Regression Neural Network Research Articles

Risk prediction of computer investment database information management system based on machine learning algorithms

In recent years, with the continuous development of the financial market, the risk prediction of computer investment database information management systems (IMS) has high practical value. At present, there are risk issues in the information management system, which may cause drawbacks to investment data processing. To address these issues, this article used Machine Learning (ML) algorithms to analyze the risk prediction of computer investment database IMS. This article introduced and utilized typical Self-Organizing Map (SOM) and Artificial Neural Network (ANN) combination algorithms, regression algorithms, and Gradient Boosting Decision Tree (GBDT) algorithms to compare and analyze the prediction accuracy of these three algorithms. This article found that the GBDT algorithm has the highest prediction accuracy. Through a large amount of experimental data, it has been proven that the average testing accuracy using regression algorithms was 3.5% higher than that using neural network algorithms. It was found that the average test accuracy using the GBDT algorithm was 7.2% higher than the average test accuracy using the regression algorithm. The study also explores the combination of physiological and behavioral data collected by wearable devices to provide more comprehensive risk assessment and decision support, which provides an important reference for the optimization of enterprise risk management. Through this innovative data source integration, this paper provides a new perspective for the application and development of machine learning algorithms in computer investment database IMS.

Mapping rapeseed (Brassica napus L.) aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery

Aboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass. However, the saturation effect makes it challenging for spectral indices to accurately reflect crop changes at higher biomass levels. It is well established that rapeseed biomass during different growth stages is closely related to phenotypic traits. This study aims to explore the potential of using optical and phenotypic metrics to estimate rapeseed AGB. Vegetation indices (VI), texture features (TF), and structural features (SF) were extracted from UAV hyperspectral and ultra-high-resolution RGB images to assess their correlation with rapeseed biomass at different growth stages. Deep neural network (DNN), random forest (RF), and support vector regression (SVR) were employed to estimate rapeseed AGB. We compared the accuracy of various feature combinations and evaluated model performance at different growth stages. The results indicated strong correlations between rapeseed AGB at the three growth stages and the corresponding indices. The estimation model incorporating VI, TF, and SF showed higher accuracy in estimating rapeseed AGB compared to models using individual feature sets. Furthermore, the DNN model (R2 = 0.878, RMSE = 447.02 kg/ha) with the combined features outperformed both the RF (R2 = 0.812, RMSE = 530.15 kg/ha) and SVR (R2 = 0.781, RMSE = 563.24 kg/ha) models. Among the growth stages, the bolting stage yielded slightly higher estimation accuracy than the seedling and early blossoming stages. The optimal model combined DNN with VI, TF, and SF features. These findings demonstrate that integrating hyperspectral and RGB data with advanced artificial intelligence models, particularly DNN, provides an effective approach for estimating rapeseed AGB.

Feature importance and model performance for prediabetes prediction: A comparative study

ObjectivesPrediabetes is a significant health condition that elevates the risk of developing type 2 diabetes and other associated complications. This study aims to (1) explore the potential of machine learning models to improve the prediction of prediabetes, (2) compare the performance of various machine learning models with traditional regression methods, and (3) identify the most influential demographic, socioeconomic, and health-related factors associated with prediabetes. MethodsThis study utilized data from the 2021 Behavioral Risk Factor Surveillance System (BRFSS) and employed comprehensive data preprocessing techniques. Logistic regression analysis was conducted to assess correlations between features and prediabetes risk. Feature importance was quantified using Adjusted Mutual Information values. Multiple machine learning models, including Random Forest, K Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Neural Network, and Logistic Regression, were used for prediction. The best model was selected and validated through cross-validation to ensure robustness. ResultsSignificant associations were observed between prediabetes and key predictors such as cholesterol levels, BMI categories, hypertension status, age groups, and income categories. Among the models tested, Random Forest demonstrated the highest accuracy and robustness, outperforming traditional regression models. ConclusionsThis study highlights the potential of machine learning to enhance prediabetes prediction and underscores the importance of identifying high-risk individuals for early intervention. The findings contribute to population health strategies by integrating advanced analytical methods with public health data.

