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 Articles 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/. Title A name given to the resource Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning Subject The topic of the resource artificial intelligence; binary spring search algorithm; neonatal hyperbilirubinemia; new-born jaundice detection; XGBoost classifier Description An account of the resource A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive. 2023 Lavoisier. All rights reserved. Creator An entity primarily responsible for making the resource Inamanamelluri H.V.S.L.; Pulipati V.R.; Pradhan N.C.; Chintamaneni P.; Manur M.; Vatambeti R. Source A related resource from which the described resource is derived Revue d'Intelligence Artificielle, Vol-37, No. 2, pp. 257-265. Publisher An entity responsible for making the resource available International Information and Engineering Technology Association Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.18280/ria.370202" target="_blank" rel="noreferrer noopener">https://doi.org/10.18280/ria.370202</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163445008&amp;doi=10.18280%2Fria.370202&amp;partnerID=40&amp;md5=a7d3e3f3e6c36aa372875ec220a7144a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163445008&amp;doi=10.18280%2fria.370202&amp;partnerID=40&amp;md5=a7d3e3f3e6c36aa372875ec220a7144a</a> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Relation A related resource ISSN: 0992499X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Inamanamelluri H.V.S.L., Information Technology, MLR Institute of Technology, Hyderabad, 500043, India; Pulipati V.R., Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, 500090, India; Pradhan N.C., Department of Electronics, Reykjavik University, Reykjavik, 101, Iceland; Chintamaneni P., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 520002, India; Manur M., Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, 560074, India; Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India