Liverpool Skyline

Department of Electronics and Electrical Engineering

Dr Ibrahim Idowu

Biography

Dr Ibrahim Idowu has a BSc degree in Information System at the Department of Computing and Communications in Lancaster University. He then joined the School of Computing at Liverpool John Moores University to complete his MSc in Computer Forensics. He pursued a PhD in Classification Techniques Using EHG Signals for Detecting Preterm Births at Liverpool John Moores University. He is a professional member of BCS The Chartered Institute for IT. His research activities are conducted as part of his engagement with the research institute of Faculty of Engineering and Technology at Liverpool John Moores University where he holds the position of Post Doctorate Research Fellow. His research work focuses on developing consultations for LCR Activate assisting SME and Academic collaborations on business and technology innovative partnerships. The broad area of his research activity includes data science, artificial intelligence, machine learning, and advanced algorithm development. His research has outputted novel publications at international conferences and journals.

Languages

English
Yoruba

Degrees

Liverpool John Moores University, United Kingdom, PhD in Computer Science
Liverpool John Moores University, United Kingdom, MSc in Computer Forensics
Lancaster University, United Kingdom, BSc in Information System

Academic appointments

Research Fellow, Faculty of Engineering and Technology, Liverpool John Moores University, 2017 - present

Publications

Conference publication

Khalaf M, Hussain AJ, Keight R, Al-Jumeily D, Keenan R, Chalmers C, Fergus P, Salih W, Abd DH, Idowu IO. 2017. Recurrent Neural Network Architectures for Analysing Biomedical Data sets Hamdan H, AlJumeily D, Hussain A, Tawfik H, Hind J. 2017 10TH INTERNATIONAL CONFERENCE ON DEVELOPMENTS IN ESYSTEMS ENGINEERING (DESE 2017), 10th International Conference on Developments in eSystems Engineering (DeSE) :232-237 >DOI >Link

Khalaf M, Hussain AJ, Al-Jumeily D, Keight R, Keenan R, Al Kafri AS, Chalmers C, Fergus P, Idowu IO. 2017. A performance evaluation of systematic analysis for combining multi-class models for sickle cell disorder data sets Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10362 LNCS :115-121 >DOI

khalaf M, Hussain A, Al-Jumeily D, Keight R, Keenan R, Fergus P, AlAskar H, Shaw A, Olatunji I. 2016. Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease Lecture Notes in Computer Science, 2016 International Conference on Intelligent Computation

khalaf M, Hussain A, Keight R, Al-Jumeily D, Keenan R, Fergus P, Idowu I. 2016. The Utilisiation of composite Machine Learning models for the Classification of Medical Datasets For Sickle Cell Disease Sixth International Conference on Digital Information Processing and Communications (ICDIPC)

Idowu I, Fergus P, Hussain A, Dobbins C, Khalaf M, Casana Eslava R, Keight R. 2016. Artificial Intelligence for Detecting Preterm Uterine Activity in Gynacology and Obstertric Care 15th IEEE International Conference on Computer and Information Technology (CIT’15) :215-220 >DOI

Khalaf M, Hussain AJ, Al-Jumeily D, Keenan R, Keight R, Fergus P, Idowu IO. 2015. Applied Difference Techniques of Machine learning Algorithm and Web-based Management System for Sickle Cell Disease Jumelly DA, Hussain A, Tawfik H, MacDermott A, Lempereur B. PROCEEDINGS 2015 INTERNATIONAL CONFERENCE ON DEVELOPMENTS IN ESYSTEMS ENGINEERING DESE 2015, International Conference on Developments in eSystems Engineering (DeSE) :231-235 >DOI >Link

Khalaf M, Hussain AJ, Al-Jumeily D, Keenan R, Fergus P, Idowu IO. 2015. Robust Approach for Medical Data Classification and Deploying Self-Care Management System for Sickle Cell Disease Wu YL, Min GY, Georgalas N, Hu J, Atzori L, Jin XL, Jarvis S, Liu L, Calvo RA. CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING, IEEE International Conference on Computer and Information :575-580 >DOI >Link

Hussain A. 2015. Advance flood detection and notification system based on sensor technology and machine learning algorithm 2015 International Conference on Systems, Signals and Image Processing (IWSSIP) >DOI

Idowu I, Fergus P, Hussain A, Dobbins C, Al-Askar H. 2014. Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems :95-100 >DOI

Journal article

Fergus P, Idowu IO, Hussain A, Dobbins C, Al-Askar H. 2014. Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction Techniques for Detecting Preterm Births Using EHG Records Huang D-S, Han K, Gromiha M. Intelligent Computing in Bioinformatics, 8590 :309-314 >DOI

Fergus P, Idowu I, Hussain A, Dobbins C. Advanced Artificial Neural Network Classification for Detecting Preterm Births Using EHG Records Heskes T. Neurocomputing,

Thesis/Dissertation

Idowu IO, Fergus , Hussain , Dobbins . Classification Techniques Using EHG Signals for Detecting Preterm Births Fergus PF, Hussain , Dobbins .