Single Machine Learning Model Research Articles

A multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models

Agricultural water demand, groundwater extraction, surface water delivery and climate exhibit complex nonlinear relationships with groundwater storage in agricultural regions. As alternatives to computationally intensive physical models, data-driven machine learning methods are frequently employed as surrogates to capture these complex relationships, owing to their high computational efficiency. Inevitably, reliance on a single machine learning model may lead to underestimation of prediction uncertainty and potentially result in reduced accuracy. This paper presents a multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models. The key contributions of the research are threefold: 1) the development of a comprehensive input variable selection process considering the lag effects and driving mechanisms of groundwater level variations; 2) the implementation of an optimization-based hyperparameter tuning method to enhance the performance of individual machine learning models; and 3) the establishment of a multi-model integration framework based on a multi-layer nesting technique. This approach combines the outputs of multiple machine learning models to consolidate predictions and expand the hypothesis space. The effectiveness of the proposed framework is demonstrated through case studies in the Huaihe River Basin, a major agricultural region in China. The results show that the multi-layer nesting and integration approach outperforms the use of individual machine learning models, providing more accurate and reliable groundwater level predictions. This framework offers valuable insights for decision-makers and water resource managers, supporting sustainable groundwater management and addressing the challenges faced in agriculturally intensive areas.

Fluorescence hyperspectral imaging technology combined with chemometrics for kiwifruit quality attribute assessment and non-destructive judgment of maturity

Dry matter content (DMC), firmness and soluble solid content (SSC) are important indicators for assessing the quality attributes and determining the maturity of kiwifruit. However, traditional measurement methods are time-consuming, labor-intensive, and destructive to the kiwifruit, leading to resource wastage. In order to solve this problem, this study has tracked the flowering, fruiting, maturing and collecting processes of Ya'an red-heart kiwifruit, and has proposed a non-destructive method for kiwifruit quality attribute assessment and maturity identification that combines fluorescence hyperspectral imaging (FHSI) technology and chemometrics. Specifically, first of all, three different spectral data preprocessing methods were adopted, and PLSR was used to evaluate the quality attributes (DMC, firmness, and SSC) of kiwifruit. Next, the differences in accuracy of different models in discriminating kiwifruit maturity were compared, and an ensemble learning model based on LightGBM and GBDT models was constructed. The results indicate that the ensemble learning model outperforms single machine learning models. In addition, the application effects of the ‘Convolutional Neural Network’-‘Multilayer Perceptron’ (CNN-MLP) model under different optimization algorithms were compared. To improve the robustness of the model, an improved whale optimization algorithm (IWOA) was introduced by modifying the acceleration factor. Overall, the IWOA–CNN–MLP model performs the best in discriminating the maturity of kiwifruit, with Accuracytest of 0.916 and Loss of 0.23. In addition, compared with the basic model, the accuracy of the integrated learning model SG-MSC-SEL was improved by about 12%–20 %. The research findings will provide new perspectives for the evaluation of kiwifruit quality and maturity discrimination using FHSI and chemometric methods, thereby promoting further research and applications in this field.

Soft-Voting Ensemble Model: An Efficient Learning Approach for Predictive Prostate Cancer Risk

Prostate cancer is increasingly common among men. However, the process of diagnosing malignant disease is relatively complicated and time-consuming. Identifying benign or malignant tumors early can assist medical professionals in choosing appropriate treatment methods. Consequently, we introduce a soft-voting ensemble model comprising several single machine learning models such as Logistic Regression, Random Forest, XGBoost, LGBM, and Support Vector Machine for the classification task with the prostate cancer dataset. The dataset was divided into two parts for training and testing with a ratio of 67:33. The confusion matrix was used to evaluate the performance of both the individual and ensemble models. Experimental results show that ensemble models achieve performance ranging from 87.88% to 96.97%, which is 3% to 9% better than individual models, surpassing recent research. Integrating the strengths of individual models helps minimize errors, resulting in optimal classification with high accuracy and overall performance in the field of machine learning.

Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model

Drought can cause great harm and loss, so accurate and efficient drought prediction has certain research significance. In previous studies, a single machine learning model is often used to predict the factors related to drought, such as precipitation and temperature, to indirectly explain the influence degree of drought, but the overall prediction accuracy is low, which can not fully and effectively predict the nonlinear and non-stationary drought characteristic information. In this paper, the variational modal decomposition model (VMD) is used to decompose the meteorological drought time series, and the convolution neural network (CNN) and the improved bidirectional long short-term memory neural network (BiLSTM) are combined to construct the meteorological drought hybrid prediction model (VMD-CBiLSTM). The research results show that using VMD-CBiLSTM model to forecast the monthly daily evapotranspiration deficit index (DEDI) of three weather stations, compared with the results predicted by VMD-LSTM, VMD-BiLSTM and VMD-ARIMA models, the average prediction accuracy R2 is increased by 0.076, 0.034 and 0.328 respectively, and the average RMSE is decreased by 0.178, 0.094 and 1.373 respectively. Compared with single model, VMD-CBiLSTM can not only reduce the uncertainty of meteorological drought prediction caused by climate model heterogeneity, but also improve the accuracy of drought prediction in arid and semi-arid regions, which can provide reference for coping with drought occurrence and drought early warning in advance.

Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data

The distribution coefficient (Kd) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, Kd depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict Kd values for radionuclide adsorption in soils surrounding nuclear facilities in Republic of Korea. We integrated and trained three sub-networks reflecting different data domains: soil adsorption factors for physicochemical conditions, X-ray fluorescence (XRF) data, and X-ray diffraction (XRD) spectra for inherent soil properties. Our multimodal model achieved high performance, with a coefficient of determination (R2) of 0.84 and root mean squared error (RMSE) of 0.89 for natural log-transformed Kd. This is the first study to develop a multimodal model that simultaneously incorporates inherent soil properties and adsorption factors to predict Kd. We investigated influential peaks in XRD spectra and also revealed that pH and calcium oxide (CaO) were significant variables in soil adsorption factors and XRF data, respectively. These results promote the use of a multimodal model to predict Kd values by integrating data from different domains, providing a cost-effective and novel approach to elucidate the mechanisms of radionuclide adsorption in soil.

A hybrid model for predicting the carbon price in Beijing: a pilot low-carbon city in China

Beijing is one of the earliest pilot low-carbon cities in China. It was one of the first cities in China to establish a pilot carbon market to achieve this goal. As an emerging market, China’s carbon pricing mechanism is not yet complete. In this context, it is crucial for market managers and companies to predict carbon prices. This study uses a Prophet-EEMD-LSTM model to predict the carbon price in the Beijing carbon market, which significantly improves prediction performance. The advantage of this hybrid model is that it considers the particularities of carbon prices including trends, cyclical changes, and volatility. Considering that the carbon market has multiple complex characteristics, the carbon price is decomposed into multiple simple sequences using the Prophet and EEMD models. These simple sequences were predicted using an LSTM model. The hybrid model outperformed both econometric and single-machine learning models in terms of carbon price prediction. Based on the findings of this study, market managers and companies can take appropriate measures to prevent carbon price risks. These findings are conducive to the smooth operation of the carbon market, thereby providing sustainable support and guidance for the development of low-carbon cities.

Research on 3D geological modeling based on boosting integration strategy

Accurate geology modeling is critical in coal mining engineering for increasing mining efficiency and safety. Traditional 3D modeling approaches rely on drill hole data to fill in unknown geographical characteristics using interpolation or machine learning methods. However, the cost of individual borehole projects is high, and borehole distribution is sparse; the filling of sparse boreholes by interpolation smoothes the geologic data to some extent, and some abnormal geologic conditions cannot be shown in the interpolated geologic data; and the classification of subsurface space by machine learning is hampered by sparse training samples and model overfitting or underfitting. As a result, this paper proposes a method to reduce the complexity and uncertainty in the process of 3D geological modeling by integrating the classification results of multiple classifiers based on the machine learning classification model and constructing a 3D geological model based on gas extraction borehole data using the Boosting integration strategy. The results reveal that the Boosting integration model outperforms a single machine learning model in geological modeling, with improved classification accuracy and resilience. A comparative investigation of different classifiers demonstrates that the Boosting integrated model outperforms a single classifier in geological modeling. The geologic profile analysis confirms the dependability and stability of the Boosting integrated model, which can provide an effective solution for 3D geologic modeling of coal mine workings and is critical for increasing mining productivity and lowering accident risks.

