What is he best known for?
The fields of study he is best known for:
- Machine learning
- Statistics
- Artificial neural network
Mahdi Hasanipanah mainly focuses on Mean squared error, Artificial neural network, Particle velocity, Structural engineering and Linear regression.
His Mean squared error research is multidisciplinary, relying on both Algorithm, Particle swarm optimization and Adaptive neuro fuzzy inference system.
Mahdi Hasanipanah frequently studies issues relating to Support vector machine and Particle swarm optimization.
His Inference system study in the realm of Adaptive neuro fuzzy inference system interacts with subjects such as Statistical function.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Overpressure, Coefficient of determination, Data mining and Performance prediction.
His research investigates the connection with Structural engineering and areas like Face which intersect with concerns in k-nearest neighbors algorithm.
His most cited work include:
- Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network (156 citations)
- Feasibility of indirect determination of blast induced ground vibration based on support vector machine (119 citations)
- Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling (115 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Mean squared error, Artificial neural network, Algorithm, Coefficient of determination and Particle swarm optimization.
He has researched Mean squared error in several fields, including Empirical modelling, Adaptive neuro fuzzy inference system, Linear regression and Support vector machine.
His studies in Artificial neural network integrate themes in fields like Data mining and Rock mass classification.
His study looks at the relationship between Algorithm and topics such as Swarm behaviour, which overlap with Kriging and Geotechnical engineering.
His work in Coefficient of determination addresses issues such as Genetic algorithm, which are connected to fields such as Differential evolution and Reliability.
Mahdi Hasanipanah focuses mostly in the field of Particle swarm optimization, narrowing it down to topics relating to Sensitivity and, in certain cases, Regression analysis and Least squares support vector machine.
He most often published in these fields:
- Mean squared error (52.56%)
- Artificial neural network (32.05%)
- Algorithm (29.49%)
What were the highlights of his more recent work (between 2020-2021)?
- Mean squared error (52.56%)
- Artificial neural network (32.05%)
- Adaptive neuro fuzzy inference system (20.51%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mean squared error, Artificial neural network, Adaptive neuro fuzzy inference system, Algorithm and Coefficient of determination.
His biological study spans a wide range of topics, including Least squares support vector machine, Support vector machine, Void ratio and Optimization problem.
Mahdi Hasanipanah works mostly in the field of Artificial neural network, limiting it down to topics relating to Particle swarm optimization and, in certain cases, Control theory.
When carried out as part of a general Adaptive neuro fuzzy inference system research project, his work on Inference system is frequently linked to work in Cultural algorithm, Statistical function and Integrated approach, therefore connecting diverse disciplines of study.
His work in the fields of Algorithm, such as Metaheuristic algorithms, intersects with other areas such as Ground vibrations.
In his research, Linear regression, Bearing capacity and Empirical modelling is intimately related to Genetic algorithm, which falls under the overarching field of Coefficient of determination.
Between 2020 and 2021, his most popular works were:
- Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting (45 citations)
- A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration (22 citations)
- GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles (17 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial neural network
Mahdi Hasanipanah mainly investigates Mean squared error, Support vector machine, Coefficient of determination, Genetic algorithm and Artificial neural network.
The various areas that Mahdi Hasanipanah examines in his Coefficient of determination study include Algorithm, Particle swarm optimization, Mathematical optimization and Linear regression.
His Genetic algorithm research includes elements of Empirical modelling and Bearing capacity.
His Artificial neural network research incorporates themes from Adaptive neuro fuzzy inference system, Pile, Statistics and Firefly algorithm.
Many of his studies involve connections with topics such as Predictive modelling and Firefly algorithm.
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