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• It is possible to obtain good classification results of cardiovascular autonomic neuropathy using supervised machine learning. • The best classifier is the random forest classifier with a F1-score of 60% on the reduced dataset. • In the ensembled data no standard clinical biochemical analytes were present in the 10 best classifiers. Using supervised machine learning to classify the severity of cardiovascular autonomic neuropathy (CAN). The aims were 1) to investigate which features contribute to characterising CAN 2) to generate an ensembled set of features that best describes the variation in CAN classification. Eighty-two features from demographic, beat-to-beat, biochemical, and inflammation were obtained from 204 people with diabetes and used in three machine-learning-classifiers, these are: support vector machine, decision tree, and random forest. All data were ensembled using a weighted mean of the features from each classifier. The 10 most important features derived from the domains: Beat-to-beat, inflammation markers, disease-duration, and age. Beat-to-beat measures associate with CAN as diagnosis is mainly based on cardiac reflex responses, disease-duration and age are also related to CAN development throughout disease progression. The inflammation markers may reflect the underlying disease process, and therefore, new treatment modalities targeting systemic low-grade inflammation should potentially be tested to prevent the development of CAN. Cardiac reflex responses should be monitored closely to diagnose and classify severity levels of CAN accurately. Standard clinical biochemical analytes, such as glycaemic level, lipidic level, or kidney function were not included in the ten most important features. Beat-to-beat measures accounted for approximately 60% of the features in the ensembled data. [ABSTRACT FROM AUTHOR]

The properties of silver nanoparticles (AgNPs) are affected by various parameters, making optimisation of their synthesis a laborious task. This optimisation is facilitated in this work by concurrent use of a T-junction microfluidic system and machine learning approach. The AgNPs are synthesized by reducing silver nitrate with tannic acid in the presence of trisodium citrate, which has a dual role in the reaction as reducing and stabilizing agent. The study uses a decision tree-guided design of experiment method for the size of AgNPs. The developed approach uses kinetic nucleation and growth constants derived from an independent set of experiments to account for chemistry of synthesis, the Reynolds number and the ratio of Dean number to Reynolds number to reveal effect of hydrodynamics and mixing within device and storage temperature to account for particle stability after collection. The obtained model was used to define a parameter space for additional experiments carried out to improve the model further. The numerical results illustrate that well-designed experiments can contribute more effectively to the development of different machine learning models (decision tree, random forest and XGBoost) as opposed to randomly added experiments. [Display omitted] • Decision tree guided synthesis is an effective strategy to design experiments. • Three models were used to test the performance of each area of parametric space. • The proposed method allowed the use of kinetics to predict the size of AgNPs. [ABSTRACT FROM AUTHOR]

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models' core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularization, axis-parallel split regularization, Null space regularization) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembles of decision trees as bagging is used at each non-leaf node which results in improved performance. The evaluation of the baseline models and the proposed oblique and rotation double random forest models is performed on benchmark 121 UCI datasets and real-world fisheries datasets. Both statistical analysis and the experimental results demonstrate the efficacy of the proposed oblique and rotation double random forest models compared to the baseline models on the benchmark datasets. [ABSTRACT FROM AUTHOR]

• Among decision tree-based MLs, RF Performance compared better than CART and XGB. • DyLEMa can reconstruct ET based on seasonal segregated atmospheric and land data. • DyLEMa ET PBIAS was reduced in temporal validation compared to the USGS ET data. • DyLEMa ET estimates are robust across various cloud contamination rates. • DyLEMa ET estimates are robust across Landsat sensor failure. This study presents the "Dynamic Land Cover Evapotranspiration Model Algorithm: DyLEMa" for continuous spatiotemporal evapotranspiration (ET) estimates across diverse land uses. DyLEMa employs a coupled Random Forest model with a novel dynamic recalibrating strategy to improve pre-optimized seasonal hyperparameters following satellite acquisition alongside land cover classes. An analysis of feature importance indicated the significant variability in ET processes across different land cover classes and seasons. Hence, DyLEMa was applied to 20 years of daily 30x30 m pixel resolution Landsat-derived ET data in Illinois to address spatial and temporal discontinuities due to cloud contamination and sensor failures. DyLEMa performance was evaluated on Eddy Covariance measurements to find out that DyLEMa predictions reduced the average PBIAS error from + 31 % to −7% compared to existing US Geological Survey ET products. Spatially, DyLEMa underscores the value of a land cover-aware approach in ET estimation under varied cloud cover rates and their ability to preserve landscape features. However, the performance of DyLEMa was affected by the quality of land cover classification, suggesting the need for a refined region-specific land cover classification. DyLEMa's flexibility and performance suggest its applicability to other regions and satellite datasets, offering a promising reduction in uncertainty of ET estimates with impacts on environmental and water resources assessments on regional scales. [ABSTRACT FROM AUTHOR]

