Support Vector Research Articles

Identification of a novel cuproptosis-related gene signature for multiple myeloma diagnosis.

Multiplemyeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis-related genes (CRGs) in the prediction of MM diagnosis. Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential signature genesfor MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in MM cell samples. The optimal gene signature was identified using LASSO and SVM-RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature-based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune-related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings. We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune-related pathways.

Process model design for positive displacement compressors and their experimental validation: Comparison of Optimal Experimental Design and Machine Learning

Valid simulation models play a critical role in enhancing efficiency in development processes and minimizing experimental efforts. As a result, ensuring accurate predictions through model discrimination, Model Calibration, and Validation (MoCaVal) has become increasingly important and is a necessary step to analyze the behavior and evaluate the effectiveness of systems during their initial design stages. Additionally, to achieve the climate objectives, it is imperative that heat pumps predominantly perform the building sector’s heating. The effectiveness of heat pumps hinges on the performance of the compressor. Therefore, there is a requirement for valid compressor models. However, currently, there is no universal compressor model that applies to all refrigerants and designs. Therefore, every refrigerant or design modification requires the development or adaptation of models. Since creating new models is a time-consuming procedure, automated techniques are advantageous.This paper compares two automated model development methods, Optimal Experimental Design (OED) and Machine Learning (ML), to create valid simulation models within the MoCaVal framework. OED is used to calibrate existing simulation models, while ML is employed to develop new models. Our comparison of the two methods focuses on a fixed-speed scroll compressor of 4 kW nominal power, using R410A and featuring 51 measurement points. Using the Full Factorial Plan (FFP) and the D-Optimal Experimental Design (D-OED), predefined models for mass flow rate, electrical power, isentropic and volumetric efficiency were calibrated. Employing the FFP, Machine Learning (ML) was also applied through support vector regression.The OED reduces experimental effort by 75–90 % compared to the FFP while only slightly increasing the average uncertainty by 0.30–0.79 %. For the FFP, MoCaVal achieved uncertainties of 0.52–3.9 % for calibrated models and 0.34–0.56 % with ML models. Therefore, within the design space, ML outperforms calibrated models based on the FFP and OED regarding uncertainty. However, since machine learning models were trained on the FFP, additional research is needed to demonstrate their potential for reducing test time and extrapolating results.

Estimation of Bali Cattle Body Weight Based on Morphological Measurements by Machine Learning Algorithms: Random Forest, Support Vector, K-Neighbors, and Extra Tree Regression

In order to forecast and model the weight of cattle a number of techniques have been used. Nonetheless, no machine algorithm has been utilized to estimate the weight of Bali cattle. This article examines the use of machine learning regression to create models for Bali cattle's body weight prediction. The response variables consist of body weight as the dependent variable and body length, girth circumference, height at wither of 228 male and 211 female cattle of similar ages (285 days). The descriptive statistics of female Bali cattle in our investigation revealed that the morphological measurements were similar to those documented by other researchers. To predict body weight on the basis of different characteristics, machine learning models such as Random Forest, Support Vector, K-Neighbors, and Extra Tree regressions have been used. Additionally, linear regression was utilized to estimate the body weight for comparison with the traditional approach. The assessment standards used included the determination coefficient, the root mean square error, the average absolute error, and the average absolute percentage error as measures of evaluation efficiency. We found that Linear Regression performs the best among all the regressors for female cattle. Similarly for male, it is about the same as extra tree regression. The machine learning algorithm (MLA) was discovered to furnish more precise estimate of the weight of the body cattle, surpassing the conventional algorithm.

Unsupervised Deep Learning Approach for Characterizing Fractality in Dried Drop Patterns of Differently Mixed Viscum album Preparations

