H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 31 Citations 4,906 322 World Ranking 6091 National Ranking 8

Research.com Recognitions

Awards & Achievements

2010 - ACM Distinguished Member

2010 - Fellow, The World Academy of Sciences

2009 - ACM Senior Member

Overview

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

  • Imputation which intersects with area such as Data mining, Curse of dimensionality, Big data, Cuckoo search and Unsupervised learning,

  • Swarm intelligence together with Supervised learning. His Markov chain study also includes

  • Econometrics together with Computational intelligence,

  • Bayesian probability that connect with fields like Artificial neural network.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics

Tshilidzi Marwala.
(2010)

424 Citations

The use of genetic algorithms and neural networks to approximate missing data in database

Mussa Abdella;Tshilidzi Marwala.
international conference on computational cybernetics (2005)

201 Citations

Missing data: A comparison of neural network and expectation maximization techniques

Fulufhelo V. Nelwamondo;Shakir Mohamed;Tshilidzi Marwala.
Current Science (2007)

183 Citations

Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques

Tshilidzi Marwala.
(2009)

138 Citations

DAMAGE IDENTIFICATION USING COMMITTEE OF NEURAL NETWORKS

Tshilidzi Marwala.
Journal of Engineering Mechanics-asce (2000)

135 Citations

FAULT IDENTIFICATION USING FINITE ELEMENT MODELS AND NEURAL NETWORKS

T. Marwala;H.E.M. Hunt.
Mechanical Systems and Signal Processing (1999)

119 Citations

EARLY CLASSIFICATIONS OF BEARING FAULTS USING HIDDEN MARKOV MODELS, GAUSSIAN MIXTURE MODELS, MEL-FREQUENCY CEPSTRAL COEFFICIENTS AND FRACTALS

Fulufhelo V. Nelwamondo;Tshilidzi Marwala;Unathi Mahola.
(2006)

118 Citations

Image Classification Using SVMs: One-against-One Vs One-against-All

Anthony Gidudu;Greg Hulley;Tshilidzi Marwala.
arXiv: Learning (2007)

105 Citations

Autoencoder networks for HIV classification

Brain Leke Betechuoh;Tshilidzi Marwala;Thando Tettey.
Current Science (2006)

96 Citations

Condition Monitoring Using Computational Intelligence Methods

Tshilidzi Marwala.
(2012)

95 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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