Decision Stumps Research Articles

A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Identifying Novel Therapeutics for the Resistant Mutant "F533L" in PBP3 of Pseudomonas aeruginosa Using ML Techniques.

Pseudomonas aeruginosa (P. aeruginosa) is a highly infectious and antibiotic-resistant bacterium, which causes acute and chronic nosocomial infections. P. aeruginosa exhibits multidrug resistance due to the emergence of resistant mutants. The bacterium takes advantage of intrinsic and acquired resistance mechanisms to resist almost every antibiotic. To overcome the drug-resistance problem, there is a need to develop effective drugs against antibiotic-resistant mutants. Therefore, in this study, we selected the F533L mutation in PBP3 (penicillin-binding protein 3) because of its important role in β-lactam recognition. To target this mutation, we screened 147 antibacterial compounds from PubChem through a machine-learning model developed based on the decision stump algorithm with 75.75% accuracy and filtered out 55 compounds. Subsequently, out of 55 compounds, 47 compounds were filtered based on their drug-like activity. These 47 compounds were subjected to virtual screening to obtain binding affinity compounds. The binding affinity range of all 47 compounds was -11.3 to -4.6 kcal mol-1. The top 10 compounds were examined according to their binding with the mutation point. A molecular dynamic simulation of the top 8 compounds was conducted to understand the stability of the compounds containing the mutated PBP3. Out of 8 compounds, 3 compounds, namely, macozinone, antibacterial agent 71, and antibacterial agent 123, showed good stability and were validated by RMSD, RMSF, and binding-free analysis. The findings of this study revealed promising antibacterial compounds against the F533L mutant PBP3. Furthermore, developments in these compounds may pave the way for novel therapeutic interventions.

Identification of Novel PI3Kα Inhibitor Against Gastric Cancer: QSAR-, Molecular Docking-, and Molecular Dynamics Simulation-Based Analysis.

Gastriccancer (GC) is a malignant tumor with global incidence and death ranking fifth and fourth, respectively. GC patients nevertheless have a poor prognosis despite the effectiveness of more advanced chemotherapy and surgical treatment options. The second most frequently mutated gene in GC is PI3Kalpha, a confirmed oncogene that results in abnormal PI3K/AKT/mTOR signaling, causing enhanced translation, proliferation, and survival, and is mutated in 7-25% of GC patients. The protein PI3Kalpha was targeted in the present study by utilizing machine learning (ML), molecular docking, and simulation. A total of 9214 molecules from the DrugBank database were chosen for the first screening. A training set for 6770 compounds tested against PI3Kalpha was assessed to create a quantitative structure-activity relationship-based machine learning model using five different classification algorithms: random forest, random tree, J48 pruned tree, decision stump, and REPTree. Furthermore, consideration was given to the random forest classifier for screening based on its performance index (Kappa statistics, ROC, and MCC). Overall, 1539 of the 9214 drug bank compounds were predicted to be active. Thereafter, three pharmacological filters, Lipinski's rule, Ghose filter, and Veber rule, were applied to test the drug-like properties of the screened compounds. Twenty-six of 1593 compounds showed excellent drug-like properties and were further considered for molecular docking. Thereafter, two compounds were screened as hits because they possessed the molecular docked position with the lowest binding energy and an excellent bonding profile. The binding stability of the selected compounds was further assessed through molecular dynamics simulations for up to 100 ns. Furthermore, compound 1-(3-(2,4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea was selected as a potential hit in the final screening by analyzing a number of parameters, including the Rg, RMSD, RMSF, H bonding, and SASA profile. Therefore, we conclude that compound 1-(3-(2, 4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea has efficient inhibitory potential against PI3Kalpha protein and could be utilized for the development of effective drugs against GC.

DeforestVis: Behaviour Analysis of Machine Learning Models with Surrogate Decision Stumps

AbstractAs the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model‐agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier‐to‐explain. Yet, rule sets can become very lengthy, with many if–else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behaviour of complex ML models by providing surrogate decision stumps (one‐level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity versus fidelity trade‐off by incrementally generating more stumps, creating attribute‐based explanations with weighted stumps to justify decision making, and analysing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case‐by‐case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

Land subsidence susceptibility mapping: a new approach to improve decision stump classification (DSC) performance and combine it with four machine learning algorithms.