Enhancing local road pavement condition prediction using Bayesian-optimized ensemble machine learning and adaptive synthetic sampling technique

ABSTRACT Pavement condition prediction helps road agencies to schedule maintenance, rehabilitation, and reconstruction, and to allocate limited funds and resources to such activities. Compared to state highways, pavement performance prediction for local roads has received relatively little attention in the literature due to perceptions of low importance, low levels of investment in data collection, poor data quality, and high variation within the data elements. Additionally, local road pavement condition data may suffer from dataset imbalance, often leading to unreliable condition predictions. Hence, this paper introduces a methodology to predict local pavement condition using various single estimator and ensemble machine learning (ML) models along with the adaptive synthetic sampling method. The study develops nine (9) Bayesian-optimised ML models: category boosting (CatBoost), adaptive boosting, decision tree, extra trees, gradient boosting, light gradient-boosting machine, k-nearest neighbour, random forest, and artificial neural network. The ensemble ML and CatBoost were found to exhibit the best model performance, with an average testing accuracy of 0.82, specificity of 0.81, sensitivity of 0.63, and F-measure of 0.61. These results underscore the efficacy of ensemble ML models in pavement condition prediction. The proposed approach can be beneficial to local road agencies in their long-term planning, scheduling, and budgeting.

A Novel Artificial Intelligence Prediction Process of Concrete Dam Deformation Based on a Stacking Model Fusion Method

Deformation effectively represents the structural integrity of concrete dams and acts as a clear indicator of their operational performance. Predicting deformation is critical for monitoring the safety of hydraulic structures. To this end, this paper proposes an artificial intelligence-based process for predicting concrete dam deformation. Initially, using the principles of feature engineering, the preprocessing of deformation safety monitoring data is conducted. Subsequently, employing a stacking model fusion method, a novel prediction process embedded with multiple artificial intelligence algorithms is developed. Moreover, three new performance indicators—a superiority evaluation indicator, an accuracy evaluation indicator, and a generalization evaluation indicator—are introduced to provide a comprehensive assessment of the model’s effectiveness. Finally, an engineering example demonstrates that the ensemble artificial intelligence method proposed herein outperforms traditional statistical models and single machine learning models in both fitting and predictive accuracy, thereby providing a scientific and effective foundation for concrete dam deformation prediction and safety monitoring.

Research on User Default Prediction Algorithm Based on Adjusted Homogenous and Heterogeneous Ensemble Learning

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the “dual-carbon” goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system.

Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. Despite significant advancements in crop yield forecasting, existing methods often struggle with integrating diverse sensor data and achieving high prediction accuracy under varying environmental conditions. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms, namely random forest (RF), partial least squares (PLS), ridge regression (RR), k-nearest neighbor (KNN) and extreme gradient boosting decision tree (XGboost), were utilized for multi-sensor data fusion, together with three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average), for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB + MS + Texture + TIR combination demonstrating the highest fusion performance (R2 = 0.660, RMSE = 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the accuracy of wheat yield prediction. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE = 0.668 t ha−1). It significantly outperformed both the second-layer ensemble methods of stacking (R2 = 0.668, RMSE = 0.673 t ha−1) and feature-weighted (R2 = 0.667, RMSE = 0.674 t ha−1), thereby exhibiting superior predictive capabilities. This finding highlighted the third-layer ensemble method’s ability to enhance predictive capabilities and refined the accuracy of wheat yield prediction through simple average ensemble learning, offering a novel perspective for crop yield prediction and breeding selection.

