Neil Ireson

Organisation, Information and Knowledge Group, Department of Computer Science, University of Sheffield

Neil Ireson

Me in Pakistan

Interests

My current research interests can broadly be defined as an exploration of Collective Intelligence, and specifically the extraction of information from social media streams. My initial research focused on the use of Machine Learning for Knowledge Acquisition, more recently I have developed Text Classification and Information Extraction techniques.

This work is carried out within the Organisation, Information and Knowledge Group at Sheffield University, under the direction of Professor Fabio Ciravenga.

Workshop/Conference Deadlines

Projects

Current

FootballWhispers

Football Whispers is a new global football digital platform has launched as the world’s first transfer predictor, giving football fans around the world the inside track on player movements. The first of its kind, Football Whispers uses a complex algorithm that can predict the likelihood of multi-million-pound football transfers taking place. Rumoured football transfers are given a Football Whispers ‘Unique Index Score’ on a scale of 1 to 5 as an indication of how likely a transfer is to happen - the higher the number calculated the more likely the player is to move clubs. Using multiple data points, the algorithm looks at the volume of conversation (chatter), the authority of the sources and the recency of the story, as well as a host of other factors. The technology behind the platform is been developed in collaboration with the Department of Computer Science's Organisations, Information and Knowledge (OAK) research group and Klood Digital.

SETA

SETA is set to create a technology and methodology that will address the challenges above and change the way mobility is organised, monitored and planned in large metropolitan areas. The solution will be based on the management of high-volume, high-velocity, multi-dimensional, heterogeneous, cross-media, cross-sectoral data and information which is sensed, crowdsourced, acquired, linked, fused, and used to model mobility with a precision, granularity and dynamicity that is impossible with today’s technologies. Such models will be used to provide always-on, pervasive services to citizens and business, as well as decision makers to support safe, sustainable, effective, efficient and resilient mobility. Differently from other initiatives that tend to focus only on transport optimisation in the inner city, we will focus on intelligent and sustainable mobility in entire metropolitan areas. A metropolitan area is a geographical and socio-economic region consisting of a densely populated urban core (the city) and its surrounding region, i.e. the counties that refer to the city as place of work or entertainment - an area that can be a hundred times bigger than the inner city. Intelligent and sustainable mobility encompasses the smarter, greener and more efficient movement of people and goods; it provides a radical change from transport as a series of separate modal journeys to an integrated, reactive, intelligent, mobility system.

Previous

WeSenseIt

In order to harness environmental data and knowledge to effectively and efficiently manage water resources, WeSenseIt, will propose to develop ;a citizen observatory of water,which will allow citizens and communities to take on a new role in the information chain: a shift from the traditional one-way communication paradigm towards a two-way communication model in which citizens become active stakeholders in information capturing, evaluation and communication.

ReDites

Real Time, Detection, Tracking, Monitoring and Interpretation of Events in Social Media

RAnDMS

Realtime Analysis of Digital Media Streams

TRIDS

Tracking Realtime Intelligence in Data Streams

EIDC

Energy Initiatives in Deprived Communities

WeKnowIt

The main objective of WeKnowIt is to develop novel techniques for exploiting multiple layers of intelligence from user-contributed content, which together constitute Collective Intelligence, a form of intelligence that emerges from the collaboration and competition among many individuals, and that seem ingly has a mind of its own.

MultiMatch

On the web, cultural heritage content is everywhere, in traditional environments such as libraries, museums, galleries and audiovisual archives, but also in popular magazines and newspapers, in multiple languages and multiple media. The aim of the MultiMATCH project is to enable users to explore and interact with online accessible cultural heritage content, across media types and languages boundaries.

Environment Agency

Examining the use of NLP techniques to facilitate the Environment Agency's Horizon Scanning Process.

AKT Project

In accordance with the need to understand the process of knowledge use, the programme of AKT is based around six challenges to ease fundamental bottlenecks in the engineering and management of knowledge. Each of these bottlenecks occurs at a vital stage in the evolution of knowledge; Acquisition, Modelling, Reuse, Retrieval, Publishing and Maintenance. My work focused on the use of Machine Learning in Information Extraction for Knowledge Acquisition.

POESIA project

POESIA seeks to develop, test, evaluate and promote a fully open-source, and extensible, state of the art, filtering and catching software solution. POESIA will filter harmful content in several channels (Web, Email, News) combining innovative technologies to achieve more effective filtering than existing products. Filtering will cover a range of modes, including image filtering, natural language text filtering, URL, PICs and JavaScript filtering. The filter will initially be deployed in English, Italian and Spanish. To cover other European languages additional work is required and should be effectively possible because of the open-source model and the portability of the technical solutions.

