Advances in Intelligent Systems: Concepts, Tools and Applications
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Notify Me We will send an email as soon as we get it in stock. Write a Testimonial Few good words, go a long way, thanks! Personalize Gift card We will send an email to receiver for gift card. Continue Shopping Checkout. Reset Pincode. Submit a Review. JK Rowling. Click on below image to change. Click on image to Zoom. Average Rating. Description The field of Intelligent Systems has expanded enormously during the last two decades with many theoretical and practical results already available, which are the outcome of the synergetic merging of classical fields such as system theory, artificial intelligence, information theory, soft computing, operations research, linguistic theory and others.
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Other Books By Author. New Book Releases. Contact Us. Monday to Saturday 9. E-Gift Coupon , click here. Insights Insights, Account, Orders. About SapnaOnline. Why Shop at SapnaOnline. Create New Account. An Intelligent Decision Support System IDSS is developed to help decision makers during different phases of decision making by integrating modeling tools and human knowledge. IDSSs are tools to help decision making process where uncertainty or incomplete information exists and where decisions involving risk must be made using human judgment and preferences. In addition, IDSS works under an assumption that the decision maker is familiar with the problem to be solved.
In that case, IDSS gives full control to the user regarding information acquisition, evaluation and making the final decision. IDSS is an interactive system, flexible, adaptable and specifically developed to support the solution of a non-structured management problem for improved decision-making Quintero et al. Besides that, an IDSS is also known as a possible theoretical model of incorporation by adapting an existing DSS system to execute in an Expert System style, such adapted systems are considered by many DSS researchers to be IDSS with the focus on the functioning of 'man and machine' together.
An IDSS is more cognitive rather than a technological system, the fundamental difference is that even basic characteristics of intelligence cannot be captured in mechanistic Malhotra et al. Most researchers agree that the purpose of IDSS is to support the solution of a non-structured management and enable knowledge processing with communication capabilities Qian et al.
IDSS can incorporate specific domain knowledge and perform some types of intelligent behaviors, such as learning and reasoning, in order to support decision-making processes Qian et al.
Catalog Record: Engineering intelligent systems : concepts, | HathiTrust Digital Library
IDSS applications are developed in various areas such as in product development and planning; management decisions; enterprise and manufacturing industries; services and etc Table 1. In this study, we categorize the application areas into seven areas i. Most of the applications are specific to problem domain in that area. From the literature, we list the application areas for the specific problem domains in Table 1. Most of IDSS applications that are listed in Table 1 use IDSS conventional name and of course they are different in purpose, implementation, design and intelligent techniques applied.
In this study we found that, there are many.
This can help to increase the IDSS products in market place as alternative tools to support and improve decision making processes for the specific problem domains. Recently, there are quite a number of computer applications that have applied intelligent techniques and used DSS concepts and components. However, some researchers claim it as an essential of DSS which uses the conventional name known as IDSS and others classified it as a member of intelligent system.
In this study, we focus our discussion on IDSS applications, which are embedded with the related intelligent techniques.
Developments and Advances in Intelligent Systems and Applications
In fact, there are various types of Artificial Intelligent technologies that are used for reasoning, machine learning, automatic programming, artificial life, data mining and data visualization. For example, the reasoning process can use specific rules, categorization, past experience, heuristics and expectations approaches.
Computer can do the reasoning through frames e. In addition, the examples of machine learning approaches are Artificial Neural Network ANN , genetic algorithms and fuzzy logic . IDSS is expected to incorporate specific domain knowledge and perform certain types of intelligent behavior such as learning and reasoning, in order to support decision making process. The need to incorporate domain knowledge and intelligent capabilities in decision support system has been identified in various forms and models by many researchers .
IDSS incorporating knowledge component through case base, rule base, knowledge acquisition subsystem or domain models and intelligent component through an intelligent advisory system, intelligent supervisor or model solver can produce the intelligent applications.
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Intelligent behaviors presented by an intelligent system are related to the abilities of gathering and incorporating domain knowledge, learning from the acquired knowledge, reasoning about such knowledge and when enquired, being able to issue recommendations and justify outcomes. These intelligent behaviors are potential intelligent techniques that can be corporated with intelligent component in IDSS. IDSS has consolidated the intelligent behaviors in its inference engine component.
In this case, with the intelligent behaviors, IDSS applications can have abilities to do learning and reasoning. These abilities are used to support the decision making processes.
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There are various types of intelligent techniques that are applied in IDSS applications such as knowledge base system Quintero et al. From the literature study, we found that most applications use Knowledge Based System, especially for rules based approach in their methodology and system implementation. In fact, most researchers agree with the advantage of using this technique. Not only it is easier to understand and implement, but the KBS using rule-based also supports the basic reasoning capabilities.
However, intelligent system and soft computing technologies are new technological platforms, whereby intelligent logic is now usually inherent in the processing of all decision support tools . Research and system developments in this field increase year by year with new ideas and approaches. They integrate more than one intelligent technique in their application system development. This approach makes IDSS applications more capable to do learning and reasoning processes.
Table 2 lists some of IDSS applications that use hybrid approach. Most researchers agree that these intelligent techniques are more suitable for learning and reasoning activities. However, some studies are needed to validate the abilities of IDSS applications with hybrid techniques, because in some cases may be single technique can produce the same result as hybrid techniques, and it also depend on the nature of the problems that to be solved.
To reach such link, management must not only face current issues of human resource management but also deal with future challenges to HRM effectively Stavrou-Costea, On the other hand, the major potential prospect for HRM is technology selection and implementation.