In this work, the gas phase photocatalytic CO 2 reduction was analyzed via machine learning, and the results were compared with those obtained in liquid phase process. Total 549 data points (268 for gas and 281 for liquid phase) were extracted from 80 published papers for this purpose. The general trends in the literature were analyzed using simple descriptive statistics first; then, the random forest (RF) regression was used for the band gap prediction while the decision tree (DT) classification was utilized to deduce heuristics for higher CO 2 reduction rates. It was found that H 2 , CO, and CH 4 are the main products in gas phase while CH 3 OH production is more dominant in liquid phase. Random forest prediction was quite successful in predicting the band gap with the root mean square error of 0.15 for testing. Decision tree models for the total gas production rates were also successful; for example, the accuracy rates for training and testing were 80% and 79% respectively for the gas phase processes. The high precision for the high gas production class allowed to deduce some rules indicating the semiconductor and co-catalysts options for high CO 2 photoreduction rates while the reaction temperatures was also found to be influential in the liquid phase. [Display omitted] • Photocatalytic CO 2 reduction was studied using machine learning. • Descriptive statistics, decision tree (DT) and random forest (RF) were used. • The bandgap prediction with RF was quite successful. • Heuristic rules were deduced for high total gas production rate using DT. • Semiconductor and co-catalyst were found to be deterministic. [ABSTRACT FROM AUTHOR]

[Display omitted] • Introduction of machine learning for modeling Sacran/CNF solution viscosity using six inputs for the first time. • High coefficient of determination (R2) of 0.99 achieved, demonstrating the effectiveness of the models. • Emphasis on key model features: shear rate or speed rate and the ratio of CNF content in solutions. • Notable importance given to shear rate, accounting for 60 % of model significance, and 31 % from CNF content ratio. • Machine learning algorithms showcased their value in analyzing viscosity data. This study explores the predictive capabilities of machine learning algorithms for Sacran/CNF solution viscosity. Leveraging data on initial CNF amount, shear rate, shear stress, speed, torque, and temperature, decision tree (DT) and random forest (RF) models were employed. Both models demonstrated strong alignment with experimental results, achieving a coefficient of determination (R2) of 0.99. The main features of those models established that 60 % of the importance is attributed to speed or shear rate, while 31 % is attributed to the ratio of CNF content in the solutions. This research underscores the potential of machine learning in effectively analyzing viscosity datasets. [ABSTRACT FROM AUTHOR]

Calcium (Ca2+) and magnesium (Mg2+) are the major scaling ions of reverse osmosis concentrate in zero-liquid discharge systems, causing performance decline. In this study, we predicted the removal of Ca2+ and Mg2+ from simulated reverse osmosis concentrate by functional polyketones (FPKs). Four amines, including 1,2-diaminopropane (DAP), 1-(2-aminoethyl) piperazine (AEP), 1-(3-aminopropyl) imidazole (API), and butyl amine (BA) used to synthesize FPKs. The effects of various factors such as the amount of adsorbent, feed water concentration, and pH were investigated for process optimization. In this study, ensemble learner artificial intelligence models, decision tree (DT), extreme gradient boost (XGB), and random forest (RF) were used to predict Ca2+ and Mg2+ removal by the FPKs. Datasets were collected experimentally using FPKs to remove Ca2+ and Mg2+ from the simulated reverse osmosis concentrate. The predictions were made by XGB, DT, and RF models for the first chosen amine for Ca2+ and then for Mg2+, subsequently, this process was repeated with each amine. The developed DT, RF, and XGB models demonstrated higher coefficients of determination for predicting Mg2+ removal by AEP and DAP (R2 = 0.841–0.935) than by API and BA (R2 = 0.774–0.801) except in the RF and XGB model results (R2 = 0.801–0.846). Overall, the XGB model displayed good results for both Ca2+ and Mg2+ removal but slight changes were observed in the AEP and BA predictions by DT and RF. Therefore, artificial intelligence models may be a viable alternative for further insight in predicting Ca2+ and Mg2+ removal by FPKs from simulated reverse osmosis concentrate. [Display omitted] • Observed ≥71% removal of Ca2+ and ≥66% of Mg2+ by the FPKs. • Studied predictive modeling of Ca2+ and Mg2+ removal from ROC by FPKs. • Tested the feasibility of ensemble AI techniques (DT, RF, and XGB). • Conducted a comparative analysis between DT, RF and XGB and a recent study. • Proposed XGB model predicted reasonably better results for Ca2+ and Mg2+ removal from ROC by FPKs. [ABSTRACT FROM AUTHOR]