Publications

2016

  • Citizens Observatories for Effective Earth Observations: the WeSenseIt Approach. Suvodeep Mazumdar, Vita Lanfranchi, Neil Ireson, Stuart Wrigley, Clara Bagnasco, Uta Wehn, Rosalind McDonagh, Michele Ferri, Simon McCarthy, Hendrik Huwald, Fabio Ciravegna. Environmental SCIENTIST Journal. 2016
  • Seeing through the Eyes of the Citizens during Emergencies. F. Ciravegna, S. Mazumdar, S.N. Wrigley, N. Ireson and P. Cudd. 12th International Conference on Information Systems for Crisis Response and Management (ISCRAM), May, Rio, Brazil.
  • Decision Graphs: Managing Decisions for Emergencies. S. Mazumdar, N. Ireson, F. Ciravegna. Poster. 12th International Conference on Information Systems for Crisis Response and Management (ISCRAM), May, Rio, Brazil
  • Understanding the barriers between citizens and authorities in environmental monitoring. F. Ciravegna, S. Mazumdar, N. Ireson, P. Cudd. 1st European Citizen Science Conference (ECSA), May, Berlin, Germany
  • Citizens Observatories for Effective Earth Observations: the WeSenseIt approach. S. Mazumdar, N. Ireson, F. Ciravegna. 1st European Citizen Science Conference (ECSA), May, Berlin, Germany
  • Geofence-driven Crowdsourcing and Citizen Science. S. Mazumdar, N. Ireson, F. Ciravegna. 1st European Citizen Science Conference (ECSA), May, Berlin, Germany
  • Seeing through the eyes of citizens: an application for Occupational Therapists. F. Ciravegna, S.Mazumdar, N. Ireson. Insigneo Showcase Event 2016, Sheffield, UK
  • Engaging Citizens and Communities for Emergencies. S. Mazumdar, S. Wrigley, N. Ireson, F. Ciravegna. International Conference on Citizen Observatories for Water Management (COWM), Venice, 7-9 June 2016
  • Citizens Observatories for Earth Observations: the WeSenseIt approach. S. Mazumdar, N. Ireson, F. Ciravegna, 10th Geo European Projects Workshop – Citizens’ Observatories for environmental policy monitoring and development, Berlin, June 2016

2015

  • Ireson Neil; Lanfranchi, Vitaveska; Mazumdar Suvodeep; Ciravegna Fabio TRIDS: Real-time Incident Monitoring with Social Media
    Proceedings of Semantics and Analytics for Emergency Response (SAFE) workshop at ISCRAM, 2015
  • Suvodeep Mazumdar, Stuart N. Wrigley, Neil Ireson, Fabio Ciravegna Geo-fence driven crowd-sourcing for Emergencies
    Proceedings of the 12th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2015): 24-27 May, Kristiansand, Norway. 2015.
  • Supplementing remote sensing using geofenced crowdsourcing for improved situation awareness. S.N. Wrigley, S. Mazumdar, N. Ireson and F. Ciravegna. Remote Sensing and Photogrammetry Society (RSPSoc), National Centre for Earth Observation (NCEO), Centre for Earth Observation Instrumentation and Space Technology (CEOI-ST) Joint Annual Conference 2015, 8-11 September, Southampton, UK.

2014

2012

2011

2010

2009

2008

2007

2006

2005

2004

2001

  • Cao YJ, Ireson N, Bull L & Miles R: An Evolutionary Intelligent Agents Approach to Traffic Signal Control. International Journal of Knowledge-based Intelligent Engineering Systems 5(4):279-289, 2001
    bibtex

2000

  • Ireson N, Cao YJ, Bull L & Miles R: A Communication Architecture for Multi-Agent Learning Systems. In S Cagnoni, et al. Real-World Applications of Evolutionary Computing: Proceedings of the EvoNet Workshops, 2000.
    bibtex
  • Cao YJ, Ireson N, Bull L & Miles R: Distributed Learning Control of Traffic Signals. In S Cagnoni, et al. Real-World Applications of Evolutionary Computing: Proceedings of the EvoNet Workshops, 2000.
    bibtex

pre 2000

  • Cao YJ, Ireson N, Bull L & Miles R: Design of a Traffic Junction Controller using Classifier System and Fuzzy Logic. In Proceedings of the Sixth International Conference on Computational Intelligence Theory and Applications, 1999.
    bibtex
  • Ireson N & Fogarty TC: Evolving decision support models for credit control. In Biethahn, J and Nissen V, Evolutionary Algorithms in Management Applications: 264-276, 1995
  • Fogarty TC, Ireson N & Bull L: Genetics based machine learning - applications in industry and commerce. In Rayward-Smith VJ, Applications of Modern Heuristic Techniques: 91-110, 1994
  • Fogarty TC & Ireson N: Evolving Bayesian classifiers for credit control; a comparison with other machine learning methods. IMA Journal of Mathematics Applied in Business and Industry 5:63-75, 1994
  • Fogarty TC, Ireson N & Battle S: Developing rule-based systems for credit card applications from data with the genetic algorithm. IMA Journal of Mathematics Applied in Business and Industry 4(1):53-59, 1992
  • Gammack JG, Fogarty TC, Battle S, Ireson N & Cui J: Human-centred decision support: the IDIOMS system, AI & Society 6:345-366, 1992
  • Fogarty TC, Ireson N & Battle S: Combining attributes and categorising attribute values for a Bayesian classifier for credit control. IMA International Conference on Control: Modelling, Computation, Information, 1992

Contact

Neil Ireson
Department of Computer Science
University of Sheffield
Regent Court
211 Portobello
Sheffield S1 4DP
UK

Phone: +44 (0)114 222 1876
Fax: +44 (0)114 278 1810