## 3 Transfer Learning Approaches  
The transfer learning approaches published in this Special Issue include Arabic handwriting recognition, literature-based drug-drug interaction, anomaly detection, and chat-based social engineering attack recognition. **Arabic handwriting recognition:** [PERSON] and [PERSON] [4] proposed an approach for Arabic handwriting recognition by leveraging the advantages of machine learning for classification and deep learning for feature extraction, resulting in the development of hybrid models. The most exceptional performance among the stand-alone deep learning models, trained on both datasets (Arabic MNIST and Arabic Character), was achieved by the transfer learning model applied to the MNIST dataset, with an accuracy of 99.67%. In contrast, the hybrid models performed strongly when using the MNIST dataset, consistently achieving accuracies exceeding 90% across all hybrid models. It is important to note that the results of the hybrid models using the Arabic character dataset were particularly poor, suggesting a possible problem with the dataset itself. **Drug-drug interaction based on the biomedical literature:** [PERSON] et al. [6] introduced a Knowledge Graph schema integrated into a Graph Neural Network-based architecture designed for the classification of Drug-Drug Interactions (DDIs) within the biomedical literature. Specifically, they present an innovative Knowledge Graph (KG)-based approach that leverages a unique graph structure, combined with a Transformer-based Language Model (LM) and Graph Neural Networks (GNNs), to effectively classify DDIs found in the biomedical literature. The KG is meticulously constructed to incorporate the knowledge present in the DDI Extraction 2013 benchmark dataset without relying on additional external information sources. Each drug pair is classified based on the context in which it appears within a sentence, making use of transfer knowledge through semantic representations derived from domain-specific BioBERT weights, which act as the initial KG states. The proposed approach is evaluated on the DDI classification task using the same dataset, and it achieves an F1-score of 79.14% across the four positive classes, surpassing the current state-of-the-art methods. **Anomaly detection for latency in 5G networks:** [PERSON] et al. [7] proposed a novel approach for detecting anomalies and issuing early warnings with application to abnormal driving scenarios involving Automated Guided Vehicles (AGVs) within the private 5G networks of China Telecom. This approach efficiently identifies high-latency cases through their proposed ConvAE-Latency model. Building on this, the authors introduce the LstmAE-TL model, which leverages the characteristics of Long Short-Term Memory (LSTM) to provide abnormal early warnings at 15 min intervals. Furthermore, they employ transfer learning to address the challenge of achieving convergence in the training process for abnormal early warning detection, particularly when dealing with a limited sample size. Experimental results demonstrate that both models excel in anomaly detection and prediction, delivering significantly improved performance compared to existing research in this domain. **Chat-based social engineering attack recognition:** [PERSON] et al. [11] employed the terminology and methodologies of dialogue systems to simulate human-to-human dialogues within the realm of chat-based social engineering (CSE) attacks. The ability to accurately discern the genuine intentions of an interlocutor is a crucial element in establishing an effective real-time defence mechanism against CSE attacks. The authors introduce 'in-context dialogue acts' that reveal an interlocutor's intent and the specific information they aim to convey. This, in turn, enables the real-time identification of CSE attacks. This research presents dialogue acts tailored to the CSE domain, constructed with a meticulously designed ontology, and establishes an annotated corpus that employs these dialogue acts as classification labels.