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Tribhuvan University

BSc CSITBachelor of Science in Computer Science and Information TechnologyBCABachelor of Computer ApplicationBITBachelor of Information TechnologyBIMBachelor of Information ManagementBBABachelor of Business AdministrationBBSBachelor of Business StudiesBBMBachelor of Business ManagementBITMBachelor of Information Technology Management

Pokhara University

BE Computer (PU)Bachelor of Engineering in Computer EngineeringBBA (PU)Bachelor of Business Administration (Pokhara University)

National Examinations Board (+2)

Class 10Class 10 (SEE)+2 Science+2 Science (Class 11 and 12)+2 Management+2 Management (Class 11 and 12)+2 Humanities+2 Humanities (Class 11 and 12)
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Elective DSS and Expert System

DSS and Expert System notes

5 chapter notes, in syllabus order. Each starts with the key points.

Unit 1 · 10 hrs

Business Decision-Making: Models, Types & ToolsUnit 1 of DSS and Expert System explores structured vs. unstructured decisions, decision-making models (rational, bounded rationality, satisficing), and tools like decision trees, payoff matrices, and cost-benefit analysis—with real-world applications in Nepalese businesses like Ncell, Daraz, and banks.11 min read

Unit 2 · 10 hrs

DSS Design: Models, Tools & EvaluationUnit 2 of DSS and Expert System covers the systematic approach to designing Decision Support Systems (DSS), including problem identification, model selection, tool integration, and evaluation metrics like usability and cost-benefit analysis—essential for building effective business intelligence tools.12 min read

Unit 3 · 10 hrs

Building DSS Systems: Models, Prototypes, and EvaluationUnit 3 of DSS and Expert System covers the practical construction of Decision Support Systems (DSS), including model-driven, data-driven, and document-driven approaches, prototyping techniques, and rigorous evaluation methods like sensitivity analysis and user testing.9 min read

Unit 4 · 8 hrs

Expert Systems: Rules, Inference, and ApplicationsUnit 4 of DSS and Expert System explores how expert systems replicate human expertise using knowledge bases, inference engines, and rule-based reasoning. Learn their architecture, types, and real-world applications in healthcare, finance, and diagnostics, with step-by-step examples and comparisons to traditional AI.6 min read

Unit 5 · 7 hrs

Fuzzy Logic, Fuzzy Sets, Fuzzy Rules, and ApplicationsUnit 5 of DSS and Expert System explores fuzzy logic principles, fuzzy set theory, fuzzy inference systems, and their applications in decision support and expert systems, contrasting them with crisp logic and traditional expert systems.10 min read