Using Ensemble StackingC Method and Base Classifiers to Ameliorate Prediction Accuracy of Pedagogical Data

Thyroid disease is spreading very rapidly among women after the age of 30 years. Therefore, it is necessary to examine the thyroid dataset for predicting the disease at early stage so that precautions can be taken to protect the dangerous condition of thyroid cancer. A decision tree is used to extract hidden patterns from the stored datasets. The objective of this research paper is to examine the thyroid disease dataset using decision tree, random forest, and classification and regression tree (CART), and after obtaining the results of these classifiers, we enhanced the results using the bagging ensemble technique. The proposed experiment was done on 3710 instances and 29 features of thyroid patients. The overall prediction depends on target variable whch is divided in sick and negative class. The accuracy of the prediction was calculated on the basis of different num-fold and seed values. Different classification algorithms are analyzed using thyroid dataset. The results obtained by individual classification algorithms like decision tree, random forest tree, and extra tree give an accuracy of 98%, 99%, and 93%, respectively. Then, we developed a bagging ensemble method combining the three basic tree classifiers and apply again on the same dataset, which gives a better accuracy of 100% in the case of seed value 35 and num-fold value 10. This proposed ensemble method can be used for better prediction of thyroid disease.

The diagnosis of kidney disease, often called chronic renal illness, is known as chronic kidney disease (CKD). Chronic kidney disease (CKD) is a widespread and chronic health issue that requires preventative measures for early identification in order to slow the disease's progression. Using complex techniques like Support Vector Machine (SVM) and Logistic Regression, this study explores the field of machine learning. For robust model construction, use Random Forest and Decision Tree. Through the use of Variance Inflation Factor (VIF) for feature engineering, the dataset is carefully refined to improve the visibility of relevant features. To address any possible data imbalance concerns, Synthetic Minority Over-sampling Technique (SMOTE) is also utilized, promoting a fairer representation of classes in the dataset. By means of a thorough assessment procedure, the study methodically pinpoints the key elements that contribute to a precise diagnosis of chronic kidney disease. Each algorithm's effectiveness is evaluated using performance indicators like recall, accuracy, precision, and Fl-score.

This article introduces a novel ensemble method named eAdaBoost (Effective Adaptive Boosting) is a meta classifier which is developed by enhancing the existing AdaBoost algorithm and to handle the time complexity and also to produce the best classification accuracy. The eAdaBoost reduces the error rate when compared with the existing methods and generates the best accuracy by reweighing each feature for further process. The comparison results of an extensive experimental evaluation of the proposed method are explained using the UCI machine learning repository datasets. The accuracy of the classifiers and statistical test comparisons are made with various boosting algorithms. The proposed eAdaBoost has been also implemented with different decision tree classifiers like C4.5, Decision Stump, NB Tree and Random Forest. The algorithm has been computed with various dataset, with different weight thresholds and the performance is analyzed. The proposed method produces better results using random forest and NB tree as base classifier than the decision stump and C4.5 classifiers for few datasets. The eAdaBoost gives better classification accuracy, and prediction accuracy, and execution time is also less when compared with other classifiers.

This article is aimed at the low accuracy of student weariness prediction in education and the poor prediction effect of traditional prediction models. It was established the SMOTE (Synthetic Minority Oversampling Technique) algorithm and random forest prediction models. This study puts forward to useing the SMOTE oversampling method to balance the data set and then use the random forest algorithm to train the classifier. By comparing the common single classifier with the ensemble learning classifier, it was found that the SMOTE and Random forest method performed more prominently, and the reasons for the increase in the AUC value after using the SMOTE method were analyzed. Using Massive Open Online Course (Mooc) synthesis student’s datasets, which mainly include the length of class, whether the mouse has moved, whether there is a job submitted, whether there is participating in discussions and completing the accuracy of the assignments. It is proved that this method can significantly improve the classification effect of classifiers, so teachers can choose appropriate teaching and teaching interventions to improve student’s learning outcomes.

