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 A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning Subject The topic of the resource CFS; Crayfish optimization; Crop yield prediction; Feature selection; Hyperparameter optimization; Information gain; Machine learning; Support vector machine; Support vector regression Description An account of the resource Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. The Author(s) 2024. Creator An entity primarily responsible for making the resource Abdel-salam M.; Kumar N.; Mahajan S. Source A related resource from which the described resource is derived Neural Computing and Applications, Vol-36, No. 33, pp. 20723-20750. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s00521-024-10226-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00521-024-10226-x</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201390106&amp;doi=10.1007%2Fs00521-024-10226-x&amp;partnerID=40&amp;md5=b9a3ac47752b2938fee8cc58e64fb577" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201390106&amp;doi=10.1007%2fs00521-024-10226-x&amp;partnerID=40&amp;md5=b9a3ac47752b2938fee8cc58e64fb577</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 9410643 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 Abdel-salam M., Faculty of Computer and Information Science, Mansoura University, Mansoura, Egypt; Kumar N., Department of CSE, Thapar Institute of Engineering and Technology, Deemed to Be University, Patiala, 147004, India; Mahajan S., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, India