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Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results.
WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting
To help mitigate this data collection bottleneck in wheat breeding, we propose a novel deep learning framework to accurately and efficiently count wheat heads to aid in the gathering of real-time data for decision making.
Regularization and False Alarms Quantification: Two Sides of the Explainability Coin
Regularization is a well-established technique in machine learning (ML) to achieve an optimal bias-variance trade-off which in turn reduces model complexity and enhances explainability.
Correspondent Banking Networks: Theory and Experiment
We employ the mathematical programming approach in conjunction with the graph theory to study the structure of correspondent banking networks.
A Swift Heuristic Method for Work Order Scheduling under the Skilled-Workforce Constraint
The considered problem is how to optimally allocate a set of jobs to technicians of different skills such that the number of technicians of each skill does not exceed the number of persons with that skill designation.
