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 Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm Description An account of the resource Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset's attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent. 2023 S. Krishnan et al. Creator An entity primarily responsible for making the resource Krishnan S.; Aruna S.K.; Kanagarathinam K.; Venugopal E. Source A related resource from which the described resource is derived Scientific Programming, Vol-2023 Publisher An entity responsible for making the resource available Hindawi Limited 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/10.1155/2023/2556066" target="_blank" rel="noreferrer noopener">https://doi.org/10.1155/2023/2556066</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156220016&amp;doi=10.1155%2F2023%2F2556066&amp;partnerID=40&amp;md5=949b253756a33dfb69fa2287bfe9d74a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156220016&amp;doi=10.1155%2f2023%2f2556066&amp;partnerID=40&amp;md5=949b253756a33dfb69fa2287bfe9d74a</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 10589244; CODEN: SCIPE 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 Krishnan S., Department of EEE, Mahendra Engineering College (Autonomous), Tamil Nadu, Namakkal, India; Aruna S.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST, Deemed to Be University, Karnataka, Bangalore, India; Kanagarathinam K., Department of Electrical and Electronics Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, India; Venugopal E., Department of Electronics and Communication Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia