Intelligent Systems

Top PDF Intelligent Systems:

INTELLIGENT SYSTEMS TECHNOLOGIES: INTELLIGENT SYSTEMS TECHNOLOGIES THE CORE FOR INFORMATION SOCIETY

INTELLIGENT SYSTEMS TECHNOLOGIES: INTELLIGENT SYSTEMS TECHNOLOGIES THE CORE FOR INFORMATION SOCIETY

Information society envelopes; script to print, mass media and new technology, information market-place, access to information, political dimension, information rich and information poor, freedom of information, protection of intellectual property, data protection and personal privacy, censorship, information professional, work of the information professional, archetypes in transitional, from archivist to records manager, information managers and managing knowledge (Feather, 2013). All society activities being transferred into information and converted into digital work, this means driven by intelligent systems technologies. New society which intelligent system is helping to create, the revolution which it has both inspired and driven. Information dependent society at is emerging from our revolution the post- industrial revolution combine both profound change and fundamental continuity tools of communication are the building blocks of information society. The convergence of technologies allows us to combine computing with telecommunications and digitization of text and image to permit almost instantaneous worldwide (and indeed extra-terrestrial) transmission of data (Feather, 2013). Now this has been speeded up by intelligent systems technologies in an advanced way. Europe, china, Singapore, and USA, Netherlands, Taiwan, Japan, Canada, Sri Lanka, South Korea are post –industrial (information society has turn information a valuable mineral above other minerals in the world) America leads the informationization and Western Europe and East Asian tigers in, and an engine to development.
Show more

20 Read more

Evolving Intelligent Systems, eIS

Evolving Intelligent Systems, eIS

Abstract—The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of research that is on the crossroads of computational intelligence and cybernetics is compressed in this short communication. Intelligent systems can be defined as systems that incorporate some form of reasoning that is typical for humans. Fuzzy Systems are well known for being able to formalize the approximate reasoning that still separates humans from machines. Artificial neural networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain. Finally, the evolution is a phenomenon that was initially used to solve optimization problems inspired by the so called ‘genetic algorithms’ due to D. E. Goldberg and ‘genetic programming’ due to J. Koza. These types of evolutionary algorithms are mimicking the natural selection that takes place in populations of living creatures over generations. More recently, the evolution of individual systems within their life-span (self-organization, learning through experience, and self-developing) has attracted the attention. These systems called ‘evolving’ came as a result of the research into the development of practical on-line algorithms that work in real-time and are close to the theoretically optimal, analytical solutions, suitable for non-stationary, non-linear problems of modeling, control, prediction, classification, clustering, signal processing. Due to the limited space and the specific purpose of this communication only the basic elements of the concept will be outlined. This concept represents, in fact, a higher level adaptation that concerns model structure as well as model parameters. It can also be considered as an extension of the multi-model concept known from the control theory, and of the on-line identification of fixed structure fuzzy rule-based models
Show more

13 Read more

INTELLIGENT SYSTEMS AND THEIR IMPLICATIONS FOR THE INDUSTRIES

INTELLIGENT SYSTEMS AND THEIR IMPLICATIONS FOR THE INDUSTRIES

In this dynamic and turbulent business environment, technology plays a pivotal role in the success of a business concern that cannot be ruled out. Thus, with the development of the technology for the business concerns has changed the face of the business. The family of intelligent systems so developed by the organizations like SAP, IBM, and GS1 etc has made the businesses more efficient and processes more effective. Intelligent systems in the business can not only provide a seamless shopping experience to the consumer, but can also help businesses streamline their operations so that they can meet the challenges of competition, shrinkage, supply chain management, inventory and store management by implementing new technologies. They can lend cutting-edge technology to industries and it only gets better with newer processes and softwares coming into the market. Industries need to shake up their feathers and fully realize the potential of intelligent systems and incorporate them in their business.
Show more

14 Read more

Intelligent systems in the context of surrounding environment

Intelligent systems in the context of surrounding environment

We should, perhaps, include an extra criterion that for a system to be truly intelligent, the feedback mechanism must in some way affect the operation of the decision- making system, whether it is punishing ‘bad’ synapses in the Minibrain neural network, changing the entries in a truth-table, or killing a bacterium. A system that keeps making the same decision regardless of how consistently successful that decision is, isn’t being intelligent. With this in mind, we might consider systems such as E. Coli (i.e. systems which employ one single strategy, and when it becomes unsuccessful simply stop) to be minimally intelligent systems. They’re nowhere near as smart as other systems, natural and artificial, but at least they know when to quit.
Show more