This paper presents a novel unsupervised deep learning methodology for the analysis of self-assembled structures formed in evaporating droplets. The proposed approach focuses on clustering these structures based on their texture similarity to characterize three different mixing procedures (turbulent, laminar, and diffusion-based) applied to produce Viscum album Quercus 10−3 according to the European Pharmacopoeia guidelines for the production of homeopathic remedies. Texture clustering departs from obtaining a comprehensive texture representation of the full texture patch database using a convolutional neural network. This representation is then dimensionally reduced to facilitate clustering through advanced machine learning techniques. Following this methodology, 13 clusters were found and their degree of fractality determined by means of Local Connected Fractal Dimension histograms, which allowed for characterization of the different production modalities. As a consequence, each image was represented as a vector in R13, enabling classification of mixing procedures via support vectors. As a main result, our study highlights the clear differences between turbulent and laminar mixing procedures based on their fractal characteristics, while also revealing the nuanced nature of the diffusion process, which incorporates aspects from both mixing types. Furthermore, our unsupervised clustering approach offers a scalable and automated solution for analyzing the databases of evaporated droplets.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I–IV NSCLC patients. Institutional 200 patients’ data were included for training and internal validation and 100 patients’ data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models’ accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors

Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions.

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

The growing demand for chicken production has emphasized the importance of maintaining optimal conditions to improve quality and productivity.The integration of Artificial Intelligence (AI) and the Internet of Things(IoT) is recommended for the efficient management of the farm's environment. A potential solution is presented in this paper, utilizing IoT-based sensor nodes with ARM Cortex M3 - LPC 1769 and LORA technology to monitor chicken farms across diverse regions.The proposed solution incorporates a low-cost edge computing server-Jetson Nano device equipped with a machine learning model to categorize and monitor live environmental conditions in poultry farms. Real-time data from various branches is collected and analyzed using machine learning classification techniques including logistic regression, K nearest neighbors, and support vector machines.The performance of these algorithms is compared to identify the most effective approach. Upon evaluation, the K nearest neighbors emerges as the superior performer, achieving an impressive accuracy of 99.72% and an execution duration of 0.087 seconds on the Jetson Nano edge computing device. This cost-effective technology is tailored for small businesses in regions where farmers can gain valuable insights from data-driven decisions and closely monitor their operations. By incorporating AIoT into farm management, the challenges faced by small-scale poultry farming can be addressed, empowering farmers with enlightened techniques to improve overall productivity and quality.

Predictive modelling of thermal conductivity in single-material nanofluids: a novel approach

BackgroundThis research introduces a novel approach for modelling single-material nanofluids, considering the constituents and characteristics of the fluids under investigation. The primary focus of this study was to develop models for predicting the thermal conductivity of nanofluids using a range of machine learning algorithms, including ensembles, trees, neural networks, linear regression, Gaussian process regressors, and support vector machines.The main body of the abstractTo identify the most relevant features for accurate thermal conductivity prediction, the study compared the performance of established feature selection algorithms, such as minimum redundancy maximum relevance, Ftest, and RReliefF, a newly proposed feature selection algorithm. The novel algorithm eliminated features lacking direct implications for fluid thermal conductivity. The selected features included temperature as a thermal property of the fluid itself, multiphase features such as volume fraction and particle size, and material features including nanoparticle material and base fluid material, which could be fixed based on any two intensive properties. Statistical methods were employed to select the features accordingly.ResultsThe results demonstrated that the novel feature selection algorithm outperformed the established approaches in predicting the thermal conductivity of nanofluids. The models were evaluated using fivefold cross-validation, and the best model was the model based on the proposed feature selection algorithm that exhibited a root-mean-squared error of validation of 1.83 and an R-squared value of 0.94 on validation set. The model achieved a root-mean-squared error of 1.46 and an R-squared value of 0.97 for the test set.ConclusionsThe developed predictive model holds practical significance by enabling nanofluids' numerical study and optimisation before their creation. This model facilitates the customisation of conventional fluids to attain desired fluid properties, particularly their thermal properties. Additionally, the model permits the exploration of numerous nanofluid variations based on permutations of their features. Consequently, this research contributes valuable insights to the design and optimisation of nanofluid systems, advancing our understanding and application of thermal conductivity in nanofluids and introducing a novel and methodological approach for feature selection in machine learning.

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies.

