Tshilidzi Marwala
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Tshilidzi Marwala has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Tankiso Moloi
Department of Accountancy, College of Business and Economics, University of Johannesburg, South Africa
9 shared publications - E. Habtemariam
Department of Politics and International Relations * School of Electrical and Information Engineering University of Kent University of the Witwatersrand Canterbury Kent Johannesburg South Africa
1 shared publication - Monica Lagazio
Department of Politics and International Relations * School of Electrical and Information Engineering University of Kent University of the Witwatersrand Canterbury Kent Johannesburg South Africa
1 shared publication
Latest Publications
- 1 December 2023
- Published by Springer Nature
- p. 49-58
Stakeholders in the Enterprise Risk Management Setting
- 1 December 2023
- Published by Springer Nature
- p. 49-58
Stakeholders in the Enterprise Risk Management Setting
- 1 December 2023
- Published by Springer Nature
- p. 75-86
The Changing Operating Environment and Potential Role Changes in Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 75-86
The Changing Operating Environment and Potential Role Changes in Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 1-10
Introduction to Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 1-10
Introduction to Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 87-100
Synopsis of the Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 87-100
Synopsis of the Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 67-73
Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 67-73
Enterprise Risk Management in the Fourth Industrial Revolution
- 1 December 2023
- Published by Springer Nature
- p. 59-66
Information Processing Steps and New Capabilities in the Enterprise Risk Management Setting
- 1 December 2023
- Published by Springer Nature
- p. 59-66
Information Processing Steps and New Capabilities in the Enterprise Risk Management Setting
- 1 December 2023
- Published by Springer Nature
- p. 35-48
The Concept of Enterprise Risk Management
- Open Access
Artificial Intelligence for Conflict Management
Militarised conflict is one of the risks that have a significant impact on society. Militarised Interstate Dispute (MID) is defined as an outcome of interstate interactions, which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support Vector Machines (SVMs) have proven to be very good prediction techniques and are introduced for the prediction of MIDs in this study and compared to neural networks. The results show that SVMs predict MID better than NNs while NNs give more consistent and easy to interpret sensitivity analysis than SVMs.
Open AccessArtificial Intelligence for Conflict Management
Militarised conflict is one of the risks that have a significant impact on society. Militarised Interstate Dispute (MID) is defined as an outcome of interstate interactions, which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support Vector Machines (SVMs) have proven to be very good prediction techniques and are introduced for the prediction of MIDs in this study and compared to neural networks. The results show that SVMs predict MID better than NNs while NNs give more consistent and easy to interpret sensitivity analysis than SVMs.