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Brian Mupini
Harare Institute of Technology
Computer Science & IT Engineering

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Risk-integrated contractor allocation in Zimbabwe’s timber value chain Tavengwa Norman; Brian Mupini
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p337-345

Abstract

In Zimbabwe, the commercial forestry industry is reliant on a large proportion of outsourced harvest and milling contractors whereas existing enterprise resource planning (ERP) systems record completed transactions instead of predicting contractor failure before assigning activities. We introduce the dynamic resource allocation framework (DRAF), a risk-integrated decision-support framework that supplements the probability of contractor failure derived from XGBoost to a non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimizer. It divides contractor-block-mill combinations, minimizes cost and expected delay, and maximizes risk-adjusted timber recovery and operational reliability. We created the solution on an 828,789-record virtual ERP dataset and calibrated it to Manicaland forestry conditions and tested with statistical, heuristic and risk-free optimization baselines. Extreme gradient boosting (XGBoost) obtained a holdout receiver operating characteristic area under the curve (ROC-AUC) of 0.965, recall of 0.999, and F1 of 0.867, showing an improvement of 0.365 over logistic regression. The optimizer developed 64 complete Pareto solutions to balanced and high-recovery scenarios and uncovered a constraint-feasibility boundary for a more conservative low-risk scenario. The satisfaction score of the 30-practitioner stakeholder assessment was 4.19 out of 5.0. The results demonstrate that embedding predictive risk into an optimization objective can optimize forestry allocation decisions and suggest that some real ERP validation is required before such measures will be broadly implemented.
Early Escherichia coli prediction in broiler chickens Nicole Chimwamafuku; Brian Mupini
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p314-324

Abstract

Poultry farming remains an important contributor to global food security and commercial livestock production. However, infectious diseases such as Escherichia coli (E. coli) cause mortality, poor feed efficiency, reduced growth performance, and economic losses in broiler production systems. This study proposes a checkpoint-based multimodal transformer-convolutional neural networks (CNN) framework for early flock-level E. coli infection risk prediction using environmental, production, behavioural, and visual poultry data. Flock monitoring records collected from 2022 to 2025 were structured across six production checkpoints: day 3, day 7, day 14, day 21, day 28, and day 31. After long-format conversion, approximately 90,000 temporal observations were used for transformer modelling, with 72,000 records for training and 18,000 for testing. The CNN component evaluated 249 poultry images across healthy, low-risk, medium-risk, high-risk, and non-broiler classes. The transformer model achieved 99.96% accuracy, while the CNN model achieved 95.58% accuracy. The integrated dashboard generated flock risk scores, contributing factors, alerts, gradient-weighted class activation mapping (Grad-CAM) explanations, and veterinary advisory recommendations, demonstrating the potential of multimodal artificial intelligence (AI) for proactive poultry health monitoring.
Co-Authors Nicole Chimwamafuku Tavengwa Norman