9 Read more

2IOE0 Interactive Intelligent Systems

2IOE0 Interactive Intelligent Systems

General Game Architecture GameState Simulator info NPC Controller info Renderer info updates actions PC Controller actions Player image sound input.. Controllers[r]

22 Read more

Intelligent Systems in Travel and Tourism

Intelligent Systems in Travel and Tourism

of it as an engineering task, and not as a cognitive science test, it poses hard questions: i) a simple problem like inquiring a flight from location A to B, with specific price and time constraints, complemented by a specific hotel, with similar constraints. In this case linking to a predefined database or to one of the CRS/GDS would be sufficient, if one ignores interoperability issues. This type of planning problem is already partially solved by existing online systems such as Expedia or Orbitz. But the need for more intelligent heuristics is obvious, when one thinks of the enormous number of scheduled flights and constraints such as different rates or different booking conditions within airline alliances, etc. ii) a complex problem which involves background knowledge of a specific traveler, e.g., the problem of traveling to Milan only when Milan plays. This needs the modeling of knowledge about the Italian city of Milan, the inference that in this case Milan refers to AC MILAN (a soccer club), the timing regarding soccer tournaments (normally at weekends). And other background knowledge might be needed such as specific weather conditions or cultural activities. In fact, the "leisure" domain might cover nearly all domains of daily life. And the information, stored in different bases with no common format and even unknown locations, needs to be extracted. Here wrapping techniques, learning accurate extraction rules and also adapting to structural changes in sites, are needed [Knoblock et al, 2000; Kushmerick, 2000]. Other non- trivial tasks within this context are
Show more

6 Read more

Expert Systems. A knowledge-based approach to intelligent systems. Intelligent System. Motivation. Approaches & Ingredients. Terminology.

Expert Systems. A knowledge-based approach to intelligent systems. Intelligent System. Motivation. Approaches & Ingredients. Terminology.

• Interesting relationships between probabilistic reasoning and qualitative reasoning in model-based systems (e.g., cost-based abduction).[r]

7 Read more

Home Intelligent Systems (HPS) For Disabled and Elderlies

Home Intelligent Systems (HPS) For Disabled and Elderlies

With the popularity of mobile devices and the emergence of smart home devices today, it is possible to control and communicate with home appliances remotely.. In this project, we will [r]

6 Read more

The application of intelligent systems to finance and business

The application of intelligent systems to finance and business

As these experiments have shown, time-series analysis is probably not the best way to use neural networks for this forecasting problem. It is debatable whether the information required to make forecasts to within £50 o f the eventual residual value is actually in the data that was used. While this error target has not been met, simple systems have been described that generate reasonable forecasts fi'om the available data. While an RMS forecast error of approximately 7% gives an error of nearly £500 in the residual value forecast, this error could be viewed as being spread over 36 payments during the 3 year hire period. To use Figure 3.2, over the 3 years from February 92 to February 95, an average E-plate vehicle would have depreciated from £11200 to £7000. This depreciation rate is approximately £117 per month. The actual hire charges are usually of the order o f 50% greater than the cost o f the anticipated depreciation due to maintenance agreements, profit margins etc., so the monthly payments on this vehicle would be approximately £175. A 7% RMS error in the residual forecast result in errors in the depreciation rate o f £8.17, which result in a final error in the monthly hire charge that is both under 5%, and £10.
Show more

