Contact Dr Desmond Bisandu
Areas of expertise
- Computing, Simulation & Modelling
Background
Dr Desmond Bisandu holds a PhD in Data Science from Cranfield University and an MSc and BSc in Computer Science from the American University of Nigeria and the University of Jos, respectively. He is currently a postdoctoral research and teaching fellow at Cranfield University. His work focuses on bridging the gap between theoretical research and practical applications, with his research focusing on developing novel algorithms aimed at addressing socio-economic challenges through advanced computational technologies.
Research opportunities
Machine Learning, Deep Learning, Artificial Intelligence, Computer Science, Algorithms Development, Air Transport Management Systems, Aerospace, Digital Aviation, Passenger Experience, Computational Science and any multidisciplinary research for solving emerging problems through innovation and new technologies.
Current activities
1. Develop novel research outputs for academia and Industrial partners (DAFNI)
2. Working and providing solutions to the UK's largest airports
3. Developing novel algorithms/approaches for the UK Aviation Systems
4. Develop course materials and teach Programming for Business Analytics
5. Programming methods for Robotics
6. Artificial Intelligence and Machine Learning for Business Analytics
7. Machine Learning and Big Data for Computational Data Analytics
8. Artificial Intelligence and Machine Learning for Robotics
9. Visualisation for Computational Engineering Sciences
10. MSc Group Project and Individual thesis supervision
Clients
- UK Research and Innovation
Publications
Articles In Journals
- Bisandu DB & Moulitsas I. (2024). Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm. Expert Systems with Applications, 247
- Bisandu DB, Soviani-Sitoiu DA & Moulitsas I. (2024). An Enhanced Deep Autoencoder for Flight Delay Prediction. Journal of Aviation/Aerospace Education & Research, 33(4)
- Alreshidi I, Bisandu D & Moulitsas I. (2023). Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability. Sensors, 23(22)
- Bisandu DB & Moulitsas I. (2023). A Deep BiLSTM Machine Learning Method for Flight Delay Prediction Classification. Journal of Aviation/Aerospace Education & Research, 32(2)
- Homaid MS, Bisandu DB, Moulitsas I & Jenkins K. (2022). Analysing the Sentiment of Air-Traveller: A Comparative Analysis. International Journal of Computer Theory and Engineering, 14(2)
- Bisandu DB, Moulitsas I & Filippone S. (2022). Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction. Neural Computing and Applications, 34(11)
- Gambo FL, Wajiga GM, Shuib L, Garba EJ, Abdullahi AA, .... (2022). Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images. ICST Transactions on Scalable Information Systems, 9(35)
- Bisandu DB, Prasad R & Liman MM. (2019). Data clustering using efficient similarity measures. Journal of Statistics and Management Systems, 22(5)
- Bisandu DB, Prasad R & Liman MM. (2018). Clustering news articles using efficient similarity measure and N-grams. International Journal of Knowledge Engineering and Data Mining, 5(4)
- Prasad R, Bisandu D & Liman M. (2018). Clustering News Articles using Efficient Similarity Measure and N-grams. International Journal of Knowledge Engineering and Data Mining, 5(1)
Conference Papers
- Bisandu DB & Moulitsas I. (2023). A Hybrid Ensemble Machine Learning Approach For Arrival Flight Delay Classification Prediction Using Voting Aggregation Technique
- Bisandu DB & Moulitsas I. (2022). A bidirectional deep LSTM machine learning method for flight delay modelling and analysis
- Bisandu DB, Homaid M, Moulitsas I & Filippone S. (2021). A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective
