By Doctor Graham Wild

Preprints

Nanyonga A; Wasswa H; Joiner K; Turhan U; Wild G, 2025, Explainable Supervised Learning Models for Aviation Predictions in Australia, http://dx.doi.org/10.20944/preprints202502.0998.v1

Nanyonga A; Wasswa H; Wild G, 2025, Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports, http://arxiv.org/abs/2501.07923v1

Nanyonga A; Wasswa H; Turhan U; Joiner K; Wild G, 2025, Exploring Aviation Incident Narratives Using Topic Modeling and Clustering Techniques, http://arxiv.org/abs/2501.07924v1

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/10.1109/HDIS60872.2023.10499521

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://arxiv.org/abs/2501.06564v1

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/10.2514/6.2023-4325

Nanyonga A; Wild G, 2025, Analyzing Aviation Safety Narratives with LDA, NMF and PLSA: A Case Study Using Socrata Datasets, http://arxiv.org/abs/2501.01690v1

Nanyonga A; Joiner K; Turhan U; Wild G, 2025, Applications of natural language processing in aviation safety: A review and qualitative analysis, http://arxiv.org/abs/2501.06210v1

Nanyonga A; Wasswa H; Wild G, 2025, Comparative Study of Deep Learning Architectures for Textual Damage Level Classification, http://dx.doi.org/10.1109/SPIN60856.2024.10511727

Nanyonga A; Wild G, 2025, Classification of Operational Records in Aviation Using Deep Learning Approaches, http://arxiv.org/abs/2501.01222v2

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://arxiv.org/abs/2501.01227v1

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/10.1109/MAES.2023.3276347

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/10.1109/SMARTGENCON60755.2023.10442471

Eqbal M; Marino M; Fernando N; Wild G, 2022, Design Factors of High- speed Turbo-electric Distributed Propulsion System, http://dx.doi.org/10.21203/rs.3.rs-1986257/v1

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, http://arxiv.org/abs/2203.15947v1

Wild G; Baxter G; Srisaeng P; Richardson S, 2021, Machine Learning for Air Transport Planning and Management, http://dx.doi.org/10.31224/osf.io/35rqj

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, http://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, http://arxiv.org/abs/2110.06457v1

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

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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