Suspicious Network Event Recognition Using Modified Stacking Ensemble Machine Learning

Child mortality is a big problem around the world, especially in low- and middle-income nations where there are big differences in health care and social conditions. This investigation seeks to create a predictive model for child mortality and pinpoint the key factors that significantly contribute to it, employing machine learning (ML) methodologies. The dataset includes various features such as parental age, maternal education, birth weight, wealth index, and access to healthcare services. Thirteen machine learning classifiers were used, categorized into four model groups: Traditional Models (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Naive Bayes), Tree-Based Models (Decision Tree, Random Forest, Extra Trees), Boosting Models (AdaBoost, Gradient Boosting, XGBoost), and Ensemble Learning Models (Soft Voting, Hard Voting, Stacking). The efficacy of each model was assessed using classification metrics, including Accuracy, Precision, Recall, and F1-Score within a 10-fold cross-validation framework to guarantee robustness. Results indicate that ensemble models, particularly AdaBoost, achieved the highest predictive accuracy, with perfect scores across all metrics (1.00). XGBoost and Stacking also demonstrated strong and consistent performance. The findings indicate that ensemble learning methods are effective in predicting child mortality and can assist policymakers and healthcare planners in identifying high-risk populations and implementing targeted interventions to reduce child mortality.

While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big part to play in the 6G paradigm that is being imagined. Future end-to-end automation of networks requires proactive threat detection, the use of clever mitigation strategies, and confirmation that 6G networks will be self-sustaining. To strengthen and consolidate the role of AI in safeguarding 6G networks, this article explores how AI may be employed in 6G security. In order to achieve this, a novel anomaly detection system for 6G networks (AD6GNs) based on ensemble learning (EL) for communication networks was redeveloped in this study. The first stage in the EL-ADCN process is pre-processing. The second stage is the feature selection approach. It applies the reimplemented hybrid approach using a comparison of the ensemble learning and feature selection random forest algorithms (CFS-RF). NB2015, CIC_IDS2017, NSL KDD, and CICDDOS2019 are the three datasets, each given a reduced dimensionality, and the top subset characteristic for each is determined separately. Hybrid EL techniques are used in the third step to find intrusions. The average voting methodology is employed as an aggregation method, and two classifiers—support vector machines (SVM) and random forests (RF)—are modified to be used as EL algorithms for bagging and adaboosting, respectively. Testing the concept of the last step involves employing classification forms that are binary and multi-class. The best experimental results were obtained by applying 30, 35, 40, and 40 features of the reimplemented system to the three datasets: NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019. For the NSL_KDD dataset, the accuracy was 99.5% with a false alarm rate of 0.0038; the accuracy was 99.9% for the UNSW_NB2015 dataset with a false alarm rate of 0.0076; and the accuracy was 99.8% for the CIC_IDS2017 dataset with a false alarm rate of 0.0009. However, the accuracy was 99.95426% for the CICDDOS2019 dataset, with a false alarm rate of 0.00113.

Ensemble learning with member optimization for fault diagnosis of a building energy system

With the recent emergence of Web-based applications and use of social networking sites, number of people are eager in expressing their views and opinions online. The sentimental analysis also referred to as opinion mining aims at processing user reviews (about products, movies, services, books, places, etc.). These reviews are often unstructured and need processing to evolve into the productive knowledge. Majority of the sentiment analysis works on the classification of opinion polarity with the use of simple classifiers. Handling diverse data distribution is one of the major issues that simple classifiers suffer. To cope up with the issue in this paper, we utilized the ensemble learners on the polarity prediction of the movie reviews. The proposed work processes the review data through some elementary steps that are conducted for the feature extraction in sentiment analysis. In addition to the feature extraction, we further perform the feature selection for the sake of dimensionality reduction. However, in contrast to the conventional simple learner, we applied the ensemble learner in the proposed model and evaluated its performance. To compare the ensemble model competence, we conducted the experiment on both individual as well as ensemble learner (random forest, AdaBoost, extra trees) and computed classification measures on both the model. IMDB dataset is used, and the polarity of a review, i.e., whether it is positive or negative, is predicted. With an extensive experimentation, it is found that results of ensemble classifiers are outperforming than individual learner in the classification of sentiment polarity.

