Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments by Małgorzata Przybyła-Kasperek, Jakub Sacewicz

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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary. ObjectiveThis study aims to develop an ensemble learning framework that adaptively integrates multimodal physiological data collected from wearable wristbands and digital cognitive metrics recorded on tablets, thereby improving the accuracy and practicality of MCI detection. …”
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Retraction: “A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study”

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BERT ensemble based MBR framework for android malware detection by Faisal S. Alsubaei, Abdulwahab Ali Almazroi, Walid Said Atwa, Abdulaleem Ali Almazroi, Nasir Ayub, Noor Zaman Jhanjhi

“…A novel framework is presented in this study for AM Detection (AMD) using BERT Ensemble (MBR) and MobileNetV2. …”
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Ensemble Transformer–Based Detection of Fake and AI–Generated News by Md. Ishraquzzaman, Mohammed Ashraful Islam Chowdhury, Shahreen Rahman, Riasat Khan

“…This work leverages advanced natural language processing, machine learning, and deep learning algorithms to effectively detect fake and AI–generated content. The utilized dataset, combined with multiple open-source datasets, comprises 43,000 real, 31,000 fake, and 80,000 AI–generated news articles and is augmented with an ensemble large language model. …”
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Improving Electrical Fault Detection Using Multiple Classifier Systems by José Oliveira, Dioeliton Passos, Davi Carvalho, José F. V. Melo, Eraylson G. Silva, Paulo S. G. de Mattos Neto

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Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei

“…This paper proposes EffiGRU-GhostNet, a deep-learning ensemble model for high-accuracy DDoS detection with minimal resource consumption. …”
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High-Precision Wideband Microwave Detection with Ensemble of Nitrogen-Vacancy Color Centers by Zhonghao Li, Wenhai Yang, Hao Zhang, Renchao Chai, Chenyu Yang, Jun Tang, Jun Liu

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Dynamic Voting-Based Ensemble Deep Learning for Closely Resembling Crop Classification by Engin Eşme, Muhammed Arif Şen, Halil Çimen

“…The results obtained report which models can more accurately detect and classify agricultural crop images. Further, the proposed ensemble approach improves accuracy and ensures greater robustness and stability. …”
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An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization by Simar Preet Singh, Abhilash Maroju, Mohammad Kamrul Hasan, Karan Tejpal

“…The fast spread of malicious URLs is a significant risk to online safety, since it makes assaults like spam, phishing, virus distribution, and vandalism of websites easier to carry out. The dynamic nature of these threats makes traditional detection techniques unable to keep up. …”
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Performance Improvement of Ensemble Empirical Mode Decomposition for Roller Bearings Damage Detection by Ali Akbar Tabrizi, Luigi Garibaldi, Alessandro Fasana, Stefano Marchesiello

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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…Empirical evaluations demonstrate that the ensemble model achieves superior detection accuracy and reduced false positive rates, thereby advancing the efficacy of intrusion detection methodologies.…”
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A feature-level ensemble machine learning approach for attack detection in IoT networks by Firoz Khan, B. S. Sunil Kumar, Sangeeta Sangani

“…Traditional AI-based intrusion detection models often struggle to maintain high accuracy over time due to Concept Drift (CD) and Class Imbalance (CI), which hinders their ability to detect evolving threats effectively. …”
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Extending radiowave frequency detection range with dressed states of solid-state spin ensembles by Jens C. Hermann, Roberto Rizzato, Fleming Bruckmaier, Robin D. Allert, Aharon Blank, Dominik B. Bucher

“…We introduce an alternative approach based on a continuous dynamical decoupling (CDD) scheme involving dressed states of nitrogen vacancy (NV) ensemble spins driven within a microwave resonator. …”
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A Wide and Weighted Deep Ensemble Model for Behavioral Drifting Ransomware Attacks by Umara Urooj, Bander Ali Saleh Al-rimy, Mazen Gazzan, Anazida Zainal, Eslam Amer, Mohammed Almutairi, Stavros Shiaeles, Frederick Sheldon

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Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers by Khawla Ali Maodah, Sharaf Alhomdy, Fursan Thabit

“…Machine learning provides dynamic options for detecting known and unknown assaults, whereas typical intrusion detection systems that depend on signature or rule-based techniques find it difficult to adjust to complex cyber threats.MethodsThis study compares the efficacy of an ensemble approach (Voting Hard and Stacking) for intrusion detection in cloud environments with individual machine learning classifiers, such as Random Forest, Decision Tree, Gradient Boosting, XGBoost, Naive Bayes, Support Vector Machine, and Logistic Regression. …”
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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method by Behnam Seyedi, Octavian Postolache

“…In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. …”
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Dual-Indexed Ensemble Kalman Filtering-Based Anti-Islanding Detection Methods for AC Microgrids by Sohaib Tahir Chauhdary, Hisham Alharbi, Abdullah S. Bin Humayd, Talal Alharbi

“…The dynamic and nonlinear characteristics of AC microgrids pose significant challenges to conventional passive islanding detection methods. …”
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Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

“…Conventional tremor detection methods have mainly focused on resting state. …”
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Federated Learning With Sailfish-Optimized Ensemble Models for Anomaly Detection in IoT Edge Computing Environment by Aravam Babu, A. Bagubali

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