Land subsidence is a worldwide threat. In arid and semiarid lands, groundwater depletion is the main factor that induce the subsidence resulting in environmental damages and socio-economic issues. To foresee and prevent the impact of land subsidence, it is necessary to develop accurate maps of the magnitude and evolution of the subsidences. Land subsidence susceptibility maps (LSSMs) provide one of the effective tools to manage vulnerable areas and to reduce or prevent land subsidence. In this study, we used a new approach to improve decision stump classification (DSC) performance and combine it with machine learning algorithms (MLAs) of naïve Bayes tree (NBTree), J48 decision tree, alternating decision tree (ADTree), logistic model tree (LMT), and support vector machine (SVM) in land subsidence susceptibility mapping (LSSSM). We employ data from 94 subsidence locations, among which 70% were used to train learning hybrid models and the other 30% were used for validation. In addition, the models' performance was assessed by ROC-AUC, accuracy, sensitivity, specificity, odd ratio, root-mean-square error (RMSE), kappa, frequency ratio, and F-score techniques. A comparison of the results obtained from the different models reveals that the new DSC-ADTree hybrid algorithm has the highest accuracy (AUC = 0.983) in preparing LSSSMs as compared to other learning models such as DSC-J48 (AUC = 0.976), DSC-NBTree (AUC = 0.959), DSC-LMT (AUC = 0.948), DSC-SVM (AUC = 0.939), and DSC (AUC = 0.911). The LSSSMs generated through the novel scientific approach presented in our study provide reliable tools for managing and reducing the risk of land subsidence.

Development of web-based learning application to enhance understanding in Philippine history

This study introduces a web-based learning system designed to enhance the recall and understanding of Philippine history through engaging quizzes and games, aligning with ISO 25010 standards. The system prioritizes functional suitability, usability, reliability, performance efficiency, and portability, with the aim of aiding users, particularly students, in remembering and studying Philippine history. Leveraging interactive features such as game content and quizzes, the system actively engages learners, promoting comprehension and retention of historical facts. Instant feedback mechanisms further contribute to users' ability to identify strengths and weaknesses in understanding Philippine history. To evaluate the system's performance, the researchers applied the Locally Weighted Learning (LWL) algorithm, adhering to ISO 25010 guidelines. The LWL algorithm, combining lazy learning and a decision stump classifier with linear weighting kernels, achieved an impressive 90% accuracy on the "Book1" dataset. This comprehensive evaluation validated the system's reliability and effectiveness, showcasing high accuracy in assessing attributes such as data accuracy, system availability, user-friendliness, responsiveness, and security assurance. The study also includes a regression analysis, indicating a moderate positive linear relationship (multiple R of 0.4243) between independent variables (functional suitability, usability, performance efficiency, reliability, and portability) and the dependent variable (software quality score). While the model explains approximately 22.38% of the variability, the non-significant p-value in the ANOVA table prompts consideration for further refinement. Overall, this research contributes to the field of educational technology by providing valuable insights into the development and evaluation of a history-focused learning application, emphasizing both practical usability and theoretical reliability within the framework of ISO 25010.

Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets

This paper investigates the performance enhancement of base classifiers within the AdaBoost framework applied to medical datasets. Adaptive boosting (AdaBoost), being an instance of boosting, combines other classifiers to enhance their performance. We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. The experiments are carried out on five datasets from the medical domain based on various types of cancers, i.e., global cancer map (GCM), lymphoma-I, lymphoma-II, leukaemia, and embryonal tumours. The evaluation focuses on the accuracy, precision, and efficiency of the base classifiers in the AdaBoost framework. The results show that the performance of Naïve Bayes, Bayes network, and voted perceptron is highly improved compared to the rest of the base classifiers, attaining accuracies as high as 94.74%, 97.78%, and 97.78%, respectively. The results also show that in most cases, the base classifiers perform better with AdaBoost compared to their performance, i.e., for voted perceptron, the accuracy is improved up to 13.34%.For bagging, it is improved by up to 7%. This research aims to identify such base classifiers with optimal boosting capabilities within the AdaBoost framework for medical datasets. The significance of these results is that they provide insight into the performance of the base classifiers when used in the boosting framework to enhance the classification performance of classifiers in scenarios where individual classifiers do not perform up to the mark.

Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department

As the COVID-19 pandemic has affected the globe, health systems worldwide have also been significantly affected. This pandemic has impacted many sectors, including health in the Kingdom of Jordan. Crises that put heavy pressure on the health systems’ shoulders include the emergency departments (ED), the most demanded hospital resources during normal conditions, and critical during crises. However, managing the health systems efficiently and achieving the best planning and allocation of their EDs’ resources becomes crucial to improve their capabilities to accommodate the crisis’s impact. Knowing critical factors affecting the patient length of stay prediction is critical to reducing the risks of prolonged waiting and clustering inside EDs. That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. Therefore, patients’ length of stay in EDs across waiting time duration is categorized as (low, medium, and high) using supervised machine learning (ML) approaches. Unsupervised algorithms have been applied to classify the patient’s length of stay in local EDs in the Kingdom of Jordan. The Arab Medical Centre Hospital is selected as a case study to justify the performance of the proposed ML model. Data that spans a time interval of 22 months, covering the period before and after COVID-19, is used to train the proposed feedforward network. The proposed model is compared with other ML approaches to justify its superiority. Also, comparative and correlation analyses are conducted on the considered attributes (inputs) to help classify the LOS and the patient’s length of stay in the ED. The best algorithms to be used are the trees such as the decision stump, REB tree, and Random Forest and the multilayer perceptron (with batch sizes of 50 and 0.001 learning rate) for this specific problem. Results showed better performance in terms of accuracy and easiness of implementation.

