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 Conference Paper Faculty Publications- Conference Papers 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 Danties, Anna; Nizar Banu, P.K.; Azar, Ahmad Taher; Kamal, Nashwa Ahmad; Tounsi, Mohamed; Njima, Chakib Ben Title A name given to the resource Machine Learning and Word Representation Techniques in Medical Transcription Data Analysis 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 2025 International Conference on Control, Automation and Diagnosis, ICCAD 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAD64771.2025.11099201" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAD64771.2025.11099201</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105014518512?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105014518512?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 Danties A., Christ University, Department of Computer Science, Bangalore, India; Nizar Banu P.K., Christ University, Department of Computer Science, Bangalore, India; Azar A.T., Prince Sultan University, College of Computer and Information Sciences, Riyadh, 11586, Saudi Arabia, Prince Sultan University, Automated Systems and Computing Lab (ASCL), Riyadh, Saudi Arabia; Kamal N.A., Cairo University, Faculty of Engineering, Giza, Egypt; Tounsi M., Prince Sultan University, College of Computer and Information Sciences, Riyadh, 11586, Saudi Arabia, Prince Sultan University, Automated Systems and Computing Lab (ASCL), Riyadh, Saudi Arabia; Njima C.B., University of Sousse, Tunisia Description An account of the resource Dermatology is the branch of medicine that deals with the diagnosis, treatment, and prevention of skin diseases. Dermatological diseases can be difficult to diagnose, treat, and manage because there are several skin conditions, each with its unique set of symptoms and causes. Underlying medical conditions, environmental causes, or hereditary characteristics can cause complex skin problems. Furthermore, because skin problems can present in a variety of ways, obtaining an appropriate diagnosis and efficient treatment may be difficult. Treating dermatological disorders is a difficult endeavor. This article proposes an integrated model to assist people in understanding and discussing the nature of dermatology. This model's capabilities include text pre-processing, audio-to-text translation, named entity recognition (NER) for extracting crucial information, and text clustering and classification based on content. The necessity for precise and efficient analysis of large amounts of text data, notably the identification and standardisation of abbreviations and the extraction of relevant information, has been identified as a problem in dermatology and medical transcription. By grouping similar cases, clustering can make it easier to spot patterns and trends in dermatological disorders. However, classification can help automatically group text data into pre-established categories, such as various kinds of skin conditions or treatments. These methods simplify data analysis, increase accuracy, and assist healthcare professionals in reaching accurate conclusions regarding patient care. This article explores the partitioning algorithm for clustering, while logistic regression is used in classification. The model analysed in this article helps dermatologists and patients understand and manage skin problems. 2025 IEEE. Subject The topic of the resource Logistic Regression; Named Entity Recognition (NER); Natural Language Processing (NLP); Partitioning Clustering Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151191-3; Language A language of the resource English Type The nature or genre of the resource Conference paper 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