Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset by Muhammad Usama Tanveer (22225360)

LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems by Muhammad Usama Tanveer (22225360)

“…This ensemble strategy ensures an optimal balance between detection performance and computational resource utilization, making it well-suited for IoT networks, where processing power and memory are limited. …”

Plant Leaf Disease Detection Using Ensemble Learning and Explainable AI by Ammar Oad (21324182)

“…The visualizations generated from multiple methods point to specific pixels’ influence on accurate and incorrect predictions, clearly illustrating the model’s decision-making process. …”

An ensemble neural network approach to forecast Dengue outbreak based on climatic condition by Panja, Madhurima

“…In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.…”
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Fuzzy Divergence Weighted Ensemble Clustering With Spectral Learning Based on Random Projections for Big Data by Ali, Tarig

“…In many real-world applications, data are described by high-dimensional feature spaces, posing new challenges for current ensemble clustering methods. The goal is to combine sets of base clusters to enhance clustering accuracy, but this makes them susceptible to low quality. …”
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Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System by M. Sajid (3070545)

“…<p dir="ltr">The ensemble deep random vector functional link (edRVFL) neural network has demonstrated the ability to address the limitations of conventional artificial neural networks. …”

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health by Belqes Alsadi (18695031)

“…Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan.</p><h3>Methods</h3><p dir="ltr">In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. …”

Enhancing Arabic Offensive Tweet Classification: An Ensemble Approach Integrating AraBERT, Neural Networks, and LSTM Models by WAHDAN, AHLAM MOHAMMAD

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PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images by Kashif Munir (6182237)

LDSVM: Leukemia Cancer Classification Using Machine Learning by Abdul Karim (417009)

“…Machine learning algorithms such as decision tree (DT), naive bayes (NB), random forest (RF), gradient boosting machine (GBM), linear regression (LinR), support vector machine (SVM), and novel approach based on the combination of Logistic Regression (LR), DT and SVM named as ensemble LDSVM model. The k-fold cross-validation and grid search optimization methods were used with the LDSVM model to classify leukemia in patients and comparatively analyze their impacts. …”

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring by Muhammad E.H. Chowdhury (17151154)

“…The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.…”

Machine learning based model for the early detection of Gestational Diabetes Mellitus by Hesham Zaky (22392529)

“…Thus, there is a need for the early detection of GDM to avoid critical health conditions in newborns and post-pregnancy complexities of mothers.</p><h3>Methods</h3><p dir="ltr">In this article, we propose a machine learning (ML)-based techniques for early detection of GDM. …”

A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies by Saurabh Tewari (22282594)

“…Also, resampling techniques are combined with nine different ML classifiers, including Decision tree, ExtraTree, Random Forest, Logistic regression, Support vector machine, K-nearest Neighbour, Naïve Bayes and Ensemble methods. Stacking and voting ensembles combine the outcomes of diverse classifiers working as team members in MCLA. …”

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021 by Kanyin Liane, Ong

“…Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. …”
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