Finding Best Voting Classifier for Diabetic Disease Classification

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

Advanced landslide susceptibility mapping and analysis of driving mechanisms using ensemble machine learning models

Breast Cancer is the most common type of cancer prevalent among female cancer patients, while it is also the second most dreaded disease, causing cancer deaths among women. This study proposes new criteria for the prediction of survival of breast cancer patients, based on the analysis performed using four ensemble machine learning techniques, which include, AdaBoost M1, Bagging, Voting, and Stacking. For this study, we have used a breast cancer dataset consisting of 23 attributes and containing 575 samples obtained from Mizoram State Cancer Institute of Aizawl, Mizoram, India. We have employed ensemble machine learning classifiers to predict the recurrence of breast cancer within a period of three years evaluated based on the comparison of their performance. We have used 10 fold cross-validation technique and ROC curve to arrive at the results. From the dataset, attributes are ranked according to their contribution towards the prediction.

The goal of this research is to apply machine learning techniques to forecast a student’s probability of being accepted into a graduate program. Applicants’ GRE and TOEFL grades, university rankings, letters of recommendation, statements of purpose, cumulative grade point averages, and prior research experience are all included in the dataset utilized for this analysis. The goal is to calculate an applicant’s expected acceptance rate. This study uses a combination of Classifiers and regressors. Different prediction models are contrasted in this study: Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Neighbors Classifier (KNC), Support Vector Classifier (SVC), Gradient Boosting Classifier (GBC), Logistic regression (LR), Support vector Regressor (SVR), Random Forest Regressor(RFR), Gradient Boosting Regressor(GBR) and Decision Tree Regressor(DTR). Using these characteristics, the models are trained and evaluated. Evaluation criteria such as accuracy, kappa value, AUC-ROC, and confusion matrix are used to find the models’ effectiveness. In order to determine which model performed the best, the assessment results are compared with one another. Based on study findings, the Gradient Boosting Classifier outperforms the other models tested by a significant margin (96 per cent). This model’s AUC-ROC of 0.97 indicates it does a decent job at separating the positive and negative categories.

Worm gearboxes are popular across various industrial applications since they offer significant gear ratios in small installation spaces. Despite having multiple advantages, worm gearboxes are subjected to higher friction due to sliding design and are prone to damage and increased transmission error over the period of operation. Delayed diagnosis of worm gearbox degradation can lead to low-quality products and/or unnecessary production line downtime. Using vibration characteristics of worm gearbox, it is possible to determine the fault and transmission error at a given period in time. In this paper, an ensemble machine learning model is trained and deployed to monitor the transmission error of worm gearbox and classify between new, operational and old conditions. 1D CNN (one dimensional convolutional neural network) model is used to automatically extract features in vibration signal of X, Y, and Z axes and predict the relevant state of worm gear. The proposed technique uses ensemble machine learning technique fusion of features extracted by multi-layer 1D CNN for three axes vibration data. The proposed method could achieve 96% accuracy and performs significantly better than traditional sequential and ensemble machine learning models on a dataset of with 7,870 samples with 800 samples labeled as new condition, 4,740 samples as operational and 2,330 as old.

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm

Malicious attacks are common among the Internet of Things (IoT) devices that are installed in several locations like offices, homes, healthcare facilities, and transportation. Due to massive amounts of data created by IoT devices, machine learning is frequently used to detect cyber-attacks on these devices. The fact that fog devices may not have the computing or memory capability to identify threats in a timely manner is a source of concern. According to this article, machine learning model selection and real-time prediction can both be offloaded from the cloud, and both jobs can be performed by fog nodes, which are distributed computing devices. A cloud-based ensemble machine learning model is constructed using the provided method, and subsequently, attacks on fog nodes are identified in real time using the model. The NSL-KDD dataset is used to evaluate the performance of this approach. The results indicate that the proposed technique is effective in terms of a variety of performance criteria, including execution time, precision, recall, accuracy, and the F1 measure.

