Projects

Digitizing Architectural Restoration Education through Virtual Reality

 

Digitizing Architectural Restoration Education through Virtual Reality

Project Team:
Yasin Ortakci, Karabuk University (Karabuk, Turkey)
Lucio Tommaso De Paolis, Universita del Salento (Lecce, Italy)
Huseyin Seker, Birmingham City University (Birmingham, UK)

Project Aim:
The aim of the project is to develop an artificial intelligence and data analytics-driven virtual reality platform to digitize architectural restoration education. The project has been funded by EU Erasmus+ as a part of Erasmus+ funding scheme “KA2 - Cooperation for Innovation and the Exchange of Good Practices” (“KA226 - Partnerships for Digital Education Readiness” programme). This 24-month project will be carried out in partnership with Karabuk University (Turkey) (Project Lead) and UNIVERSITA DEL SALENTO (Italy; Project Partner).





Staffordshire Connected & Intelligent Mobility Innovation Accelerator (SCIMIA)

 

Staffordshire Connected & Intelligent Mobility Innovation Accelerator (SCIMIA)

Project Team:
Professor Huseyin Seker (Academic Lead for SCIMIA)

Project Aim:
SCIMIA is a dedicated project led by Staffordshire University driving research and innovation through collaborative knowledge exchange between the university and Staffordshire LEP SMEs to develop innovative solutions for the intelligent mobility market. Each SME partnership will receive up to 12 months of support from the Staffordshire University to develop new to company and/or new to market product or service. The project has secured ERDF funding of over £1M from the European Regional Development Fund (ERDF) to deliver the project in November 2020-June 2023. During this period, we will be working with 45 SMEs to develop new to company and/or new to market product or service within the context of intelligent mobility. For further information, please see here.





Bioinformatics for Metabolomics

 

The project is in partnership with Staffordshire University (UK) and Tokyo Medical University (Japan)

Project Team:
Professor Masahiro Sugimoto
Professor Huseyin Seker

Project Aim:
The main of the project is to develop novel, robust and cost-effective bioinformatics methods that deal with extremely high-dimensional and imbalanced data sets for the analysis of metabolomics data with a particular application in cancer.





ToSmartEADs:

Towards a smart, explainable and accurate knowledge extraction for complex data science problems

The project has been funded by The Spanish Ministry of Science, Innovation & Universities and will be carried out in partnership with Andalusian Research Institute in Data Science and Computational Intelligence of the University of Jaen (Spain).

Project Team:
Prof María José Del Jesús Díaz (The University of Jaen, Jaen, Spain)
Prof Pedro González (The University of Jaen, Jaen, Spain)
Assoc Prof Cristóbal J. Carmona (The University of Jaen, Jaen, Spain)
Prof Huseyin Seker (Staffordshire University, UK)s

Project Aim:
The main aim of the project is to develop more advanced data analytics and machine learning methods for the real time analysis of data stream and extremely high dimensional data with applications in several disciplines, with a particular emphasis on enterprise-related problems.







BHGE PII Ltd

Industrial data analytics project funded by InnovateUK-KTP (with BHGE PII Ltd)

Project Team:
Dr Huseyin Seker (University of Northumbria )
Steven Farnie (BHGE PII Ltd)
John Elliott (BHGE PII Ltd)
Dr Amine Aitsiali (University of Northumbria & BHGE PII Ltd)

Project Aim:
Development of an Artificial Intelligence-based data analytics tool, integrated with PII Ltd’s in-house processes, to automate the analysis of oil and gas pipeline inspection

Lucion Environmental Ltd

Industrial data analytics project funded by InnovateUK-KTP (with Lucion Environmental Ltd)

Project Team:
Prof Steve Lockley (University of Northumbria )
Dr Huseyin Seker (University of Northumbria )
Charles Pickles (Lucion Environmental Ltd)
Paul Hayball (Lucion Environmental Ltd)
Alexei Holgate (Lucion Environmental Ltd)

Project Aim:
Development of next generation of mobile applications for the 3D building modelling of buildings and management of hazardous materials

Data Stream Analysis

Data Stream Analysis using sub-group discovery (funded by Spanish Government)

Project Team:
Prof María José Del Jesús Díaz
Dr Cristóbal J. Carmona
Mr Angel Miguel Garcia Vico (Jaen University, Spain)
Dr Huseyin Seker (University of Northumbria )

Project Aim:
Development of sub-group discovery-based method for the efficient analysis of data stream




Institute of Coding

Project Team:
IoC Consortium Members

Project Aim:
The Institute of Coding is a partnership for digital transformation and funded by The Office for Students

The institute brings industry, government, higher education and outreach partners together to create new courses, develop existing skills and provide support that attracts fresh talent into digital careers. We empower organisations to bridge the digital skills gap. Working with our partners, we are determined to:
-Break down the barriers that discourage people from tech education and careers
-Provide different ways to access digital education
-Design events, courses and training that suit the lifestyles of learners.