The increasing number of space debris and space-active targets makes the space environment more and more complex. Space target recognition, a crucial component of space situational awareness, is of paramount importance to space security. Firstly, this paper elucidates the fundamental principles of ensemble learning, analyzes its characteristics and fusion method, and provides a comprehensive comparison of three common ensemble learning methods. Secondly, this paper analyzes the basic attributes and characteristics of space targets and categorizes the hierarchy of space target recognition. Again, the paper reviews the advances in the application of ensemble learning in space target recognition, focusing on three aspects: space target recognition datasets, the ensemble of traditional machine learning models, and ensemble deep learning. Subsequently, classical machine learning and ensemble learning algorithms are tested on a self-built space target simulation dataset, and we find that Stacking performs well on this dataset. Finally, the paper discusses future research directions.

Hepatitis C is a viral infectious disease caused that affects 58 million people globally making it a public health crisis, and it is reported that 20-30% of cases develop to severe liver disease. This creates the need for data-driven based method that such as Machine learning that depends on data inputs in making accurate outputs with high precision. This study explores the use of Machine learning algorithms in optimizing the detection of hepatitis C status of patients. This research utilizes a publicly available dataset that contains 12 explanatory features and one target feature that addresses the patient’s diagnosis status. Data preprocessing techniques such as checking of missing values, assessment for possible outliers and Exploratory data analysis was carried on the dataset to improve the quality of the dataset for predictive analysis. The EDA results reviews that majority of the features had significant relationship (p-value < 0.05) with the target variable. Also, we observed the distribution of the disease diagnosis by gender reviews that females showed higher prevalence, and when we consider age, older people are more likely to contract the HCV. This result aligns with research findings from recent studies. The dataset was scaled and separated using a ratio split of 80:20 (80% of data for training). We applied SMOTE algorithm to the training data to balance the diagnosis distribution of the target variable. The model was trained using a wide parameter space using a cross-validation fold of 10 to select the best parameters. The ML models evaluated in this research include Logistic Regression, Support Vector Machine, Random Forest, XGBOOST, Decision Tree, and Extra Trees. The model performance metrics shows that XGBOOST was superior with an accuracy score of 98%, followed by Random Forest (97%), Decision Tree (94%), Extra Trees (93%), SVM (92%), and Logistic Regression (87%). The Kappa statistics for XGBOOST which was estimated as 0.8827 shows that difference between the predicted instances and the actual instances was minimal. The performance metrics of the XGBOOST was also contrast with ML algorithms trained by recent researchers in detecting HCV and it shows our model was more precise underscoring the significance of datapreprocessing, feature-scaling, SMOTE and hyperparameter tuning in predictive analysis in healthcare.

The exponential rise in digital transactions has made credit card fraud a critical threat to financial institutions and consumers worldwide. Traditional rule-based fraud detection systems are increasingly ineffective against sophisticated and evolving fraudulent patterns due to their inability to adapt to new tactics and handle large-scale transaction data. This paper proposes an end-to-end FraudX that classifies transactions as legitimate or fraudulent using ensemble machine learning and real-time predictive analytics. The system utilizes historical transaction data in CSV format, applies advanced preprocessing and feature engineering techniques, and employs XGBoost as the primary classifier, alongside comparative models such as Logistic Regression and Random Forest]. Experimental results on a publicly available credit card fraud dataset demonstrate that XGBoost achieves superior performance with an accuracy of 99.8%, precision of 92.5%, and recall of 88.3%. The system is implemented as a modular web application with a Flask backend and SQLite database, offering an interactive dashboard for transaction screening, detailed reporting, and administrator-led model retraining. This work provides a scalable, efficient, and reliable solution for real-time fraud detection in financial ecosystems. Keywords: Credit card fraud detection, ensemble learning, XGBoost, machine learning, Flask web application, financial security, predictive analytics, imbalanced data.

