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Food Security in the Economy of the Future: Transition from Digital Agriculture to Agriculture 4.0 Based on Deep Learning
Popkova E.G., Sergi B.S.
and the prospects for agricultural development, demonstrating how Agriculture 4.0 based on AI and deep learning must
AI-based Retrospective Study for Revealing Diagnostic Errors in Chest X-ray Screening
Liauchuk, V., Tarasau, A., Kovalev, V.
diagnostic reports provided by the radiologists. We apply a Deep Learning-based CAD system to the cases from
Editorial: Applications of artificial intelligence, machine learning, and deep learning in plant breeding
Eftekhari M., Ma C., Orlov Y.L.
in artificial intelligence (AI) technologies, including machine learning (ML) and deep learning (DL
Strategic Directions for Smart Agriculture Based on Deep Learning for Future Risk Management of Food Security
Popkova E.G., Litvinova T.N., Zemskova O.M., Dubkova M.F., Karpova A.A.
directions for developing smart agriculture based on deep learning for risk management. The contribution
Automated detection of COVID-19 coronavirus infection based on analysis of chest X-ray images by deep learning methods; [Компьютерная система обнаружения COVID-19 по рентгеновским снимкам легких методами глубокого обучения]
Shchetinin E.Y., Sevastyanov L.A.
the load on the healthcare systems. One of effective methods for detecting COVID-19 is deep learning models
Multimodal text-emoji fusion using deep neural networks for text-based emotion detection in online communication
Kusal S., Patil S., Kotecha K.
deep learning framework for multimodal emotion detection. The proposed framework leverages the feature
A Review of Artificial Intelligence Based Research Results in Agriculture and Livestock Industry
Lu, S., Du, X., Zhouli, Y., Kou, Y.
In recent years, China's agricultural and animal husbandry industry has gradually developed
Lightweight Deep Learning-Based Model for Traffic Prediction in Fog-Enabled Dense Deployed IoT Networks
Ateya A.A., Soliman N.F., Alkanhel R., Alhussan A.A., Muthanna A., Koucheryavy A.
traffic management and avoids network congestion. The developed AI algorithm is a deep learning model
Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model
Hammad M., Chelloug S.A., Alkanhel R., Prakash A.J., Muthanna A., Elgendy I.A., Pławiak P.
in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI
Towards accurate and efficient diagnoses in nephropathology: An AI-based approach for assessing kidney transplant rejection
Fayzullin A., Ivanova E., Grinin V., Ermilov D., Solovyeva S., Balyasin M., Bakulina A., Nikitin P., Valieva Y., Kalinichenko A., Arutyunyan A., Lychagin A., Timashev P.
characteristics, improving reproducibility in clinical practice. This study aims to develop an AI-based solutions

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