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Dr Maysam Abbod

Dr Maysam Abbod
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Summary

Education

Dr Maysam F. Abbod (MIEE, CEng) He received BSc degree in Electrical Engineering fromUniversity ofTechnology in 1987. PhD in Control Engineering fromUniversity ofSheffield in 1992. From 1993 to 2006 he was with the Department of Automatic Control and Systems Engineering at theUniversity ofSheffield as a research associate and senior research fellow

Responsibility

Consultency Work

  • November 2001 Modelling of DNA mismatch repair expression and microsatellite instability in transitional cell carcinoma of the bladder, Department of Urology, Faculty of Medicine, University of Sheffield.
  • August 2000 Model-based data-mining from chromium process production information for London and Scandinavian Metallurgical Co Ltd.
  • July 1999 Developments of a neuro-fuzzy tool for hardness estimation of quenched steel rolls, Forgemasters Rolls, Sheffield.
  • March - April 1996 Development of a fuzzy logic tool for estimation of electric load on national grid. EA Technology, Chester.

Newest selected publications

Chen, J., Abbod, M. and Shieh, JS. (2019) 'Integrations between Autonomous System and Modern Computing Techniques: A Mini-review'. Sensors, 19 (18). pp. 1 - 18. ISSN: 1424-8220 Open Access Link

Journal article

Al-dmour, A. and Abbod, M. (2019) 'The Implementation of Sys Trust Principles and Criteria for Assuring Reliability of AIS: Empirical Study'. International Journal of Accounting and Information Management, 27 (3). pp. 461 - 491. ISSN: 1834-7649 Open Access Link

Journal article

Singh, RSS., Chiew, WY., Singh, BSS. and Abbod, M. (2019) 'A new fault detection system using wireless communication - assisted with analog relays for grid electrical lamp pole network'. Journal of Theoretical and Applied Information Technology, 97 (8). pp. 2359 - 2369. ISSN: 1992-8645 Open Access Link

Journal article

Liu, Q., Cai, J., Fan, S., Abbod, M., Shieh, J-S., Kung, Y. and et al. (2019) 'Spectrum analysis of EEG signals using CNN to model patient’s consciousness level based on anesthesiologists’ experience'. IEEE Access, 7. pp. 53731 - 53742. ISSN: 2169-3536 Open Access Link

Journal article

Liao, Y-H., Wang, Z-C., Zhang, F-G., Abbod, MF., Shih, C-H. and Shieh, J-S. (2019) 'Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit'. Sensors (Basel, Switzerland), 19 (8). pp. 1866 - 1866. ISSN: 1424-8220 Open Access Link

Journal article
More publications(301)