Prediction of HHV of fuel by Machine learning Algorithm: Interpretability analysis using Shapley Additive Explanations (SHAP)

This study presents a novel approach using machine learning techniques to estimate waste materials' higher heating value (HHV), which plays a crucial role in waste-to-energy generation efficiency. The study utilizes a dataset comprising ultimate and proximate analysis of 16 different waste types. It employs six machine learning models: Extra Trees, Random Forest, Support Vector, Decision Tree, Extreme Gradient Boosting, and Multivariate Linear Regressors. The investigation explores the relationships between the features and outcomes through Spearman correlation, feature importance analysis, SHAP dependence, and decision plots, providing the interpretability of the model's predictions. The models are fine-tuned with hyperparameters for six feature sets, enabling researchers to anticipate HHV based on their specific input. The results demonstrate high accuracy in predicting HHV, with R2 ranging from 0.83 to 0.98, RMSE from 2.25 to 0.79, and MAPE from 6.01 to 0.92%. The study further reveals that higher carbon and hydrogen content increases HHV, while higher oxygen and ash content results in decreased HHV. Notably, Carbon, Ash content and Hydrogen content are the significant features with mean absolute SHAP values of2.17, 0.65, and 0.37, respectively. The proposed alternative prediction method has practical implications for waste-to-energy generation research and practice, facilitating informed decision-making in this field.

Price Prediction Using Web Scraping and Machine Learning Algorithms in the Used Car Market

The development of technology increases data traffic and data size day by day. Therefore, it has become very important to collect and interpret data. This study, it is aimed to analyze the car sales data collected using web scraping techniques by using machine learning algorithms and to create a price estimation model. The data needed for analysis was collected using Selenium and BeautifulSoup and prepared for analysis by applying various data preprocessing steps. Lasso regression and PCA analysis were used for feature selection and size reduction, and the GridSearchCV method was used for hyperparameter tuning. The results were evaluated with machine learning algorithms. 
 Random Forest, K-Nearest Neighbor, Gradient Boost, AdaBoost, Support Vector and XGBoost regression algorithms were used in the analysis. The obtained analysis results were evaluated together with Mean Square Error (MSE), Root Mean Square Error (RMSE) and Coefficient of Determination (R-square). When the results for data set 1 were examined, the model that gave the best results was XGBoost Regression with 0.973 R2, 0.026 MSE and 0.161 RMSE values. When the results for data set 2 were examined, the model that gave the best results was K-Nearest Neighbor Regression with 0.978 R2, 0.021 MSE and 0.145 RMSE values.

Building a predictive model of low birth weight in low- and middle-income countries: a prospective cohort study

BackgroundLow birth weight (LBW, < 2500 g) infants are at significant risk for death and disability. Improving outcomes for LBW infants requires access to advanced neonatal care, which is a limited resource in low- and middle-income countries (LMICs). Predictive modeling might be useful in LMICs to identify mothers at high-risk of delivering a LBW infant to facilitate referral to centers capable of treating these infants.MethodsWe developed predictive models for LBW using the NICHD Global Network for Women’s and Children’s Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India (2 sites: Belagavi, Nagpur), Pakistan, and Bangladesh between January 2017 – December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine.ResultsWe report a rate of LBW of 13.8% among the eight Global Network sites from 2017–2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW.ConclusionsPredictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk.

A nonlinear kernel SVM classifier via [formula omitted] soft-margin loss with classification performance

Recent advance in linear support vector machine with the 0-1 soft-margin loss (L0/1-SVM) shows the ability to solve the 0-1 loss problem directly. However, its theoretical and algorithmic requirements restrict us from directly extending the linear solving framework to its nonlinear kernel form. The lack of an explicit expression of the Lagrangian dual function of L0/1-SVM is a major shortcoming among them. By applying the nonparametric Representation Theorem, we propose a nonlinear model for support vector machine with 0-1 soft-margin loss, called L0/1-KSVM, which skillfully incorporates the kernel technique, and more importantly, follows the success in systematically solving its linear problem. The optimal condition is theoretically explored and a working set selection alternating direction method of multipliers (ADMM) algorithm is introduced to obtain its numerical solution. Furthermore, we first introduce a closed-form definition to the support vector (SV) of L0/1-KSVM. Theoretically, we prove that all SVs of L0/1-KSVM are only located on the parallel decision surfaces. The experimental results show that L0/1-KSVM has much fewer SVs compared to its linear counterpart and the other six nonlinear benchmark SVM classifiers, while maintaining good prediction accuracy.

