A Strategy For Collecting And Evaluating Information On Engineering Consultant Offices Using Neural Networks: A Case Study
Bibliographic record
Abstract
This paper presents a strategy for collecting and evaluating information on engineering consultant offices using information technology (IT) and a neural network approach. This strategy is effective in selecting the most appropriate engineering consultant offices and their services. The overall process consists of four main stages: data collection, data evaluation, interface design, and information transformation to a neural network model. The emergence of IT has been the natural result of the development and spread of the computer, and its applications in many fields, which have played a major role in overcoming many obstacles. This has resulted in the creation of new environments, and has facilitated new routine tasks. Programmers have competed to create programs that are easy to use, and many now offer complete packages, including software applications that enable engineers to conduct their work faster and more efficiently. The use of neural networks in general has significantly increased during the past few years, and successful applications in many areas are expected to lead to further interest and confidence in the field of civil engineering. It has been demonstrated that neural networks can outperform conventional modeling in solving various complex engineering problems; however, reports of their practical application in evaluating engineering consultant offices are limited in the literature. The use of Active Server Pages (ASP) and the Internet Information Server (IIS) is proposed in the design of a novel system for data collection and generating standardization processing information. Finally, the results are transferred to a neural network model for evaluation. The proposed site has three user modes: free, public, and administrator. The public users are encouraged to visit this portal to access services, such as finding consultant offices local to new consultation services. Consultants are required to register as premium members to publish their profile for public viewing. Administrators are responsible for managing, updating, and administrating the contents of the website. Five steps are required to construct 1 Associate Professor, Dept. of Building Science & Technology, P. O. Box 30973, Al-Khobar 31952, King Faisal University, Saudi Arabia, Phone 009663/857-4179, FAX 009663/857-8883, mnjadid@yahoo.com 2 IT Engineer, Dept. of the New University Campus Projects, P. O. Box 1982, Dammam 31451, King Faisal University, Saudi Arabia, Phone 009663/857-4179, FAX 9663/857-7000/2130, mobin_pk@yahoo.com June 14-16, 2006 Montreal, Canada Joint International Conference on Computing and Decision Making in Civil and Building Engineering
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".