In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision. This paper proposes an Evidential Decision Tree, and an Evidential Random Forest. These two models use a distance and a degree of inclusion to allow the model to group observations whose response elements are included in each other into a single node. Experimental results showed better performance for the presented methods compared to other evidential models and to recent Cautious Random Forests when the data is noisy. The models also offer a better robustness to the overfitting effect when using datasets that are effectively uncertainly and imprecisely labeled by the contributors. The proposed models are also able to predict rich labels, an information that can be used in other approaches, such as active learning. • Source uncertainty and imprecision can be modeled in richer labels. • Richer labels improve classification performance. • Evidential decision tree and evidential random forest are robust to overfitting. [ABSTRACT FROM AUTHOR]

Optimization of SO 2 emission and the process cost in catalytic hydrodesulfurization (HDS) is of great importance for the petroleum industry. Given that the process of HDS is complicated, machine learning-based models are suitable for the purpose of process optimization in which the cost and separation efficiency can be optimized efficiently. In this investigation, we are working with a data collection on the HDS process to model via machine learning models. Pressure, temperature, initial sulfur content, and catalyst dose constitute the inputs for the models. Outputs include sulfur concentration (ppm), emission of gas (%), and HDS process cost ($). To model the process, for the first time, four tree-based ensemble methods are developed including Gradient Boosting, Extreme gradient boosting, Random Forest, and Extra Trees to optimize the HDS process. The models tuned on the available dataset and then the best ones selected for each output For sulfur concentration the extra tree model is the most accurate and for other outputs extreme gradient boosting has the best performance. For the models, the R2 scores for outputs are 0.983, 0.982, and 0.995, respectively. [ABSTRACT FROM AUTHOR]

The Notification Oriented Paradigm (NOP) emerges as an alternative to develop and execute applications. The NOP brings a new inference concept based on precise notifying collaborative minimal entities. This inference implicitly allows achieving decoupled solutions, thereby enabling parallelism at a granularity level as fine-grained as possible in the envisaged computational platform. Previous research has proposed a digital circuit solution based on the NOP model, which is called NOP to Digital Hardware (DH), as a sort of High-Level Synthesis (HLS) prototype tool. The results with NOP-DH were encouraging indeed. However, the previous NOP-DH works lack benchmarks that exploit well-known algorithms against known HLS tools, such as the Vivado HLS tool, which is one of the suitable commercial HLS solutions. This work proposes evaluating the NOP-DH applied to develop the well-known Random Forest algorithm. The Random Forest is a popular Machine Learning algorithm used in several classification and regression applications. Due to the high number of logic-causal evaluations in the Random Forest algorithm and the possibility of running them in parallel, it is suitable for envisaged benchmark purpose. Experiments were performed to compare NOP-DH, and two Vivado HLS approaches (an ad hoc code and a hls4ml tool-based code) in terms of performance, amount of logic elements, maximum frequency, and the number of predictions per second. Those experiments demonstrated that NOP-DH circuits achieve better results concerning the number of logical elements and prediction rates, with some scalability limitations as a drawback. On average, the NOP-DH uses 52.5% fewer resources, and the number of predictions per second is 4.7 times higher than Vivado HLS. Finally, our codes are made publicly available at https://nop.dainf.ct.utfpr.edu.br/nop-public/nop-dh-random-forest-algorithm. [ABSTRACT FROM AUTHOR]

