What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Zaher Mundher Yaseen mainly investigates Mean squared error, Artificial intelligence, Artificial neural network, Statistics and Support vector machine.
The concepts of his Mean squared error study are interwoven with issues in Coefficient of determination, Correlation coefficient and Time series.
His research integrates issues of Reliability and Machine learning in his study of Artificial intelligence.
His work in the fields of Feedforward neural network overlaps with other areas such as Empirical modelling.
His research in Statistics focuses on subjects like Streamflow, which are connected to Process and Regression analysis.
His Support vector machine study incorporates themes from Extreme learning machine, Spline, Multivariate statistics and Regression.
His most cited work include:
- An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction (235 citations)
- Artificial intelligence based models for stream-flow forecasting: 2000–2015 (191 citations)
- Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq (135 citations)
What are the main themes of his work throughout his whole career to date?
Zaher Mundher Yaseen spends much of his time researching Mean squared error, Artificial neural network, Statistics, Artificial intelligence and Support vector machine.
His Mean squared error research integrates issues from Correlation coefficient, Coefficient of determination, Extreme learning machine, Algorithm and Multilayer perceptron.
Zaher Mundher Yaseen interconnects Structural engineering and Streamflow in the investigation of issues within Artificial neural network.
His work on Regression, Linear regression and Predictive modelling as part of general Statistics research is frequently linked to Scale, thereby connecting diverse disciplines of science.
His research ties Machine learning and Artificial intelligence together.
The study incorporates disciplines such as Shear strength and Predictability in addition to Support vector machine.
He most often published in these fields:
- Mean squared error (28.90%)
- Artificial neural network (18.81%)
- Statistics (20.64%)
What were the highlights of his more recent work (between 2020-2021)?
- Mean squared error (28.90%)
- Algorithm (8.72%)
- Artificial intelligence (17.43%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Mean squared error, Algorithm, Artificial intelligence, Support vector machine and Statistics.
His work deals with themes such as Soil science, Correlation coefficient, Salp swarm algorithm, Range and Metaheuristic optimization algorithms, which intersect with Mean squared error.
His Algorithm research is multidisciplinary, relying on both Genetic algorithm, Swarm behaviour and Series.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Wavelet and Wavelet transform.
His Support vector machine research includes themes of Uncertainty analysis, Artificial neural network, Coefficient of determination, Sensitivity and Principal component analysis.
His study in the field of Predictive modelling, Regression and Markov chain Monte Carlo also crosses realms of Filter.
Between 2020 and 2021, his most popular works were:
- Reliability-based structural design optimization: hybridized conjugate mean value approach (18 citations)
- The Application of Soft Computing Models and Empirical Formulations for Hydraulic Structure Scouring Depth Simulation: A Comprehensive Review, Assessment and Possible Future Research Direction (16 citations)
- Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia. (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Zaher Mundher Yaseen mostly deals with Artificial intelligence, Support vector machine, Algorithm, Mean squared error and Rainwater harvesting.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
His Support vector machine study incorporates themes from Artificial neural network, Statistics and Feature selection.
His work in the fields of Artificial neural network, such as Mean absolute percentage error, overlaps with other areas such as Environmental pollution.
The various areas that he examines in his Statistics study include Ensemble forecasting, Bay and Overfitting.
His study looks at the intersection of Mean squared error and topics like Predictability with Uncertainty analysis.
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