Personalisation of asthma management plans is important because asthma patients experience different triggers and symptoms as a result of the high heterogeneity level of the condition. Although this makes context-awareness suitable to support asthma management, existing context-aware solutions do not allow patients to personalise their management plans. This research proposes an approach to develop context-aware solutions allowing the personalisation of asthma management plans. It is derived on the basis of the literature review and a qualitative research that includes both asthma patients and carers. A prototype to illustrate the application of the approach is demonstrated.

In this work we will examine and develop a system that can assist people in Activities of Daily Life (ADL). This study focuses on resolving conflicts for the requests from different users' profiles, for instance - elderly, adult and young. The objective of the system is to present a dialogue manager which is able to detect multi-user semantic conflict and to resolve the conflict for improved dialogue informing about its decisions using a system interface avatar. The system is also able to prioritize requests that occurred among the services of multiple home appliances, as well as to deal with conflicting entities involving a single device. We investigated whether the multi-user context awareness by a Virtual Assistant adds value to the Smart Home concept in recognizing multi-user conflicts dynamically. This work has proposed a preference based method for resolving conflict and evaluated the developed system in a smart home environment.

In recent years, e-learning systems have played an increasingly important role in higher education and, in particular, in enhancing learning experiences for individuals who have learning difficulties. However, it appears that many of the people involved in the development and implementation of e-learning tools overlook the needs of dyslexic students. As such, these students lack access to the resources on offer to other students and this leads to a disability divide. In order for educational initiatives to be effective, learning experiences need to be tailored to the individual needs of each student and the materials and tools employed should be aligned with the student's needs, capabilities and learning objectives. The objective of this research was to propose an ontology that will facilitate the development of learning methods and technologies that are aligned with dyslexia types and symptoms. The paper commences with a discussion of domain ontology and examines how learning objectives that take into consideration a student's capabilities and needs can be matched with appropriate assistive technology in order to deliver effective e-learning experiences and educational resources that can be consistently employed. The ontology employed within this study was developed using Ontology Web Language (OWL), an information processing system that allows applications to handle both the content and the presentation of the information available on the web. Two characteristics were employed within this research to describe each resource: dyslexia type and the features of assistive technologies that were deemed to be most appropriate for educational experiences targeted at each dyslexia type.

As a result of the widespread, and accelerating, dissemination of digital technologies, there has been a profound effect on how people learn. These effects have been greatly debated over the years. While many educational developers have agree on the effects technologies have brought on educational performance, little attention has been made to the relationship between such technologies effects and individual differences such as individuals who suffer dyslexia. There are readily available Assistive Technologies that offer potential to help students with learning difficulties such as dyslexia. This paper will attempt to tease out these technologies and dyslexia types, hence attempt at providing a solution. The focus should not be on correlating one technology to one disability. Instead of identifying different pedagogical needs such as help with reading and writing. Then, trying to meet those needs by looking at the whole range of ways that available.

This paper aims to address the problems low literacy (LL) users face when searching for information online. The first part of this paper summarizes the problems that LL user's face, and establishes a set of design principles for interfaces suitable for LL users. This is followed by a description of how these design principles are mapped to a novel interface for interactive data retrieval. The interface was realized into a working system and evaluated against a traditional web interface for both high literacy (HL) and LL users. The suitability of the designs was analyzed using performance data, subjective feedback and an observational analysis. The findings from the study suggest that LL users perform better and prefer the proposed designs over a traditional web interface.

This paper presents an extensible schema for capturing laboratory gene variance data with its meta-data properties in a semi-structured environment. This paper also focuses on the issues of creating a local and task specific component database which is a subset of global data resources. An XML based genetic disorder component database schema is developed with adequate flexibilities to facilitate searching of gene mutation data. A web based search engine is developed that allows researchers to query a set of gene parameters obtained from local XML schema and subsequently allow them to automatically establish a link with the public domain gene databases. The application applies AJAX (asynchronous Javascript and XML), a cutting-edge web technology, to carry out the gene data searching function.

In this study, a neural network based approach is used to predict the presence of Maturity Onset Diabetes type 3, referred as MODY3 type II diabetes mellitus. The study has used backpercolation neural network algorithm to predict the specific genetic mutation that causes the MODY3 type II diabetes mellitus. A set of coded numeric values are assigned for numeric representation of genetic data that are available in public domain repositories. A point mutation is introduced in a portion of the nucleotide for the mutation prediction to train the data set. The study has demonstrated that backpercolation neural network algorithm is useful to train and to predict gene point mutation that leads to MODY3 type II diabetes.

This paper proposes a framework based on the context of the Web data for interactions with different biological data resources so that a consolidated view of data can be achieved. A formal description of context of resources and their relationships have been described here using context graph. The context increases the interoperability by providing the description of the resources and the navigation plan for accessing the Web based databases. A higher level construct is developed to implement the context in RDF for Web interactions. The interaction among the resources is achieved by using a context based integration domain. The integration domain allows to navigate and execute the query plan within the resource databases.

Many algorithms are available for quantitative and qualitative analysis of protein spot in gel electrophoresis images and majority of the these algorithms use geometric and image processing techniques to match protein spots. These algorithms do not take into consideration the electrophoretic mobility of the proteins and they only match similar protein spots rather than matching similar proteins. The approach presented in this paper uses a novel technique based on electrophoretic mobility to match protein spots between source and target images. The algorithm identifies the protein spot in the target image which lies on the same line of path as it is in the source image. A shape matching algorithm using Generalized Hough Transform and Canny Edge Detection method is used to determine the shape variance. The method describes here gives an accuracy of 90% or more to identify the same or similar proteins from the target image. Finally, a dedicated target based database has been created to store a set of finite values of an element spot for correlating the 3D protein structure.

This paper presents a novel approach to integrate biological data from multiple resources into a single page using image object keying. In this interactive approach, a gel electrophoresis protein spot is selected by the user which initiates the retrieval of corresponding 3D structure of the protein. It provides a set of operators to access and to collect elements content of the biological data resources for integration. The approach utilises a number of tools, namely, meta data extractor, mapping linker, dispatcher and result integrator for unifying the data collected from multiple data sources.

This paper reviews 'database interoperability' problems and suggests a framework for integrating molecular biology databases. It suggests a component database for gene mutation data which is subject-oriented and dedicated to a particular research laboratory, and outlines how to develop a cooperative environment for molecular biology databases to share information based on a common view. The paper also establishes the concept of image object keying for multi-database querying and it proposes a matching algorithm using gel electrophoresis images. This image object keying method could replace the conventional SQL scripts for multi-database querying.