Time-domain inversion of random dynamic loads on offshore platforms based on optimized ensemble learning

<title>Abstract</title> Telecommunication companies globally face the critical challenge of subscription fraud, which threatens both financial stability and national security. This research addresses this issue by developing an advanced fraud detection model specifically for Ethio Telecom. The model utilizes Ensemble and Adaptive Learning techniques to enhance detection accuracy by combining multiple classifiers. The study used a dataset of 1,000,000 Call Detail Records (CDRs) collected over two months known for increased fraudulent activity3. After filtering out irrelevant data and aggregating multiple call records per subscriber, the dataset was refined to 349,164 records. Initially, 16 features were analyzed, with four excluded for lacking relevance. The remaining 11 features, excluding the target variable, underwent preprocessing including data cleaning, transformation, and balancing4. Feature selection, utilizing Correlation Matrix and Random Forest importance analysis, led to the removal of four additional features, resulting in a final set of 8 key features, including INT_DIALLED, RATIO_INT_TOTAL, and RATIO_UNIQUE_TOTAL4. Three individual models, namely Decision Tree (DT), Logistic Regression (LR), and Artificial Neural Network (ANN), were implemented alongside ensemble methods such as Bagging, Boosting, Stacking, and Voting, and adaptive models like Hoeffding Tree and Adaptive Random Forest45. The findings of this research recommend Stacking and Adaptive Random Forest (ARF) as robust tools for subscription fraud detection.

The selection of exact material for shielding analysis is challenging in radiation protection. The primary objective of shielding analysis is to reduce radiation exposure to the occupational worker at their workplace. Generally, high-density concrete is selected as the shielding material to prevent accidental exposure to gamma and neutron radiation. Composite material or multilayer shielding materials are generally used to optimise the cost of concrete with maximum benefit to the society of occupational radiation workers. A surrogate model for concrete's overall strength using cement, fly ash, and coarse and fine aggregates is created using machine learning and ensemble learning. Ensemble learning in machine learning solves underfitting and overfitting problems when fitting a regression model for shielding analysis. As density increases, concrete overall strength decreases. Several samples of various types of concrete (different compositions) are collected as input data. Finally, a multi-attribute decision-making method is applied to select the appropriate type of concrete. The research presents the ensemble learning based regression technique coupled with multi attribute decision making method to recommend the exact variety of concrete for shielding gamma and neutron radiation.

Explaining Great Lakes water level variability through interpretable ensemble machine learning.

Heart disease is a complicated disorder that is becoming increasingly common. Finding the best treatment plan for any heart disease patient requires early detection. The main goal of this study is to improve the prediction of heart disease by implementing ensemble learning techniques with machine learning (ML) models. The dataset used in this study, comprising 319,755 records from the Centers for Disease Control and Prevention, was downloaded from the Kaggle website. The SMOTE-ENN hybrid approach was used to address class imbalance. To increase the consistency and distribution of numerical variables, data preprocessing entailed standardization and the creation of dummy variables. The machine learning (ML) models Random Forest, Logistic Regression, Support Vector Machine, and Extra Trees were applied to the processed dataset without and with bagging, boosting, and stacking ensemble methods. Stacking, which combined SVM and Logistic Regression, outperformed the baseline models and other ensemble techniques, returning the highest recall score of 0.837731. This study underlines the significance of data balancing and ensemble learning for accurate forecasts based on medical datasets, which are typically large. The findings highlight how ML can enhance early diagnosis and intervention in the treatment of cardiac disease.

