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Richard Kitondua Lubanzadio

Publications (3)

Optimization of Logistics Cost through Computer Analysis and Simulation of Transport Problem-solving Algorithms

Pascaline Kizodisa Mbilankazi, Camile Likotelo Binene, Mukoko Matondo Pamela, Télesphore Nsumbu Lukamba, Francis Mayala Lemba, Richard Kitondua Lubanzadio & Ruffin Ngoie Mpoy · Asian Journal of Research in Computer Science · 2026

In a global context characterized by increasingly complex supply chains, growing competitive pressure on distribution costs, and the need to strengthen logistics robustness in both private and public sectors, transport optimization has become a major challenge. This research aims...

Open access Research Article 10.9734/ajrcos/2026/v19i5865

Hybrid Packet Learning Approaches for Early Diabetes Detection and Optimization of Therapeutic Strategies: A Comparative Analysis of Stack, Replacement and Reinforcement Models

Joël Mangoma-Joël, Levi Lubaki Budiena, Evariste Kantshia Bakatubia, Thierry Honorius Kanza, Pierre Kafunda Katalay & Richard Kitondua Lubanzadio · Asian Journal of Research in Computer Science · 2026

This thesis focuses on the comparative analysis of ensemble learning methods, including bagging, boosting, and stacking, in the context of early detection of diabetic retinopathy and personalized treatment. The main objective is to propose an intelligent system capable of estimat...

Open access Research Article 10.9734/ajrcos/2026/v19i4846

Comparison and Optimization of the Robustness of Recommendation Models in the Face of Noised Data in E-Commerce

Bruce Mbombi Bakondolo, Télesphore Nsumbu Lukamba, Christophe Ebaka Bongelo, Jean Cibamba Kanyinda, Blaise Kapalala Kapenda, Pierre Kamuina Kambayi, Gédéon Mbala Mbuyamba & Richard Kitondua Lubanzadio · Asian Journal of Research in Computer Science · 2026

Recommendation systems play a crucial role in e-commerce, but their performance is often degraded by noisy data such as accidental clicks, erroneous implicit interactions, and ambiguous user behavior. This study compares the robustness of three recommendation approaches: collabor...

Open access Research Article 10.9734/ajrcos/2026/v19i5860