Bibliographic record
Abstract
AICTE sponsored International E-conference on Data Analytics, Intelligent Systems and Information Security (ICDIIS’20) was held on 11th and 12th December 2020, in Dr.Mahalingam College of Engineering Technology, Pollachi, Tamilnadu, India. ICDIIS’20 provided a forum for academicians, students and research scholars to present their innovative ideas, models, applications, research accomplishments and concepts of diversified areas. The theme of the conference was emerging interdisciplinary area. Hence, a wide range of theories, methodologies & algorithms were explored among the participants during the conference. ICDIIS’20 was held online with the help of Google Meet network, due to the current restrictions caused by the coronavirus infection COVID-19 pandemic. In ICDIIS’20, the inaugural speech was provided by an expert member from industry Mr.Sivaramakrishnan Senthattty, HCL Technologies Ltd, Chennai, India. This conference was modeled with three tracks namely Data Science and Analytics as track1, Intelligent Systems as track 2, Information Security as track 3. Each track was scheduled parallel with sessions of keynote and technical presentation. Totally, 9 keynote sessions and 12 technical sessions were over in two days of the conference. All these sessions were well attended by 164 external participants and 219 internal participants from the host institution. Every keynote session was scheduled for one hour out of which 15 minutes allocated for interaction. Similarly, every technical session was scheduled for 90 minutes in which each paper was given 10 minutes for presentation and 5 minutes for interaction between the authors, session chair and participants. The interaction was very active and useful. We were greatly privileged to have invited keynote speakers from different countries such as Australia, Canada, India, Japan, Nigeria and USA. To this conference, 152 papers were received through easy chair conference paper submission system from different countries such as Bangladesh, China, Ecuador, Greenland, India, Iran, Nigeria, Oman, Saudi Arabia and Uzbekistan. Out of which, 68 papers get accepted and registered. List of Committee of ICDIIS’20, National Advisory Committee, Keynote Speakers, Session Chairs, Reviewers, are availble 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.529 | 0.383 |
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".