Rhodes>Mathematics>Research>Artificial Intelligence Research Group (AIRG)

Rhodes Artificial Intelligence Research Group (RAIRG)

Rhodes Artificial Intelligence Research Group (RAIRG) is based in the Department of Mathematics. The academics in the group have expertise in applied mathematics, computer science, Bayesian techniques, artificial intelligence, signal processing, statistics, and modern astronomical data processing/analysis. Interdisciplinary research thrusts are a major ambition of the group. We welcome MSc, Ph.D., and postdoc candidates willing to learn and contribute. 

Staff:

Dr Marcellin Atemkeng (Coordinator), email: m.atemkeng@ru.ac.za 

Dr Patrice Okouma, email: p.okouma@ru.ac.za 

Mr Sisipho Hamlomo, email: S.Hamlomo@ru.ac.za

 

Available Projects:

Honours

MSc

PhD

 

Current students:

Irene Nandutu
 

Degree: PhD

Subject: Applied mathematics

Field of Study: Machine Learning
Research: Understanding the integration of sensor technology and machine learning to mitigate conservation problems, with a keen focus on animal-vehicle collisions.
Based: Grahamstown
Future aims: Irene intends to improve her personal and professional development skills. She further plans to continue enriching herself and others' technical knowledge by contributing to interdisciplinary research and artificial intelligence. As well as engage in developing sustainable solutions with a high social and environmental impact.
 
 
Nicole Oyetunji
Student_RAIRG
 
Degree: Masters of Science

Subject: Applied mathematics

Field of Study: Machine Learning
Research: Towards an artificial intelligence-based agent for characterizing the organization of prime numbers
Based: Grahamstown
Future aims: She hopes to continue her journey of building skills for contributing towards a transition to sustainable practices in Research and Artificial Intelligence in the developing world.
 
 
Masechaba Sydil Kupa

 

Degree: Masters of Science
Subject: Physics
Field of Study: Machine Learning
Research: Automating Image Segmentation and Classifying Radio Galaxies Using Deep Neural Network
Based: Cape Town
Future aims: Simultaneous to my MSC, I am a Junior Software Developer at SARAO( South African Radio Astronomy Observatory). My Future endeavors involve proceeding with the development of technical tools that will be useful in Astronomy and later translated to solve social problems. 
 

 

Avuya Deyi

 

Degree: Masters of Science
Subject: Applied Mathematics
Field of Study: Machine Learning
Research: Tackling deep learning models compression for medicinal plant classification. 
Based: Grahamstown
Future aims: His interest in the field of machine learning is rooted from the passion he has for mathematics. He hopes to work for a corporate company as a data scientist. He holds an honours degree in Applied Mathematics and Information Systems.
 
sisipho hamlomo

 

Degree: MSc
Subject: Statistics
Field of Study: Machine Learning
Research: Detection of credit fraud using statistical and machine learning techniques
Based: Grahamstown
Future aims: His future plan is to join the industry once the degree is fully completed, more specifically he wants to be in the consulting industry where he would be able to extensively apply his mathematical skills in solving the daily challenges that arise in real life.  
 
 
Vanqa Kamva

 

Degree: Honours
Subject: Pure and applied mathematics
Field of Study: Big data and interferometric techniques
Research: Understanding and modeling aliasing source sidelobes contamination in the field of view of a radio interferometer
Based: Grahamstown
Future aims: I am aspiring to develop myself to a more sophisticated scientist particularly in the study of mathematical sciences, I also wish to extend my interest to fields like computer science and modern physics.
 
 

Myren Govender

 

Degree: Honours [Graduated]
Subject:  Applied mathematics
Field of Study: Machine learning
Research: Exploring the effects of downsampling in deep convolutional neural networks
Based: Grahamstown
Future aims:  He wants to further his education and eventually become a lecturer in applied mathematics.
 
  
 

Selected Publications

2022:
  • Atemkeng, M.; Okouma, P.; Maina, E.; Ianjamasimanana, R.; Zambou, S. Radio Astronomical Antennas in the Central African Region to Improve the Sampling Function of the VLBI Network in the SKA Era? Sensors 2022, 22, 8466.
  • Fendji, J.L.K.E., Tala, D.C., Yenke, B.O. and Atemkeng, M., 2022. Automatic Speech Recognition using limited vocabulary: A survey. Applied Artificial Intelligence36(1), p.2095039.
  • Nandutu, I.; Atemkeng, M.; Mgqatsa, N.; Toadoum Sari, S.; Okouma, P.; Rockefeller, R.; Ansah-Narh, T.; Ebongue Kedieng Fendji, J.L.; Tchakounte, F. Error Correction Based Deep Neural Networks for Modeling and Predicting South African Wildlife–Vehicle Collision Data. Mathematics 2022, 10, 3988. 
  • Ianjamasimanana, R., Koribalski, B.S., Józsa, G.I., Kamphuis, P., de Blok, W.J.G., Kleiner, D., Namumba, B., Carignan, C., Dettmar, R.J., Serra, P. and Smirnov, O.M., 2022. The extended H i halo of NGC 4945 as seen by MeerKAT. Monthly Notices of the Royal Astronomical Society513(2), pp.2019-2038.
  • Nandutu, I.; Atemkeng, M.; Okouma, P. Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives. Sensors 2022, 22, 2478.
2021:
  • Nandutu, I., Atemkeng, M., & Okouma, P. (2021). Integrating AI ethics in wildlife conservation AI systems in South Africa: a review, challenges, and future research agenda. AI & SOCIETY, 1-13.
  • Brima, Y., Atemkeng, M., Tankio Djiokap, S., Ebiele, J., & Tchakounté, F. (2021). Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images. Diagnostics11(8), 1480.
  •  Nguegnang, G. M., Atemkeng, M., Ansah-Narh, T., Rockefeller, R.,Mulongo, J., & Garuti, M. A. (2021). Predicting fuel consumption in power generation plants using machine learning and neural networks. IEEE International Conference on Electrical, Computer and Energy Technologies (ICECET) Cape Town-South Africa, 9-10 December 2021.
  • Fendji, J. L. E. K., Taira, D. M., Atemkeng, M., & Ali, A. M. (2021). WATS-SMS: A T5-Based French Wikipedia Abstractive Text Summarizer for SMS. Future Internet13(9), 238.
  • Atemkeng, M.,  Perkins,  S., Kenyon, J., Hugo, B. and Oleg Smirnov (2021).  Xova: Baseline-Dependent Time and Channel Averaging for Radio Interferometry, Proceedings of the Astronomical Data Analysis Software and Systems (ADASS) conference, Spain.
   2020:
  • Atemkeng, M., Smirnov, O., Tasse, C., Foster, G., & Makhathini, S. (2020). Fast algorithms to approximate the position-dependent point spread function responses in radio interferometric wide-field imaging. Monthly Notices of the Royal Astronomical Society.
  • Fountsop, A. N., Ebongue Kedieng Fendji, J. L., & Atemkeng, M. (2020). Deep Learning Models Compression for Agricultural Plants. Applied Sciences10(19), 6866.
  • Tchakounté F, Pagore AE, Kamgang JC, Atemkeng M. (2020). CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme for Android Apps based on Sentiment Analysis of Reviews. Future Internet, 12(9), 145.
  • Ebiele F, Ansah-Narh T, Djiokap S, Proven-Adzri E, Atemkeng M. (2020). Conventional Machine Learning based on Feature Engineering for Detecting Pneumonia from Chest X-rays. ACM Annual Conference of the South African Institute of Computer Scientists and Information Technologists, Cape Town Sep 14, 2020.
  • Tchakounté, F., Faissal, A., Atemkeng, M., & Ntyam, A. (2020). A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake News. Information11(6), 319.
  • Cotton, W.D., Thorat, K., Condon, J.J., Frank, B.S., Józsa, G.I.G., White, S.V., Deane, R., Oozeer, N., Atemkeng, M., Bester, L. and Fanaroff, B. (2020). Hydrodynamical backflow in X-shaped radio galaxy PKS 2014− 55. Monthly Notices of the Royal Astronomical Society495(1), pp.1271-1283.
  • Mulongo, J., Atemkeng, M., Ansah-Narh, T., Rockefeller, R., Nguegnang, G. M., & Garuti, M. A. (2020). Anomaly Detection in Power Generation Plants Using Machine Learning and Neural Networks. Applied Artificial Intelligence34(1), 64-79.
 
 

Last Modified: Thu, 03 Nov 2022 19:25:15 SAST