A designed predictive modelling strategy based on data decomposition and machine learning to forecast solar radiation

Consistent with the United Nations Sustainable Development Goal 7, the design and optimization of clean energy resources (e.g., solar energy) is a highly motivating task for all researchers globally who continue to build research synergies that can tackle the future likelihood of energy crises due to socioeconomic and strategic environmental policy. In this paper, a weekly solar radiation (SR) forecasting model is designed using a robust local mean decomposition (RLMD) technique unified with a random forest (RF) algorithm to generate a fully optimised hybridized RLD-RF model that has a promising capability to forecast the SR values. In the first stage of model design, the RLMD, a frequency resolution method, is applied to decompose the original SR time series into amplitude modulation subseries (AMs), frequency modulation subseries (FMs), and the low-frequency product functions (PFs) to reveal the internal structure of the model construction data to incrementally optimize the RLD-RF model where only PFs were used. Subsequently, the statistically significant lagged subseries at a week ahead forecasting horizon (t – 1) of the low-frequency PFs with residual components are extracted individually, via partial autocorrelation function (PACF), to capture the historical behaviour of frequency-resolved SR component in order to build a robust modelling framework. Consequently, the random forest (RF) algorithm is employed to forecast each of the subseries using PACF-based lagged inputs to construct a fully optimised hybrid RLMD-RF predictive model. RLMD-RF is benchmarked against a baseline RF, M5tree, and multiple linear regression (MLR), Artificial neural network (ANN) and Gaussian process regression (GPR) algorithms, including their hybridized counterparts (i.e., RLMD-M5tree, RLMD-MLR, RLMD-ANN, and RLMD-GPR) using statistical score metrics in the independent testing phase. The results generated at test sites in Queensland State, Australia that have high solar energy potential confirm that the RLMD-RF method can produce quality predictions of weekly solar radiation against the benchmarking comparison models. For instance, RLMD-RF for Barcaldine are higher in terms (EWI= 0.938, ENS= 0.878) against RLMD-MLR (EWI = 0.845, ENS = 0.705), the RLMD-M5tree (EWI = 0.836, ENS = 0.684), RLMD-ANN (EWI = 0.836, ENS = 0.715), RLMD-GPR (EWI = 0.839, ENS = 0.716), the RF (EWI = 0.720, ENS = 0.564), the M5tree (EWI = 0.692, ENS = 0.522), the MLR (EWI = 0.683, ENS = 0.508), the ANN (EWI = 0.708, ENS = 0.519) and the GPR (EWI = 0.708, ENS = 0.520). Similarly, the RLMD-RF also outperformed in Rockhampton, Clermont, and Lockyer Valley stations as compared to other models. This research establishes the practical usefulness of hybridised RLMD-RF modelling framework for accurate SR forecasting and advocates its possible consideration in renewable and sustainable energy production and monitoring systems that can aid in decision-making by energy utilities and stakeholders (e.g., climate and energy policy experts).

How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression

This study uses Artificial Neural Networks (ANNs) and multiple linear regression (MLR) models to explore the relationship between gait dynamics and the metabolic cost. Six nonlinear metrics—Lyapunov Exponents based on Rosenstein’s algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on Wolf’s algorithm (LyEW)—were utilized to predict the metabolic cost during walking. Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg–Marquardt backpropagation algorithm and compared with MLR models. Performance was assessed based on the mean squared error (MSE) and correlation coefficients. ANN models outperformed MLR, with DFA and Lyapunov Exponent models showing higher R2 values, indicating stronger predictive accuracy. The results suggest that gait’s nonlinear characteristics significantly impact the metabolic cost, and ANNs are more effective for analyzing these dynamics than MLR models. The study emphasizes the potential of focusing on specific nonlinear gait variables to enhance assistive device optimization, particularly for hip exoskeletons. These findings support the development of personalized interventions that improve walking efficiency and reduce metabolic demands, offering insights into the design of advanced assistive technologies.