Student outcomes are of great importance in higher education institutions. Accreditation bodies focus on them as an indicator to measure the performance and effectiveness of the institution. Forecasting students’ academic performance is crucial for every educational establishment seeking to enhance performance and perseverance of its students and reduce the failure rate in the future. The main goal of this study is to predict the performance of undergraduate first-level students in the Computer Department during the years 2016 to 2021 to enhance their performance in future by discovering the best algorithm use to analyze the educational data to identify the students’ academic performance. The secondary data was collected by reviewing the Student Affairs Department at the Faculty of Specific Education at Damietta University, in addition to the Statistics Department at the university. The dataset contained 830 instances after excluding 139 instances of missing values, irrelevant rows, and outliers. The dataset was divided into train (577 instances (70%)), test (253 instances (30%)) and involved six features such year, midterm, practical exam, writing exam, final total degree, and grade. This paper use five machine learning (ML) algorithms which was selected according to the literature review and high accuracy in predicting educational data mining: For the purpose of comparison, a number of different machine learning algorithms, such as Random Forest, Decision Tree, Naive Bayes, Neural Network, and K-Nearest Neighbours, were utilized and evaluated with evaluation metrics such as confusion matrix, accuracy, precision, recall, and F-measure. The Random Forest and Decision Tree classifiers emerged as the top-performing algorithms, accurately categorizing 250 instances when predicting students' performance in the statistics course. This was determined based on the findings of the study. Out of a total of 253 instances that were included in the testing set, they only made three incorrect classifications.

Diagnosing a parasitic disease is a very difficult job in clinical practice. In this study, we constructed a machine learning model for diagnosis prediction using patient information. First, we diagnosed whether a patient has a parasitic disease. Next, we predicted the proper diagnosis method among the six types of diagnostic terms (biopsy, endoscopy, microscopy, molecular, radiology, and serology) if the patient has a parasitic disease. To make the datasets, we extracted patient information from PubMed abstracts from 1956 to 2019. We then used two datasets: the prediction for parasite-infected patient dataset (N = 8748) and the prediction for diagnosis method dataset (N = 3780). We then compared four machine learning models: support vector machine, random forest, multi-layered perceptron, and gradient boosting. To solve the data imbalance problem, the synthetic minority over-sampling technique and TomekLinks were used. In the parasite-infected patient dataset, the random forest, random forest with synthetic minority over-sampling technique, gradient boosting, gradient boosting with synthetic minority over-sampling technique, and gradient boosting with TomekLinks demonstrated the best performances (AUC: 79%). In predicting the diagnosis method dataset, gradient boosting with synthetic minority over-sampling technique was the best model (AUC: 87%). For the class prediction, gradient boosting demonstrated the best performances in biopsy (AUC: 88%). In endoscopy (AUC: 94%), molecular (AUC: 90%), and radiology (AUC: 88%), gradient boosting with synthetic minority over-sampling technique demonstrated the best performance. Random forest demonstrated the best performances in microscopy (AUC: 82%) and serology (AUC: 85%). We calculated feature importance using gradient boosting; age was the highest feature importance. In conclusion, this study demonstrated that gradient boosting with synthetic minority over-sampling technique can predict a parasitic disease and serve as a promising diagnosis tool for binary classification and multi-classification schemes.

The main challenges of predictive analytics revolve around the handling of datasets, especially the disproportionate distribution of instances among classes in addition to classifier-suitability issues. This unequal spread causes imbalance learning and severely obstructs prediction accuracy. In this paper, the performances of six classifiers and the effect of data balancing (DB) and formation approaches for predicting pregnancy outcome (PO) were investigated. Synthetic minority oversampling technique (SMOTE), resampling with and without replacement, were adopted for data imbalance treatment. Six classifiers including random forest (RF) were evaluated on each resampled dataset with four test modes using Waikato Environment for Knowledge Analysis and R programming libraries. The results of analysis of variance performed separately using F-measure and root mean squared error showed that mean performance of classifiers across the datasets varied significantly (F=117.9; p=0.00) at 95% confidence interval, while turkey multi-comparison test revealed RF(mean=0.78) and SMOTE (mean=0.73) as having significantly different means. The RF model on SMOTE produced each PO class accuracy ≥0.89, area under the curve ≥ 0.96 and coverage of 97.8% and was adjudged the best classifier-DB method pair. However, there was no significant difference (F=0.07, 0.01; p=1.000) in the mean performances of classifiers across test data modes respectively. It reveals that train/test data modes insignificantly affect classification accuracy, although there are noticeable variations in computational cost. The methodology significantly enhance the predictive accuracy of minority classes and confirms the importance of data-imbalance treatment, and the suitability of RF for PO classification.

