2010 - Fellow, The World Academy of Sciences
2010 - ACM Distinguished Member
2009 - ACM Senior Member
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.
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.
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.
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.
Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics
The use of genetic algorithms and neural networks to approximate missing data in database
Mussa Abdella;Tshilidzi Marwala.
international conference on computational cybernetics (2005)
Missing data: A comparison of neural network and expectation maximization techniques
Fulufhelo V. Nelwamondo;Shakir Mohamed;Tshilidzi Marwala.
Current Science (2007)
Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques
DAMAGE IDENTIFICATION USING COMMITTEE OF NEURAL NETWORKS
Journal of Engineering Mechanics-asce (2000)
FAULT IDENTIFICATION USING FINITE ELEMENT MODELS AND NEURAL NETWORKS
T. Marwala;H.E.M. Hunt.
Mechanical Systems and Signal Processing (1999)
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.
Image Classification Using SVMs: One-against-One Vs One-against-All
Anthony Gidudu;Greg Hulley;Tshilidzi Marwala.
arXiv: Learning (2007)
Condition Monitoring Using Computational Intelligence Methods
Autoencoder networks for HIV classification
Brain Leke Betechuoh;Tshilidzi Marwala;Thando Tettey.
Current Science (2006)
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