## 4 Framework
### HPA-C Framework
The primary driving force behind our effort is to improve feature selection for applications where computing efficiency and interpretability are crucial. Direct comparisons with deep learning models that do not use feature selection are not straightforward because they are based on a different paradigm. Specifically in medical or specialized domains, combining transfer learning with pre-trained models like SWIN transformers may be a promising path toward further improvement. This work can be expanded in the future by comparing HPA-C against cutting-edge deep learning models and hybrid metaheuristic-based feature selection methods. Transparency is crucial in applications like medical diagnosis, where using visualization techniques like Grad-CAM could offer deeper insights into model decision making. In addition to meeting the changing requirements of intelligent classification systems, these directions would improve HPA-C's wider application. For future work, we can use model parallelism and distributed training with frameworks like DeepSpeed or PyTorch Distributed to scale training across numerous nodes. We can maximize computational efficiency by using NVIDIA's Apex for low-precision training, such as mixed precision (FP16). This might greatly speed up calculations without sacrificing accuracy. We can Use Numba and CuPy to implement GPU-accelerated computations for feature selection and preprocessing. This can result in improved execution speed and make handling large datasets easier. ## 6 Conclusions

HPA-C is a novel feature selection algorithm for classification using agricultural datasets. It leverages a variety of biological activities, including cooperative hunting, foraging, and echolocation, to accomplish superior exploration and exploitation capabilities. When used along with a custom convolutional neural network (CNN), HPA-C delivered exceptional classification performance by achieving accuracy levels of up to 99.8% on key agricultural datasets, including Plant Leaf Diseases, Weed Detection, Fruits-360, and Fresh n Rotten. These results not only surpass those of methods like GWO and GA but also demonstrate the algorithm's ability to extract highly discriminative features, even in scenarios where performance gains are thought to be saturated. These improvements, though seemingly incremental, are transformative in agricultural applications where even fractional gains in accuracy can reduce misclassifications and enhance decision making. HPA-C outperformed benchmark techniques in both accuracy and macro F1 score, particularly in challenging datasets like Fresh n Rotten, where it achieved a 12.8% improvement over traditional methods like RelieFF. Beyond agriculture, the competitive performance of HPA-C on supplementary datasets shows its potential for broader applicability, including in domains like medical imaging and environmental monitoring. This study also focused attention on critical challenges to pave the way for future innovation. Computational resource limitations, particularly GPU memory constraints, restricted the exploration of more advanced architectures like Vision Transformers (ViTs). Addressing these challenges through model compression, distributed training, and low-precision computing could unlock even greater performance gains. Additionally, the lack of explicit handling of dataset imbalance presents an opportunity to integrate advanced balancing techniques, such as SMOTE, ADASYN, or generative approaches like GANs, to further enhance generalization. Extending the framework to high-stakes domains like medical imaging, where explainability and domain adaptation are paramount, represents another exciting avenue for future research. In conclusion, this work not only establishes HPA-C as a state-of-the-art feature selection algorithm but also sets a new benchmark for hybrid optimization techniques in machine learning.