Mitigating anomalous electricity consumption in smart cities using an AI‐based stacked‐generalization technique

AbstractEnergy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non‐technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the main issue in maintaining grid stability and cause revenue loss to the utility. The automatic metering infrastructure (AMI) system has reduced grid instability but it has opened up new ways for NTLs in the form of different cyber‐physical theft attacks (CPTA). Machine learning (ML) techniques can be used to detect and minimize CPTA. However, they have certain limitations and cannot capture the energy consumption patterns (ECPs) of all the users, which decreases the performance of ML techniques in detecting malicious users. In this paper, we propose a novel ML‐based stacked generalization method for the cyber‐physical theft issue in the smart grid. The original data obtained from the grid is preprocessed to improve model training and processing. This includes NaN‐imputation, normalization, outliers' capping, support vector machine‐synthetic minority oversampling technique (SVM‐SMOTE) balancing, and principal component analysis (PCA) based data reduction techniques. The pre‐processed dataset is provided to the ML models light gradient boosting (LGB), extra trees (ET), extreme gradient boosting (XGBoost), and random forest (RF), to accurately capture all consumers' overall ECP. The predictions from these base models are fed to a meta‐classifier multi‐layer perceptron (MLP). The MLP combines the learning capability of all the base models and gives an improved final prediction. The proposed structure is implemented and verified on the publicly available real‐time large dataset of the State Grid Corporation of China (SGCC). The proposed model outperformed the individual base classifiers and the existing research in terms of CPTA detection with false positive rate (FPR), false negative rate (FNR), F1‐score, and accuracy values of 0.72%, 2.05%, 97.6%, and 97.69%, respectively.

Modeling Spatio-Temporal Dynamics of BMPs Adoption for Stormwater Management in Urban Areas

Nonpoint source (NPS) pollution is a severe problem in the U.S. and worldwide. Best management practices (BMPs) have been widely used to control stormwater and reduce NPS pollution. Previous research has shown that socio-economic factors affect households’ adoption of BMPs, but few studies have quantitatively analyzed the spatio-temporal dynamics of household BMP adoption under different socio-economic conditions. In this paper, diverse regression approaches (linear, LASSO, support vector, random forest) were used on the ten-year data of household BMP adoption in socio-economically diverse areas of Washington, D.C., to model BMP adoption behaviors. The model with the best performance (random forest regression, R2 = 0.67, PBIAS = 7.2) was used to simulate spatio-temporal patterns of household BMP adoption in two nearby watersheds (Watts Branch watershed between Washington, D.C., and Maryland; Watershed 263 in Baltimore), each of which are characterized by different socio-economic (population density, median household income, renter rate, average area per household, etc.) and physical attributes (total area, percentage of canopy in residential area, average distance to nearest BMPs, etc.). The BMP adoption rate was considerably higher at the Watts Branch watershed (14 BMPs per 1000 housing units) than at Watershed 263 (4 BMPs per 1000 housing units) due to distinct differences in the watershed characteristics (lower renter rate and poverty rate; higher median household income, education level, and canopy rate in residential areas). This research shows that adoption behavior tends to cluster in urban areas across socio-economic boundaries and that targeted, community-specific social interventions are needed to reach the NPS control goal.

Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)

We study the problem of downlink channel estimation in multi-user massive multiple input multiple output (MIMO) systems. To this end, we consider the use of Bayesian compressive sensing approach in which the clustered sparse structure of the channel in the angular domain is employed to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An expectation propagation (EP) algorithm is developed to approximate the intractable joint distribution of the sparse vector and its support with a distribution from an exponential family. The approximate distribution is then used for direct estimation of the channel. The EP algorithm assumes that the model parameters are known a priori. Since these parameters are unknown, we estimate these parameters using the expectation maximization (EM) algorithm. The combination of EM and EP referred to as EM-EP algorithm is reminiscent of the variational EM approach. Simulation results show that the proposed EM-EP algorithm outperforms several recently-proposed algorithms in the literature.