Random Forest is an ensemble classification approach. It aims to design a discrete finite group of decision trees constructed based on bootstrap samples and random attribute selection. Random Forests have strong generalization capacities due to the variance in the training and attribute couple subsets used for constructing different decision trees in the forest. However, to construct a robust and effective random forest, two main issues need to be taken into account namely: (1) increasing the accuracy and diversity of decision trees; (2) decreasing the number of decision trees. In this paper, a genetic algorithm-based approach to tackle the aforementioned challenges related to random forest construction is proposed. Three objectives are taken into consideration. First, strengthening the classification accuracy of individual decision trees as well as that of the forest. Second, making use of diversity measures among the decision trees to improve the generalization of the constructed model. Third, minimizing the number of trees in the forest and finding an optimal subset of the random forest. An experimental evaluation on several datasets from the UCI Machine Learning Repository is conducted. The obtained results show that the proposed approach outperforms state-of-the-art classical as well as evolutionary random forest construction methods. Finally, the proposed approach is used to build a reliable random forest model for detecting Botnet traffic in Internet of Things environment. • We propose a genetic algorithm to construct an improved random forest. • We design a theoretic solution allowing a smart convergence to the best solutions. • We propose a Powerset system to optimize the encoding of the forests in chromosomes. • Our objective function optimizes the accuracy and diversity of the trees of models. • Application to the detection of Botnet traffic in Internet of Things networks. [ABSTRACT FROM AUTHOR]

For the purpose of carbon capture, utilization, and storage, carbon dioxide (CO 2) injection in coal formations can enhance methane recovery and mitigate climate change. However, measuring CO 2 adsorption isotherms using experimental or mathematical models can be time-consuming, expensive, and inaccurate. Thus, this study presents a machine-learning framework that predicts CO 2 adsorption in coal formations based on various coal properties and testing conditions. Machine-learning (ML) framework was applied using a dataset of 1,064 points collected for different coal samples at different operating conditions to predict the CO 2 adsorption in coal surface. The ML techniques include decision tree regression (DT), random forests (RF), gradient boost regression (GBR), K-nearest neighbor (KNN), artificial neural network (ANN), function network (FN), and adaptive neuro-fuzzy inference system (ANFIS). The applied framework determines CO 2 adsorption as a function of coal's physical and chemical properties (moisture, ash, volatile matter, and fixed carbon content), the vitrinite reflectance of the coal samples, and testing conditions (pressure and temperature). Classical statical tools such as R2, root mean square error (RMSE), and average absolute percentage error (AAPE) were used to evaluate the model's performance analysis. The results demonstrated the ability to determine CO 2 adsorption for varying coal types and at different temperature and pressure conditions. The statistical measures suggested that RF, GBR, and KNN are very reliable ML models, with RF being the best. At low operating pressure (P < 4 MPa), CO 2 adsorption is impacted by any pressure changes, while it is stabilized at high-pressure values and becomes more dependent on the rock properties at high operating pressure (P > 4 MPa). The introduced ML framework offers a technique to evaluate the capability of different algorithms and accurately estimate CO 2 adsorption without the requirement of additional experimental measurements or complicated mathematical techniques. [ABSTRACT FROM AUTHOR]

The Decision Tree algorithm is one of the first machine learning algorithms developed. It is used both as a standalone model and as an ensemble of many cooperating trees like Random Forest, AdaBoost, Gradient Boosted Trees, or XGBoost. In this work, a new version of the Decision Tree was developed for classifying real-world signals using Gaussian distribution functions and a fuzzy decision process. The research was carried out on Power Quality Classification Dataset hosted on the platform kaggle.com for accessing the algorithm's classification. The proposed algorithm modification produces a sparse tree of multidimensional Gaussian kernels performing fuzzy, proximity-based division of solution space instead of a typical Decision Tree performing definite space restrictions. The machine learning model can cluster samples based on the common pattern and evaluate the input's similarity in a fuzzified fashion. The studies were conducted for the prediction of 6 classes of current. The proposed modification achieved the best accuracy and F1 score compared to the default Decision Tree and other machine learning algorithms. [ABSTRACT FROM AUTHOR]

