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Ulster University, United Kingdom

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7 Papers

Semantic Approach for Prospectivity Analysis of Mineral Deposits

Topics: Decision Support Systems; Geographic Information Retrieval; Intelligent Data Fusion; Spatial Data Quality and Uncertainty

Authors: Sławomir Wójcik ¹ , Taha Osman ² and Peter Zawada ¹

Affiliations: ¹ International Geoscience Services (IGS) Ltd., United Kingdom , ² Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005871001800189

Proceedings of GISTAM 2016, ISBN: 978-989-758-188-5

Quick Abstract

Early mineral exploration activities motivates innovative research into cost-effective methods for automating the process of mineral deposits’ prospectivity analysis. At the heart that process is the development of a knowledge base that is not only capable of consuming geodata originating from multiple sources with different representation format and data veracity, but also provides for the reasoning capabilities required by the prospectivity analysis. In this paper, we present an integrative semantic-driven approach that reconciles the representation format of sourced geodata using a unifying metadata model, and encodes the prospectivity analysis of geological knowledge both at the schemata modelling level and through more explicit reasoning rules operating on the semantically tagged geodata. The paper provides valuable insights into the challenges of representation, inference, and query of geospatially-tagged geological data and analyses our initial results into the prospectivity analysis of mineral deposits.

Related Ontology Subjects/Areas/Topics (10)

Artificial Intelligence	Data Engineering	Health Engineering and Technology Applications
Business Analytics	Decision Support Systems	Knowledge-Based Systems
Cardiovascular Technologies	Decision Support Systems, Remote Data Analysis	Symbolic Systems
Computing and Telecommunications in Cardiology

Domain-Specific Relation Extraction - Using Distant Supervision Machine Learning

Topics: Machine Learning; Information Extraction

Authors: Abduladem Aljamel , Taha Osman and Giovanni Acampora

Affiliation: Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005615100920103

Proceedings of KDIR 2015/IC3K, ISBN: 978-989-758-158-8

Quick Abstract

The increasing accessibility and availability of online data provides a valuable knowledge source for information analysis and decision-making processes. In this paper we argue that extracting information from this data is better guided by domain knowledge of the targeted use-case and investigate the integration of a knowledge-driven approach with Machine Learning techniques in order to improve the quality of the Relation Extraction process. Targeting the financial domain, we use Semantic Web Technologies to build the domain Knowledgebase, which is in turn exploited to collect distant supervision training data from semantic linked datasets such as DBPedia and Freebase. We conducted a serious of experiments that utilise the number of Machine Learning algorithms to report on the favourable implementations/configuration for successful Information Extraction for our targeted domain.

Related Ontology Subjects/Areas/Topics (9)

Artificial Intelligence	Information Extraction	Machine Learning
Computational Intelligence	Knowledge Discovery and Information Retrieval	Soft Computing
Evolutionary Computing	Knowledge-Based Systems	Symbolic Systems

Si elegans: A Computational Model of C. elegans Muscle Response to Light

Authors: Alicia Costalago Meruelo , Pedro Machado , Kofi Appiah and T. Martin McGinnity

Affiliation: Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005712201210126

Proceedings of NeBICA 2015/NEUROTECHNIX, ISBN: 978-989-758-161-8

Quick Abstract

It has long been the goal of computational neuroscientists to understand animal nervous systems, but their vast complexity has made it very difficult to fully understand even basic functions such as movement. The C. elegans nematode offers the opportunity to study a fully described connectome and link neural network to behaviour. In this paper a model of the responses of the body wall muscle in C. elegans to a random light stimulus is presented. An algorithm has been developed that tracks synapses in the nematode nervous system from the stimulus in the phototaxis sensory neurons to the muscles cells. A linear second order model was used to calculate the isometric force in each of the C. elegans body wall muscle cells. The isometric force calculated resembles that of previous investigations in muscle modelling.

Bloodstain Pattern Analysis - A New Challenge for Computational Intelligence Community

Topics: Pattern Recognition: Fuzzy Clustering and Classifiers; Industrial, Financial and Medical Applications

Authors: Giovanni Acampora ¹ , Autilia Vitiello ² , Ciro Di Nunzio ³ , Maurizio Saliva ⁴ and Luciano Garofano ⁵

Affiliations: ¹ Nottingham Trent University, United Kingdom , ² University of Salerno, Italy , ³ University ”Magna Graecia” of Catanzaro, Italy , ⁴ Azienda Sanitaria Locale ASL Napoli 3 Sud, Italy , ⁵ Arma dei Carabinieri and Italian Academy of Forensic Sciences, Italy

Digital Object Identifier: 10.5220/0005155602110216

Proceedings of FCTA 2014/IJCCI, ISBN: 978-989-758-053-6

Quick Abstract

Bloodstain pattern analysis (BPA) is a forensic discipline that plays a key role in tracing events which caused a bloodshed at a crime scene. Indeed, BPA supports worldwide investigation agencies (US FBI, Italian Carabinieri and so on) in interpreting the morphology and distribution of bloodspots at a crime scene in order to enable a potentially complete reconstruction of the dynamics of the act of violence with a consequent identification of potential suspects for that crime. However, in spite of its importance, this forensic discipline is still based on completely manual approaches, making the analysis of a crime scene long, tedious and potentially imperfect. This position paper is aimed at proving that computational intelligence methodologies can be efficiently integrated with image processing techniques to support forensic investigators in increasing their performance in examining bloodstains, both in terms of time and accuracy of analysis. A preliminary study involving the application of fuzzy clustering has been carried out in order to validate our opinion and stimulate computational intelligence community to face this new challenge towards a formal definition of Forensic Intelligence.

