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This study introduces an innovative approach integrating artificial intelligence (AI) and statistical modelling techniques to enhance the prediction of earth dam slope stability. Utilizing advanced methodologies, including Classification and ...

• AI innovation for slope stability prediction, omitting safety factor calculations.
• Using CART models, it predicts earth dam slope stability from 6 key inputs.
• CART's decision tree format is user-friendly and valuable.

This paper introduces a new method for training decision trees and random forests using CKKS homomorphic encryption (HE) in cloud environments, enhancing data privacy from multiple sources. The innovative Homomorphic Binary Decision Tree (HBDT) ...

• The ISP method allows for gathering a large number of historical training samples.
• The SRVI values of the same crop planted in neighboring years range from 0.91 and 1.2.
• A distinct northward expansion trend is evident for all four ...

Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification ...

• Devising an expert system based on bagging classifier.
• Enhancing the performance using state-of-art data balancing technique.
• Evaluation based on state-of-art feature selection technique.
• Validation of the proposed technique on ...

Risks of death from Breast Cancer (BC) are drastically rising in recent years. The diagnosis of breast cancer is time-consuming due to the limited availability of diagnostic systems such as dynamic MRI, X-rays etc. Early detection and diagnosis ...

Anomaly detection is a process that detects unlike observations from entire data points. Also, detection of anomalies in unlabelled data particularly is very tedious task using unsupervised learning models. It is a very big problem in banking, ...

Predicting the Earth Skin Temperature (TS) using artificial intelligence (AI) has the potential to offer valuable insights into environmental changes and their impacts. TS has significant nonlinearity due to several meteorological parameters, ...

• Malaysian regional earth skin temperature (TS) forecasting adopted using machine learning.
• Deep learning and tree-based models were developed for the forecasting process.
• Pearson correlation was conducted to determine the ...

• Pediatric readmissions burden patients, the family network and the health system.
• Machine learning approaches can predict potentially avoidable 30-day pediatric hospital readmission.
• The algorithm XGBoost with bagging imputation ...

Pediatric readmissions are a burden on patients, families, and the healthcare system. In order to identify patients at higher readmission risk, more accurate techniques, as machine learning (ML), could be a good strategy ...

• A data collection method acquiring data from tobacco curing sources is developed.
• The variation laws of impact factors of multi-source curing data are explored.
• A multi-sensor data fusion scheme with embedded XGBoost is proposed ...

The quality and economic value of cigarettes depend heavily on the quality of tobacco curing, which is greatly influenced by the temperature rise time during the process. Accurately setting the temperature rise time is crucial in creating the ...

• Propose an improved random forest based on the improvement of decision trees.
• Improve the evaluation mechanism for the classification effect of decision trees.
• Propose a method for quantifying the diversity between decision trees.

Random forest is one of the most widely used machine learning algorithms. Decision trees used to construct the random forest may have low classification accuracies or high correlations, which affects the comprehensive performance of the random ...

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their ...

Significant changes have been observed from 2019 in the COVID period as study styles shifted from traditional to hybrid learning. This paper predicted the applicability of the hybrid learning mode of education in the programming labs for the ...

Global mortality rates are high due to heart disease, it is crucial to early analyzing the risk factors of prediction and diagnosis are crucial. Decision trees are a reliable machine-learning method for this problem. Four decision tree algorithms CART (...

• A new method is developed for Multiple Criteria Decision-Making & Verification.
• Classification and statistical methods are used for Decision-Verification.
• This method is applied to robot selection in the pharmaceutical industry.

This study is a double subject that links Multiple Criteria Decision-Making (MCDM) to the problem of robot selection. The study is contained in three folds. The first fold addresses this problem for industry in general, as well as the ...

The aim of this study was to use machine learning model for prediction of running costs of public transport buses in Karachi, which is the most widely used mode of shared mobility in this city. To achieve this objective, classification and ...

Tree ensemble algorithms as RandomForest and GradientBoosting are currently the dominant methods for modeling discrete or tabular data, however, they are unable to perform a hierarchical representation learning from raw data as NeuralNetworks does ...

Educational data mining (EDM) is the application of data mining in the educational field. EDM is used to classify, analyze, and predict the students’ academic performance, and students’ dropout rate, as well as instructors’performance in order to ...

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Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high ...

Classification trees are a popular machine learning tool for studying a variety of problems, including prediction, inference, risk factors identification, and risk groups classification. Classification trees are basically developed under the ...

The study introduces a novel approach to classify faulty bearings using a combination of the Teager-Kaiser Energy Operator (TKEO) and Artificial Intelligence. The TKEO signal is used for statistical feature extraction to distinguish between healthy and ...

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• Fear test behaviors in sheep were classified by combining CNNs and optical flow methods.

Determining the temperament related traits of sheep, such as the coping style with various stress factors such as people, a new environment and social isolation, is essential in terms of improving animal welfare and increasing ...