Heart Disease Prediction Using Hybrid Model Integrating Artificial Neural Network, Decision Tree, and Logistic Regression

Heart disease, a leading cause of global mortality, necessitates accurate prediction for timely intervention. This study proposes a hybrid model amalgamating LR, DT and ANN algorithms to enhance heart disease prediction. Using a Kaggle dataset comprising 1025 patient records with 14 features, including age, sex, chest pain, and cholesterol levels, the hybrid model achieved an impressive 88% precision. This outperforms individual models, with DT achieving 99% accuracy, LR with 80%, and ANN with 86%. Evaluation metrics demonstrate competitive performance, affirming the hybrid model as a robust tool for cardiovascular ailment prediction. The study underscores the efficacy of combining diverse algorithms, leveraging their strengths for more effective predictive modeling in cardiovascular health assessment.

Aging trajectory prediction of lithium-ion batteries based on mechanical-electrical features via nonlinear autoregressive and regression neural networks

The prediction of the lifespan of lithium-ion batteries (LIBs) is crucial for gaining early insights into battery degradation, enabling the optimization of battery usage and management strategies. This work assesses the expansion and aging characteristics of LIBs under various operating conditions and develops a combined machine-learning method to predict the aging trajectory of LIBs based on mechanical and electrical features. To avoid errors caused by secondary data processing, five mechanical-electrical features are directly extracted from the stress and electrochemical characteristic curves to reflect the battery aging state. A nonlinear autoregressive (NAR) neural network is adopted to predict the change trend of aging features (AFs) using historical data. Subsequently, the nonlinear regression (NR) neural network is employed for aging trajectory prediction using the aging feature dataset, which consists of both historical and predicted data. The experimental results demonstrate that the proposed method can ensure that the mean absolute error (MAE) in aging trajectory prediction is within 0.25 %. The method comprehensively analyzes and utilizes the mechanical-electrical properties of LIBs to enhance the predictive accuracy, expanding the scope of the predictive model in practical applications.

Real-Time prediction of pool fire burning rates under complex heat transfer effects influenced by ullage height: A comparative study of BPNN and SVR

This research utilizes machine learning methods to forecast the complex, non-linear thermal phenomena, along with heat transfer mechanisms, that influence the burning rate of pool fires, especially with changes in ullage height. Experiments involving pool fires were systematically designed and carried out, incorporating different diameters and ullage heights. Heptane was used as the representative alkane fuels. A dataset containing more than 70,000 sets of data was created as a training dataset for training the Backpropagation Neural Network (BPNN) and Support Vector Regression (SVR) models. During the optimization of machine learning model parameters, this study is based on Particle Swarm Optimization (PSO) with the principle of intelligent optimization to efficiently and accurately screen and optimize the key parameters of the model. The combustion duration, pool dimensions, and non-dimensional ullage height were input into a machine-learning model to predict the burning rate. By comparing against experimental data, the model was found to be able to predict the dynamic evolution of the burning rate of the pool fire in a real-time manner. The SVR model demonstrates greater predictive accuracy in comparison to the BPNN model, and the relative prediction error remains within ± 20 %, which fully proves its effectiveness and generalization ability in the prediction of pool fire burning rate. The insights gained will offer substantial scientific backing for enhanced fire monitoring systems, while highlighting the capability of advanced machine learning methodologies to predict the intricate, real-time thermal dynamics and heat transfer characteristics of burning liquid fuels.

Unlocking the Secrets of Corn: Physiological Responses and Rapid Forecasting in Varied Drought Stress Environments

