Extra Trees Algorithm Research Articles

Three‐layer deep learning network random trees for fault detection in chemical production process

AbstractWith the development of technology, the chemical production process is becoming increasingly complex and large‐scale, making fault detection particularly important. However, current detection methods struggle to address the complexities of large‐scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long‐ and short‐term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three‐layer deep learning network random trees (TDLN‐trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher‐level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method.

Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data

ABSTRACT Changeover events occur in every industrial production when a machine is prepared and setup for production of the next product variant. Changeover times must be acquired with a high degree of validity for product cost calculations, order sequencing, and work schedules. The novelty of this article is a Machine Learning (ML) approach to automatically detect changeover events in production on manufacturing machines without direct human feedback. The machine learning approach uses several algorithms to classify different phases of the changeover process. The changeover of a milling process was defined using different phase concepts (2-phases, 6-phases, 23-phases) to be applicable to other types of manufacturing machines. Different machine learning methods were compared. The best results for the F1 score were achieved with the Random Forest, the CatBoost, and the Extra Trees algorithm (2-phases: 99.4–99.7%, 6-phases: 85.2–85.9%, 23-phases: 77.7–79.4%). It is shown that detecting changeover events can be realized based on data from an NC of a manufacturing machine without data from external sensors (2-phases: 98.9%, F1 score).

A Developed Model Based on Machine Learning Algorithms for Phishing Website Detection

Abstract: Users are accessing websites for many purposes, such as obtaining information about a particular topic, buying items, accessing their accounts, etc. Cybercriminals use phishing websites to attain the sensitive information of the users, like usernames and passwords, credit card details, etc. Detecting phishing websites helps in protecting the information and the money of people. Machine learning algorithms can be applied to detect phishing websites. In this paper, a model based on various machine learning algorithms is developed to detect phishing websites. The machine learning algorithms used in this model are Decision Tree, Random Forest, Extra Trees, K-Nearest Neighbors, Multilayer Perceptron and Support Vector Machine. The dataset of phishing websites is taken from the Kaggle website. The algorithms mentioned above of the developed model are compared together to identify which algorithm has better classification results. The extra trees algorithm offers the best results for accuracy, precision, and F1-Score. This paper also compares the developed model with a previous model that uses the same dataset and relies upon decision tree, random forest, and support vector machine to determine which model has better classification report results. The developed model, depending on the Decision Tree and SVM, offers better classification results than those of the previous models. The developed model is compared with another preceding model relying upon Decision Tree and Random Forest algorithms to determine which model generates better results for accuracy, precision, recall/sensitivity, and F1-Score. The developed model, depending on the Decision Tree, presents better results for accuracy, recall, and F1-Score than the results of accuracy, sensitivity, and F1-Score for the preceding model based on the Decision Tree.

An Intrusion Detection Model Based on Extra Trees Algorithm with Dimensionality Reduction and Oversampling

With the advancement of the university information process, more and more application systems are running on the campus network, and the information system becomes larger and more complex. With the rapid growth of network users and the popularization and deepening of computer knowledge, the campus network has been transformed from an experimental network for education and scientific research into an operational network that attaches equal importance to education, scientific research, and service. As the most important transmission carrier of digital information, how to ensure its security has become an urgent issue for colleges and universities. Therefore, this paper uses advanced intrusion detection technology to design the corresponding model to solve the security problem of the campus network. When traditional machine learning algorithms train network intrusion data sets, they are prone to problems such as too many feature dimensions, overfitting, and imbalance of data sets, which lead to lower accuracy and low time efficiency of intrusion detection algorithms. In order to solve the above problems, this paper proposes an intrusion detection model based on Extra Trees, which uses linear discriminant analysis to reduce the dimension of data, then uses oversampling to reduce the influence of imbalance of sample categories of the network intrusion dataset, and finally uses Extra Trees algorithm to train the model. The experimental results show that after LDA reduction and oversampling, using the Extra Trees classification model can improve the overall recognition performance of imbalanced data sets under multi-classification and satisfy the network intrusion detection.

