Intelligent Decision Support

Intelligent decision support system (IDSS) makes extensive use of machine learning techniques systems to describe components of management systems. Applications include Flexible manufacturing systems (FMS), intelligent marketing decision support systems and medical diagnosis systems. An intelligent decision support system should behave like a human consultant: supporting decision makers by gathering and analysing evidence, identifying and diagnosing problems, proposing possible courses of action and evaluating such proposed actions.
The machine learning techniques, embedded in intelligent decision support system, enable these tasks to be performed by a computer, while emulating human capabilities as closely as possible. Many IDSS implementations are based on expert systems that encode knowledge and emulate the cognitive behaviours of human experts using predicate logic rules, and have been shown to perform better than the original human experts in some circumstances. Expert systems typically combine knowledge of a particular application domain with an inference capability to enable the system to propose decisions or diagnoses. Accuracy and consistency can be comparable to (or even exceed) that of human experts when the decision parameters are well known (e.g. if a common disease is being diagnosed).
Research in Machine Learning intelligent decision support focused on enabling systems to respond to novelty and uncertainty in more flexible ways is starting to be used in IDSS. Intelligent agents that perform complex cognitive tasks without any need for human intervention have been used in a range of decision support applications. A range of AI techniques such as case based reasoning, decision trees, support vector machines, and deep learning have also been used to enable decision support systems to perform better in uncertain conditions.
Medical Data Analysis
Iris Dataset
Servo Machine
Natural Language Database Query
Conversational System
Restaurant Voice Orders