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 Hemanth, K.S.; Harisha Naik, T.; Kartik, N.; Kumar, N. Nanda; Senthilkumar, S.; Ramya, R. Title A name given to the resource Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound Classification 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 Journal of Information Technology Management;Volume;18;Issue;1;pp.123-140 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22059/jitm.2026.1062557" target="_blank" rel="noreferrer noopener">https://doi.org/10.22059/jitm.2026.1062557</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038337718?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038337718?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 Hemanth K.S., Department of Computer Science, Christ University, Bengaluru, India; Harisha Naik T., Department of Computer Applications, Presidency College, Bangalore, India; Kartik N., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Kumar N.N., Department of Computer Science and Engineering, Excel Engineering College, Tamilnadu, Komarpalayampalyam, India; Senthilkumar S., Department of Computer Science and Engineering, Vinayaka Mission`s Kirupananda Variyar Engineering College, (Vinayaka Mission`s Research Foundation), Salem, India; Ramya R., Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamilnadu, Erode, India Description An account of the resource The necessity for efficient breathing sound classification systems originates from respiratory diseases, which impair oxygen-carbon dioxide exchange and impact lung function. Feature extraction and pattern categorization are general components of such systems. Because of their effectiveness with big datasets, deep neural networks have acquired popularity recently in the category of breathing sounds. Enhancing medical care requires cooperation amongst researchers, medical professionals, and patients. An attentional deep learning model with inverse transform sampling is presented in this study to classify respiratory diseases from audio data. Robust models were developed to classify and detect respiratory elements using the Respiratory Sound dataset. The primary objectives include effectively determining lung sounds and determining respiratory illnesses. The architectures of CNN, VGG16, and ResNet50 were developed to extract features and categorize data. Also, the pre-trained models ResNet50 and VGG16 identify critical characteristics in spectrum pictures more accurately. Inverse transfer sampling is used to rectify class imbalance in respiratory datasets. The models achieved 98% accuracy with the CNN model, 83% accuracy with VGG16, and 95% accuracy with ResNet50. Moreover, LSTM and CRNN models offer more information on how respiratory illnesses are classified. 2026, Hemanth K S, Harisha Naik T, N Kartik, N Nanda kumar, S Senthilkumar and Ramya R. Subject The topic of the resource CNN; Deep Neural Network; Inverse Transform Sampling; Pre-trained Models; Respiratory Diseases Publisher An entity responsible for making the resource available University of Tehran Relation A related resource ISSN: 20085893; 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 Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online