Understanding the intricate relationship between drought stress and corn yield is crucial for ensuring food security and sustainable agriculture in the face of climate change. This study investigates the subtle effects of drought stress on corn physiological, morphological, and spectral characteristics at different growth stages, in order to construct a new drought index to characterize drought characteristics, so as to provide valuable insights for maize recovery mechanism and yield prediction. Specific conclusions are as follows. Firstly, the impact of drought stress on corn growth and development shows a gradient effect, with the most significant effects observed during the elongation stage and tasseling stage. Notably, Soil and Plant Analyzer Development (SPAD) and Leaf Area Index (LAI) are significantly affected during the silking stage, while plant height and stem width remain relatively unaffected. Secondly, spectral feature analysis reveals that, from the elongation stage to the silking stage, canopy reflectance exhibits peak–valley variations. Drought severity correlates positively with reflectance in the visible and shortwave infrared bands and negatively with reflectance in the near-infrared band. Canopy spectra during the silking stage are more affected by moderate and severe drought stress. Thirdly, LAI shows a significant positive correlation with yield, indicating its reliability in explaining yield variations. Finally, the yield-related drought index (YI) constructed based on Convolutional Neural Network (CNN), Random Forest (RF) and Multiple Linear Regression (MLR) methods has a good effect on revealing drought characteristics (R = 0.9332, p < 0.001). This study underscores the importance of understanding corn responses to drought stress at various growth stages for effective yield prediction and agricultural management strategies.

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.

Python-based machine learning estimation of thermo-hydraulic performance along varying nanoparticle shape, nanofluid and tube configuration

In this research article, a Python-based machine learning model prediction study was conducted based on the study results obtained from sudden expansion tubes containing different expansion angles, dimpled fin structures and nanofluids, whose thermo-hydraulic performance was previously examined. In the study, Artificial Neural Network and Ridge regression models were used to make predictions on the average Nusselt number (Nu), average Darcy friction factor (f) and performance evaluation criteria (PEC). Physical variations of the sudden expansion tube were taken into account and a detailed comparison of the results was made. A superior average Nu was acquired as 172.45 %, 22.05 %, 17.18 %, 13.65 %, and 7.76 % compared to Ag-MgO/H2O, Al2O3/H2O (blade), CoFe2O4/H2O, Al2O3/H2O (cylindrical), and Al2O3/H2O (platelet), respectively. The highest Performance Evaluation Criteria (PEC) for Re= 2000 based on Al2O3/H2O (platelet) shows an increase of 4.84 %, 12.08 %, 11.76 %, 66.05 %, and 148.94 % compared to Al2O3/H2O (cylindrical), Al2O3/H2O (blade), CoFe2O4/H2O, Fe3O4/H2O, and Ag-MgO/H2O, respectively. From the results obtained, it was determined that Python-based Machine Learning approach which facilitates custom optimizations showed a significant performance with small margins of error in predicting the heat transfer parameters. The lowest error rates of machine learning and polynomial ridge regression models ranged from 0.2 % to 5.4 % for the unseen test set and the application of Python-based algorithms provided considerable savings in calculation time compared to conventional methods. On the other hand, using machine learning models with feature engineering has been found to increase model performance by at least 30 %. In these years when studies on the predictions of thermo-hydraulic studies are very rare in the literature, this study is intended to facilitate scientists, engineers and academicians who will further study on this subject.

Rate coefficients for C and O2 reactive collisions relevant to interstellar clouds from QCT and machine learning.

The chemical reactions between certain interstellar molecules are exothermic in nature and barrierless in the entrance channel, allowing these reactions to occur rapidly even at low astronomical temperatures, e.g., C and O2 interaction. Obtaining detailed rovibrational transition parameters for the reaction between C and O2, such as state-selected rate coefficients, is crucial for studying the associated atmospheric and astronomical environments. Hence, this work presents an approach that combines quasi-classical trajectory calculations with machine learning techniques based on Neural Network (NN) and Gaussian Process Regression (GPR) to determine state-selected rate coefficients. Employing this approach, we significantly reduced the computational requirements while simultaneously obtaining the accurate state-selected reaction cross sections and rate coefficients for the collision of C and O2. Both the NN-based and GPR-based models established in this work accurately predict the results calculated from explicit numerical calculations in the explored temperature range of 50-1500K, achieving a coefficient of determination R2 > 0.96. Most importantly, the current work provides the most comprehensive dataset of rovibrational rate coefficients of v = 0-4, j = 0-70 → v' = 0-15 for the astrophysical modeling of the C-O2 collision system.