A Study on Variable Selections and Prediction for Diabetes Pregnancy Dataset Using Data Mining With Machine Learning Approaches

Data mining aims to convert raw data into valuable insights that inform decision-making, predictions, and business-related research. Machine learning, a branch of artificial intelligence (AI), creates algorithms and models that empower computers to learn from data and make data-driven predictions or decisions. This paper considers diabetes-related parameters, namely gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, and blood glucose, for applying machine learning techniques to find diabetes and suitable variables for future predictions in diabetes using Gaussian Process, Linear Regression, Multilayer Perceptron, Simple Linear Regression, SMOreg, decision stump, M5P, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction

IntroductionThere are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process.Related workDecision trees and their ensemble algorithms are considered to be suitable methods for pharmaceutical applications due to their excellent performance and good interpretability. We review existing decision trees or decision tree ensemble algorithms in the medical field and point out their shortcomings.MethodThis study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST).ExperimentThe two-drug combination data sets are collected from two cell lines with three classes (additive, antagonistic and synergistic effects) to test our method. Experimental results show that both our DST and EDST perform better than other methods. Besides, the rules generated by our methods are more compact and more accurate than other rule-based algorithms. Finally, we also analyze the extracted knowledge by the model in the field of bioinformatics.ConclusionThe novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.

Optimal Variable Selections and Prediction Methods for Soil Chemical Property Using Data Mining and Machine Learning Approaches

The main objective of the research is to convert raw data into actionable knowledge. Creating a decision tree involves recursively dividing the data into subsets based on different attributes, aiming to achieve homogeneity or minimize variance within each subset. This paper considers agriculture and its soil chemical-related dataset for applying data mining techniques to find suitable variables for future predictions. The tenfamiliar ML approaches are Gaussian processes,linear regression, multilayer perceptron, simple linear regression, SMOreg, decision stump, M5P, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

Optimal Sparse Recovery with Decision Stumps

Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood. We here obtain a tight finite sample bound for the feature selection problem in linear regression using single-depth decision trees. We examine the statistical properties of these "decision stumps" for the recovery of the s active features from p total features, where s

An approach for classification of lung nodules

The main objective of the proposed work is to develop an automated computer-aided detection (CAD) system to classify lung nodules using various classifiers from computed tomography (CT) images. One of the most important steps in lung nodule detection is the classification of nodule and non-nodule patterns in CT. The early detection of the condition helps lower the mortality rate. The developed CAD systems consist of segmentation, feature extraction, and classification. In this work, a filter method is used to segment the infected region. Later, we extracted features through and fed into classifiers such as Decision Stump (DS), Random Forest (RF), and Back Propagation Neural Network (BPNN). The experimentation was conducted on LIDC-IDRI dataset, and the results with BPNN outperformed those with DS and RF classifiers.

Risk Prediction and Machine Learning: A Case-Based Overview.

Risk Prediction and Machine Learning: A Case-Based Overview.

Morphometric analysis of dry atlas vertebrae in a northeastern Thai population and possible correlation with sex

The uppermost segment of the cervical vertebra or atlas (C1) is a critically important anatomical structure, housing the medulla oblongata and containing the grooves for the C1 spinal nerve and the vertebral vessels. Variations of the C1 vertebra can affect upper spine stability, and morphometric parameters have been reported to differ by population. However, there are few data regarding these parameters in Thais. The use of this bone to predict sex and age has never been reported. This study aimed to examine C1 morphometry and determine its ability to predict sex. Twelve diameter parameters were taken from the C1 vertebrae of identified skeletons (n = 104, males [n, 54], females [n, 50]). Correlation analysis was also performed for sex and age, which were predicted using machine learning algorithms. The results showed that 8 of the 12 measured parameters were significantly longer in the male atlas (p < 0.05), while the remaining 4 (distance between both medial-most edges of the transverse foramen, transverse dimension of the superior articular surface, frontal plane passing through the canal's midpoint, and anteroposterior dimension of the inferior articular surface) did not differ significantly by sex. There was no statistically significant difference in these parameters on the lateral side. The decision stump classifier was trained on C1 parameters, and the resulting model could predict sex with 82.6% accuracy (root mean square error = 0.38). Assertation of the morphometric parameters of the atlas is important for preoperative assessment, especially for the treatment of atlas dislocation. Our findings also highlighted the potential use of atlas measurements for sex prediction.

