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Item type:Publication, Quantifying Emergent Behaviors in Agent-Based Models using Mean Information Gain(RELX Group (Netherlands), 2025-01-01)5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving Asset Allocation in a Fast Moving Consumer Goods B2B Company: An Interpretable Machine Learning Framework for Commercial Cooler Assignment Based on Multi-Tier Growth Targets(Institute of Electrical and Electronics Engineers Inc., 2025-11-10)In the fast-moving consumer goods (FMCG) industry, deciding where to place physical assets, such as commercial beverage coolers, can directly impact revenue growth and execution efficiency. Although churn prediction and demand forecasting have been widely studied in B2B contexts, the use of machine learning to guide asset allocation remains relatively unexplored. This paper presents a framework focused on predicting which beverage clients are most likely to deliver strong returns in volume after receiving a cooler. Using a private dataset from a well-known Central American brewing and beverage company of 3,119 B2B traditional trade channel clients that received a cooler from 2022-01 to 2024-07, and tracking 12 months of sales transactions before and after cooler installation, three growth thresholds were defined: 10%, 30% and 50% growth in sales volume year over year. The analysis compares results of machine learning models such as XGBoost, LightGBM, and CatBoost combined with SHAP for interpretable feature analysis in order to have insights into improving business operations related to cooler allocation; the results show that the best model has AUC scores of 0.857, 0.877, and 0.898 across the thresholds on the validation set. Simulations suggest that this approach can improve ROI because it better selects potential clients to grow at the expected level and increases cost savings by not assigning clients that will not grow, compared to traditional volume-based approaches with substantial business management recommendations2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhanced Underwater 3-D Reconstruction: Case Study at Los Organos Reef Using 3dFeatureUp and 3dVisualAppUp(Institute of Electrical and Electronics Engineers Inc., 2026)In recent years, photogrammetry for tridimensional (3-D) reconstruction of underwater environments has gained significant interest, offering a nonintrusive approach to study and monitor aquatic ecosystems. However, underwater photogrammetry requires specialized equipment capable of capturing high-resolution images with adequate and uniform lighting. This article introduces two innovative methodologies, 3-D reconstruction based on Feature Enhancement (3dFeatureUp) and 3-D reconstruction based on Visual Appearance Enhancement (3dVisualAppUp), which are designed to enhance the accuracy and visual quality of 3-D underwater models. These methodologies incorporate novel algorithms for water image enhancement (WaterImgEnh) and water image restoration (WaterImgRest), aiming to address the challenges posed by underwater image acquisition such as light attenuation, color distortion, and visibility reduction. This approach is validated through the 3-D reconstruction of Los Organos Reef, located in Piura, Peru, employing a combination of standard and specialized underwater cameras along with a customized lighting system. The results demonstrate significant improvements in both the structural characteristics and visual appearance of the 3-D models, as compared to those generated by traditional methods or using specialized underwater cameras alone. The 3dFeatureUp method significantly enhances the structural features of the model by merging images from multiple cameras, while the 3dVisualAppUp methodology improves the visual quality of the models by correcting color imbalances and removing water effects.