### Research Objectives, Motivation, and Contributions  
One critical aspect of safeguarding safety, efficiency, and reliability in container terminal operations is detecting and mitigating errors that may occur during crane operations.  
Errors in quay container crane operations can have significant implications, ranging from potential damage to cargo and infrastructure to disruptions in supply chain logistics. Moreover, human error, equipment malfunction, and environmental contingencies contribute to the complexity of error detection and pose challenges to ensuring uninterrupted operations [14]. System failures can be prevented by identifying and anticipating errors, resulting in a more robust and durable system. Error prediction in container crane operations involves using data analysis, machine learning, and deep learning methodologies to predict prospective errors or anomalies that may occur during the operation of container cranes [6].  
This research aims to develop proactive strategies for error detection and prediction by harnessing the power of deep learning, data analytics, and predictive modelling, thereby enhancing container terminal operations' safety, efficiency, and reliability. Addressing this problem requires a comprehensive understanding of container terminals' operational dynamics and the application of advanced AI technologies and methodologies for error detection, diagnosis, and prediction [15].  
By analysing diverse data sources, including crane sensor data, past operation logs, weather conditions, and other pertinent aspects, predictive models can be created to foresee and minimise problems in advance. Implementing this proactive strategy enhances safety, effectiveness, and overall efficiency in container terminal operations.  
Research findings in this domain have significant implications for the maritime industry, port authorities, terminal operators, and other stakeholders involved in container terminal operations. The objective is to enhance operational safety, minimise downtime, optimise resource utilisation, and improve overall terminal performance by effectively detecting and mitigating errors in operational data. The scope of this paper lies in exploring how deep learning can enhance error detection, prediction, and time series analysis in container crane operations, which is currently a critical need in the maritime industry and will help to enhance operational efficiency and safety.  
The potential of this research extends beyond container cranes to other industries with imbalanced data challenges--where rare events or anomalies occur can benefit significantly. Other industrial sectors can significantly benefit from this research, such as manufacturing, in identifying equipment failures or defects early in production lines; the energy sector, in predicting anomalies in power generation or distribution systems; and transportation, in detecting irregularities in vehicle performance or safety systems. Additionally, organisations can target interventions and preventive measures by identifying critical elements associated with errors.  
For instance:  
Maintenance: Priorities maintenance schedules for components most likely to cause errors;  
Process Optimisation: Adjust operational procedures based on error patterns;  
Resource Allocation: To minimise disruptions, allocate resources (personnel, materials) strategically.  
This research is motivated by the urgency of improving error recognition and prediction in container cranes in the multibillion-dollar container shipping industry, which is the backbone of the current worldwide economy. Traditional rule-based techniques frequently fail because they cannot manage complicated, dynamic settings and uneven data distributions. Deep learning, particularly LSTM networks, provides a viable solution to these shortcomings. By capturing temporal connections and learning from previous data, LSTMs can model complex patterns and increase accuracy.