184 Read more

Aspects of Intelligent Systems Explanation

Aspects of Intelligent Systems Explanation

Content based recommender systems, on the other hand, recommend products that are based on the items content, rather than other user ratings. For example, if a customer has bought, or shown an interest in CD’s recorded by the rock band, called the Manic Street Preachers, then other items known to have been recorded but assumed not to be owned by this customer may be recommended. The retailing website Amazon uses content based recommendations. However, in practice, these methods would be combined to provide a hybrid solution. [11] and [55] have described one major problem associated with CF systems: their computations are often based on incomplete data – this may result from an insufficient data sample from which CF recommendations are based, and as a result, this means that recommendations are mostly correct but occasionally wrong. Explanation facilities can be of benefit in such systems because they can assist the user in either detecting or estimating the likelihood of errors in the recommendation. [11] and [53] identify many benefits which are similar to those identified of benefit to KBS explanation facilities described in section 2. These include greater user acceptance of the recommender system as a decision making aid. [55] has examined content based explanations and lists transparency, trustworthiness, user performance (i.e., time to make a decision and quality of choice), user satisfaction, persuasiveness, the degree to which a user would be convinced to buy as likely benefits of explanation facilities for recommender systems.
Show more

12 Read more

Engineering Health and Medicine: An Intelligent Systems Approach at Intelligent Systems Engineering at Indiana University

Engineering Health and Medicine: An Intelligent Systems Approach at Intelligent Systems Engineering at Indiana University

• Health Monitoring—Imaging, Implantable and Wearable Sensors • Data Analysis and Reduction • Predictive Virtual Individuals Simulations and in Vitro Tissue Samples/Organ on a Chip • Hea[r]

16 Read more

End-user interactions with intelligent and autonomous systems.

End-user interactions with intelligent and autonomous systems.

recognition systems, network device alarms, smart home applications, music recommender, healthcare decision or fraud detection systems, are quickly becoming mainstream. Consumers and business specialists now often interact with systems on a daily basis in a form of “human-in-the loop” learning (e.g., [8, 1]). Yet, interacting with even well-designed systems is limited and often uninformative for the end user because of the internal complexity and current “black box” nature of most intelligent systems.

5 Read more

Acceptance of Intelligent Ticketing Systems in Developing Countries

Acceptance of Intelligent Ticketing Systems in Developing Countries

Information communication technologies bring the revolution into all business sectors, and transportation sector is not an exception. Ticketing system has changed from traditional to intelligent, which provides information and service to the consumer. In developed countries such systems are implemented and operate successfully, while in the developing countries electronic ticketing and other similar innovative solutions face specific challenges. These challenges are related to information era and changes in consumer behaviour, caused by the development of information and communication technologies. In these new conditions the motives of consumers to choose electronic ticketing has become an extremely important factor of success. Lack of integrity of consumer behaviour and technology acceptance (electronic ticketing in particular) was identified in previous scientific research, especially taking into consideration recent conditions of developing countries. The aim of this article is to evaluate the consumers’ behaviour and acceptance of intelligent systems, such as electronic ticketing, in order to identify factors, influencing and encouraging the customers to use electronic ticketing systems. In this study extended technology acceptance model with trust element was used to measure the consumer behaviour. The sample for this research has been taken from China and Pakistan populations and consists of 432 participants from both countries.
Show more

10 Read more

NLP Techniques in Intelligent Tutoring Systems

NLP Techniques in Intelligent Tutoring Systems

Many Intelligent Tutoring Systems (ITSs) aim to help students become better readers. The computational challenges involved are (1) to assess the students’ natural language inputs and (2) to provide appropri- ate feedback and guide students through the ITS cur- riculum. To overcome both challenges, the following non-structural Natural Language Processing (NLP) techniques have been explored and the first two are already in use: word-matching (WM), latent semantic analysis (LSA, Landauer, Foltz, & Laham, 1998), and topic models (TM, Griffiths & Steyvers, 2007).
Show more

6 Read more

Intelligent CPAP systems: clinical experience

Intelligent CPAP systems: clinical experience

eliminated rapidly. Flattening of the flow-time curve causes a slower approach to the final pressure over the next several minutes. When there is no snoring or flattening the pressure gradually decreases with a time constant of 20 minutes. Thus, in general the system acts pre- emptively to increase the pressure before obstructive apnoeas occur. In addition, the AutoSet Clinical and Portable systems, which are intended for use on the first treatment night, detect apnoeas, classify them into those with an open airway and those where the airway is closed, and optionally increase the mask pressure in the presence of closed airway apnoeas. Hypopnoeas are ignored unless there is associated evidence of snoring or silent airflow limitation.
Show more

6 Read more

Sequencing Content in Intelligent Tutoring Systems

Sequencing Content in Intelligent Tutoring Systems

A new arrangement of sections allows resuming where students left off, more section types provide a more general descriptive language and the addition of Computer Adaptive Testing comp[r]

