Ð԰ɵç̨

Dr Lipika Deka hails from the beautiful state of Assam, renowned for its tea and one-horned rhinos, in the North-East of India. She completed her basic education in Shillong, a hill-station and located in the same state as the world's rainiest place, Mawsynram.  

Dr Deka is a Computer Engineering by training and her research interest primarily lies in the area of concurrency control techniques such as for the purpose of consistent, architecture preserving, online software updates in for example the autonomous vehicles and IoT devices and systems, with an ultimate goal of reducing e-waste by facilitating longer lives of digital items. Additionally, Dr Deka works actively in the development of efficient and suitable machine learning techniques for Intelligent Transportation Systems and downstream space applications such as smart agriculture (particularly for adaptation to climate change) as well as soil and water analysis.

Dr Deka holds a PhD in Computer Science and Engineering from Indian Institute of Technology (IIT) Guwahati, India in the area of concurrency control techniques and developed her interest in the intersection of machine learning and a number of global challenges as a Post Doc at Loughborough University UK before joining Ð԰ɵç̨.

The Institute of Artificial Intelligence (IAI)

The Ð԰ɵç̨ Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Software Technology Research Laboratory

Journal Articles: 

1. Bushra, B., Bazneh, L., Deka, L., Wood, P., McGowan, S., and Das, D.. 2023. Temporal modelling of long-term heavy metal concentrations in aquatic ecosystems. Journal of Hydroinformatics. doi: 10.2166/hydro.2023.151.

2. Allidina, T., Deka, L., Paluszczyszyn, D. and Elizondo, D., 2022. Selecting Non-Line of Sight Critical Scenarios for Connected Autonomous Vehicle Testing. Software, 1(3), pp.244-264.

3. Rivera, A.J., Pérez-Godoy, M.D., Elizondo, D., Deka, L. and del Jesus, M.J., 2022. Analysis of clustering methods for crop type mapping using satellite imagery. Neurocomputing, 492, pp.91-106.

4. Khan, N., Elizondo, D. A., Deka, L. and Molina-Cabello, M.A., Fuzzy Logic Applied to System Monitors, in IEEE Access, volume. 9, pp. 56523-56538, 2021, DOI: 10.1109/ACCESS.2021.3072239.

5. Ayres, N., Deka, L., Paluszczyszyn, D., Continuous Automotive Software Updates through Container Image Layers. Electronics 2021, volume. 10, 739. DOI: 10.3390/electronics10060739

6. González-Enrique, J., Ruiz-Aguilar, J.J., Moscoso-López, J.A., Urda, D., Deka, L., Turias, I.J., Artificial Neural Networks Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain). Sensors 2021, volume. 21, DOI: 10.3390/s21051770.

7. Vu, LH., Passow, BN., Paluszczyszyn, D., Deka, L., Goodyer, E., Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Data. IEEE Intelligent Transportation Systems Magazine. 2021, volume.13, DOI: 10.1109/MITS.2019.2926274.

8.  Olowofoyeku, O., Shell, J., Goodyer, E., Deka, L. Healthcare Facility Coverage for Malaria and Sickle Cell Diseases Treatment: A Spatial Analysis of Ikorodu Local Government Area of Lagos State. International Journal of Health, Wellness & Society. Jun2020, Volume 10 Issue 2, p33-51. DOI: 10.18848/2156-8960/CGP/v10i02/33-51

9. Kenyon, A., Deka, L., Elizondo, D., Are Public Intrusion Datasets Fit for Purpose? Characterising the State of the Art in Intrusion Event Datasets. Elsevier’s Computers & Security, 2020, volume. 99, DOI: 10.1016/j.cose.2020.102022.

10. Islam, M., Rahman, M., Khan, S. M., Chowdhury, M., Deka. L., Development and performance evaluation of a connected vehicle application development platform. Transportation Research Record. 2020. Volume 2674. Issue 5. DOI: 10.1177/0361198120917146

11. Khan, SM., Chowdhury, M., Morris, EA., Deka, L. Synergizing Roadway Infrastructure Investment with Digital Infrastructure: Motivations, Current Status and Future Direction. Journal of Infrastructure Systems. December 2019, Volume. 25. DOI: 10.1061/(ASCE)IS.1943-555X.0000507

12. Pantzartzis, E., Deka, L., et al., Sustainable management of NHS assets backlog maintenance. Built Environment Project and Asset Management. Volume 6, No. 5, 2016. DOI: 10.1108/BEPAM-09-2015-0055.

13. Katrakazas, C., Quddus, M., Chen, W., Deka, L., Planning Techniques for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies. Vol 60, November 2015. DOI: 10.1016/j.trc.2015.09.011.

14. Das, DB, Thirakulchaya, T, Deka, L, Hanspal, N. Environmental Processes, Vol 2, No 1, 2015. DOI: 10.1007/s40710-014-0045-3

15. Mills, G. R. W., Deka, L., Price, A. D. F.., Mahadkar, S.., Pantzartzis, E., Sellars, P. Critical Infrastructure Risk in NHS England: Predicting the impact of asset portfolio age. International Journal of Strategic Property Management. Volume 6, No. 5, June 2015. DOI:10.3846/1648715X.2015.1029562.

