### Deep Learning Overview  
Deep learning is a branch of artificial intelligence (AI) that involves training computer systems to perform tasks such as recognising patterns, making decisions, and solving problems without being explicitly programmed to do so. It uses algorithms known as neural networks, which are inspired by the structure and function of the human brain [16]. Data-driven machine health monitoring is becoming increasingly prevalent in modern production systems because low-cost sensors are widely deployed and connected to the internet. Deep learning offers valuable methods for processing and evaluating large-scale operational data [17]. Deep learning enables computers to recognise patterns, classify information, and make highly accurate predictions by learning from large amounts of data. Deep learning employs neural networks that consist of numerous layers, known as deep architectures, to acquire knowledge from input and generate predictions. This technology has applications in various fields, including image and speech recognition, natural language processing, autonomous vehicles and, in this case, the manufacturing industry.  
Several key factors make the utilisation of deep learning paramount in error detection, diagnosis, and prediction in various industries, including quay container crane operations:  
1. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated exceptional capability in handling complex and high-dimensional data [18]. In container crane operations, where data streams from various sensors, control systems, and operational logs are abundant and diverse, deep learning models can effectively process these data to identify subtle patterns and anomalies indicative of errors.
2. Deep learning models excel in capturing temporal dependencies and dynamic relationships within sequential data [19]. In container crane operations, where crane movements, cargo handling processes, and environmental conditions evolve, deep learning algorithms can analyse historical data to predict future states, enabling proactive error detection and prevention [20].
3. The scalability and adaptability of deep learning models make them well-suited for real-time and offline monitoring and decision-making in dynamic operational environments. By continuously learning from new data and adapting to changing conditions, deep learning-based error detection systems can enhance operational safety, optimise resource utilisation, and mitigate potential risks in container crane operations [21; 22].  
Overall, the importance of utilising deep learning for error detection, diagnosis, and prediction in container crane operations lies in its ability to harness the power of advanced data analytics to enhance operational safety, efficiency, and resilience in maritime logistics. As technology advances, leveraging deep learning algorithms will become increasingly indispensable for ensuring container terminals' seamless and secure operation in the global supply chain ecosystem.  
By leveraging advanced deep learning techniques, the research aims to address the inherent complexities of error detection and prediction in container crane operations, where operational data are represented as discrete Boolean values over time. The proposed algorithm seeks to effectively capture temporal dependencies and subtle patterns within Boolean time series data, enabling accurate anticipation of potential errors before occurrence during crane operations. Through empirical validation and performance assessment, this paper endeavours to demonstrate the effectiveness and applicability of the proposed deep learning algorithm in enhancing operational safety, efficiency, and reliability within container terminal management.