Alam, Md. Golam RabiulReza, Md. TanzimYeasin, Sakib RayhanRahat, Md. Atik HasanNakib, Shafaat JamilMitra, Debjoty2026-05-032026-05-0320262026ID 24341218ID 22101162ID 24241257ID 22141001http://hdl.handle.net/10361/28144Cataloged from PDF version of thesis.Includes bibliographical references (pages 147-150).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2026.General aviation records a fatal accident rate of approximately one per 100,000 flight hours, with loss-of-control and stall events remaining leading preventable causes. Existing flight safety systems rely on fixed expert-defined thresholds and cannot detect complex multi-sensor anomaly patterns or identify specific event types in real time. This thesis proposes a lightweight two-stage deep learning framework for real-time aviation anomaly detection and multi-label event classification on raw flight sensor data from the NGAFID General Aviation Training Set. Stage 1 uses an ensemble of two novel Transformer architectures, the Cross-Sensor Patch Transformer (CSPT) and the Hierarchical Cross-Sensor Transformer (HiCST), each incorporating a cross-sensor multi-head attention module that explicitly models inter-sensor dependencies before temporal processing. Root Mean Square ensemble fusion achieves an anomaly-class F1-score of 0.8815, recall of 0.9076, and AUPRC of 0.9523 on a test set with a 339:1 class imbalance ratio. Stage 2 uses MHANet, which introduces per-sensor independent linear projections to classify each anomalous timestep into any combination of ten simultaneous event types, achieving a macro F1 of 0.9563 and subset accuracy of 0.9627. The complete pipeline runs in 6.08 milliseconds per sensor reading on a standard CPU, confirming real-time feasibility. Both stages outperform all established deep learning and classical machine learning baselines while using significantly fewer parameters, demonstrating that domain-aware architectural specialization consistently outperforms general-purpose approaches for aviation safety monitoring.150 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Aviation anomaly detectionMulti level classificationTransformerHierarchical frameworkTime-series dataElectric transformers.Airplanes--Maintenance and repair.Multisensor data fusion.Time-series analysis--Data processing.Thesis