16. Deka, L., Quddus, M. Network-level Accident-mapping: Distance Based Pattern Matching using Artificial Neural Network. Accident Analysis and Prevention, Vol. 65, April 2014. DOI:10.1016/j.aap.2013.12.001

17. Deka, L., Barua, G. Consistent Online Backup in Transactional File Systems, IEEE Transaction on Knowledge and Data Engineering. Volume 26, Issue 11, 2014. DOI: 10.1109/TKDE.2014.2302297.

18. Deka, L., Barua, G. Implementation and Evaluation of Consistent Online Backup in Transactional File Systems, CSI Journal of Computing. Volume 2, No 3, 2013

19. Hanspal, N., Babatunde, A., Deka, L., Das, DB. Artificial Neural Network (ANN) Modeling of Dynamic Effects on Two-phase Flow in Homogenous Porous Media. Journal of Hydroinformatics, Volume 15 No. 2, 2013. DOI: 10.2166/hydro.2012.119

Publications in Conference Proceedings

1. Moore, P., Deka, L., Amaugo, A., Budd, L., and Ison, S., Bikeshare: A review of the trip determinants related to socio-economic disadvantage. To be presented in World Conference on Transport Research Society in July 2023

2. García Aguilar, I., Deka, L., Marcos Luque Baena, R., Domínguez Merino, E., López Rubio, E., 2023. Minimal Optimal Region Generation for Enhanced Object Detection in Aerial Images using Super-Resolution and Convolutional Neural Networks. To be presented in 17th International Work-Conference on Artificial Neural Networks (IWANN2023)  Portugal in June 2023

3. Mifsud, R., Deka, L. Lahiri, I., An Optimised BERT Pretraining Approach for Identification of Targeted Offensive Language: Data Imbalance and Potential Solutions. IEEE Sponsored International Conference on Computing and Communication Systems. Shillong . India. March 2023

4. Bushra, B, Bazneh, L., Deka, L., Wood P., Das, D. Assessment of Long-Term Heavy Metal Contamination in Aquatic Ecosystems Using a Combination of Secondary Data Analysis Techniques. In the Proceedings of the 39th IAHR World Congress (Granada, 2022). doi:10.3850/IAHR-39WC2521711920221418

5. Thurnhofer-Hemsi, K., López-Rubio, E., Roé-Vellvé, N. and Deka, L., Super-Resolution of 3D MRI Corrupted by Heavy Noise with the Median Filter Transform, In the Proceedings of 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp. 3015-3019.

6. Jesús Rivera, A., Dolores Pérez-Godoy, M., Elizondo, D., Deka, L., José del Jesus, M., A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops.  In the Proceedings of International Workshop on Soft Computing Models in Industrial and Environmental Applications. 2020.

7. Wysocki, O., Deka, L., Elizondo, D., Kropiwnicki, J., Czyżewicz, J. Heavy duty vehicle fuel consumption modelling using artificial neural networks. In the proceedings of The 25th IEEE International Conference on Automation and Computing, Lancaster, UK, September 2019

8.  Ayres, N., Deka, L., Passow, B., Virtualisation as a means for dynamic software update within the automotive E/E architecture. In the proceedings of the 2019 IEEE Smart World Congress, Leicester, UK, August 2019.

9. Wysocki, O., Deka, L., Elizondo, D., Kropiwnicki, J., Czyżewicz, J. Heavy Duty Vehicle Fuel Consumption Modelling Based on Exploitation Data by Using Artificial Neural Networks. In the proceedings of the International Work-Conference on Artificial Neural Networks. Gran Canaria, Spain, June 2019.

10. Molina-Cabello, MA., Luque-Baena, RM., López-Rubio, E., Deka, L., Road pollution estimation using static cameras and neural networks. In the proceedings of the International Joint Conference on Neural Networks (IJCNN) held by IEEE World Congress on Computational Intelligence, Rio de Janeiro, Brazil, 2018.

11. Vu,L., Passow, B., Paluszczyszyn, D., Deka, L., Goodyer, E., Neighboring Link Travel Time Inference Method Using Artificial Neural Network .In the proceedings of The 2017 IEEE Symposium Series on Computational Intelligence. December 2017.

12. Deka, L., Quddus, M. A. Trip-Based Weighted Trajectory Matching Algorithm for Sparse GPS Data. In the proceedings if the 94th Annual Meeting of the Transportation Research Board, Washington, D.C., January, 2015.

13. Deka, L., Quddus, M. Network-level Accident-mapping: Distance Based Pattern Matching using Artificial Neural Network. In the proceedings of the 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., January, 2014.

