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James Chambua

College of Information and Communication Technologies

Computer Science and Engineering

Biography

Dr. James Chambua is a Lecturer and Postgraduate Studies Coordinator at the Computer Science and Engineering Department at the University of Dar-es-Salaam. He is also a mentor and supervisor for the Artificial Intelligence for Development in Africa (AI4D Africa) Scholarship Programme, an initiative funded by the International Development Research Centre (IDRC) and managed by the African Centre for Technology Studies (ACTS). Dr. Chambua holds a Bachelor of Science with Computer Science, and a Master of Science in Computer Science focusing on localization of Open-Source Software to Swahili, from the University of Dar-es-Salaam. He also holds a doctorate degree in Computer Science and Technology specializing in Artificial Intelligence and Machine Learning Algorithms from the Beijing Institute of Technology - China. 

His research focuses on understanding, designing and extending AI/ML algorithms for recommendation, retrieval and classification systems, evaluating the impact of users’ review texts and auxiliary information. Dr. Chambua research has explored and leveraged artificial neural networks, specifically Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM) and Convolution Neural Networks (CNN), to capture semantic similarities, linguistic patterns and user sentiments. His research works have been published in international journals including Artificial Intelligence Review, Expert Systems with Applications, Neurocomputing, Weather, Knowledge-Based Systems, IEEE Transactions on Knowledge and Data Engineering and UDSM library.

Research Interest

  • Understand, design and extend AI/ML algorithms
  • Recommender systems
  • Information Retrieval
  • software Engineering
  • Information Systems

 

Contacts

Email:

Projects

Position: Researcher / ICT Expert July – September, 2020 

Ministry of Finance and Planning, URT

Tasks: 

  • Assessment of the adoption, usefulness and analysis of the Government electronic Payment Gateway (GePG) performance in enhancing Non-Tax Revenue collection.
  • Designing data collection instruments, performing data analysis and report writing.

Position: Researcher  December, 2020 – May, 2021 

Tanzania Revenue Authority, TRA

Tasks: 

  • Analyzed and evaluated the performance of the selected information systems implemented by TRA in enhancing tax revenue collection.
  • Assessed the existing ICT management including Technical Human Resource (ICT Skills Audit) capacity to support the implemented and expected ICT systems.

    established the current status of existing ICT systems in TRA in terms of their functionalities, utilization, relevance and investment cost.

Position: Research Assistant Sept, 2004 – June, 2008   

Kilinux Project – Swedish International Development Agency, Dar-es-Salaam (Tanzania)

Tasks:

  • Customized and translated Open Office 3.1 and Mozilla Firefox to Kiswahili
  • Developed Tux Koti la Rangi (Kiswahili Painting App for kids)

Publications

  1. Prince, R., Niu, Z., Khan, Z. Y., ChambuaJ., Yousif, A., Patrick, N., et al. (2025). Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network. Comput. Biol. Med. 186:109659. doi: https://doi.org/10.1016/j.compbiomed.2025.109659
  2. Duma, R.A., Niu, Z., Nyamawe, A., Tchaye-Kondi, J., ChambuaJ., & Yusuf, A.A.,  DHMFRD – TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-15193-4
  3. James Chambua, (2022) “Rating Prediction based on Optimal Review Topics: A Proposed Latent Factors-Optimal Topics Hybrid Approach” [J]. University of Dar es Salaam Library Journal, 17 (1), pages 38 – 53. https://dx.doi.org/10.4314/udslj.v17i1.4 
  1. Shanshan Wan, Ying Liu, Dongwei Qiu, James Chambua, Zhendong Niu (2022) “A dual learning-based recommendation approach” Knowledge Based System, 254. https://10.1016/j.knosys.2022.109551 

 

  1. James Chambua, Zhendong Niu, (2021) “Review Text Rating Prediction Approaches: Preference Knowledge Learning, Representation and Utilization” [J]. Artificial Intelligence Review, https://doi.org/10.1007/s10462-020-09873-y  (SCI)