As network‐based services continue to expand and cyber threats grow increasingly sophisticated, the deployment of effective network intrusion detection systems (NIDS) has become crucial for the protection of sensitive data and the preservation of digital infrastructure integrity. These systems are indispensable in ensuring the security and reliability of digital environments. This study presents an all‐encompassing approach that integrates feature selection, ensemble learning, clustering, and diverse analytical methods to improve the precision and effectiveness of network intrusion detection systems. Using data sets that have been preprocessed and classified using the Random Forest and XGBoost algorithms, we extract key features and categorize key variables using K‐means clustering, thus constructing a robust feature set for intrusion detection. Our experimental results demonstrate high precision in detecting benign and malicious activities using the CIC‐IDS‐17 and UNSW‐NB15 datasets. These outcomes underscore the efficacy of our proposed approach in monitoring network traffic, effectively identifying threats, and preventing potential intrusions.

Predictive models for early identification of heart disease must be precise and efficient because it is a major worldwide health concern. To improve classification performance while protecting data privacy, this study investigated a combined method that uses ensemble learning, feature selection, and federated learning (FL). The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. We used particle swarm optimization (PSO) for feature selection, which optimized the most relevant features in conjunction with voting and stacking approaches to further increase the model's performance. In addition, federated learning was implemented to allow decentralized training while preserving sensitive medical data. The results highlight the effectiveness of combining these techniques in the detection of heart disease, providing a scalable and privacy-preserving solution for real-world healthcare applications. Two benchmark datasets were used to validate the proposed approach, ensuring the reliability and generalizability of the findings. Furthermore, we used four performance metrics, namely accuracy, precision, recall, and F1score, to evaluate the selected models. Finally, federated learning was included to handle privacy issues and guarantee safe access to private medical data. This distributed method allows model training without centralizing patient data, so it is compatible with strict data privacy rules. With up to 95% precision, our method shows a notable increase in prediction accuracy according to the testing results. This work offers a strong, scalable, and safe solution for the early identification of cardiovascular diseases by combining ensemble learning, feature selection, and federated learning, opening the way for more general uses in medical diagnostics.

Non-adherence to prescribed medication is a major public health concern that escalates the risk of morbidity and death as well as incurring extra expenses associated with hospitalisation. According to the World Health Organization (WHO), only 50% of people suffering from chronic diseases follow the treatment recommendations despite the counsel provided to patients on the importance of medication adherence (MA). Early detection of non-communicable disease (NCD) patients poorly adhering to recommended medications using analytics based on machine learning (ML) may improve the outcomes of NCD patients positively. This paper presents a systematic review of literature involving the application of ML in evaluating MA amongst NCD patients. The articles considered in this study were extracted from Web of Science, Google Scholar, PubMed, and IEEE Explore. Twenty-five articles in total met the criteria for inclusion. These were articles that utilised ML techniques to analyse MA in NCDs, with patients suffering from diabetes (n = 8), hypertension (n = 3), cardiovascular disease (CVD) and statin adherence (n = 6), cancer (n = 3), respiratory diseases (n = 2), and other NCD conditions (n = 3). The proportion of days covered (PDC) was typically used to evaluate MA. It emerged from the study that for MA to be considered high, the adherence threshold should be at least 75% of the PDC, a universally accepted threshold. In MA analytics research and practice, a PDC ≥80% threshold is typically regarded as a high level of adherence to prescription medication. Logistic regression (LR) (n = 12), random forest (RF) (n = 11), support vector machine (SVM) (n = 7), neural net (n = 6), ensemble learning (n = 6), MLPs (n = 4), XGBoost (n = 3), Bayesian network (BN) (n = 3), and gradient boosting (n = 3) were the most frequently applied ML techniques in the analytics of MA amongst NCD patients. It should be underscored that leveraging standard ML, deep learning (DL), and ensemble learning has enormous potential for measuring MA amongst NCD patients based on various analytics such as prediction, regression, classification, and clustering. Moreover, a further study could be conducted to comprehend how the application of alternative ML-based techniques can be used to measure MA among patients with chronic infectious diseases.