The detection of cryptographic attacks is a vital aspect of maintaining cybersecurity, especially as digital infrastructures become increasingly intricate and susceptible to sophisticated threats. This systematic review aims to examine and compare a range of machine learning approaches applied to cryptographic attack detection, focusing on their performance in terms of detection rates, efficiency, and overall effectiveness. A comprehensive review and meta-analysis were conducted, focusing on existing research that utilized machine learning models for identifying cryptographic attacks. The models included in the review were Naïve Bayes, C4.5, Random Forest, Decision Tree, K-Means, and Particle Swarm Optimization (PSO) combined with Neural Networks. Studies were selected based on their relevance to cryptographic security, with particular attention paid to performance metrics like classification accuracy, precision, recall, and area under the curve (AUC). The findings indicated that the C4.5 decision tree model achieved a high classification rate of 98.8%, while both Random Forest and Decision Tree models performed with an accuracy of 99.9%, making them highly suitable for real-time attack detection. Additionally, the PSO + Neural Network model showed enhanced detection precision, illustrating the value of integrating optimization techniques with machine learning models. The use of machine learning, especially with ensemble methods such as Random Forest and Decision Trees, proves to be highly effective for cryptographic attack detection. The study underscores the necessity for customized machine learning solutions in cybersecurity, balancing both high accuracy and operational efficiency. Further research should focus on the real-world deployment of hybrid models to confirm their practical effectiveness.

Educational data mining has illustrated an increasing demand for extracting and maneuvering data from academic backdrop, to generate prolific information which is indispensible for decision making. Therefore in this paper, an attempt has been made to deploy various data mining techniques including base and meta learning classifiers across our pedagogical dataset to foretell the performance of students. Among several contemporary ensemble approaches, researchers have practiced widespread learning classifiers viz. boosting to predict the performance of students. As exploitation of ensemble methods is considered to be significant phenomenon in classification and prediction mechanisms, therefore analogous method (boosting) has been applied across our pedagogical dataset. The entire results have been evaluated with 10-fold cross validation, once pedagogical dataset has been subjected to base classifiers including j48, random tree, naive bayes and knn. In addition, techniques such as oversampling (SMOTE) and undersampling (Spread subsampling) have been employed to further draw a comparison among ensemble classifiers and base classifiers. These methods were exploited with the key objective to observe any improvement in prediction accuracy of students.

This study uses Random Forest algorithm to model students' final year mark in an engineering technology module taught by the University of South Africa. The algorithm uses a supervised learning classification technique to map the different assessment marks and the final mark. Hence, the latter are labelled instances whereas the former constitute the features. Random Forest (RF) has been applied to Structural Analysis 3, which takes into consideration the graduate attribute concept or level of competence as far as assessments are concerned. Firstly, the RF is subjected to imbalanced binary classes, then balanced classes are achieved by Synthetic Minority Oversampling Technique (SMOTE) and class weights adjustment techniques. The results showed that SMOTE brought an improvement in accuracy of 3%. It was also revealed that an increase of 4, 15 and 9% in precision, recall and F1-Score were observed in predicting non-competent students. An increase of 4 and 3% was noticed in the case of the precision and F1-Score respectively in predicting competent students, whereas the recall did not display any change. Despite the RF with SMOTE overperformed standard RF and RF class weights adjustment, all three algorithms were good candidates in the prediction of student performance. RF-SMOTE could be suggested as a guiding instrument when dealing with imbalanced data.

With the booming development of sports events, the demand for athlete performance evaluation has begun to increase. The visualization analysis of basketball player data is significant for the analysis of individual value and potential of athletes. However, existing data analysis methods have problems such as insufficient efficiency, low accuracy, and insufficient attention to the inherent attributes of sports events. Therefore, to better analyze the composition data of basketball team members, the study combines Synthetic Minority Over-Sampling Technique (SMOTE) and Random under-sampling techniques to handle the imbalance in the athlete dataset, and then uses Random Forest (RF) to extract data features. Based on this data processing, a ensemble learning method based on SMOTE, Random under-sampling, and RF (SRR)-Voting is proposed to predict athlete performance. The results demonstrated that in the player dataset analysis, the Precision, Recall, and F1 of the research method were 0.9486, 0.9588, and 0.9752, which were superior to comparative methods. The proposed method had a ROC value of 0.94 and a fitting value of 0.17, indicating better fitting performance. This indicates that the designed player composition data visualization analysis method based on ensemble learning can effectively analyze the various attribute data of competitive players and ordinary players, providing effective data support for the comprehensive quality analysis of competitive players.

The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess the efficiency of nine different models: Naïve Bayes, Logistic Regression, Random Forest, Decision Tree, Gated Recurrent Unit (GRU), Multi-layer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Convolutional Neural Network (CNN). Concluded comprehensive investigation and performance evaluation, the study validates that ensemble methods such as Random Forest and Decision Tree, together with deep learning models like LSTM and GRU, significantly exceed conventional models in terms of accuracy and detection abilities. The best-performing models attained up to 99.99% accuracy in recognizing malicious network activity. This exceptional performance validates that the strong potential of participating intelligent learning-based methods into ZTA to create scalable and dynamic security solutions with high accuracy. These findings illustrate the value of ML/DL in enhancing the threat detection layer of ZTA, eventually providing a stronger resistance to advanced attacks cyber threats.