Tree-based ensemble algorithms (T E A s) have made significant advances in recent years due to their simple algorithmic design. However, when the proportion of the 'most informative' features is low, the performance of conventional T E A s degrades significantly. The primary rationale for performance degradation is that traditional algorithmic design appears to be biased toward the least informative features, and the sub-space selection procedure contains uninformative features. This paper proposes a logically randomized forest (L R F) algorithm by incorporating two different enhancements into existing T E A s. The first enhancement is made to address the issue of biasness by performing feature-level engineering. The second enhancement is the approach by which individual feature sub-spaces are selected. To derive the first enhancement, we use the graph-theoretic principle of minimal vertex cover to construct a relevant assemblage of features. Following that, the permutation-based feature importance technique is employed to calculate the 'informativeness' of the relevant features in order to infuse logical randomness into the individual trees in the forest. For the second enhancement, the stratified sampling method is used to ensure that the most informative features are present in all newly created feature sub-spaces. Consequently, individual trees are generated using the Roulette wheel-based selection (R W S) algorithm. The proposed algorithm has been evaluated on two real-world genomic data sets, ten hybrid-synthetic classification data sets, and twenty multidisciplinary benchmark data sets with varying characteristics. The experimental findings demonstrate that the L R F outperforms the existing benchmark and cutting-edge T E A s. • Proposed a novel tree-based ensemble algorithm. • Appropriate for applications with sparsely populating most informative features. • Feature engineering by graph-theoretic principal and permutation-based algorithm. • Forest generation by stratified sampling and Roulette wheel selection algorithm. • Proposed algorithm outperformed the benchmark and state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Herein, we analyze the effects of critical materials, electrode preparation methods, and operational descriptors on the discharge capacity and cycle life of the Na-ion batteries. An extensive dataset is constructed from literature and analyzed using machine learning. It is found that alloy-based anodes have the highest average discharge capacity, while the carbon group exhibits higher cycle life performance; it is also deduced that the discharge capacity is improved when alloy-based anodes are coupled with metal oxide cathodes. Random forest models are reasonably good for providing rough predictions for discharge capacity and demonstrating the relative significance of descriptors; the root mean square error for training and testing are 75 mAh/g and 157 mAh/g, respectively, for the anode, and 13 mAh/g and 48 mAh/g, respectively, for the cathode studies. Anode and cathode types are the most influential descriptors for the model, as expected, while the synthesis conditions and crystal structure are also effective. Decision tree classification of cycle life (cycle number at which 80% of peak discharge capacity is retained) is also quite successful in leading heuristic rules for electrode preparation. Material synthesis conditions are highly influential for high cycle life for the anode, while solvent selection seems to be also significant for cathode studies. [Display omitted] • Performance of Na-ion batteries was studied using machine learning. • Dataset contained 1227 data points obtained from 335 published articles. • Random forest was used to develop predictive models for discharge capacity. • Decision tree was used to develop heuristics for cycle life. • Significant factors affecting high performance were also identified. [ABSTRACT FROM AUTHOR]

• The main objective of this work is to develop regression models for forecasting self-compacting concrete compressive strength that are based on machine learning. • The correctness of the model can be evaluated based on the RMSE value, as well as MSE, MAE, and R2. • The Random forest algorithm also performs better than the other models found from the analysis. Self-Compacting Concrete (SCC) has congested structural components and an inaccessible position. Mixing concrete multiple times becomes time-consuming and expensive. Due to a lack of competence in mixture design, analyzing appropriate mixture components and their influence on SCC's mechanical behavior might be a real-time concern in the construction sector. The work intends to create machine learning-based regression models to predict SCC compressive strength. A laboratory set of data comprising 99 SCC samples was used for this purpose. SCC's machine-learning regression model has many input and output parameters. Python machine learning was used to compare actual strengths. Linear regression, Lasso regression, Ridge regression, multi-layer perceptron regression, decision tree regression, and random forest regression are machine learning prediction methods. RMSE, MSE, MAE, and R2 measure model accuracy. The Random Forest model can efficiently estimate self-compressing concrete compression strength, according to the results. The RF model forecasts concrete's compressive strength accurately. [ABSTRACT FROM AUTHOR]

Modal parameters are crucial in investigating structural vibration problems and virtual sensing of vibration responses. This work proposes machine learning (ML) models such as Decision Tree, Random Forest, Adaboost Trees and RUSboost Trees to correlate 13 finite element (FE) modal shapes with the experimental ones. A catamaran's FE model was assembled and used as a case study. Firstly, a full FE model with 93591 DOFs was reduced to 174 DOFs using SEREP. Secondly, as the experimental mode shapes have a low spatial resolution (only six measured DOFs), another goal of this work is to expand these 6 DOFs to the 174 DOFs of the reduced FE model, that had not been measured. The ML models were used to correlate and expand the experimental mode shapes, thus, solving their lack of spatial resolution. As a result, the ML models identified the most relevant parameters for the scaling process of each of the 13 numerical modes using a supervised database. Adaboost Trees and Random Forest were used to correlate and expand five mode shapes each and RUSboost Trees were used to correlate and expand three mode shapes. In conclusion, Adaboost Trees and Random Forest models showed better performance during the correlation process. • Machine Learning models were used to correlate and expand the experimental modes. • Relevant degrees of freedom and modes were identified in the modes' correlation task. • Decision Tree, Random Forest, Adaboost and RUSboost were used in modes' correlation. • Supervised databases were used to correlate and expand modes at unmeasured locations. [ABSTRACT FROM AUTHOR]

