Bibliographic record
Abstract
It is with great pleasure that I extend my warmest welcome to all participants of the 2023 4th International Conference on Power, Energy, and Electrical Engineering (PEEE 2023). This conference was held in Kuala Lumpur, Malaysia during November 9-11, 2023. It served as a platform for researchers, academics, and industry professionals to come together and exchange ideas, research findings, and best practices in the field of power, energy, and electrical engineering. This year we have around twenty participants from Malaysia, Spain, UAE, Germany, China, Korea, Canada, Australia, Egypt and Kuwait, whom we wish fruitful discussions. Conference program was highlighted by three keynote and invited speakers: Prof. Saad Mekhilef, University of Malaya; Prof. Jesús Toribio, University of Salamanca; Prof. Hussain Shareef, UAE University. With a lineup of renowned keynote speakers, engaging panel discussions, and paper presentations, this conference promised to be a valuable opportunity for all attendees to gain insights, network with peers, and contribute to the advancement of the field. I would like to express my sincere gratitude to the organizing committee, sponsors, and all individuals involved in making PEEE 2023 a reality. Your dedication and hard work have been instrumental in bringing together this esteemed gathering of experts and professionals. I am confident that PEEE 2023 was a rewarding and enriching experience for all involved, and I look forward to the continued growth and impact of this important conference in the years to come. Prof. Saad Mekhilef, University of Malaya, Malaysia Conference Chair List of Committee is available in this pdf.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.468 | 0.333 |
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".