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 Support vector machine, Artificial neural network, Mean squared error, Soft computing and Adaptive neuro fuzzy inference system.
His study in Support vector machine is interdisciplinary in nature, drawing from both Firefly algorithm, Data mining and Radial basis function.
The study incorporates disciplines such as Genetic programming, Wavelet transform, Time horizon and Pan evaporation in addition to Artificial neural network.
His work deals with themes such as Coefficient of determination, Correlation coefficient and Meteorology, which intersect with Mean squared error.
His Soft computing research incorporates elements of Intrusion detection system, Intrusion prevention system, Mechanical engineering, Wireless sensor network and Cloud computing.
He combines subjects such as Computational fluid dynamics and Neuro-fuzzy with his study of Adaptive neuro fuzzy inference system.
His most cited work include:
- A systematic literature review on agile requirements engineering practices and challenges (225 citations)
- Coupling a firefly algorithm with support vector regression to predict evaporation in Northern Iran (189 citations)
- Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (189 citations)
What are the main themes of his work throughout his whole career to date?
Shahaboddin Shamshirband mainly investigates Adaptive neuro fuzzy inference system, Support vector machine, Artificial neural network, Soft computing and Artificial intelligence.
His Adaptive neuro fuzzy inference system study incorporates themes from Wind power, Wind speed and Neuro-fuzzy.
His Support vector machine research includes elements of Firefly algorithm, Data mining, Radial basis function and Mean squared error, Statistics.
His research in Artificial neural network intersects with topics in Algorithm and Genetic programming.
His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
Shahaboddin Shamshirband usually deals with Fuzzy logic and limits it to topics linked to Intrusion detection system and Wireless sensor network.
He most often published in these fields:
- Adaptive neuro fuzzy inference system (31.28%)
- Support vector machine (21.81%)
- Artificial neural network (17.84%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (15.86%)
- Machine learning (9.91%)
- Random forest (1.98%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Machine learning, Random forest, Support vector machine and Deep learning.
Artificial neural network is the focus of his Artificial intelligence research.
His biological study spans a wide range of topics, including Data mining, Expression, Streamflow, Inflow and Autoregressive model.
The study of Machine learning is intertwined with the study of Fuzzy logic in a number of ways.
He has included themes like Correlation coefficient, Decision tree, Significant wave height, Wave height and Algorithm in his Support vector machine study.
His Mean squared error course of study focuses on Adaptive neuro fuzzy inference system and Shear velocity.
Between 2019 and 2021, his most popular works were:
- Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (51 citations)
- Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain. (35 citations)
- Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Support vector machine, Machine learning, Random forest and Mean squared error.
The concepts of his Support vector machine study are interwoven with issues in Radial basis function, Decision tree, Neuro-fuzzy, Algorithm and Wavelet transform.
His research integrates issues of Groundwater model and Resampling in his study of Machine learning.
His Mean squared error study is concerned with the larger field of Statistics.
His research integrates issues of Wind speed, Adaptive neuro fuzzy inference system, Pan evaporation and k-nearest neighbors algorithm in his study of Statistics.
His Correlation coefficient research incorporates themes from Artificial neural network, Recurrent neural network, Data mining, Deep learning and Hydrogeology.
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