Exploring the chemical space of BRAF Inhibitors: A cheminformatic and Machine learning analysis

BRAF kinase inhibitors have shown promise in treating melanoma patients with BRAF-V600 mutations. However, their clinical benefits are limited due to the development of acquired resistance. To address this issue, we conducted a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure–activity relationship, and landscape of human BRAF inhibitors. The final dataset comprised 3,952 molecules. Physicochemical property visualization for chemical space visualization has demonstrated that molecules from Group 1 (potent/active class) generally have slightly higher MW, RB, NumHAcceptors, and TPSA than molecules from Group 2 (intermediate/inactive class). The principal component analysis (PCA) shows that Group 1 data spreads widely and overlaps with the other group, which occupies only the left area on the plot. This suggests that molecules’ chemical structure or scaffolds are more diverse for Group 1 (Potent and Active) compounds than for Group 2 (Intermediate and Inactive) compounds. Murcko scaffold analysis has shown a greater scaffold diversity in the Active, Intermediate, and Inactive classes than in the Potent class. However, in the four bioactivity-defined classes for our BRAF dataset, molecules are uniformly distributed, with a small number of highly populated scaffolds and a large number of singletons. Furthermore, scaffold visualization has identified 12 representative Murcko scaffolds. Scaffolds 1 S1C1, S2C1, S2C2, S3C2, and S4C2 are highly favorable due to their high scaffold enrichment factor values. Based on scaffold analysis, the study investigated and summarized the local structure–activity relationships (SARs). In addition, the global SAR landscape was explored through quantitative structure–activity relationship (QSAR) modeling and structure–activity landscape visualization. Out of a total of 14 candidate models for BRAF inhibitors, a QSAR classification model that includes all 3952 molecules has been identified as the best model. The model was built using the PubChem fingerprint and extra trees algorithm and achieved an accuracy of 0.920 for the training set, 0.699 for the 10-fold cross-validation set, and 0.733 for the test set. Through a detailed analysis of the structure–activity landscapes, a total of sixteen significant consensus activity cliff (AC) generators were identified (ChEMBL molecule IDs: 4795335, 1822247, 3665863, 5083036, 5086749, 4061139, 1822242, 3665859, 1822250, 3641129, 3641043, 514688,3641178, 3697934, 3641046, and 368991). These generators offer valuable information on the SAR (Structure-Activity Relationship) for medicinal chemistry. The findings from this study provide new insights and guidelines for hit identification and lead optimization in developing novel BRAF inhibitors.

Comparative assessment of supervised machine learning algorithms for predicting geometric characteristics of laser cladded inconel 718

Laser cladding, an innovative surface modification and coating preparation process, has emerged as a research hotspot in material surface modification and green remanufacturing domains. In the laser cladding process, the interaction between laser light, powder particles, and the substrate results in a complicated mapping connection between process parameters and clad layer quality. This work aims to shed light on this mapping using fast evolving machine learning algorithms. A full factorial experimental design was employed to clad Inconel 718 powder on an A286 substrate comprising 64 groups. Analysis of variance, contour plots, and surface plots were used to explore the effects of laser power, powder feeding rate, and scanning speed on the width, height, and dilution rate of the cladding. The performance of the predictive models was evaluated using the index of merit (IM), which includes mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). By comparing the performance of the models, it was found that the Extra Trees, Random forest regression, Decision tree regression, and XGBoost algorithms exhibited the highest predictive accuracy. Specifically, the Extra Trees algorithm outperformed other machine learning models in predicting the cladding width, while the RFR algorithm excelled in predicting the associated height. The DTR algorithm demonstrated the best performance in predicting the cladding dilution rate. The R2 values for width, height, and dilution rate were found to be 0.949, 0.954, and 0.912, respectively, for these three models.

Efficient Email Spam Classification with N-gram Features and Ensemble Learning

In this paper, we present an innovative approach to enhancing email spam classification using N-gram features, TF-IDF weighting, SMOTE oversampling, and ensemble learning techniques such as Decision Trees, Random Forests, and Ensemble Extra Trees. Our methodology involves preprocessing the dataset to extract N-gram features, applying TF-IDF weighting to highlight important terms, and addressing class imbalance through SMOTE. We then train and evaluate multiple classification models and find that the Ensemble Extra Trees algorithm outperforms others in terms of accuracy, precision, recall, and F1-score. Our experiments on benchmark datasets confirm the efficacy of our approach, showcasing significant improvements in spam detection accuracy and highlighting the potential of ensemble learning for email spam classification. This research contributes to the advancement of spam filtering technologies, providing a robust and efficient solution for accurately identifying and categorizing spam emails.

Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower (R2 = 0.58), while the fitting accuracy for other indicators was higher (R2 ≥ 0.59). The performance and prediction accuracy of ERT (R2 = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation.

A generalized model for monitor units determination in ocular proton therapy using machine learning: A proof-of-concept study

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.

Prediction of Global Horizontal Irradiance Using an Explainable Data Driven Machine Learning Algorithms

Estimating global horizontal irradiance (GHI) with a high level of accuracy and precision is very challenging due to the volatile climate parameters and location constraints. To overcome this challenge, several machine learning (ML)-based techniques such as Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), and Extra Trees (ET) are implemented to forecast the GHI. The first stage of model development is to select the optimal subset of features by using the variance inflation factor feature selection method. In the second stage, the selected features are fed into the ML models and trained. The predictive performance of the ML models is improved the result of removal of insignificant input features. The predictive accuracy of the ML models is compared and evaluated by performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). Conclusively, after feature selection it is seen that the ET algorithm outperforms the others because of its lowest MAE and RMSE value of 3.01 and 1.748, respectively, as compared to the other models, indicating its relevancy, legitimacy, and viability for the estimation of GHI. The higher R 2 value of 0.99 obtained by the ET model indicates that it is best fitted with the dataset. Additionally, optimal shapely additive explanation values have been used as feature attributions for determining the magnitude and direction of the impact of each feature on the outcome.

Prediction of lateral lymph node metastasis in rectal cancer patients based on MRI using clinical, deep transfer learning, radiomic, and fusion models.

Lateral lymph node (LLN) metastasis in rectal cancer significantly affects patient treatment and prognosis. This study aimed to comprehensively compare the performance of various predictive models in predicting LLN metastasis. In this retrospective study, data from 152 rectal cancer patients who underwent lateral lymph node (LLN) dissection were collected. The cohort was divided into a training set (n=86) from Tianjin Union Medical Center (TUMC), and two testing cohorts: testing cohort (TUMC) (n=37) and testing cohort from Gansu Provincial Hospital (GSPH) (n=29). A clinical model was established using clinical data; deep transfer learning models and radiomics models were developed using MRI images of the primary tumor (PT) and largest short-axis LLN (LLLN), visible LLN (VLLN) areas, along with a fusion model that integrates features from both deep transfer learning and radiomics. The diagnostic value of these models for LLN metastasis was analyzed based on postoperative LLN pathology. Models based on LLLN image information generally outperformed those based on PT image information. Rradiomics models based on LLLN demonstrated improved robustness on external testing cohorts compared to those based on VLLN. Specifically, the radiomics model based on LLLN imaging achieved an AUC of 0.741 in the testing cohort (TUMC) and 0.713 in the testing cohort (GSPH) with the extra trees algorithm. Data from LLLN is a more reliable basis for predicting LLN metastasis in rectal cancer patients with suspicious LLN metastasis than data from PT. Among models performing adequately on the internal test set, all showed declines on the external test set, with LLLN_Rad_Models being less affected by scanning parameters and data sources.

Comparison of features elimination methods for geomagnetic data classification

The main stages of processing and feature selection methods for their further use in machine learning algorithms for building models that are designed to predict auroras are considered. The aim of this work is to compare the methods of feature selection when constructing a model for diagnosing the presence of auroras based on the intellectual analysis of geomagnetic data. Data from the Lovozero Observatory (LOZ) for nine years (2012–2020) were used as data for processing. A distinctive feature of the data is their heterogeneity: the set contains both categorical (binary and non-binary) and quantitative data. We consider such feature selection methods as principal component analysis, support vector machines, recursive feature elimination, and the Extra-Trees algorithm. The results of the study showed that the use of selected features based on the analysis in the projection of the principal components will overcome the curse of dimensionality, eliminate noise and reduce model overfitting.

Feature selection for effective prediction of SARS-COV-2 using machine learning.

