الفهرس 
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Abstract
Decision-making and decision implementation are the fundamental of many modern disciplines. Decision making in all of modern information technology and management is presented as a rational selection between alternatives.
Development and implementation of Decision Support Systems to support intelligent decision-making is an area of research that has gained an importance in recent years. Due to the increased complexity of decision making, active involvement of the user and the computer in an intelligent way are necessary in the decision process.
In this thesis we present an Intelligent Decision Support System (IDSS) to handle decision-making using both deterministic and probabilistic data. On the one hand, we have the first type of data, which is the architecture of the IDSS is based on Artificial Neural Networks as the main intelligent system component. On the other hand, the IDSS that handles probabilistic data is based on the intelligent component of Bayesian Belief Networks (BBN) Simulator.
To test the software quality of the main component of the IDSS, a quality resource metrics software package (RSM) is used to test the software quality of sample implementation of the intelligent components in the system.