Addressing flood risk by integrating flood hazard, vulnerability and exposure is crucial for managing growing flood threats under warming climate. Among various flood risk indicators, flood hazard is significant, as it quantifies the spatial extent, depth and intensity of flooding. Hence, accurate assessment of flood hazard is essential for effective flood risk assessment. Recent developments in machine-learning (ML) techniques enhanced flood hazard assessment through integrating multi model dataset and increased predictive accuracy. The present investigation developed a weighted ensemble ML technique by integrating - Generalised Linear Model, Support Vector Machine, Random Forest, and Maximum Entropy models and tested in a tropical river basin (Kadalundi River basin) of southern India, prone to frequent flooding. Results show that weighted ensemble ML technique outweigh standalone models with an accuracy over 90% (AUROC 0.910) and a kappa index of 0.769. Subsequently, the Analytical Heirarchy Process (AHP) technique is applied to estimate flood vulnerability and exposure by combining various physical and socio-economic parameters. Finally, improved flood risk is estimated by combining hazard, vulnerability and exposure. The proposed methodology, integrating ensemble ML technique with physical and socio-economic factors is useful for effective disaster risk reduction and achieving Sustainable Development Goals (SDGs), particularly SDG 11 and SDG 13.

Indeed, understanding the behavior of headed stud shear connectors in composite steel and concrete construction is essential for ensuring structural integrity and optimal performance. This research focuses on the sensitivity and parametric assessment of the behavior of headed stud shear connectors in composite steel and concrete construction using ensemble machine learning techniques. The study aims to uncover hidden correlations and patterns in the data using a detailed database from 464 push tests, where connectors are welded within the ribs of both trapezoidal and re-entrant steel decks. These patterns provide insights into the performance of shear connectors under various conditions, including different welding methods. The application of ensemble machine learning offers an opportunity to understand complex relationships between variables that may not be immediately evident through conventional analysis. Within the study context, eight types of ensemble machine learning models are implemented and applied to estimate the shear capacity of shear studs and conduct feature importance and partial dependence analysis. The outcomes of this research contribute to a deeper understanding of the factors influencing the performance of shear connectors, providing valuable input for structural design and evaluation in composite construction practices. As a result, this research not only enriches the current academic discourse on shear connectors but also offers pragmatic insights for professionals in the field, thereby bridging the gap between theoretical research and real-world applications in composite construction practices.

A common health problem around the globe, chronic kidney disease (CKD) must be identified early in order to be effectively managed. The accuracy of CKD diagnosis may be increased with the use of machine learning approaches, especially ensemble learning. In order to determine which model performs best for CKD detection, this research will compare and contrast several ensemble learning strategies. Ten distinct models are evaluated in the study: Bagging, Random Forest, Gradient Boosting, Ada Boosting, XGBoost, K-Nearest Neighbours (KNN), Decision Tree, Decision Tree after Pruning, Logistic Regression, and Linear Discriminant Analysis. A CKD dataset is used to evaluate these models based on criteria including accuracy, precision score, and recall score. The comparative study results demonstrate how ensemble learning techniques might raise CKD detection accuracy. The findings provide crucial details about the optimal model for CKD detection, which can help with early diagnosis and better patient outcomes. KEYWORDS: Chronic Kidney Disease (CKD), Ensemble Learning, Machine Learning, Accuracy, Early Diagnosis

Drill-and-blast tunnel construction in the Himalayan region often encounters complex geological and hydrogeological conditions, leading to significant water leakage that impacts project cost and stability. This study aims to enhance leakage prediction accuracy using ensemble machine learning techniques. Initial leakage estimates were made using Panthi’s semi-empirical approach for the Nilgiri-II Hydropower Project. A dataset comprising rock mass quality, topography, and permeability features was used to train four ensemble models: Bagging, Boosting (XGBoost), Voting, and Stacking. Among these, Bagging outperformed others with an R2 of 0.99, followed by Voting and Stacking (both R2 = 0.97). Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE) plots were used to interpret model predictions and identify key influencing features such as hydrostatic head (Hstatic), distance to the valley side (D), and joint parameters. These results demonstrate that ensemble learning, particularly bagging, is highly effective in modeling water leakage in challenging Himalayan tunnel environments.