This study investigates the application of operational research techniques to optimize investment decisions in peer-to-peer (P2P) lending platforms, focusing on balancing risk and return for investors. The study proposes a multi-objective decision-making model that leverages data from the Lending Club, the largest P2P marketplace in the United States, to minimize risk and maximize returns. To address the data imbalance, the model uses classification techniques including logistic regression, decision trees, random forests, and light gradient boosting machines (LGBM), which are supported by the synthetic minority oversampling technique (SMOTE). While a convolutional neural network (CNN) predicts net present value (NPV), logistic regression is used to assess risk. The nondominated sorting genetic algorithm II (NSGA-II) is then used for portfolio optimization, producing returns of over 7% with risk levels that are comparable with conventional methods. Sensitivity analysis highlights the importance of investment allocation strategies by emphasizing that portfolio returns are more sensitive to changes in investments than risk. This study contributes to the operational research literature on risk management, investment modeling, and practical decision support systems in financial services by integrating advanced AI-based computational methods and optimization tools. HIGHLIGHTS The multi-objective model seeks to balance risk reduction with return maximization. The LGBM, logistic regression, random forest, and decision tree models are assessed. The NSGA-II algorithm is used to optimize the portfolio model. A sensitivity analysis is used to evaluate the investment amounts. The results provide wisdom on return optimization and risk reduction in P2P lending.

Microorganisms are fundamental to ecosystems, human health, and various industrial processes. The rapid and accurate identification of microorganisms is crucial for diagnosing diseases, monitoring environmental changes, and advancing biotechnological applications. Traditional methods of microbial identification, including culturing and microscopy, are often labor-intensive and time-consuming, necessitating the development of computational techniques to automate and improve accuracy. This project presents a comprehensive approach to microbe prediction by leveraging advanced machine learning algorithms applied to a dataset containing genomic and morphological features of ten different microorganism species. The study investigates the performance of four classification algorithms — K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, and Decision Trees — to classify microorganisms based on multiple quantitative features extracted from images and genetic data. The methodology involves extensive data preprocessing steps such as cleaning, normalization, polynomial feature expansion, and dimensionality reduction using Principal Component Analysis (PCA) to enhance model performance. Rigorous experimentation and comparative analysis reveal that ensemble methods, particularly Random Forest, provide superior classification accuracy, robustness, and generalizability across unseen data. In addition to model development, a user-friendly web interface built using Streamlit facilitates real-time microorganism prediction, making the system accessible to researchers and practitioners without deep technical expertise. The platform enables the input of feature values and instantly returns predicted microorganism classes along with relevant biological descriptions and preventive measures, thereby bridging the gap between computational predictions and practical microbiological insights. This project contributes to the emerging intersection of microbiology and machine learning by demonstrating an effective pipeline from raw data to deployable predictive tool. Its implications span environmental monitoring, medical diagnostics, and industrial microbiology, emphasizing the potential for machine learning to revolutionize microbial identification and understanding. Key Words – K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, and Decision Trees, Stream lit user interface software.

This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healthcare problem, often leading to prolonged hospitalization and increased medical costs. Conventional risk assessment scales are limited in their ability to predict PIs accurately, prompting the exploration of ML techniques to address this challenge.We utilized data from 7,292 patients admitted to a tertiary care hospital in Shanghai between May 2017 and July 2023, with a final dataset of 2,972 patients, including 158 with PIs. Seven ML algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Extra Trees (ET), K-Nearest Neighbors (KNN), and Decision Trees (DT)-were used in conjunction with SMOTE, SMOTE+ENN, Borderline-SMOTE, ADASYN, and GAN to balance the dataset and improve model performance.Results revealed significant improvements in model performance when SMOTE and its variants were used. For instance, the XGBoost model hadan AUC of 0.996 with SMOTE, compared to 0.800 on raw data. SMOTE+ENN and Borderline-SMOTE further enhanced the models' ability to identify minority classes. External validation indicatedthat XGBoost, RF, and ET exhibited the highest stability and accuracy, with XGBoost having an AUC of 0.977. SHAP analysis revealed that factors such as anesthesia grade, age, and serum albumin levels significantly influenced model predictions.In conclusion, integrating SMOTE with ML algorithms effectively addressed a data imbalance and improved the prediction of perioperative PIs. Future work should focus on refining SMOTE techniques and exploring their application to larger, multi-center datasets to enhance the generalizability of these findings, and especially for diseaseswith a lowincidence.