Research on internal leakage detection of the ball valves based on stacking ensemble learning

Abstract Natural gas is an important clean energy source that is mainly transported through pipelines. The ball valve is a crucial piece of control equipment for the pipeline transportation system for natural gas, and the failure of internal leakage of the ball valve will seriously affect the natural gas transmission and increase the risk of sudden safety accidents. In response to the problems of the limitations of a single machine learning model in the traditional ball valve internal leakage rate prediction methods and failure to qualitatively analyze unilateral and bilateral internal leakage recognition of ball valve, a study of ball valve internal leakage detection based on Stacking ensemble learning is proposed. A total of 15 time and frequency domain feature parameters were obtained by feature extraction of 125 and 96 sets of raw acoustic emission signals from the ball valve; the parameters of a single machine learning model were adjusted by Bayesian optimization grid search. An internal leakage rate prediction model and an internal leakage recognition model are constructed, and the proposed model is compared and analyzed with a single model through a field ball valve internal leakage test. The results indicate that the Stacking ensemble learning model outperforms each single machine learning model in terms of SMAPE (17.2583), RMSE (1.1009), and MAE (0.9375) for internal leakage rate prediction. The Stacking ensemble learning model outperformed the single machine learning model in terms of accuracy (1), recall (1), precision (1), FAR(0), and F1-score (1) for internal leakage recognition. Stacking ensemble learning significantly enhances the model's ability to detect internal ball valve leaks.

Unlocking Potential of Pyrochlore in Energy Systems via Soft Voting Ensemble Learning.

In traditional machine learning (ML)-based material design, the defects of low prediction accuracy, overfitting and low generalization ability are mainly caused by the training of a single ML model. Here, a Soft Voting Ensemble Learning (SVEL) approach is proposed to solve the above issues by integrating multiple ML models in the same scene, thus pursuing more stable and reliable prediction. As a case study, SVEL is applied to develop the broad chemical space of novel pyrochlore electrocatalysts with the molecular formula of A2B2O7, to explore promising pyrochlore oxides and accelerate predictions of unknown pyrochlore in the periodic table. The model successfully established the structure-property relationship of pyrochlore, and selected six cost-effective pyrochlore from the periodic table with a high prediction accuracy of 91.7%, all of which showed good electrocatalytic performance. SVEL not only effectively avoids the high costs of experimentation and lengthy computations, but also addresses biases arising from data scarcity in single models. Furthermore, it has significantly reduced the research cycle of pyrochlore by ≈ 22 years, offering broad prospects for accelerating the development of materials genomics. SVEL method is intended to integrate multiple AI models to provide broader model training clues for the AI material design community.

Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.

Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models

The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process.

Rapid and Non-Destructive Determination of the Protein and Fat Contents in Wheat by Hyperspectral Imaging Combined with AdaBoost-SVR Modeling

Wheat protein and fat contents directly affect the flavor and yield of liquor. This study utilized hyperspectral imaging (HSI) technology along with an integrated learning model to rapidly and non-destructively detect protein and fat contents in wheat. The hyperspectral data of the wheat grains was preprocessed using various methods. The competitive adaptive reweighted sampling (CARS) algorithm, the successive projections algorithm (SPA), and the combination of the two algorithms (CARS-SPA) were used to extract the characteristic wavelengths. Three support vector regression (SVR) models were created to forecast wheat protein and fat contents using full-band spectral data and characteristic wavelengths. These models are gray wolf optimized support vector regression (GWO-SVR), particle swarm optimized support vector regression (PSO-SVR), and support vector regression-based integrated learning (AdaBoost-SVR). Comparing the models showed that the AdaBoost-SVR model, utilizing the Eigen wavelengths extracted through the CARS-SPA and CARS algorithms, stood out as the most accurate in predicting wheat protein and fat contents, with Rp 2 values of 0.9789 and 0.9651, respectively. The study findings indicate that combining HSI with AdaBoost-SVR integrated learning modeling enables rapid, non-destructive determination of wheat protein and fat contents with greater accuracy than using a single machine learning (ML) model.

Bayesian optimization-enhanced ensemble learning for the uniaxial compressive strength prediction of natural rock and its application

Engineering disasters, such as rockburst and collapse, are closely related to structural instability caused by insufficient bearing capacity of geological materials. Uniaxial compressive strength (UCS) holds considerable significance in rock engineering projects. Consequently, this study endeavors to devise efficient models for the expeditious and economical estimation of UCS. Using a dataset of 729 samples, including the Schmidt hammer rebound number, P-wave velocity, and point load index data, we evaluated six algorithms, namely Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Extra Trees (ET) and utilized Bayesian Optimization (BO) to optimize the aforementioned algorithms. Moreover, we applied model evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Variance Accounted For (VAF), Nash-Sutcliffe Efficiency (NSE), Weighted Mean Absolute Percentage Error (WMAPE), Coefficient of Correlation (R), and Coefficient of Determination (R2). Among the six models, BO-ET emerged as the most optimal performer during training (RMSE ​= ​4.5042, MAE ​= ​3.2328, VAF ​= ​0.9898, NSE ​= ​0.9898, WMAPE ​= ​0.0538, R ​= ​0.9955, R2 ​= ​0.9898) and testing (RMSE ​= ​4.8234, MAE ​= ​3.9737, VAF ​= ​0.9881, NSE ​= ​0.9875, WMAPE ​= ​0.2515, R ​= ​0.9940, R2 ​= ​0.9875) phases. Additionally, we conducted a systematic comparison between ensemble and traditional single machine learning models such as decision tree, support vector machine, and K-Nearest Neighbors, thus highlighting the advantages of ensemble learning. Furthermore, the enhancement effect of BO on generalization performance was assessed. Finally, a BO-ET-based Graphical User Interface (GUI) system was developed and validated in a Tunnel Boring Machine-excavated tunnel.

EcGBMsub: an integrative stacking ensemble model framework based on eccDNA molecular profiling for improving IDH wild-type glioblastoma molecular subtype classification.

IDH wild-type glioblastoma (GBM) intrinsic subtypes have been linked to different molecular landscapes and outcomes. Accurate prediction of molecular subtypes of GBM is very important to guide clinical diagnosis and treatment. Leveraging machine learning technology to improve the subtype classification was considered a robust strategy. Several single machine learning models have been developed to predict survival or stratify patients. An ensemble learning strategy combines several basic learners to boost model performance. However, it still lacked a robust stacking ensemble learning model with high accuracy in clinical practice. Here, we developed a novel integrative stacking ensemble model framework (ecGBMsub) for improving IDH wild-type GBM molecular subtype classification. In the framework, nine single models with the best hyperparameters were fitted based on extrachromosomal circular DNA (eccDNA) molecular profiling. Then, the top five optimal single models were selected as base models. By randomly combining the five optimal base models, 26 different combinations were finally generated. Nine different meta-models with the best hyperparameters were fitted based on the prediction results of 26 different combinations, resulting in 234 different stacked ensemble models. All models in ecGBMsub were comprehensively evaluated and compared. Finally, the stacking ensemble model named "XGBoost.Enet-stacking-Enet" was chosen as the optimal model in the ecGBMsub framework. A user-friendly web tool was developed to facilitate accessibility to the XGBoost.Enet-stacking-Enet models (https://lizesheng20190820.shinyapps.io/ecGBMsub/).

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Machine learning forecasting of solar PV production using single and hybrid models over different time horizons

This study uses operational data from a 180 kWp grid-connected solar PV system to train and compare the performance of single and hybrid machine learning models in predicting solar PV production a day-ahead, a week-ahead, two weeks ahead and one month-ahead. The study also analyses the trend in solar PV production and the effect of temperature on solar PV production. The performance of the models is evaluated using R2 score, mean absolute error and root mean square error. The findings revealed the best-performing model for the day ahead forecast to be Artificial Neural Network. Random Forest gave the best performance for the two-week and a month-ahead forecast, while a hybrid model composed of XGBoost and Random Forest gave the best performance for the week-ahead prediction. The study also observed a downward trend in solar PV production, with an average monthly decline of 244.37 kWh. Further, it was observed that an increase in the module temperature and ambient temperature beyond 47 °C and 25 °C resulted in a decline in solar PV production. The study shows that machine learning models perform differently under different time horizons. Therefore, selecting suitable machine learning models for solar PV forecasts for varying time horizons is extremely necessary.