Antibiotic-resistant bacteria have proliferated at an alarming rate as a result of the extensive use of antibiotics and the paucity of new medication research. The possibility that an antibiotic-resistant bacterial infection would progress to sepsis is one of the major collateral problems affecting people with this condition. 31,000 lives were lost due to sepsis in England with costs about two billion pounds annually. This research aims to develop and evaluate several classification approaches to improve predicting sepsis and reduce the tendency of underdiagnosis in computer-aided predictive tools. This research employs medical datasets for patients diagnosed with sepsis, and it analyses the efficacy of ensemble machine learning techniques compared to nonensemble machine learning techniques and the significance of data balancing and conditional tabular generative adversarial nets for data augmentation in producing reliable diagnosis. The average F Score obtained by the nonensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. Nonensemble techniques, such as Decision Tree, achieved an F score of 0.90, an AUC of 0.90, and an accuracy of 90%. Histogram-basedgradient boosting classification tree achieved an F score of 0.96, an AUC of 0.96, and an accuracy of 95%, surpassing the other models tested. Additionally, when compared to the current state-of-the-art sepsis prediction models, the models developed in this study demonstrated higher average performance in all metrics, indicating reduced bias and improved robustness through data balancing and conditional tabular generative adversarial nets for data augmentation. The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.

Bankruptcy is a legal proceeding involving a person or a business, where they are unable to pay the debt. Financial investors, banks, money lenders, and the government seek to know the status of bankruptcy of firms as it carries huge financial risk. The prediction of bankruptcy will help all the stakeholders of the company. To model bankruptcy prediction, traditional statistical methods like multiple discriminant analysis and Machine Learning (ML) models like Decision Trees, Support Vector Machines, and Ensemble have been utilized. In existing works, homogeneous base estimators are used while developing ensemble algorithms. This study uses a bi-level classification technique (a heterogeneous ensemble ML technique) to predict bankruptcy. To train the classifier, the features extracted are Altman z-score parameters and market-based measures. Unlike previous studies, this study uses an indicator of corporate governance as a feature. The outcome of this study is an improvement in the performance of the ML model using the bi-level classification technique. An F1-score of 0.98 and 97.8% accuracy is achieved with features including Tobin’s Q and bi-level classification technique as an ML model. It outperforms the 96% accuracy of the random forest algorithm.KeywordsBankruptcy PredictionMachine learningBi-level classificationTobin’s Q

The importance of Power Transmission System PTS fault detection and classification is increasing day by day as because consumption of electricity is increasing. Short circuit fault in Power Transmission Line Network PTLN can cause severe damage to the power transmission system as well as economic loss. Power Transmission System requires new methods to detect and classify fault behaviour to prevent it from heavy damage. Machine Learning ML algorithms can be very effective to classify and detect various types of faults within the PTLN. There are variety of ML algorithms to recognise and classify the faults but as complexity of PTS is increasing day by day, reliability of these algorithms is decreasing. This study uses various types of ML algorithms to generate predictive models to evaluate what kind of algorithm is more appropriate to recognise and classify faults within the PTLN. Faults investigated in this research work include (L-L) double line fault, (L-L-L) three phase fault, (L-G) line to ground fault, (L-L-G) double line to ground fault, and (L-L-L-G) three phase fault with the involvement of the ground. The data was evaluated using six (06) ML algorithms that are Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbor (Knn), Random Forest, XGBoost (XGB) and Naive Bayes (NB) for recognise of fault and classification within the PTLN. The performance of ML algorithms obtained by comparing the results and determine which algorithm is fast and more accurate. These results can be used to create more effective ML algorithms for PTS. The results indicate that the application of ML algorithms for PTS task could improve the PTLN yield and save time for technical teams.