What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Machine learning, Support vector machine, Deep learning and Big data.
His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Natural hazard.
His work focuses on many connections between Machine learning and other disciplines, such as Linear discriminant analysis, that overlap with his field of interest in Multivariate statistics.
His research in Support vector machine intersects with topics in Cluster analysis, Data mining and Fuzzy logic.
His research in Deep learning tackles topics such as State which are related to areas like Systems engineering and Popularity.
Wind power, Biofuel, Efficient energy use, Neuro-fuzzy and Solar energy is closely connected to Robustness in his research, which is encompassed under the umbrella topic of Big data.
His most cited work include:
- Flood prediction using machine learning models: Literature review (237 citations)
- An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines (183 citations)
- Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters (99 citations)
What are the main themes of his work throughout his whole career to date?
Amir Mosavi spends much of his time researching Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Adaptive neuro fuzzy inference system.
The study incorporates disciplines such as Predictive modelling and Big data in addition to Artificial intelligence.
Machine learning and Robustness are commonly linked in his work.
His Artificial neural network study combines topics in areas such as Intelligent decision support system and Biological system.
The various areas that he examines in his Deep learning study include Ensemble forecasting, State, Urban planning and Taxonomy.
His Perceptron study deals with Mean squared error intersecting with Radial basis function, Correlation coefficient and Coefficient of determination.
He most often published in these fields:
- Artificial intelligence (42.55%)
- Machine learning (31.96%)
- Artificial neural network (21.01%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (42.55%)
- Artificial neural network (21.01%)
- Compressive strength (2.69%)
In recent papers he was focusing on the following fields of study:
Amir Mosavi focuses on Artificial intelligence, Artificial neural network, Compressive strength, Marketing and Adaptive neuro fuzzy inference system.
When carried out as part of a general Artificial intelligence research project, his work on Deep learning and Multilayer perceptron is frequently linked to work in Volume rate, therefore connecting diverse disciplines of study.
His Artificial neural network research incorporates elements of HVAC, Efficient energy use, Mathematical optimization and Benchmark.
His biological study deals with issues like Cement, which deal with fields such as Soft computing.
His Adaptive neuro fuzzy inference system study combines topics from a wide range of disciplines, such as Dimensionless quantity, Shear stress, Transverse plane, Distribution and Machine learning.
Borrowing concepts from Food processing, Amir Mosavi weaves in ideas under Machine learning.
Between 2020 and 2021, his most popular works were:
- Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete (2 citations)
- Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation (2 citations)
- Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation (2 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Compressive strength, Composite material, Aggregate, Context and Cement.
His Compressive strength study incorporates themes from Ultimate tensile strength, Compaction and Volume.
His study on Silica fume, Crumb rubber and Absorption of water is often connected to Satin bowerbird as part of broader study in Composite material.
His Aggregate research incorporates elements of Concrete slump test, Formwork, Bagasse ash, Grout and Process engineering.
A majority of his Context research is a blend of other scientific areas, such as Sunshine duration, Correlation coefficient, Kernel, Gaussian process and Support vector machine.
His biological study spans a wide range of topics, including Bagasse, Waste disposal, Parametric statistics and Soft computing.
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