Enterprise AI Analysis
Modeling uncertainty in course selection using singular value decomposition-based energy measures within neutrosophic frameworks
This analysis explores a novel approach for decision-making under uncertainty, leveraging Complex Neutrosophic Soft Set (CNSS) theory and Singular Value Decomposition (SVD) based energy measures. It addresses the limitations of traditional models by incorporating both magnitude and phase information from complex-valued memberships, enhancing precision in complex, real-world scenarios like course selection.
Key Impact Metrics
Our advanced CNSS model delivers measurable improvements in decision accuracy and robustness.
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Decision Accuracy Improvement
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Highest Energy Score (z2)
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Uncertainty Reduction Factor
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Optimal Preference Score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow: CNSS Energy-Based Decision Algorithm
Decide alternatives and parameters
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Expert's opinion in CNSS form
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Construct Complex neutrosophic soft decision matrix
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Generate representation matrix and transpose
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Determine AND-product and OR-product
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Find score matrices
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Determine upper and lower energies (eigenvalues)
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Calculate total energy for each alternative
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Arrange energies from highest to lowest
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Choose alternative with highest energy
Comparative Analysis of Energy-Based Models
| Method | Ranking (z1, z2, z3) | Optimal Choice | Key Strengths |
|---|---|---|---|
| Proposed CNSS Energy Model | z2 > z1 > z3 | z2 (Data Science) |
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| Neutrosophic Soft Set Energy [36] | z2 > z1 > z3 | z2 |
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| Fuzzy Soft Set Energy [37] | z3 > z1 > z2 | z3 |
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| Khan et al. [44] (Multi-Attribute) | z2 (4.176), z1 (2.304), z3 (3.639) | z2 |
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| Saeed and Shafique [45] (Multi-Attribute) | z2 (4.599), z1 (3.805), z3 (3.926) | z2 |
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Course Selection for 'John'
Scenario: John, a science student, needs to select the most suitable course from Software Engineering (z1), Data Science (z2), and Digital Marketing (z3) offered at LIMS College. Experts evaluate options based on interest level, cost-effectiveness, and future career opportunities. The process involves uncertainty and vagueness.
Results: The proposed algorithm yielded energy values of -1.196 for Software Engineering (z1), 1.784 for Data Science (z2), and -1.392 for Digital Marketing (z3). Data Science (z2) achieved the highest preference score (100% after normalization), making it the most suitable choice.
Key Takeaway: The CNSS energy model successfully processed complex, uncertain data to provide a clear, data-driven recommendation, highlighting its practical utility in academic decision-making.
Optimal Course Identified
Data Science Highest Energy Score (z2)Calculate Your Potential AI ROI
Estimate the financial and operational benefits of implementing our advanced AI solutions in your enterprise.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrate CNSS energy-based decision-making into your operations.
Discovery & Strategy
Comprehensive assessment of your current decision-making processes, data infrastructure, and specific challenges. Define clear objectives and a tailored strategy for CNSS model integration.
Model Development & Customization
Design and develop the CNSS energy-based algorithms, customizing them to your unique datasets and parameters. Includes SVD implementation and validation against historical data.
Pilot & Validation
Deploy the CNSS model in a controlled pilot environment. Gather feedback, fine-tune parameters, and validate decision accuracy and robustness using real-world scenarios.
Full Integration & Training
Seamlessly integrate the validated CNSS model into your existing enterprise systems. Provide extensive training for your teams to ensure effective adoption and utilization.
Continuous Optimization & Support
Ongoing monitoring, performance analysis, and iterative improvements to the CNSS model. Dedicated support to ensure sustained high performance and adaptability to evolving needs.
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