Information for the user in design of intelligent systems

Recommendations are made for improving intelligent system reliability and usability based on the use of information requirements in system development. Information requirements define the task-relevant messages exchanged between the intelligent system and the user by means of the user interface medium. Thus, these requirements affect the design of both the intelligent system and its user interface. Many difficulties that users have in interacting with intelligent systems are caused by information problems. These information problems result from the following: (1) not providing the right information to support domain tasks; and (2) not recognizing that using an intelligent system introduces new user supervisory tasks that require new types of information. These problems are especially prevalent in intelligent systems used for real-time space operations, where data problems and unexpected situations are common. Information problems can be solved by deriving information requirements from a description of user tasks. Using information requirements embeds human-computer interaction design into intelligent system prototyping, resulting in intelligent systems that are more robust and easier to use

An Intelligent System For Arabic Text Categorization

Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. In this paper, an intelligent Arabic text categorization system is presented. Machine learning algorithms are used in this system. Many algorithms for stemming and feature selection are tried. Moreover, the document is represented using several term weighting schemes and finally the k-nearest neighbor and Rocchio classifiers are used for classification process. Experiments are performed over self collected data corpus and the results show that the suggested hybrid method of statistical and light stemmers is the most suitable stemming algorithm for Arabic language. The results also show that a hybrid approach of document frequency and information gain is the preferable feature selection criterion and normalized-tfidf is the best weighting scheme. Finally, Rocchio classifier has the advantage over k-nearest neighbor classifier in the classification process. The experimental results illustrate that the proposed model is an efficient method and gives generalization accuracy of about 98%

A Conexionist Intelligent System for Accounting

Neural networks are a computing paradigm developed from artificial intelligence and brain modelling’s fields, which lately has become very popular in business. Many researchers are seeing neural networks systems as solutions to business problems like modelling and forecasting, but accounting and audit were also touched by the new technology. The purpose of this paper is to present the ability of an artificial neural networks model to forecast and recognize patterns while analyzing company’s sales evolution. The monthly sales evolutions are considered a time-series and the target is to observe the ability of the investigated model to make predictions.accounting, neural networks, predictions, time-series, hybrid intelligent systems

A reconfigurable hybrid intelligent system for robot navigation

Soft computing has come of age to o er us a wide array of powerful and e cient algorithms that independently matured and in uenced our approach to solving problems in robotics, search and optimisation. The steady progress of technology, however, induced a ux of new real-world applications that demand for more robust and adaptive computational paradigms, tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms and neural networks. As noted in the literature, they are signi cantly more powerful than individual algorithms, and therefore have been the subject of research activities in the past decades. There are problems, however, that have not succumbed to traditional hybridisation approaches, pushing the limits of current intelligent systems design, questioning their solutions of a guarantee of optimality, real-time execution and self-calibration. This work presents an improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search algorithm and the Voronoi diagram generation algorithm

Development of an Intelligent System for IoT using Web Services and Cyber Physical Approaches

The Internet of Things (IoT) is changing the way we perceive information. It has inspired solutions for a variety of everyday problems. With the advent of IoT, the internet will house several ldquointelligent ldquoobjects capable of making their own decisions and communicate with each other in an efficient manner. Cyber-Physical Systems (CPSs) represent a new paradigm of future intelligent systems. They consist of loosely coupled subsystems which interact with mechanisms of Service oriented Architecture (SoA). One of the most important goals for many organizations is to satisfy their clientsrsquo service level agreements with respect to the response time and throughput. Web services are one of the popular technologies to achieve SOA solutions.Web service is a very important candidate technology to achieve SOA requirements that allows the service providers to publish their services to many service consumers. nbs

An intelligent System for Diagnosis Schizophrenia and Bipolar Diseases based on Support Vector Machine with Different Kernels

Bipolar disorder and schizophrenia overlap in symptoms and may share some underlying neural substrates. The discrimination between the two diseases is one of the problems that face psychiatric experts. This paper will propose some solutions to this problem based on the artificial methods. The support vector machine (SVM) is used for discrimination based on measuring of the patient EEG rhythms. The large set of features included in the EEG rhythms is reduced into smaller set of features after Fast Fourier Transform (FFT) segmentation. Different kernels are applied on the SVM which are linear, polynomial, quadratic and radial basis function. The application of SVM with different kernels for the EEG discrimination of the patients suffering from schizophrenia and bipolar diseases is the core of this work. Experimental results have shown that the proposed algorithms will solve the discrimination between the two diseases using EEG rhythms and the support vector machine with linear and quadratic kernels have achieved a high performance rate equal to 98 % and 97.667% respectively compared to the other kernels

Exploring Design Space For An Integrated Intelligent System

Understanding the trade-offs available in the design space of intelligent systems is a major unaddressed element in the study of Artificial Intelligence. In this paper we approach this problem in two ways. First, we discuss the development of our integrated robotic system in terms of its trajectory through design space. Second, we demonstrate the practical implications of architectural design decisions by using this system as an experimental platform for comparing behaviourally similar yet architecturally different systems. The results of this show that our system occupies a "sweet spot" in design space in terms of the cost of moving information between processing components