8,365 research outputs found
Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach
Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy
The Relationship between Fuzzy Reasoning and Its Temporal Characteristics for Knowledge Management
The knowledge management systems based on artificial reasoning (KMAR) tries to provide computers the capabilities of performing various intelligent tasks for which their human users resort to their knowledge and collective intelligence. There is a need for incorporating aspects of time and imprecision into knowledge management systems, considering appropriate semantic foundations. The aim of this paper is to present the FRTES, a real-time fuzzy expert system, embedded in a knowledge management system. Our expert system is a special possibilistic expert system, developed in order to focus on fuzzy knowledge.Knowledge Management, Artificial Reasoning, predictability
Library Cataloguing and Role and Reference Grammar for Natural Language processing Applications
Several potential application of natural language processing have proven to be intractable. In this paper, we provide and overview of methods from library cataloguing and linguistics that have not yet been adopted by the natural language processing community and which could be used to help solve some of these problems
Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
Decision support is a probabilistic and quantitative method designed for
modeling problems in situations with ambiguity. Computer technology can be
employed to provide clinical decision support and treatment recommendations.
The problem of natural language applications is that they lack formality and
the interpretation is not consistent. Conversely, ontologies can capture the
intended meaning and specify modeling primitives. Disease Ontology (DO) that
pertains to cancer's clinical stages and their corresponding information
components is utilized to improve the reasoning ability of a decision support
system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider
disease manifestations and provides physicians with treatment solutions from
similar previous cases for reference. The proposed DSS supports natural
language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease
classification with the help of the ontology
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
- …