Creative Fuse North East

Project Team:
CFNE Consortium Members

Project Aim:
Creative Fuse North East project has been funded by EU European Regional Development Fund and Arts & Humanities Research Council

Creative Fuse North East is delivered through a truly unique partnership between the North East’s five universities − Newcastle, Durham, Northumbria, Sunderland and Teesside. Academics will work alongside industry, cultural organisations, charities and the public sector, to explore how creative, digital and IT firms can have a sustainable future in the region adding value to the region’s broader employment base.

Next-Generation Genome Sequencing

Analysis of next-generation genome sequencing (very high-dimensional) data: Prediction of age and BMI as a case study

(funded by Turkish Ministry of Education and Suleyman Demirel University)

Project Team:
Dr Ferdi Sarac (University of Northumbria and Suleyman Demirel University)
Dr Huseyin Seker (University of Northumbria)

Project Aim:

There has been extensive number of studies in the development of feature selection methods for classification problems. However, the literature suggests that there is a significant gap and algorithmic challenges in regression domain, in particular for very high dimensional data. One of such problems exists in next-generation genome sequencing data. In this study, deep-learning-based support vector regression model will be developed to predict age and BMI from CpD islands in DNA that has been obtained from next generation genome sequencing data. This will also include the application of multi-output regression model.

Prediction of peptide binding affinity

Prediction of peptide binding affinity using amino acid scales and machine learning methods

(funded by Turkish Ministry of Education, De Montfort University and Suleyman Demirel University)

Project Team:
Dr Ferdi Sarac (University of Northumbria and Suleyman Demirel University)
Dr Huseyin Seker (University of Northumbria)

Project Aim:

Given the number of peptides that exits on earth and complexity in biological manual experiment for measuring peptide’s binding affinity, there is a need for the development of bioinformatics method. Therefore, the project aims at developing a predictive model for the prediction of peptide affinity using peptide sequence information and amino acid scales.

Protein’s Thermal Characteristics

Modelling protein’s thermal characteristics using sequence information and amino acid scales

Project Team:
Dr Hana Hussain (The Public Authority of Applied Education and Training, Kuwait)
Dr Asmaa Al Naqi (The Public Authority of Applied Education and Training, Kuwait)
Dr Haidar Almohri (Siemens Kuwait)
Dr Huseyin Seker (University of Northumbria)

Project Aim:

Given the number of proteins that exit on earth and complexity in biological manual experiment for measuring their thermal characteristics (e.g., melting temperature), there is a need for the development of bioinformatics method. Therefore, the project aims at developing a predictive model for the prediction of proteins’ thermal characteristics using their sequence information and amino acid scales as well as other biological information. This project is further expected to lead a mathematical model to synthetically design novel proteins that may be needed in pharmaceutical industry.



Data-Driven Palliative Care

Data-driven resource management in palliative care using hybrid artificial intelligence approach

Project Team:
Mr Fernando Antonio Torres (St. Cuthbert's Hospice, Newcastle upon Tyne)
Dr Huseyin Seker (University of Northumbria)

Project Aim:

Health and social care has become a data-driven. One of the challenging areas in this domain is resource management. Therefore, this study aims at developing a data-driven model using a hybrid artificial Intelligence technique to accurately predict resource requirement within certain time-frame with a case study in palliative care.

Molecular Characterization of Infectious Viruses

Molecular characterization of infectious viruses using their sequence information and advanced bioinformatics methods

Project Team:
Manal Ahmad Alshehri (KACST, Saudi Arabia)
Dr Manee Manee (KACST, Saudi Arabia)
Dr Badr Alshomrani (KACST, Saudi Arabia)
Dr Huseyin Seker (University of Northumbria)

Project Aim:

The main aim of this project is to analyse DNA and protein sequences of viruses that cause infectious diseases, namely Influenza, Zika, HIV and Ebola viruses. The analysis will be carried out by considering their timeframes and geographical locations, which is expected to help understand how these viruses have evolved over time and what has changed according to their geographical locations. This is further expected to help reveal similarities and differences between all these viruses.