110 Read more

Building Intelligent Tutoring Systems: An Overview

Building Intelligent Tutoring Systems: An Overview

The chapters in this part of the book provide the reader with two examples of authoring systems and an example of an ITS. Chapter 19 presents a thorough comparative analysis between CTAT, a well-known authoring tool for cognitive tutors, and ASTUS, a new cognitive tutor authoring tool. Through examples, the chapter addresses many limitations of CTAT and shows how ASTUS copes with these limitations. Chapter 20 is about ASSISTMENT, a suite of web-based tools that help researchers to easily design, build and then compare different ways of teaching students in order to improve their achievement. A randomized controlled experiment conducted using these tools is described. Chapter 21, the last in this part, presents ANDES, one of the most popular ITSs. ANDES is an intelligent homework helper for physics. That is, it replaces students’ pencil and paper as they do problem-solving homework. The author presents ANDES’ behavior, the development experience, evaluations of its pedagogical effectiveness and recent progress on dissemination/scale-up.
Show more

15 Read more

On Intelligent Transportation Systems and Road Congestion

On Intelligent Transportation Systems and Road Congestion

This study makes a set of important contributions to the IS and the transportation economics literatures as follows. By focusing on a long-lasting public concern on road congestion, this study contributes to the emerging literature on societal impacts of IS by examining how ITS affects road congestion. Our empirical evidence shows that the adoption of IT-enabled transportation management systems benefits the society by saving time and money incurred by road congestion. We also contribute to the burgeoning “Green IT” literature (e.g., Melville 2010; Malhotra et al., 2013) by providing preliminary but encouraging evidence that ITS reduces excessive CO2 emissions due to road congestion. Second, our study contributes to the transportation economics literature by showing that an IT-enabled intervention on congestion is likely to reduce traffic. Third, by integrating the IS with the transportation literature, we propose a new interdisciplinary research that sheds light on the increasing role of IT in transportation, an approach that, to our knowledge, few prior studies have attempted.
Show more

19 Read more

Knowledge management in intelligent tutoring systems

Knowledge management in intelligent tutoring systems

expensive than in FARMER case. With the source code of WARMR this problem would be easily solvable and is not a parameter to be considered in performance. The generation of rules takes the same time because the final output is essentially the same (both systems solve the same problem). Table 5.5 summarize the quality of the Ruleminator. The rules are the same for both WARMR that FARMER and as can be seen from the tables many rules are generated even though the most interesting are in the top positions. The rules showed in previous subsection can be founded in the first 30 rules in the case of Algebra dataset and in the first 50 rules in the case of SumC dataset. We report some of the generated output of Ruleminator and analyze in more detail the gener- ated rules. For the Algebra data set in the very first positions we find the following rules:
Show more

91 Read more

Intelligent Control Systems Using Soft Computing.pdf

Intelligent Control Systems Using Soft Computing.pdf

For many decades, it has been a goal of engineers and scientists to develop a machine with simple elements similar to one found in the human brain. References to this subject can be found even in 19 th century scientific literature. During the 1940s, researchers desiring to duplicate the human brain, developed simple hardware (and later software) models of biological neurons and their interconnection systems. McCulloch and Pitts in 1943[8] published the first systematic study on biological neural networks. Four years later the same authors explored the network paradigms for pattern recognition using a single- layer perceptron. Along with the progress, psychologists were developing models of human learning. One such model, that has proved most fruitful, was due to D. O. Hebb, who, in 1949, proposed a learning law that became the starting point for artificial neural networks training algorithm [9]. Augmented by many other methods, it is now well recognized by scientists as indicative of how a netwo rk of artif icial neuro ns could exhib it learn ing behav ior. In the 1950s and 1960s, a group of researchers combined these biological and psychological insights to produce the first artificial neural network [9], [10]. Initially implemented as electronic circuits, they were later converted into a more flexible medium of computer simulation. However, from 1960 to 1980, due to certain severe limitations on what a NN could perform, as pointed out by Minsky [11], neural network research went into near eclipse. The discovery of training methods for a multi-layer network of the 1980s has, more than any other factor, been responsible for the recent resurgence of NN.
Show more

493 Read more

Show all 10000 documents...