Notice bibliographique
Résumé
2024 8th International Conference on Data Mining, Communications and Information Technology (DMCIT 2024), was successfully held on May 25, 2024, which was organized by Asia Pacific Institute of Science and Engineering (APISE), media supported by Internet of Things Technologies, Modern Electronics Technique, Journal of Xidian University, Journal of Information and Intelligence, OPTICAL COMMUNICATION TECHNOLOGY, Telecommunication Engineering. The conference was held in Hong Kong from May 24-26, 2024 as planned. Considering that some participants could not attend in person, the conference was adjusted as a hybrid conference, as a combination of on-line and off-line conference. The proceedings of this year’s edition comprised three main categories: “1. Advanced Methods and Algorithms”; “2. Applied Technologies in Various Domains”; “3. Case Studies and Practical Implementations”. All these submissions were rigorously reviewed by the Program Committee. The conference attracted 45 submissions in total, and out of 27 papers were accepted, including countries like Canada, France, China, India, New Zealand, Spain, Thailand, etc. On the conference day, 8 oral presentations and 18 poster presentations were arranged according to the participants’ choices. Each presenter was given 15 minutes to deliver their presentation, including 3 minutes Q&A. Two awards, one best oral presentation award and one best poster presentation award were selected by the end of the conference. The conference was inaugurated with the esteemed Dr. Simon Fong from the University of Macau, Macau S.A.R., China, and Dr. Ka-Chun Wong from the City University of Hong Kong, Hong Kong S.A.R., China, both delivering outstanding opening remarks. The conference was honored by the presence of four eminent keynote speakers who graced the event with their distinguished speeches. Professor Xianbin Wang from Western University, Canada, Professor Raymond Chi-Wing Wong from The Hong Kong University of Science and Technology, Hong Kong, Professor Steven Guan from Xi’an Jiaotong-Liverpool University, China, and Professor Chin-Chen Chang from Feng Chia University, Taiwan ROC, each shared their latest and profoundly insightful research perspectives. The technical session and poster session were formally presided over by Prof. Jiwat Ram, who delivered exemplary and thought-provoking remarks. The DMCIT 2024 conference is dedicated to showcasing the most recent findings and scholarly work in the realms of Data Mining, Communications, Information Technology, and associated fields. Through a combination of oral presentations and poster sessions, the event facilitates a platform for participants to engage in the exchange of innovative concepts, forge professional or academic alliances, and seek out international collaborators for prospective joint ventures. We express our collective gratitude to all participants for their invaluable contributions. The shared knowledge and spirited discussions have been the lifeblood of our gathering, igniting a passion for innovation and collaboration. We are deeply inspired by the intellectual curiosity and the pursuit of knowledge that have been the defining attributes of our conference. The synergy of ideas and the collaborative spirit have laid a robust foundation for the ongoing evolution and progress within our disciplines. Looking forward, we anticipate the enduring impact of this conference, confident that the seeds of thought sown here will flourish into a bountiful harvest of academic achievements. List of Committees are available in this Pdf.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,537 | 0,408 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».