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 Jankatti, Santosh Kumar; Srinivasaiah, Raghavendra; Parveen, Mohammad Shahina; Kenchannavar, Harish H.; Sudha, Danthuluri; Narah, Srihari Sharma Karigiri; Shivaraj, Mahadev Title A name given to the resource NN-SVM: a hybrid neural networksupport vector machine framework for accurate pneumonia detection from chest X-rays Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived IAES International Journal of Artificial Intelligence;Volume;15;Issue;2;pp.1349-1361 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v15.i2.pp1349-1361" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v15.i2.pp1349-1361</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036100483?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036100483?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 Jankatti S.K., Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India; Srinivasaiah R., Department of Artificial Intelligence and Data Science Engineering, CHRIST University, Bengaluru, India; Parveen M.S., Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bengaluru, India; Kenchannavar H.H., Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bengaluru, India; Sudha D., Department of Computer Science and Engineering (Data Science), Dayananda Sagar College of Engineering, Bengaluru, India; Narah S.S.K., Dayananda Sagar University, Bengaluru, India; Shivaraj M., Department of Electronics and Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India Description An account of the resource We present neural network (NN)-support vector machine (SVM), hybrid NN-SVM framework for three-class pneumonia detection (normal, bacterial, and viral) from chest X-rays (CXRs). Pretrained NN backbone is fine-tuned for radiographic textures; global average pooling (GAP) yields embeddings that feed calibrated radial basis function (RBF)-SVM. Standardized preprocessing (resize, normalization) and class-aware augmentation are applied. We report accuracy, precision, recall, F1-score, area under the curve (AUC), confusion matrices, and per-class receiver operating characteristic (ROC). Statistical significance is assessed via DeLong (AUC), McNemar (accuracy), and paired bootstrap (F1-score). Gradient-weighted class activation mapping (grad-CAM) supports interpretability; external validation and domain adaptation (batch normalization re-estimation and temperature scaling) assess robustness. NN-SVM attains 97.46% accuracy with strong macro-F1 and AUC. Compared with SoftMax head, SVM improves margin separation and calibration. We present NN-SVM, hybrid deep learning approach that combines transfer-learned convolutional neural networks (CNNs) with SVM classifier to automatically diagnose pneumonia from CXRs into three clinically relevant categories: viral pneumonia, bacterial pneumonia, and normal. We use pre-trained CNN to extract robust image embeddings after standardized preprocessing (resizing and normalization) and train RBF-kernel SVM on resulting features. Performance is evaluated with accuracy, precision, recall, F1-score, and confusion matrices. On labeled CXR dataset, NN-SVM achieves 97.46% accuracy, demonstrating strong diagnostic capability that can reduce radiologist burden and support timely clinical decision-making. This is an open access article under the CC BY-SA license. https://creativecommons.org/licenses/by-sa/4.0/ Subject The topic of the resource Chest X-ray; CNN-SVM; Deep learning; Medical imaging; Pneumonia; Transfer learning Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; 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; Gold Open Access Format The file format, physical medium, or dimensions of the resource online