The Home Page · The Integral Worm, Inc. · My Resume · My Show Car · My White Papers · Organizations I Belong To
Human Computer Interaction (IFSM303) · Web Information Architecture (IFSM387) · Database Project Overview (IFSM420) · Expert System Project (IFSM425)
Information Systems and Security (IFSM430) · Information Systems Analysis & Design (IFSM436) · Project Management (IFSM438)
Legal Aspects of Information Systems (IFSM474) · Enterprise Network System Design (IFSM498)
Paper: An Introduction to Data Mining ·
Artificial Intelligence Repository of Journal Articles and White Papers
This presentation is an introduction to artificial intelligence: expert systems components. Topics covered are the following: defining artificial intelligence; expert systems key terms; expert systems requirements; expert systems components; and selecting appropriate problems for expert systems.
This presentation is an introduction to artificial intelligence: knowledge engineering. Topics covered are the following: knowledge engineering, requirements of expert systems (ES), functional requirements of ES, structural requirements of ES, components of ES/KBS, knowledge base, inference engine, working memory, expert system, explanation facility, user interface, will ES work for my problem.
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
This presentation covers best practices to be followed during project planning phase of the Expert System Development Lifecycle (ESDLC). Topics covered are as follows: domain expert selection, problem identification, domain selection, basic requirements, type of problems, and project planning.
This presentation covers knowledge acquisition for artificial intelligence. Topics covered are as follows: knowledge acquisition, types of knowledge, knowledge acquisition paradox, difficulties of knowledge acquisition, knowledge acquisition methods, repertory grid analysis, reasoning methods, RGA input for selecting a computer language, automatic knowledge acquisition techniques, knowledge representation, propositional logic, predicate logic, rules, semantic nets, frames, frames systems, and comparisons of KR methods.
This presentation covers case-based and model-based reasoning for artificial intelligence. Topics covered are as follows: case-based reasoning, case-based reasoning components; case base, retriever, adapter, refiner, executor, and evalutator; and model-based reasoning.
This presentation covers agent technology for artificial intelligence. Topics covered are as follows: expert systems, overcoming expert systems limitations, agent, what is an agent, definition of an agent, agents versus expert systems, how is an agent different from other software, types of agents, deliberate versus reactive, interface versus information, mobile versus stationary, and why a mobile agent.
This presentation covers data mining within artificial intelligence. Topics covered are as follows: motivation, synonym, process of data mining, operation of data mining, data mining techniques, business application, application selection, and current issues.
This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.
The Home Page · The Integral Worm, Inc. · My Resume · My Show Car · My White Papers · Organizations I Belong To