14. Deka, L. and Barua, G., On-line Consistent Backup in Transactional File Systems, In the proceedings of the First ACM Asia-Pacific Workshop on Systems (APSys2010) held in conjecture with SIGCOMM, August 2010, New Delhi.

Ayres, N., Deka, L., Paluszczyszyn, D., Continuous Automotive Software Updates through Container Image Layers. Electronics 2021, volume. 10, 739. DOI: 10.3390/electronics10060739

Katrakazas, C., Quddus, M., Chen, W., Deka, L., Planning Techniques for autonomous on-road driving: State-of-the-art and future research directions. Transportation Research Part C: Emerging Technologies. Vol 60, November 2015. DOI: 10.1016/j.trc.2015.09.011.

Deka, L., Quddus, M. Network-level Accident-mapping: Distance Based Pattern Matching using Artificial Neural Network. Accident Analysis and Prevention, Vol. 65, April 2014. DOI:10.1016/j.aap.2013.12.001

Deka, L., Barua, G. Consistent Online Backup in Transactional File Systems, IEEE Transaction on Knowledge and Data Engineering. Volume 26, Issue 11, 2014. DOI: 10.1109/TKDE.2014.2302297

• Artificial Intelligence
• Data Science
• Concurrency Control
• Dynamic Software Update
• Intelligent Transportation System
• Machine Learning for Water Quality and Water Distribution Networks
• Machine Learning for Agriculture

• Machine Learning for Satellite Downstream Applications (such as air quality)

Data Structures and Algorithms, Devices and Networks, Data Management.

 

 

• PhD in Computer Science and Engineering
• MTech in Computer Science and Information Technology
• BEng in Computer Science and Engineering

Dr Deka has taught on many courses including, Computer Science, Software Engineering and Information Systems 

Faculty Staff Leadership Award in 2019

Lead of European Volunteers Network, Association of Computing Machinery - Womens Chapter (). 

Vice-Chair of Association of Computing Machinery - UK Womens Chapter ()

IEEE Professional Member (current)

Association of Computing Machinery Professional Member (Past)

 

 

Co-I on STAGE I (2022-23): EIT Food Seedbed pre-incubation for OPTIcut and selected for Stage 2 of the Seedbed Programme. Co-funded by EIT Food Partner Universities and the European Union On advisory board (2022 - 2026) for  a THIS Institute fellowship. Project Title: Integrated sustainable and resilient healthcare buildings.

Participant (2022 -2026) on a project funded by Spanish Govt. under Knowledge Generation Projects 2021. Ministry of Science and Innovation. Project title: Development of Advanced Models for the Characterisation of Bifacial Photovoltaic Systems. This includes PhD student exchange and travel.  Project Partner (2020-2024) on an EPSRC Discipline Hopping Award. Title of Project: Membrane-Cyber-Physical System (m-CPS) for Smart Water Treatment.

Entrepreneurial Lead (2020) on INNOVATE UK’s ICURe project on OPTIcut: a decision support system to reduce fruit waste, enhance quality control and maximise profitability in the banana supply chain.

Academic Supervisor (2018 - 2020) on a Knowledge Transfer Partnership project with Geospatial Insight Ltd, UK. The partnership’s objective is to develop and embed machine learning capabilities within a range of products involving Downstream Satellite Applications in the area of Agriculture.

Ð԰ɵç̨ PI (2015-2018) on Transport Catapult sponsored project titled IMPART – Intelligent Mobility Partnership programme with the Transport Systems Catapult, this project is part of the Transport Systems Catapult University Partner Programme, led by Loughborough, managed by Ð԰ɵç̨, and in partnership with Nottingham Trent and Coventry Universities.

 

1. Noel Khan  : Automatic Determination of Both Sensorimotor Primitives and Topology for Robots with Unknown or Dynamic     Morphology

2. Reem Bashir:  Machine Learning based effective detection technique of emerging automotive cybersecurity incidents

3. Hussein Hennaoui: Use of Mechatronics and Machine Learning in Autonomous Systems in Variable Dune Areas.

4. Patrick Moore: Breaking the cycle of immobility? Evaluating the impact of Leicester’s e-bike scheme on urban mobility.

5. Deborah Fakehinde:  iMuseum: Intelligent, interactive virtual museum for conserving and celebrating the history of Leicester’s multicultural population.

6. Mohammad Bakar: Federated learning for improving security in water distribution system

Completed Research Students:

1. Anthony Kenyon (2023) :  Deep Sequence Modelling in Cybersecurity

 2. Tanvir Allidina (2022) :  Non-line of sight test scenario generation for connected autonomous vehicles

 3. Nick Ayres (2021): Enhancing the Automotive E/E Architecture Utilising Container-Based Electronic Control Units

 4. Olukemi Olowofoyeku:  Dynamic Spatial Agent-Based Facility Location for Healthcare Coverage

 5. Phuong Thi Mai Nguyen:  Multi-objective Optimization in Road Transport

 6. Luong Huy Vu:  Estimation of Travel Time using Temporal and Spatial Relationships in Sparse Data