## 5 Discussion  
Timely error detection and prediction are essential in optimising crane performance. They are crucial in ensuring safety by preventing accidents, protecting equipment from damage, and reducing human errors. As a result, these practices enhance productivity and lower operating expenses.  
Understanding predictive analytics' limitations is critical. First and foremost, progress is impossible without an extensive and high-quality training dataset. Second, having a clear description of the concept to be forecasted and past occurrences is critical. One of the challenges in predictive analytics is identifying a needle in a haystack and predicting its occurrence. However, there are certain limitations of traditional methods. These limitations in container crane operations are as follows:  
1. Human-Induced Errors: Human operators still play a significant role in quay crane operations despite automation advancements. Their decisions and actions can lead to errors, affecting productivity and safety [64].
2. Complex Dynamics: Container cranes operate in a dynamic environment with varying container weights, wind conditions, and vessel movements. Predicting errors in such a complex system is challenging [65; 66; 67].
3. Efficiency and Downtime: Minimising downtime due to errors is critical. Timely error prediction can prevent costly delays and optimise crane utilisation [68; 69].
4. Imbalanced data: A dataset is called imbalanced when there are substantial disparities in the number of examples of each class, resulting in one or more classes being underrepresented in the dataset [70]. This phenomenon has been established in several real-world datasets gathered in industrial settings.  
Therefore, this research developed a novel, custom-built algorithm to address these limitations and challenges by focusing on detecting and predicting errors before they convert into faults and failures.  
The following points address any challenges and limitations encountered during the implementation:  
1. Because there was a significant time difference between the data array files, the data array files could not be merged for analysis; instead, analysis was performed on each data array file separately.  
\begin{table}
\begin{tabular}{c c} \hline
**Timestamp** & **var\_831** \\ \hline  
\end{tabular}
\end{table}
Table 3: _Cont._For time series forecasting, the data were scaled down from milliseconds to seconds, because the data in milliseconds were not continuous, i.e., (1, 2, 3,..., 999).
3. There may be a good chance of error as the dataset size was small, so the LSTM model could not properly and correctly predict new data. By adding more data, this issue can be resolved.  
Despite the promising results, implementing deep learning models for error prediction in real-world crane operations remains challenging. These include data quality issues, the interpretability of complex models, and integration with existing systems. Future research directions may address these challenges through advanced model architectures, data fusion techniques, and collaborative efforts between academia and industry stakeholders. The avenues for future research can advance with improved data quality and quantity, which are currently lagging.