Related Ontology Subjects/Areas/Topics (21)

Artificial Intelligence	Health Engineering and Technology Applications	Pattern Recognition
Biomedical Engineering	Human-Computer Interaction	Pattern Recognition: Fuzzy Clustering and Classifiers
Biomedical Signal Processing	Industrial, Financial and Medical Applications	Physiological Computing Systems
Computational Intelligence	Methodologies and Methods	Sensor Networks
Computer-Supported Education	Neural Networks	Signal Processing
Domain Applications and Case Studies	Neurocomputing	Soft Computing
Fuzzy Systems	Neurotechnology, Electronics and Informatics	Theory and Methods

Modelling and Analysis of Retinal Ganglion Cells Through System Identification

Topics: Computational Neuroscience; Neuroinformatics and Bioinformatics; Bio-Inspired and Humanoid Robotics; Image Processing and Artificial Vision Applications; Intelligent Artificial Perception and Neural Sensors

Authors: Dermot Kerr ¹ , Martin McGinnity ² and Sonya Coleman ¹

Affiliations: ¹ University of Ulster, United Kingdom , ² Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005069701580164

Proceedings of NCTA 2014/IJCCI, ISBN: 978-989-758-054-3

Quick Abstract

Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and response automatically.

Related Ontology Subjects/Areas/Topics (21)

Artificial Intelligence	Human-Computer Interaction	Neurotechnology, Electronics and Informatics
Bio-Inspired and Humanoid Robotics	Image Processing and Artificial Vision Applications	Pattern Recognition
Biomedical Engineering	Intelligent Artificial Perception and Neural Sensors	Physiological Computing Systems
Biomedical Signal Processing	Methodologies and Methods	Sensor Networks
Computational Intelligence	Neural Networks	Signal Processing
Computational Neuroscience	Neurocomputing	Soft Computing
Health Engineering and Technology Applications	Neuroinformatics and Bioinformatics	Theory and Methods

Si elegans - Computational Modelling of C. elegans Nematode Nervous System using FPGAs

Authors: Pedro Machado ¹ , John Wade ¹ and T. M. Mcginnity ²

Affiliations: ¹ University of Ulster, Ireland , ² University of Ulster and Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0005169301690176

Proceedings of NeBICA 2014/NEUROTECHNIX, ISBN: 978-989-758-056-7

Quick Abstract

It has long been the goal of computational neuroscientists to understand and harness the parallel computational power of the mammalian nervous system. However, the vast complexity of such a nervous system has made it very difficult to fully understand even the most basic of functions such as movement and learning and accordingly there has been increasing attention paid to the development of emulations of simpler systems. One such system is the C. elegans nematode, which has been widely studied in recent years and there now exists a vast wealth of biological knowledge about its nervous structure, function and connectivity. The Si elegans EU FP7 project aims to develop a Hardware Neural Network (HNN) to accurately replicate the C. elegans nervous system behaviour to enable neuroscientists to better understand these basic functions. To fully replicate the C. elegans biological system requires powerful computing technologies, based on parallel processing, for real-time computation and therefore will use Field Programmable Gate Arrays (FPGAs) to achieve this. In this paper an overview of the complete hardware system required to fully realise Si elegans is presented along with an early small scale implementation of the hardware system.

INVESTIGATION OF OPERATING PARAMETERS FOR A SEMEN QUALITY ANALYSIS SYSTEM

Topics: Biomedical Instrumentation; Biomedical Equipment; Biomedical Sensors

Authors: S. Atherton , C. R. Evans , P. Roach , D. C. Hughes , G. McHale and M. I. Newton

Affiliation: Nottingham Trent University, United Kingdom

Digital Object Identifier: 10.5220/0001429600130016

Proceedings of BIODEVICES 2009/BIOSTEC, ISBN: 978-989-8111- 64-7

Quick Abstract

To increase the success rate of Artificial Insemination (AI) in animals, it is important that the semen sample is of a high quality. The quality is related to both the number and motility of sperm present. Numerous methods of analysing semen samples exist, but these are generally expensive and/or laboratory based. A useful alternative would be an inexpensive simple system that could be used in the field immediately prior to insemination. We present a time of flight (ToF) technique using a quartz crystal microbalance (QCM). In this system the sperm are introduced at one end of a liquid filled swim channel and self propel to a QCM sensor at the other end. A chemical coating is applied to the QCM to bind the sperm and from the frequency change the number of attached sperm and their ToF can be measured. We report the effect of temperature and the introduction of small quantities of progesterone into the swim channel on the sperm ToF. Results show the QCM can be used to detect the arrival of the sperm and that increasing temperature and the presence of progesterone are both shown to decrease the ToF.

Related Ontology Subjects/Areas/Topics (5)