Reconstructing urban vegetation evolution in China using multimodal deep learning and 30-years Landsat archive

To retrospect the impact of urbanization on vegetation, consistent and reliable maps that provide urban vegetation estimates are required. In this study, we make the first attempt to employ deep learning for urban vegetation mapping of 2120 cities in China using a 30-years Landsat archive (UV-30). We present a multimodal deep learning (MDL) model to combine features from 5 categories, namely reflective bands, remote sensing indices, texture variables, topographical variables, and time labels. We normalized elevation data locally to remove the inter-city differences, while retaining topographic details. We used time labels to account for spectral variability over time and Landsat sensors. The performance of the MDL models was compared with a vanilla deep neural network, random forest, and support vector regression. Results indicated that the fusion of different categories of features improved the prediction accuracy. The MDL performed best in most validation cities, and our results were superior to the fractional maps aggregated from high-resolution datasets in homogeneous urban landscapes. We further analyzed urban vegetation dynamics across the urban core-new town-outskirt gradients of different geographical regions and city sizes. We found a gradually diminishing de-greening trend in urban China, especially in urban cores and new towns. Nevertheless, we observed a more pronounced degradation of urban vegetation in the south regions of China compared to the north, and smaller cities show no evident urban vegetation increase. The suggested framework is valuable for urban vegetation assessments and facilitates a thorough understanding and management of urban ecosystems.

Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

AbstractModeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low‐quality well data which is sparsely and noisily sampled across time and space. The lack of consistent well data makes it difficult to evaluate the impact of 2017 and 2019 wet years on CV groundwater following a severe drought during 2012–2015. A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). The hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non‐parametric regression with GP is performed. We demonstrate the efficacy of GP‐DNN regression for modeling non‐stationary features in the well data with fast and reliable uncertainty quantification, as validated to be statistically consistent with the empirical data distribution from 90 blind wells across CV. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.

Prediction of municipal waste generation using multi-expression programming for circular economy: a data-driven approach.

The existing surge in municipal waste generation (MWG), characterized by swiftly changing and uncontrollable factors, poses a significant challenge to sustainable development. This prompted the need for improved predictive models to guide strategic waste management within the circular economy framework. This study aims to develop a predictive model using multi-expression programming (MEP) to assess MWG. The model was developed using historical data on socioeconomic and environmental factors and validated via comparative analyses with artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) using various evaluation metrics. The parametric and sensitivity analyses of the MEP model were also conducted. The MEP, ANN, RF, and MLR models have a coefficient of determination (R2) (for testing datasets) of 0.977, 0.974, 0.957, and 0.964, respectively. The MEP model is superior in terms of accuracy and performance for the prediction of MWG when compared to the other three models. The sensitivity analysis revealed the relative importance of each input variable in the established MEP model. The novelty of this research lies in the application of MEP to predict MWG and the formulation of a new mathematical model that links socioeconomic and environmental factors with MWG. The model can be used by waste management authorities to optimize waste collection, transportation, and disposal infrastructure for an effective circular economy and sustainable development. This model also aids in the development of effective waste management policies.

Breath Analysis Using Quartz Tuning Forks for Predicting Blood Glucose Levels Using Artificial Neural Networks.

Diabetes Mellitus (DM), a widespread metabolic disorder, poses lifelong health implications, demanding timely diagnosis and cautious monitoring for effective disease management. Traditional blood glucose tests are invasive and require medical expertise for intermittent checking, motivating the investigation of alternative, noninvasive methods. This study introduces an approach employing breath analysis through a set of 12 quartz tuning fork-based sensors enhanced using nanomaterials and dedicated artificial neural network (ANN) algorithms for data interpretation. The breath analysis methodology involves capturing unique breath signatures using the frequency-based sensor array. The accompanying neural network classification algorithm, customized for the sensor data, enables precise classification of data from 245 individuals as diabetic, prediabetic, or healthy. A neural network regression algorithm predicted blood glucose values and was compared with the actual values obtained from medical blood glucose measurement. The clinical relevance of the predicted blood glucose has been examined using error grids. The sensor array coupled with the ANN algorithm can identify diabetic, prediabetic, and control samples with 97% test accuracy. Blood glucose was predicted using neural network regression with a correlation coefficient of 0.89 and a mean square error of 0.13.

