Suchergebnisse - Special Issue on Timely Advances of Deep Learning with applications and Data Driven Modeling

Special Issue: Data-Driven Discovery in Geosciences: Opportunities and Challenges.

Autoren: Chen, Guoxiong Cheng, Qiuming Puetz, Steve

Quelle: Mathematical Geosciences; Apr2023, Vol. 55 Issue 3, p287-293, 7p

Advanced Research in Technology and Information Systems.

Autoren: Pavaloaia, Vasile-Daniel Martin-Rojas, Rodrigo Sulikowski, Piotr

Quelle: Electronics (2079-9292); Apr2025, Vol. 14 Issue 8, p1677, 7p

Schlagwörter: ARTIFICIAL intelligence, ARTIFICIAL neural networks, INFORMATION technology, NATURAL language processing, MACHINE learning, IN-vehicle computing, REINFORCEMENT learning

Preface for Special Issue on "Artificial Intelligence within Multibody System Dynamics".

Autoren: Gerstmayr, Johannes Orzechowski, Grzegorz Tasora, Alessandro

Quelle: Multibody System Dynamics; Jul2025, Vol. 64 Issue 3, p333-334, 2p

EDITORIAL SPECIAL ISSUE: PART IV-III-II-I SERIES.

Autoren: KARACA, YELİZ BALEANU, DUMITRU MOONIS, MAJAZ et al.

Quelle: Fractals; 2023, Vol. 31 Issue 10, p1-58, 58p

Schlagwörter: SYSTEMS theory, MULTIDISCIPLINARY design optimization, CHAOS theory, ARTIFICIAL intelligence, MATHEMATICAL analysis, COMPUTER science, SCIENTIFIC computing, FRACTIONAL programming

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics^,.

Autoren: Tedeschi, Luis O¹

Quelle: Journal of Animal Science. May2019, Vol. 97 Issue 5, p1921-1944. 24p.

Schlagworte: Artificial intelligence, Computer programming, Deep learning, Machine learning, Mathematical models, Simulation methods & models

A ERSF-VIPA framework: scalable wildlife movement modelling for conflict mitigation.

Autoren: Chen, Xiaoyi Li, Jie Cao, Xinyu et al.

Quelle: Movement Ecology. 10/30/2025, Vol. 13 Issue 1, p1-14. 14p.

Next‐Generation Vaccines for Co‐Circulating PEDV and TGEV: Integrating Nucleic Acid Platforms, Mucosal Delivery, and AI–Driven Antigen Design.

Autoren: Hu, Xiaojun Wang, Zhenshan Wang, Shen et al.

Quelle: Transboundary & Emerging Diseases. 12/4/2025, Vol. 2025, p1-26. 26p.

TRANSFORMING VETERINARY PRACTICE WITH ARTIFICIAL INTELLIGENCE (AI): A COMPREHENSIVE REVIEW OF APPLICATIONS FOR VETERINARY PRACTITIONERS.

Autoren: Nair, Sudheesh S.¹ snair@rossvet.edu.kn

Quelle: Journal of Indian Veterinary Association. Dec2024, Vol. 22 Issue 3, p36-52. 17p.

Schlagworte: Artificial intelligence, Health outcome assessment, Veterinary medicine, Veterinary services, Animal health, Animal diseases

The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature.

Autoren: Ngo, Kaitlyn Mekhail, Simone Chan, Virginia et al.

Quelle: Nutrients. Nov2025, Vol. 17 Issue 22, p3515. 45p.

Recent Advancement In Healthcare Management By Using Technology.

Autoren: Isha, Christian Bhadoria, Adarsh S. Patani, Pragnesh

Quelle: Journal of Advanced Zoology. 2024, Vol. 45 Issue 2, p539-548. 10p.

GeoAI and its Role in Planetary Health: Navigating the Path to Global Sustainability

Autoren: ABHIJEET SARKAR

Advancing Dairy Cattle Farming: Integrating Herd Management, Automation and Artificial Intelligence for Elevated Productivity and Sustainable Practices.

Alternate Title: Süt Sığırcılığında İlerleme: Sürü Yönetimi, Otomasyon ve Yapay Zekâ Entegrasyonu ile Artan Verimlilik ve Sürdürülebilir Uygulamalar.

Autoren: YILMAZ, Kübra Benan¹ kubrabenanyilmazz@hotmail.com

Quelle: Bozok Veterinary Sciences. Jun2024, Vol. 5 Issue 1, p23-30. 8p.

PROCEEDINGS ONE HUNDRED AND TWENTY EIGHTH ANNUAL MEETING OF THE UNITED STATES ANIMAL HEALTH ASSOCIATION.

Quelle: Proceedings of the Annual Meeting of the United States Animal Health Association. 10/10/2024, Vol. 128, p1-339. 336p.

Land Carbon Cycle Modeling : Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning

Autoren: Yiqi Luo Benjamin Smith

Resource Type: eBook.

Schlagworte: Carbon cycle (Biogeochemistry), Carbon

Categories: SCIENCE / Global Warming & Climate Change, NATURE / General, NATURE / Ecology, NATURE / Environmental Conservation & Protection, NATURE / Natural Resources, SCIENCE / General, SCIENCE / Earth Sciences / General, SCIENCE / Life Sciences / Ecology, SCIENCE / Environmental Science, SCIENCE / Chemistry / Environmental

Editorial: Introduction to the Issue on Domain Enriched Learning for Medical Imaging.

Autoren: Monga, Vishal Acton, Scott T. Seghouane, Abd-Krim et al.

Quelle: IEEE Journal of Selected Topics in Signal Processing; Oct2020, Vol. 14 Issue 6, p1068-1071, 4p

Exploring Deep Learning Models for Pneumonia Classification in Chest Radiological Images: A Systematic Revie.

Autoren: Pamungkas, Yuri Siswanto, Putri Alief Eljatin, Dwinka Syafira et al.

Quelle: International Journal of Robotics & Control Systems; 2025, Vol. 5 Issue 4, p2290-2310, 21p

Schlagwörter: PNEUMONIA, CHEST X rays, MACHINE learning, MEDICAL technology, DEEP learning, CONVOLUTIONAL neural networks

Survey, taxonomy, and emerging paradigms of societal digital twins for public health preparedness.

Autoren: Rehan, Muhammad Waqas Rehan, Muhammad Maaz

Quelle: NPJ Digital Medicine; 8/13/2025, Vol. 8 Issue 1, p1-25, 25p

Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024)

Autoren: Kun Zhang Hang Luo Tang Yao et al.

Resource Type: eBook.

Schlagworte: Big data, Business--Data processing

Categories: BUSINESS & ECONOMICS / Management Science

Data‐Driven Modeling for Urban Flood Warning Systems: A Case Study in the Guarará Basin, Brazil.

Autoren: Hossoda, Dário Hachisu Perez, Raphael Ferreira Tercini, João Rafael Bergamaschi et al.

Quelle: Journal of Flood Risk Management; Sep2025, Vol. 18 Issue 3, p1-13, 13p

Schlagwörter: FLOOD warning systems, MACHINE learning, FLOOD risk, FLOODS, PRECIPITATION forecasting, SUSTAINABLE urban development, WATERSHEDS, PREDICTION models

Geografische Kategorien: BRAZIL, SAO Paulo (Brazil)

Toward Safer Flight Training: The Data-Driven Modeling of Accident Risk Network Using Text Mining Based on Deep Learning.

Autoren: Zhuang, Zibo Hou, Yongkang Yang, Lei et al.

Quelle: International Journal of Computational Intelligence Systems; 11/26/2024, Vol. 17 Issue 1, p1-21, 21p