With rise in variants of SARS-CoV-2, it is necessary to classify the emerging SARS-CoV-2 for early detection and thereby reduce human transmission. Genomic and proteomic information have less frequently been used for classifying in a machine learning (ML) approach for detection of SARS-CoV-2. With this aim we used nucleoprotein and viral proteomic evolutionary information of SARS-CoV-2 along with the charge and basicity distribution of amino acids from various strains of SARS-CoV-2 to generate a disease severity model based on ML. All sequence and clinical data were obtained from GISAID. Proteomic level calculations were added to comprise the dataset. The training set was used for feature selection. Select K- Best feature selection method was employed which was cross validated with testing set and performance evaluated. Delong's test was also done. We also employed BIRCH clustering on SARS-CoV-2 for clustering the strains. Out of six ML models four were successful in training and testing. Extra Trees algorithm generated a micro-averaged F1-score of 74.2% and a weighted averaged area under the receiver operating characteristic curve (AUC-ROC) score of 73.7% with multi-class option. The feature selection set to 5, enhanced the ROC AUC from 73.7 to 76.4%. Accuracy of the selected model of 86.9% was achieved. The unique features identified in the ML approach was able to classify disease severity into classes and had potential for predicting risk in newer variants.

Machine learning-based predictive model for the differential diagnosis of ≤ 5 cm gastric stromal tumor and gastric schwannoma based on CT images

The clinical symptoms of ≤ 5 cm gastric stromal tumor (GST) and gastric schwannoma (GS) are similar, but the treatment regimens are different. This study explored the value of computed tomography (CT) combined with machine learning (ML) algorithms to find the best model to discriminate them. A total of 126 patients with GST ≤ 5 cm and 35 patients with GS ≤ 5 during 2013–2022 were included. CT imaging features included qualitative data (tumor location, growth pattern, lobulation, surface ulcer status, necrosis, calcification, and surrounding lymph nodes) and quantitative data [long diameter (LD); short diameter (SD); LD/SD ratio; degree of enhancement (DE); heterogeneous degree (HD)]. Patients were randomly divided into a training set (n = 112) and test set (n = 49) using 7:3 stratified sampling. The univariate and multivariate logistic regression analysis were used to identify independent risk factors. Five ML algorithms were used to build prediction models: Support Vector Machine, k-Nearest Neighbor, Random Forest, Extra Trees, and Extreme Gradient Boosting Machine. The analysis identified that HDv, lobulation, and tumor growth site were independent risk factors (P < 0.05). We should focus on these three imaging features of tumors, which are relatively easy to obtain. The area under the curve for the SVM, KNN, RF, ET, and XGBoost prediction models were, respectively, 0.790, 0.895, 0.978, 0.988, and 0.946 for the training set, and were, respectively, 0.848, 0.892, 0.887, 0.912, and 0.867 for the test set. The CT combined with ML algorithms generated predictive models to improve the differential diagnosis of ≤ 5 cm GST and GS which has important clinical practical value. The Extra Trees algorithm resulted in the optimal model.

Energy-aware and Context-aware Fault Detection Framework for Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.

A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf

Efficient and safe drilling operations require real-time identification and mitigation of downhole vibrations like stick-slip, which can significantly diminish performance, reliability, and efficiency. This pioneering research introduces a robust machine learning approach combining model-agnostic regression techniques with Bayesian Optimized Extra Trees (BO_ET) to accurately predict stick-slip events in real-time using downhole sensor data. The model is rigorously tested and validated on a substantial offshore dataset comprising over 78,000 data points from a Norwegian continental shelf (NCS) oil field. The key input features encompassing real-time downhole and surface drilling parameters are carefully selected, including critical variables such as collar rotational speed, shock risks, annular pressure, torque, mud flow rate, drill string vibration severity, and other relevant measurements. These parameters offer significant insights into the occurrence of harmful stick-slip vibrations. Among several sophisticated machine learning models, the Extra Trees (ET) algorithm demonstrates superior performance with the lowest errors of 5.5056 revolutions per minute (r/min) Mean Absolute Error (MAE) and 9.9672 r/min Root Mean Square Error (RMSE) on out-of-sample test data. Further hyperparameter tuning of the ET algorithm via Bayesian Optimization dramatically reduces errors down to 0.002156 MAE and 0.024495 RMSE, underscoring the significant innovation and advantages of the proposed approach. By seamlessly incorporating real-time downhole sensor data and drill string mechanics, the model enables reliable identification of stick-slip events as they occur downhole. This grants opportunities to optimize critical drilling parameters including revolutions per minute (RPM), weight-on-bit (WOB), mud flow rates, and more to effectively mitigate stick-slip severity and improve the rate of penetration (ROP). Integrating the approach into automatic driller systems on offshore rigs offers immense benefits for drilling operations through substantially increased efficiency, fewer premature failures, lower costs, and significantly improved productivity and safety. Overall, this research strongly emphasizes the immense transformative potential of advanced data analytics and machine learning in enabling more efficient, economical, and sustainable drilling practices. The proposed model demonstrates clear superiority over existing methods and establishes a robust and reliable platform for real-time stick-slip prediction and mitigation, maximizing drilling performance.Graphical abstract

Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Ensuring the maturity of green waste compost is crucial to composting processes and quality control of compost products. However, accurate prediction of green waste compost maturity remains a challenge, as there are limited computational methods available. This study aimed to address this issue by employing four machine learning models to predict two indicators of green waste compost maturity: seed germination index (GI) and T value. The four models were compared, and the Extra Trees algorithm exhibited the highest prediction accuracy with R2 values of 0.928 for GI and 0.957 for T value. To identify the interactions between critical parameters and compost maturity, The Pearson correlation matrix and Shapley Additive exPlanations (SHAP) analysis were conducted. Furthermore, the accuracy of the models was validated through compost validation experiments. These findings highlight the potential of applying machine learning algorithms to predict green waste compost maturity and optimise process regulation.

Assessing the impact of employing machine learning-based baseline load prediction pipelines with sliding-window training scheme on offered flexibility estimation for different building categories

The present study is focused on assessing the impact of the performance of baseline load prediction pipelines on the estimation (by the grid operator) accuracy of the flexibility offered by different categories of buildings. Accordingly, the corresponding impact of employing different machine learning (ML) algorithms, with sliding-window and offline training schemes, for hour-ahead baseline load prediction has been investigated and compared. Using a smart meter measurements dataset, training window sizes and the most promising pipeline for each building category are first identified. Next, the consumption profiles of five buildings (belonging to each category), with the regular operation (baseline load) and while offering flexibility, are physically simulated. Finally, the identified pipelines are used for predicting the baseline loads, and the resulting error in estimating the provided flexibility is determined. Obtained results demonstrate that the identified most promising prediction pipeline (extra trees algorithm with a sliding window of 5 weeks) offers a notably superior performance compared to that of offline training (average R2 score of 0.91 vs. 0.87). Employing these pipelines permits estimating the provided flexibility with acceptable accuracy (flexibility index's mean relative error between -2.45% to +2.79%), permitting the grid operator to guarantee fair compensation for buildings' offered flexibility.

Prediction of Nucleophilicity and Electrophilicity Based on a Machine-Learning Approach.

Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In the past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) and electrophilicity (E), which proved to be a useful tool for the rationalization of chemical reactivity. In this study, a holistic prediction model was developed through a machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical and solvent features, was developed for this purpose. With 1115 nucleophiles, 285 electrophiles, and 22 solvents, the dataset is currently the largest one for reactivity prediction. The rSPOC model trained with the Extra Trees algorithm showed high accuracy in predicting Mayr's N and E parameters with R2 of 0.92 and 0.93, MAE of 1.45 and 1.45, respectively. Furthermore, the practical applications of the model, for instance, nucleophilicity prediction of NADH, NADPH and a series of enamines showed potential in predicting molecules with unknown reactivity within seconds. An online prediction platform (http://isyn.luoszgroup.com/) was constructed based on the current model, which is available free to the scientific community.

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

Application of machine learning (ML) algorithms to spectroscopic data has a great potential for obtaining hidden correlations between structural information and spectral features. Here, we apply ML algorithms to theoretically simulated infrared (IR) spectra to establish the structure-spectrum correlations in zeolites. Two hundred thirty different types of zeolite frameworks were considered in the study whose theoretical IR spectra were used as the training ML set. A classification problem was solved to predict the presence or absence of possible tilings and secondary building units (SBUs). Several natural tilings and SBUs were also predicted with an accuracy above 89%. The set of continuous descriptors was also suggested, and the regression problem was also solved using the ExtraTrees algorithm. For the latter problem, additional IR spectra were computed for the structures with artificially modified cell parameters, expanding the database to 470 different spectra of zeolites. The resulting prediction quality above or close to 90% was obtained for the average Si-O distances, Si-O-Si angles, and volume of TO4 tetrahedra. The obtained results provide new possibilities for utilization of infrared spectra as a quantitative tool for characterization of zeolites.