Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chaitra, P.C.; Saravana Kumar, R. Title A name given to the resource Optimization Enabled Ensemble Learning for Leukemia Classification Using Microarray Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications;Volume;16;Issue;2;pp.903-926 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.58346/JOWUA.2025.I2.056" target="_blank" rel="noreferrer noopener">https://doi.org/10.58346/JOWUA.2025.I2.056</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013323002?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013323002?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chaitra P.C., Computer Science and Engineering, Christ University, Kengeri, India, Computer Science and Engineering, Dayananda Sagar Academy of Technology &amp; Management, Visvesvaraya Technological University, Belagavi, India; Saravana Kumar R., Computer Science and Engineering, Dayananda Sagar Academy of Technology &amp; Management, Visvesvaraya Technological University, Belagavi, India Description An account of the resource Leukemia classification involves identification and categorization of various leukemias, a cluster of blood malignancies influencing white blood cells. Proper classification is crucial for selecting the appropriate treatment modalities and predicting outcomes in patients. Historically, leukemia classification was based on clinical and morphological characteristics, but new developments in genomics like microarray and next-generation sequencing tools have facilitated more accurate molecular classifications. Machine learning (ML) and deep learning (DL) methods have transformed leukemia classification by enabling automation of analysis in large and intricate datasets to ensure more accurate and efficient leukemia subtype classification. The primary goal of this research is to suggest a new leukemia classification method using microarray data. Leukemia microarray data first undergoes preprocessing, after which feature selection is performed through Serial Exponential-Secretary Bird Optimization Algorithm (SE-SBOA). SE-SBOA is an optimization method that embeds the exponential weighted moving average concept (EWMA) into Secretary Bird Optimization Algorithm (SBOA). The method helps to find the best feature subset, improving model performance at lower complexity. Lastly, leukemia classification is done using the proposed ensemble method that combines Graph Neural Network (GNN), Multi-Layer Perceptron (MLP) and Random Forest. Utilizing the advantages of GNN, MLP and Random Forest, the model proposed herein attains higher classification accuracy and proves to outperform traditional methods. Experimental results demonstrate that the SE-SBOA-based Ensemble Learning technique outperformed standard methods, attaining an accuracy of 95.9%, a precision of 96.1%, a recall of 96.2%, and an F1-score of 96.2%. 2025, Innovative Information Science and Technology Research Group. All rights reserved. Subject The topic of the resource Deep Learning; Disease Detection; Leukemia Classification; Microarray Data; Optimization Algorithm Publisher An entity responsible for making the resource available Innovative Information Science and Technology Research Group Relation A related resource ISSN: 20935374; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Format The file format, physical medium, or dimensions of the resource online