve HGGs monitoring. MATERIAL AND METHODS Features were extracted from each T1 post-contrast and FLAIR images by using the FLAIR hyperintensity and enhancement automatic segmentation tool (Pyradiomics software, v2.2.0). The radiomic dataset underwent Z-score normalization, feature reduction, and feature selection. Highly correlated features were excluded using a threshold of 0.9 person-correlation-coefficient. The ‘F_regression’ function was used to select the most important features concerning the ccfDNA concentration, Overall survival (OS), and progression-free survival (PFS). This dataset was used as input for four machine learning models: Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), and least absolute shrinkage and selection operator (LASSO). Hyperparameter tuning was performed at the first training iteration of each model using a 4-fold cross-validation GridSearchCV and model-specific parameters. The train-test strategy involved randomizing datasets into 80% training and 20% testing subsets across 100 iterations, ensuring robustness and reducing biases. RMSE values of each run were averaged based on sample test frequencies. Root means square error (RMSE) was used to assess the model’s performance. RESULTS 405 features were successfully extracted from T1 post-contrast and FLAIR images from both tumoral masks. A total of 287 were found to be highly correlated and, therefore, removed from the dataset. The ‘F_regression’ with a 90-percentile threshold separates 12 features divided into 3 shapes features relative to FLAIR segmentation, 6 features relative to the T1 post-contrast image, and 3 features for the FLAIR image. The SVR achieves the best RMSE performances when compared to other models when predicting the ccfDNA concentration, reaching an RMSE of 8.58. An analogous strategy was used for the models built to predict PFS and OS reaching an RMSE of 5.89 and 6.77, respectively. CONCLUSION Machine-learning models based on radiomic features combined with clinical variables are valuable tools for predicting OS and PFS in HGGs’ patients. The radiomic signature provides additional information compared to the tumoral volume to forecast ccfDNA levels. Combining both techniques could provide more precise assessment of disease status.

Spatio-Temporal Model to Forecast COVID-19 Confirmed Cases in High-Density Areas of Malaysia

The coronavirus 2019 disease has spread across the world. The number of coronaviruses 2019 (COVID-19) cases throughout Malaysia is high in the densely populated state of Selangor. In assisting the early preventive measures, this study utilises time series methods to model and forecast the number of daily positive cases in three Selangor districts: Petaling, Hulu Langat, and Klang. Specifically, the study compares the effectiveness of the Autoregressive Integrated Moving Average (ARIMA), a univariate model and the Generalized Space-Time autoregressive integrated (GSTARI), a multivariate model. For the GSTARI model, uniform and inverse distance weights represent the relationship between locations. The analysed data are from January to August 2021, and the lowest root mean square error (RMSE) is chosen as the best model. The results show GSTARI (1,1) with both spatial weights outperformed ARIMA (0,1,1) in Petaling and Klang but not in Hulu Langat. However, the average RMSE values show that the most accurate and effective for forecasting the number of daily confirmed positive cases in Selangor is using GSTARI. In conclusion, by utilising advanced time series methods such as spatial analysis, this study provides important insights into forecasting trends of infectious diseases like COVID-19 and can help in early preventive measures.