Application of TOPSIS, VIKOR and COPRAS for ideal investment decisions
Bibliographic record
Abstract
An increase in investment is required to support the growth and expansion of the industrial sector in a given country. But the planning and ranking of investments must consider financial resource constraints, high investment risk, the frequency of needs and goals, as well as an unfavorable pattern of investments in the production sectors and industries. To ascertain ranking and economic viability for the investment sectors, this study used optimization techniques called TOPSIS, VIKOR, and COPRAS. From 2020 to 2022, this study was conducted for the Debre Berhan City Administration. The study's findings include investment criteria and outline the importance of certain investment areas. The final findings of this study show that, according to TOPSIS, VIKOR, and COPRAS, the current industrial investment pattern is not ideal investment priorities need to be changed. As a result of the above three optimization techniques; spinning, weaving, and finishing of textile fabric’s sector have been ranked first.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".