Reconstructing a Fine Resolution Landscape of Annual Gross Primary Product (1895–2013) with Tree-Ring Indices

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century.

Entrepreneurial firm creation and economic uncertainty: an explainable artificial intelligence approach

ABSTRACT Through this paper, we examined the relationship between macroeconomic factors and new entrepreneurial firms in France during the 2000–2020 period; this relationship included several uncertainties and shocks (the 9/11, the 2008 global financial crisis, the Brexit, COVID-19 Pandemic, and so forth). To this aim, we applied various advanced machine learning (ML) techniques, including the eXtreme Gradient Boosting (XGBoost) algorithm, Light Gradient-Boosting Machine (LightGBM) algorithm, Neural Network (NN), Random Forest (RF), and Linear Regression (LR). We also investigated SHapley Additive exPlanation (SHAP) framework for interpretation and analysis purposes. Furthermore, we compared the performance and forecasting accuracy of the five models. The study revealed three major results. First, the XGBoost algorithm provides the most accurate prediction of a firm’s creation. Second, a heterogeneous relationship exists between new business creation and the business cycle. Third, higher GDP and unemployment levels are associated with greater procyclicality and countercyclicality in entrepreneurship, respectively. Finally, the results of the study suggest the importance of developing effective financial assistance during economic downturns to encourage faster recovery in the formation of new businesses.

Simulation and optimization of cheese whey additive for value-added compost production: Hyperparameter tuning approach and genetic algorithm

Cheese whey is a difficult and costly wastewater to treat due to its high organic matter and mineral content. Although many management strategies are conducted for whey removal, its use in composting is limited. In this study, the effect of cheese whey in the composting of sewage sludge and poultry waste on compost quality and process efficiency was investigated. Also, valid and consistent simulations were developed with Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Neural Network Regression (NNR) Machine Learning (ML) algorithms. The results of all physicochemical parameters determined that 3% of cheese whey addition for both feedstocks improved the composting process's efficiency and the final product's quality. The best results obtained through hyperparameter tuning showed that Gaussian Process Regression (GPR) was the most effective modeling tool providing realistic simulations. The reliability of these simulations was verified by running the GPR process 50 times. MdAPE demonstrated the validity and consistency of the created process simulations. Moreover, a genetic algorithm was used to optimize these dependent simulations and achieved almost 100% desirability. Optimization studies showed that the effective cheese whey ratios were 3.2724% and 3.1543% for sewage sludge and poultry waste, respectively. Optimization results were compatible with the results of experimental studies. This study provides a new strategy for the recovery of cheese whey as well as a new perspective on the effect of cheese whey on both physicochemical parameters and composting phases and the modeling and optimization processes of the results.

Performance of artificial neural network compared to multi-linear regression in prediction of countermovement jump height

Previous research has used primarily linear regression models to predict jump height and establish contributors of performance. The purpose of this study was to compare the performance of artificial neural network (ANN) and multi-linear regression (MLR) in prediction of countermovement jump (CMJ) height and investigating the contribution of kinematic variables to CMJ performance. Thirty-four healthy young male athletes performed a total of 204 CMJ while eight kinematic variables (the hip, knee, and ankle angles at the begging of the concentric phase of CMJ, the hip and knee take-off angles, and the shoulder, hip, and knee maximum angular velocities) were used as inputs to ANN and MLR to predict CMJ height. The correlation coefficients between the jump height and the predicted value by the developed models indicated that ANN predict CMJ height better than MLR (R2 = 0.68 compared to R2 = 0.44). Moreover, the root mean squared error of prediction showed better performance of the ANN rather than the MLR (4.8 cm compared to 5.3 cm). The shoulder and hip maximum angular velocities were the most important contributors, and then the hip and knee take-off angles contributed to CMJ height. In conclusion, implementing ANN to identify key variables of performance may also be relevant for other sport skills.