## 6 Conclusions  
This study presents the innovative BCHO-TCN LightGBM model and offers valuable new insights and practical benefits for industry use, especially in predicting out-of-stock situations. Our research highlights its superior accuracy and reliability compared to conventional methods. The model has the best predictive performance of any that combines \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{5}{c}{**TP 90**} & \multicolumn{5}{c}{**k-Fold 10**} \\ \cline{2-9}  & **Accuracy** & **NPV** & **PDV** & **Threat Score** & **Accuracy** & **NPV** & **PDV** & **Threat Score** \\ \hline Perceptron & 84.61 & 70.24 & 89.05 & 74.35 & 72.21 & 81.75 & 77.44 & 78.09 \\ \hline CNN-LSTM & 75.81 & 82.48 & 88.46 & 83.14 & 72.30 & 86.41 & 74.17 & 81.92 \\ \hline BiLSTM & 83.69 & 82.63 & 82.18 & 68.44 & 93.40 & 91.61 & 75.68 & 84.52 \\ \hline Rabbit-TCN LightGBM & 92.66 & 89.22 & 91.17 & 83.07 & 90.67 & 92.84 & 88.12 & 91.15 \\ \hline Hawk-TCN LightGBM & 90.65 & 83.91 & 79.25 & 87.25 & 93.49 & 87.70 & 92.88 & 90.09 \\ \hline Vulture-TCN LightGBM & 92.51 & 93.95 & 82.41 & 89.24 & 85.25 & 94.61 & 95.89 & 90.26 \\ \hline BCHO-TCN LightGBM & 94.52 & 95.16 & 94.81 & 95.76 & 94.81 & 95.60 & 96.28 & 95.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative discussion table for TP and K-fold. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Best** & **Mean** & **Variance** \\ \hline Perceptron & 93.49 & 79.86 & 50.45 \\ \hline CNN-LSTM & 92.42 & 83.77 & 41.86 \\ \hline BiLSTM & 93.24 & 86.19 & 34.28 \\ \hline Rabbit-TCN Light GBM & 91.15 & 82.63 & 54.56 \\ \hline Hawk-TCN Light GBM & 90.09 & 83.39 & 50.63 \\ \hline Vulture-TCN Light GBM & 90.26 & 79.98 & 48.54 \\ \hline BCHO-TCN Light GBM & 95.86 & 94.98 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistical analysis based on k-fold for threat score. Buzzard Coney Hawk Optimization with LightGBM and convolutional neural networks. LightGBM utilizes a fast and scalable algorithm capable of handling large datasets. It helps to increase training speed and contributes to effective memory utilization. This advancement shows great potential for industries that rely heavily on inventory management, such as retail, manufacturing, and supply chain logistics. By implementing this model in real-world scenarios for out-of-stock prediction, businesses can improve their inventory management efficiency, reduce instances of stockouts, and enhance overall customer satisfaction. Using advanced methods and strategies, the model allows for proactive decision-making, the optimization of inventory levels, and the simplification of supply chain operations. Ultimately, the BCHO-TCN LightGBM model has practical benefits that include improving business performance, reducing revenue losses caused by stock shortages, and promoting operational efficiency in different industry sectors. The results show that when using product listingsfrom the Amazon India dataset, the models achieve high levels of accuracy, as well as excellent NPV, PDV, and threat scores, with values of 94.52%, 95.16%, 94.81%, and 95.76%, respectively. Likewise, at k-fold of 10, the model attains values of 94.81%, 95.60%, 96.28%, and 95.86% for the same metrics. While these achievements are very promising, we should also consider the potential limitations of the proposed method, such as data quality, scalability, and model interpretability. Moreover, in the future, improving the interpretability of the model and developing post-processing techniques for actionable insights are important aspects to consider. Conceptualization, A.E. ; methodology, A.E. ; software, A.E. ; writing--original draft preparation, A.E. ; review, editing and supervision, A.A. and K.I. All authors have read and agreed to the published version of the manuscript. This research received no external funding.