• Machine learning techniques enable real time process control and product quality prediction. • Proposed method assists process development in accordance with QbD approach. • Tree based regression techniques outperformed neural network and gradient boosting techniques. • Gradient boosting regression model performance depends on hyperparameters tuning. • Neural network requires large datasets for training and validation. As continuous manufacturing of biotherapeutics gains steam, there is an increasing interest in using machine learning (ML) techniques for real time prediction of product quality and for process control. This paper focuses on application of different ML techniques for predicting critical process attributes that are pertinent to capture and polishing chromatography. Data from pH, UV, and conductivity sensors are acquired and pre-processed. For the present case study, tree-based regression techniques (decision tree and random forest) outperformed in all cases. The final model, random forest regression model, resulted in prediction errors of <5% for all predicted attributes. The results showed that random forest models exhibit optimal performance for smaller process-scale datasets with low chances of overfitting, are computationally inexpensive and do not require a graphics processing unit. The proposed approach is well suited for implementation in a continuous mAb train for real time prediction of chromatography process attributes. [ABSTRACT FROM AUTHOR]

In today's world, a massive amount of data is available in almost every sector. This data has become an asset as we can use this enormous amount of data to find information. Mainly health care industry contains many data consisting of patient and disease-related information. By using the machine learning technique, we can look for hidden data patterns to predict various diseases. Recently CVDs, or cardiovascular disease, have become a leading cause of death around the world. The number of death due to CVDs is frightening. That is why many researchers are trying their best to design a predictive model that can save many lives using the data mining model. In this research, some fusion models have been constructed to diagnose CVDs along with its severity. Machine learning(ML) algorithms like artificial neural network, SVM, logistic regression, decision tree, random forest, and AdaBoost have been applied to the heart disease dataset to predict disease. Randomoversampler only for multi-class classification to make the imbalanced dataset balanced. To improve the performance of classification, a weighted score fusion approach was taken. At first, the models were trained. After training, two algorithms' decision was combined using a weighted sum rule. A total of three fusion models have been developed from the six ML algorithms. The results were promising in the performance parameter. The proposed approach has been experimented with different test training ratios for binary and multiclass classification problems, and for both of them, the fusion models performed well. The highest accuracy for multiclass classification was found as 75%, and it was 95% for binary. [Display omitted] • Three weighted score-based fusion models to predict the severity of Cardiovascular disease. • Application of machine learning in health care for Cardiovascular disease prediction. • Binary and multi-class classification of patients with and without Cardiovascular disease. • Empirical results of comparisons confirm the proposed fusion model's superiority over single machine learning algorithms. [ABSTRACT FROM AUTHOR]

Machine learning methods are increasingly employed in various seismic and petrophysical methods for parameter estimation, interpretation, prediction, and classification. Reservoir facies classification assists the interpretation of seismic data as an important step in petroleum exploration and production monitoring. In this study, we estimate a reservoir facies model by integrating random forest (RF) algorithms and geostatistics modeling. The Surmeh Formation with the Jurassic age is known as one of the most important hydrocarbon reservoirs in the Middle East. The upper part of the Surmeh Formation is equivalent to the Arab Formation, which includes sequences of evaporitic carbonate facies in the study area. Well log data including DT, GR, RHOB, and PHI are used in the RF method for reservoir facies classification. Cross-validation verifies the high accuracy of our classification, with an average accuracy of 95%. The predicted reservoir facies consistently describe the carbonate and evaporitic facies with the geological information of this formation. The decision tree diagrams of the RF algorithm give valuable information on decision limitations and how to select features for efficient computation. We use the classification results for facies modeling. The comparison between facies models and drilling core data shows that the APE value of the sequential indicator simulation model is less than that of the indicator kriging model. • The random forest method was employed to classify seismological and well datasets. • The geostatistics and random forest methods were used for electrofacies modeling. • The decision tree helped to select facies classification for efficient computation. • Integrated geostatistics and random forest methods were used for the first time. [ABSTRACT FROM AUTHOR]

