What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial neural network, Artificial intelligence, Computational intelligence, Algorithm and Machine learning.
His Artificial neural network research includes themes of Feature, Data mining, Genetic algorithm, Independent component analysis and Fault.
His Data mining research is multidisciplinary, incorporating elements of Entropy, Multilayer perceptron and Expectation–maximization algorithm.
His studies deal with areas such as Economic problem, Economic data, Condition monitoring and Pattern recognition as well as Artificial intelligence.
Tshilidzi Marwala interconnects Field, Game theory, Management science and Economic model in the investigation of issues within Computational intelligence.
His study in Algorithm is interdisciplinary in nature, drawing from both Modal, Modal analysis, Finite element method, Frequency response and Numerical analysis.
His most cited work include:
- Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics (149 citations)
- The use of genetic algorithms and neural networks to approximate missing data in database (136 citations)
- Missing data: A comparison of neural network and expectation maximization techniques (102 citations)
What are the main themes of his work throughout his whole career to date?
Tshilidzi Marwala mainly investigates Artificial intelligence, Artificial neural network, Machine learning, Data mining and Pattern recognition.
His research brings together the fields of Genetic algorithm and Artificial intelligence.
His Genetic algorithm research focuses on subjects like Simulated annealing, which are linked to Particle swarm optimization.
His research integrates issues of Fuzzy logic, Fault, Bayesian probability and Condition monitoring in his study of Artificial neural network.
Tshilidzi Marwala has included themes like Principal component analysis and Missing data in his Data mining study.
His studies in Missing data integrate themes in fields like Deep learning, Estimation and Swarm intelligence.
He most often published in these fields:
- Artificial intelligence (40.92%)
- Artificial neural network (27.53%)
- Machine learning (19.69%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (40.92%)
- Algorithm (10.52%)
- Machine learning (19.69%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Algorithm, Machine learning, Missing data and Deep learning.
Tshilidzi Marwala studies Artificial neural network, a branch of Artificial intelligence.
As a member of one scientific family, Tshilidzi Marwala mostly works in the field of Artificial neural network, focusing on Credit default swap and, on occasion, Hybrid Monte Carlo.
The study incorporates disciplines such as Rejection sampling, Monte Carlo method and k-means clustering, Cluster analysis in addition to Algorithm.
The various areas that Tshilidzi Marwala examines in his Machine learning study include Bayesian probability, Directed acyclic graph and Causal model.
His research investigates the link between Missing data and topics such as Swarm intelligence that cross with problems in Imputation, Data mining and Unsupervised learning.
Between 2016 and 2021, his most popular works were:
- A new T-S fuzzy model predictive control for nonlinear processes (43 citations)
- Implications of the Fourth Industrial Age for Higher Education (20 citations)
- Blockchain and Artificial Intelligence. (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Missing data, Algorithm, Machine learning and Deep learning.
His research in Artificial intelligence intersects with topics in Management science, Gaussian process, Blockchain, Smart contract and Pattern recognition.
His work deals with themes such as Rejection sampling, Hybrid Monte Carlo, k-means clustering, Cluster analysis and Monte Carlo method, which intersect with Algorithm.
His study in Machine learning is interdisciplinary in nature, drawing from both Collaborative learning, Estimation and Presentation.
His study on Deep learning also encompasses disciplines like
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Imputation which intersects with area such as Data mining, Curse of dimensionality, Big data, Cuckoo search and Unsupervised learning,
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Swarm intelligence together with Supervised learning.
His Markov chain study also includes
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Econometrics together with Computational intelligence,
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Bayesian probability that connect with fields like Artificial neural network.
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