A Literature Survey on Applications of Neural Networks for Human-Computer Interaction

This article presents an overview of the neural networks approach to user modelling and intelligent interface. We analyse and discuss activities in user modelling and intelligent interface. Activities of various neural networks models are introduced to illustrate how user modelling problems can be solved by neural networks. The practical utility of neural networks in supporting user modelling and intelligent interface is demonstrated by reviewing a selection of neural networks developed in this area. Structured summaries are provided for comparative purpose.     

Learning classification rules from database in the context ofknowledge acquisition and representation

A method for learning knowledge from a database is used to address the bottleneck of manual knowledge acquisition. An attempt is made to improve representation with the assistance of experts and from computer resident knowledge. The knowledge representation is described in the framework of a conceptual schema consisting of a semantic model and an event model. A concept classifies a domain into different subdomains. As a method of knowledge acquisition, inductive learning techniques are used for rule generation. The theory of rough sets is used in designing the learning algorithm. Examples of certain concepts are used to induce general specifications of the concepts called classification rules. The basic approach is to partition the information into equivalence classes and to derive conclusions based on equivalence relations. In a sense, what is involved is a data-reduction process, where the goal is to reduce a large database of information to a small number of rules describing the domain. This completely integrated approach includes user interface, semantics, constraints, representations of temporal events, induction, etc     

A conceptual design aid environment for expert-database systems

With the actual penetration of expert systems into the business world, the question is, how the expert system idea can be used to enhance the existing information systems with more intelligence in usage and operation. This interest is not surprising due to the advancement of the fifth generation of computer technology, and avid interest in the field of Artificial Intelligence. Therefore design of an information system for an application becomes more complex, and the inability of the human designer to deal with it increases. For designing intelligent systems, we have to be able to forecast the behavior of the information system more precisely before implementing it, i.e. we'have to support the specification process.Clearly the technology, such as Data base systems, is leading on efficiency issues as those needed for the construction, retrieval and manipulation of large shared data base. On the other hand, the AI techniques have improved significantly with function such as deductive reasoning and natural language processing. It is important to find way to merge these technologies into one mainstream of computing. A meeting point for the two areas is the issue of conceptual knowledge modelling, so that models can be created that will define the role and the ways to use data in AI systems. In the framework of this study, one possible expert system design aid environment has been suggested to assist the designer in his work.In a conceptual modelling environment a model is given for analysing complex real world problems known as the Conceptual Knowledge Model (CKM), represented by a Graphical and a Formal Representation. The Graphical Representation consists of three graphs: Conceptual Requirement Graph, Conceptual Behavior Graph, and Conceptual Structure Graph. These graphs are developed by involving the expert during the design process. The graphs are then transformed into first-order predicate logic to represent the logical axioms of a theory, which constitutes the knowledge base of the Expert System. The model suggested here is a step towards closing the gap between the theory of the conventional data base theory and AI databases.     

Prediction of Road Traffic using a Neural Network Approach

A key component of the daily operation and planning activities of a traffic control centre is short-term forecasting, i.e. the prediction of daily to the next few days of traffic flow. Such forecasts have a significant impact on the optimal regulation of the road traffic on all kinds of freeways. They are increasingly important in an environment with increasing road traffic problems. The present paper aims at presenting the effectiveness of a neural network system for prediction based on time-series data. We only use one parameter, namely traffic volume for the forecasting. We employ artificial neural networks for traffic forecasting applied on a road section. Recurrent Jordan networks, popular in the modelling of time series, is examined in this study. Simulation results demonstrate that learning with this type of architecture has a good generalisation ability.