A
Amir Mosavi
Researcher at Óbuda University
Publications - 491
Citations - 12774
Amir Mosavi is an academic researcher from Óbuda University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 38, co-authored 432 publications receiving 6209 citations. Previous affiliations of Amir Mosavi include University of Debrecen & National Yunlin University of Science and Technology.
Papers
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Journal ArticleDOI
An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
Bahram Choubin,Bahram Choubin,Ehsan Moradi,Mohammad Golshan,Jan Adamowski,Farzaneh Sajedi-Hosseini,Amir Mosavi +6 more
TL;DR: In this paper, the authors employed two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach.
Journal ArticleDOI
Flood prediction using machine learning models: Literature review
TL;DR: In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.
Journal ArticleDOI
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Amir Mosavi,Amir Mosavi,Amir Mosavi,Mohsen Salimi,Sina Ardabili,Timon Rabczuk,Shahaboddin Shamshirband,Annamária R. Várkonyi-Kóczy +7 more
TL;DR: There is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models.
Posted Content
Sustainable Business Models: A Review
Saeed Nosratabadi,Amir Mosavi,Shahab Shamshirband,Edmundas Kazimieras Zavadskas,Andry Rakotonirainy,Kwok Wing Chau +5 more
TL;DR: In this article, a comprehensive review of sustainable business models literature in various application areas is provided, which provides an insight into the state-of-the-art of sustainability business models and future research directions.
Journal ArticleDOI
COVID-19 outbreak prediction with machine learning
Sina Ardabili,Amir Mosavi,Pedram Ghamisi,Filip Ferdinand,Annamária R. Várkonyi-Kóczy,Uwe Reuter,Timon Rabczuk,Peter M. Atkinson +7 more
TL;DR: A comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models suggests machine learning as an effective tool to model the outbreak.