## 4 Framework
### HPA-C Framework
By addressing the identified challenges and exploring the proposed future directions, this framework has the potential to revolutionize classification tasks across a wide range of applications. Its impact extends beyond smart agriculture, offering a scalable, efficient, and highly accurate solution for real-world problems. We believe this research opens the door to a new era of hybrid optimization-driven machine learning, with far-reaching implications for both academia and industry. The findings presented here are a testament to the transformative power of interdisciplinary innovation, and we are confident that this work will inspire further advancements in the field. A.R.--investigation, software, and manuscript draft preparation; M.M.R.--formal analysis and manuscript draft review; S.A.H.--methodology and resources; T.A.--data curation and project administration; S.R.N.--conceptualization, supervision, and manuscript draft preparation. All authors have read and agreed to the published version of the manuscript. The authors did not receive support from any organization for the submitted work. This research work did not involve any human and/or animal participants. The datasets used in this study are publicly available in Mendeley and Kaggle and are referenced in the manuscript as [31; 32; 33; 34; 35; 36; 37; 38; 39]. The code used/developed in this study will be made available upon receiving a reasonable request. The authors declare no competing interests related to the submitted work. The following abbreviations are used in this manuscript: \begin{tabular}{l l} HPA-C & Hybrid Predator Algorithm for Classification \\ CNN & Convolutional Neural Networks \\ \end{tabular} 

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