Effective hybrid feature subset selection for multilevel datasets using decision tree classifiers

Feature selection is one of the most significant procedures in machine learning algorithms. It is particularly to improve the performance and prediction accuracy for complex data classification. This paper discusses a hybrid feature selection technique with the decision tree-based classification algorithm. The feature selected using information gain (IG) is combined with the features selected from ReliefF which generates the resultant feature subset. Then the resultant feature subset is in turn combined with a correlation-based feature selection (CFS) method to generate the aggregated feature subset. To perform classification accuracy on the aggregated feature subset, different decision trees-based classification algorithm such as C4.5, decision stumps, naive Bayes tree, and random forest with ten-fold cross-validation have been deployed. To check the prediction accuracy of the proposed work eight different multilevel University of California, Irvine (UCI) machine learning datasets have been used with minimum to maximum numbers of features. The main objective of the hybrid feature selection is to improve the classification accuracy, prediction and to reduce the execution time using standard datasets.

Recognize corrupted data packeted while transferring data through ensemble machine learning techniques

In today’s world, every technology is moving towards cloud storage which makes file transfer protocols a cornerstone for any platform to run smoothly. Therefore, identifying damaged files is a crucial responsibility in the area of data management and integrity. In this study, we suggest an AdaBoost-based machine learning technique for identifying damaged files. AdaBoost is an ensemble method that combines many weak classifiers into one powerful classifier. In our method, we train weak classifiers called decision stumps using a dataset that includes both damaged and healthy files. The final prediction was decided by a weighted majority vote of all the weak classifiers. We evaluated our method on a dataset generated by collecting metadata information of files and passed it to the algorithms. We used the AdaBoost approach as a base algorithm for comparison along with more established techniques like Naive Bayes, Logistic Regression, and Linear Discriminant Analysis. The results show that the AdaBoost algorithm is effective in detecting corrupted files, and it performs better than other traditional methods. Additionally, our method is computationally efficient and can be easily integrated into existing data management systems. It is expected to have a positive impact on data integrity and management in various fields such as digital forensics, cloud computing, and storage systems.

Investigation of the Differential Power of Young's Internet Addiction Questionnaire Using the Decision Stump Tree.

Background Internet addiction is one of the serious consequences of recent advances in the use of social media. Early detection of Internet addiction is essential because of its harms and is necessary for timely and effective treatment. Aim The aim of this study was to use data mining and an artificial intelligence algorithm to estimate the differential power of each question in the Young Internet Addiction Test and build a decision stump model to predict which item in the questionnaire can be representative of the whole questionnaire. Methods This is a descriptive study conducted at the University of Tabriz, in which 256 undergraduate students were selected in randomized cluster sampling, and they completed Young's IAT (Internet Addiction Test) questionnaire and some demographic questions. The data were statistically analyzed with SPSS and were divided into two groups, normal and addicted, by using a cut-off point. Also, the data of the subjects was used to model the decision stump tree in WEKA. The clustering item was the normal and addicted specifier. Results The study shows that Cronbach's alpha of the IAT is 0.88, which shows good internal integration of subjects that are used to develop the model in WEKA (the Waikato Environment for Knowledge Analysis). Data analysis showed that by using the second question of this questionnaire as the root of the decision stump tree model, it is possible to distinguish between Internet addicts and healthy users with 82% accuracy using this model. Conclusion The study shows innovative ways in which decision stump trees and data mining can help to improve methods used in Clinical Psychotherapy and Human Science. Regarding this, the study showed that early detection of Internet addiction would be possible by using the 2nd question of the IAT. Also, early detection can result in cost-effectiveness for the whole healthcare system.

Comparative Analysis of Machine Learning Technique to Improve Software Defect Prediction

One of the most active areas of research in the software engineering community is defect prediction. The gap between data mining and software engineering must be bridged to increase the rate of software success. Before the testing phase, software defect prediction predicts where these flaws will most likely occur in the source code. Methods for predicting software defects are widely used to investigate the impact area in software using various techniques (clustering, statistical methods, neural networks, and machine learning models). The goal of this research is to examine various machine learning algorithms for predicting software defects. There have been many fault prediction techniques introduced, but no single technique or approach can be used for all types of datasets. To achieve maximum accuracy, different machine learning algorithms such as Bayesian Net, Logistic Regression, Multilayer Perceptron, Ruler Zero-R, J48, Lazy IBK, Support Vector Machine, Neural Networks, Random Forest, and Decision stump were used to uncover the maximum subset of defects that could be predicted. This research concern is to find out defects using five NASA data sets JM1, CM1, KC1, KC2, and PC1. Logistic Regression has been shown to have the best output relative to others (93%).

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) ( i.e ., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) ( i.e ., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random: 70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE = 0.75 cm, MAE = 0.59 cm, NSE = 0.98), while the SCS model had the lowest performance (RMSE = 4.77 cm, MAE = 2.64 cm, NSE = 0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively.