By Doctor Graham Wild

Preprints

Wild G, 2026, War in Social Media: Soft Systems Modelling of NATO Fracture Through Grey Zone Operations, http://dx.doi.org/10.20944/preprints202601.1500.v1

Somerville A; Lynar T; Joiner K; Wild G, 2026,

Virtual Reality Flight Simulation: A Quasi-Transfer of Training Study, http://dx.doi.org/10.20944/preprints202601.0964.v1

Nanyonga A; Joiner K; Turhan U; Wild G, 2025, Deep Learning Approaches for Classifying Aviation Safety Incidents: Evidence from Australian Data, http://dx.doi.org/10.20944/preprints202508.2196.v1

Nanyonga A; Wild G, 2025, Utilizing AI for Aviation Post-Accident Analysis Classification, http://dx.doi.org/10.48550/arxiv.2506.00169

Nanyonga A; Wild G, 2025, Classification of Operational Records in Aviation Using Deep Learning Approaches, http://dx.doi.org/10.48550/arxiv.2501.01222

Nanyonga A; Wasswa H; Turhan U; Molloy O; Wild G, 2025, Sequential Classification of Aviation Safety Occurrences with Natural Language Processing, http://dx.doi.org/10.36227/techrxiv.173895150.01666498/v1

Nanyonga A; Wasswa H; Wild G, 2025, Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports, http://dx.doi.org/10.48550/arxiv.2501.07923

Nanyonga A; Wasswa H; Turhan U; Joiner K; Wild G, 2025, Exploring Aviation Incident Narratives Using Topic Modeling and Clustering Techniques, http://dx.doi.org/10.48550/arxiv.2501.07924

Nanyonga A; Wasswa H; Wild G, 2025, Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset, http://dx.doi.org/10.48550/arxiv.2501.07925

Nanyonga A; Wasswa H; Molloy O; Turhan U; Wild G, 2025, Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences, http://dx.doi.org/10.48550/arxiv.2501.06564

Nanyonga A; Wasswa H; Turhan U; Molloy O; Wild G, 2025, Sequential Classification of Aviation Safety Occurrences with Natural Language Processing, http://dx.doi.org/10.48550/arxiv.2501.06490

Nanyonga A; Wild G, 2025, Analyzing Aviation Safety Narratives with LDA, NMF and PLSA: A Case Study Using Socrata Datasets, http://dx.doi.org/10.48550/arxiv.2501.01690

Nanyonga A; Joiner K; Turhan U; Wild G, 2025, Applications of natural language processing in aviation safety: A review and qualitative analysis, http://dx.doi.org/10.48550/arxiv.2501.06210

Nanyonga A; Wasswa H; Wild G, 2025, Comparative Study of Deep Learning Architectures for Textual Damage Level Classification, http://dx.doi.org/10.48550/arxiv.2501.01694

Nanyonga A; Wasswa H; Turhan U; Joiner K; Wild G, 2025, Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language Processing, http://dx.doi.org/10.48550/arxiv.2501.01227

Pollock L; Wild G, 2024, Data-driven shape sensing of a hypersonic inlet ramp, http://dx.doi.org/10.31224/3817

Wild G, 2024, Airbus A32x vs Boeing 737 Safety Occurrences, http://dx.doi.org/10.48550/arxiv.2405.00044

Nanyonga A; Wasswa H; Wild G, 2024, Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models, http://dx.doi.org/10.48550/arxiv.2403.04788

Wild G; Richardson S, 2022, Automated formative assessments marking, feedback, and analytics with multiple choice face-to-face quizzes, http://dx.doi.org/10.31219/osf.io/vsr4q

Wild G, 2022, Improving Capstone Research Projects: Using Computational Thinking to Provide Choice and Structured Active Learning, https://arxiv.org/abs/2203.15947v1

Beling C; Wild G, 2021, The association between emotional intelligence and decision making for pilots, http://dx.doi.org/10.31224/osf.io/gqfsc

Wild G; Baxter G; Srisaeng P; Richardson S, 2021, Machine Learning for Air Transport Planning and Management, http://dx.doi.org/10.48550/arxiv.2112.01301

Pollock L; Wild G, 2021, An initial review of hypersonic vehicle accidents, https://arxiv.org/abs/2110.06438v1

Pollock L; Wild G, 2021, Passive Phased Array Acoustic Emission Localisation via Recursive Signal-Averaged Lamb Waves with an Applied Warped Frequency Transformation, https://arxiv.org/abs/2110.06457v1

Wild G, 2021, On the Origins and Relevance of the Equal Transit Time Fallacy to Explain Lift, https://arxiv.org/abs/2110.00690v1

Nanyonga A; Wasswa H; Joiner K; Turhan U; Wild G, A Multi-Head Attention-Based Transformer Model for Predicting Causes in Aviation Incident, http://dx.doi.org/10.20944/preprints202502.1196.v1

Eqbal M; Marino M; Fernando N; Wild G, Design Factors of High- speed Turbo-electric Distributed Propulsion System, http://dx.doi.org/10.21203/rs.3.rs-1986257/v1

Nanyonga A; Joiner K; Turhan U; Wild G, Does the Choice of Topic Modeling Technique Impact the Interpretation of Aviation Incident Reports? A Methodological Assessment, http://dx.doi.org/10.20944/preprints202503.2171.v1

Nanyonga A; Wasswa H; Joiner K; Turhan U; Wild G, Explainable Supervised Learning Models for Aviation Predictions in Australia, http://dx.doi.org/10.20944/preprints202502.0998.v1

Wild G; Baxter G; Srisaeng P; Richardson S, Machine Learning for Air Transport Planning and Management, http://dx.doi.org/10.31224/osf.io/35rqj

Nanyonga A; Joiner K; Turhan U; Wild G, Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia, http://dx.doi.org/10.20944/preprints202503.2251.v1

Wild G, Potential Designs for Miniature Distributed Optical Fiber Smart Sensors Systems for Use in Aerospace Flight Vehicles, http://dx.doi.org/10.20944/preprints202503.0993.v1

Nanyonga A; Wasswa H; Joiner K; Turhan U; Wild G, Predicting Probable Causes of Aviation Incidents Using NLP and Variational Autoencoders, http://dx.doi.org/10.36227/techrxiv.174431146.68041296/v1

Nanyonga A; Joiner K; Turhan U; Wild G, Semantic Topic Modeling of Aviation Safety Reports: A Comparative Analysis Using BERTopic and PLSA, http://dx.doi.org/10.20944/preprints202505.1509.v1

Somerville A; Lynar T; Wild G, The Nature and Costs of Civil Aviation Flight Training Safety Occurrences, http://dx.doi.org/10.2139/ssrn.4191514

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