A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system

Globally, the construction industry is experiencing an increase in energy demand, which has significant environmental and economic repercussions. To address these issues, it is now possible for buildings, vehicles, and renewable energy sources to collaborate and function as an advanced, integrated, and environmentally favorable system that meets the high energy demands of contemporary buildings. To attain maximum efficiency, however, it is necessary to create reliable energy demand forecasting models. In this research, by introducing the energy model of a neighbourhood with buildings with solar panels and electric vehicles, the final balance of energy production and consumption for each building and the whole neighbourhood as a micro grid is predicted. DesignBuilder is used to model neighbourhood buildings, and K-Nearest neighbor (KNN), Regression Support Vector (SVR), Adaptive Boosting (AdaBoost), and Deep neural networks (DNN) algorithms in machine learning are used to predict the final energy balance. a comparative analysis of the performance of the KNN, SVR, AdaBoost, and DNN algorithms was conducted to determine which algorithm is the most effective in predicting energy balance. Finally, the Root Mean Square Error (RMSE) has been used to validate the prediction models. The results show that the KNN, SVR, AdaBoost, and DNN algorithms had RMSE values of 0.56, 0.92, 0.95, and 0.53, respectively. Among these algorithms, the DNN and KNN algorithms had more accurate results than the other used algorithms and as a result of this research, An accurate forecast of neighbourhood energy balance was made. This study takes a novel approach by developing a model that takes into account an integrated system of houses, solar cells, and electric consumption for each building in a neighborhood, which can help to optimize energy consumption and reduce environmental impact.

Machine Learning-based Time Series Modelling for Large-Scale Regional Wind Power Forecasting: a Case Study in Ontario, Canada

Recently, time series forecasting has acquired considerable academic and industrial interests in various areas for different applications. Machine learning (ML) algorithms are known for their ability to capture the chaotic temporal non-linear relations in time series data. In this paper, various ML-based algorithms are employed and analyzed for time series forecasting of “regional wind power” in Ontario, Canada. To this end, the meteorological and spatial parameters with seasonal and temporal features are filtered and selected by a proposed deep feature selection approach. Then, multiple ML algorithms, including artificial neural network (ANN), deep neural network (DNN), long short-term memory (LSTM), bagging tree (BT), and support vector machine/regression (SVM/SVR), are used for training one-step ahead forecasting models. Finally, a comprehensive assessment of the constructed models is conducted based on different error criteria metrics. By evaluating and analyzing the performance of the models using testing data, it is observed that SVR/SVM is one of the most promising robust ML-based forecasting models. This technique results in reliable generic models that perform well with new data, where the testing MAPE % reaches a value of 13 %. Almost a similar MAPE is obtained from the ensemble modeling approach, which means combining process of the generated ML-based models does not significantly improve the predictions, in comparison with the developed SVR/SVM model. On the other hand, when constructing the multi-step ahead forecasting models, the predictions obtained from the multi-input multi-output (MIMO) LSTM approach are more reliable with higher accuracies. In other words, it is shown that the performance of the MIMO multi-step strategy is superior to the direct multi-step forecasting method, while employing algorithms with recursive properties.

QSAR ANALYSIS OF HDAC6 INHIBITORS

Histone deacetylase inhibitors are the most important class of drugs for the treatment of oncologies and other diseases due to their effect on cell growth, differentiation and apoptosis. Among the known eighteen histone deacetylases, Histone deacetylase 6 (HDAC6), which is involved in oncogenesis, cell survival, and cancer cell metastasis, is of high importance. Using 2D molecular descriptors RDKit, simplex descriptors, as well as methods of Random Forest (RF), Gradient Boosting (GBM), Support vectors (SVM), a number of adequate classi cation models of Quantitative Structure-Activity Relationship (QSAR) are proposed. For the models constructed using simplex descriptors, a structural interpretation was carried out, which made it possible to describe molecular fragments that increase and decrease the activity of HDAC6 inhibitors. The results of the structural interpretation were used for the rational molecular design of potential HDAC6 inhibitors, for which ADMET properties were also evaluated. Models built using 2D RDKit descriptors are freely available on the github platform (https://github.com/ovttiras/HDAC6-inhibitors).

Real‐Time Crossing‐Gate Rod Breakage Detection Based on Sequential‐Dataset Classification for the Railway‐Telemeter System

In our previous studies, we proposed crossing‐gate rod breakage detection methods based on machine learning techniques for the telemeter system installed by the Shikoku Railway Company (JR Shikoku) on railway lines in the Shikoku area to collect real‐time equipment data. However, these methods involve batch processing of many training data over several days, making them unsuitable for real‐time detection because of the long computation time. Therefore, this study improves these methods by performing sequential processing for real‐time detection for installation in the railway field. A one‐class support vector machine‐based detection method was applied to sequential processing to perform detection at each sampling period every few seconds. The proposed method is evaluated and its effectiveness is demonstrated in two representative cases of crossing‐gate rod breakage. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.