Notice bibliographique
Résumé
The international conference on Applied Scientific Computational Intelligence using Data Science (ASCI-2020) has been grappled its importance to discuss the research challenges in the field of Data Science by applying scientific techniques in terms of computational intelligence. The initiative has been taken by Department of Computer Applications, Manipal University Jaipur, Rajasthan, India. Manipal University Jaipur (MUJ) was launched in 2011 on an invitation from the Government of Rajasthan, as a self-financed State University. MUJ has redefined academic excellence in the region, with the Manipal way of learning; one that inspires students of all disciplines to learn and innovate through hands on practical experience. In line with Manipal University’s legacy of providing quality education to its students, the campus uses the latest in technology to impart education. Thus, ASCI-2020 drives this legacy of MUJ to join forces and showcase exorbitant research and industry inventiveness, and to recognize and hear from experts in the field of Data Science. Data science is a huge diverted field. Different kind of algorithms, scientific techniques, processes and systems are used in data science to pull out knowledge and insights from Big Data i.e. structured and unstructured data. It is a concept to data analysis; unify statistics, machine learning and their related methods. This conference aims to reveal into advanced methodologies, prototypes, systems, tools, and techniques of data science from academia, industry and government agencies scientists and practitioners. Whether it is information technology or hardware, banking to healthcare, automation and innovation are revolutionary in almost everything. Cities and infrastructure are becoming smarter, health care is being integrated and education is becoming super-focused. The conference will bring together all topics of interest to those who are inclined towards computing and data science using intelligence techniques. ASCI-2020 proceeding has tried to fetch innovative facts and information from academician, research scholars and scientists in terms of their research results and key findings from all the aspects of data science and computational intelligence. The manuscripts of ASCI-2020 has been called for three tracks of data science and computational intelligence. The first track focused on Big Data Management. Then, second track emphasized the Computational Intelligence Techniques, and third track concentrated on Data Science Applications. Subsequently, these tracks has been formulated on the sub themes of Data Science and Computational Intelligence evolved with emerging fields in this present scenario. The sub themes of first track described Heuristic and Nature Inspired Search, Fuzzy and Rough Sets, Reinforcement Learning, ANN and Deep Neural Networks, Auto Encoder, GAN, Transfer Learning, Data Optimization, Data and Network Outsourcing Services. In addition, the second track includes the topics related to Algorithms and Models, Cognitive Computing Development, Business Intelligence and Strategies, Machine Learning and Statistics, Machine Learning Tools and Techniques, Fielded Applications, Generalization as Search, Machine Translation, Data Communication and Intelligence and Natural Language Processing. Finally, track three has been intended on interdisciplinary topics such as Predictive and Statistical Analysis, Application in Computer Vision, Natural Language Processing, Time Series Data, Computational Mathematics, Drug Discovery and Genomic Sequencing, Data Analysis for Improving Defence Security, Cyber Security Analytics, Data Recommendation in Social Networks, Data Analysis of Customer Need in E-commerce, Search Keyword Analysis, Web and Digital Media and Business Analytics in Agriculture. This conference received 235 papers from across the world such as United States, United Kingdom, United Arab Emirates, Norway, Russia, Saudi Arabia, Turkey, Ukraine, Ethiopia, Canada, Morocco, Poland, Uzbekistan, Ghana, Ecuador and Bangladesh etc. Out of 235 manuscripts 91 high quality papers are selected with 38 % accepting ratio. As a whole, 35 international authors submitted their papers, and 350 authors submitted their manuscript from the country. All the manuscripts have been discussed the new findings in the field of data science and computational intelligence. Authors of the proceedings focused on latest trends such as different data models for classification and prediction in various applications. Nevertheless, this proceeding also bring together different theories and paradigm of neural network, fuzzy systems for computational intelligence. List of Editors are available in the 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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,602 | 0,434 |
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 ».