No one can deny the ever-increasing importance of oil since it has influenced every aspect of humans' life. One of the most important pressure-volume-temperature (PVT) properties of crude oil, which is needed in a majority of production and reservoir engineering calculations, is the saturation pressure (bubble point pressure (P b)). Having accurate knowledge about P b is significant for both academia and industry. This communication concentrates on providing reliable experimental data from constant composition expansion (CCE) test as well as rigorous compositional models to predict saturation pressure of crude oils based on oil composition (H 2 S, N 2 , CO 2 , C 1 to C 7+), reservoir temperature, C 7+ specifications (molecular weight and specific gravity). Seven advanced machine learning approaches, namely, decision trees (DTs), random forest (RF), extra trees (ETs), cascade-forward back-propagation network (CFBPN), and generalized regression neural networks (GRNN) as well as multilayer perceptron (MLP) and radial basis function (RBF) neural networks were used for modeling. The CFBPN and MLP models were trained by three different training algorithms, namely scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt (LM). The modeling was done based on a databank consisting of 206 data points (130 points were previously published in the literature plus 76 points determined experimentally in this study). The results show that the DT model could provide the most reliable prediction with an average absolute percent relative error (AAPRE) of 4.43%. The efficiency of various equations of state (EoS) and empirical correlations were checked. According to the results, Peng-Robinson (PR) and the correlation developed by Elsharkawy were the most efficient EoS and empirical correlation with AAPRE values of 8.46% and 12.05%, respectively. Then, the sensitivity analysis revealed that the Pb was extremely affected by methane and C 7+ mole percent. Finally, the Leverage approach confirmed the validity of the employed data, detecting only 7 points as outliers. [Display omitted] • Bubble point pressures (Pb) of 76 crude oil systems are experimentally measured by CCE test. • 206 data points (76 from this study and 130 from literature) are collected to model Pb. • Advanced intelligence models are used for modeling based on crude oil composition and temperature. • The data are also modeled with equations of state and existing empirical correlations. • The DT model outperforms all intelligent models, empirical correlations, and equations of state. [ABSTRACT FROM AUTHOR]

• Development of a method for preparation of continuous nano drugs. • Machine learning computational study on drug nanonization solubility. • Validation and training the neural model using the experimental data. Drug solubility is a critical parameter in the pharmaceutical industry for developing efficient processes for production of nanomedicine at industrial scale. Several attempts have been made in recent years to investigate and obtain this parameter using various data mining methods, including neural networks. In this study, to reduce the error rate in predicting solubility, three methods including Multi-layer Perceptron (MLP), decision tree, and random forest have been applied to 32 rows of experimental data collected from literature for solubility of a model drug in supercritical CO 2. Afterwards, the results of these models are examined and compared with measured data to calibrate and validate the developed models. Finally, the mean squared error improved to 1.77 e −5 in Random Forest Model. MLP and decision tree models mean squared errors are equal to 6.72 e −5 and 3.28 e −5, respectively which is a good result, especially when we can guarantee that the model did not have more problems in predicting the drug solubility and can be used as reliable methods in the pharmaceutical area. [ABSTRACT FROM AUTHOR]

• A learning-augmented method is developed for AC OPF. • Results have shown minimal constraints violation and loss of optimality. • Reduce AC OPF solution time by 15–100 times. Due to the high nonlinearity of AC optimal power flow (OPF), numerous efforts have been made in recent decades to find efficient methods. Machine learning (ML) has proven to significantly reduce the computational costs in many real-world problems. Thus, this paper develops a learning-augmented method for solving AC OPF, which integrates both power network equations and ML to yield near-optimal solutions. More specifically, ML models are developed to first predict bus voltage magnitudes and angles. Then, physics-based network equations are employed to calculate the power injection at different buses. Three ML algorithms, i.e., random forest, multi-target decision tree, and extreme learning machine, are explored and compared. To evaluate the efficiency of the proposed learning-augmented AC OPF solver, the MATPOWER Interior Point Solver is adopted as a baseline. Case studies on both 500-bus and 4918-bus test networks show that the proposed learning-augmented method has reduced the computational time by 15–100 times depending on the network size with a minimal loss in optimality. [ABSTRACT FROM AUTHOR]