Over the past few centuries, petrochemical disasters have occurred both internationally and domestically, with a large number of casualties. Due to the high purity and quantity of combustible materials, the consequenc...
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This paper presents a study focused on the analysis of phytosanitary production. The objective is to predict pro-duction per unit, in order to optimize production efficiency, as an essential key in the supply chain. T...
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This paper presents a study focused on the analysis of phytosanitary production. The objective is to predict pro-duction per unit, in order to optimize production efficiency, as an essential key in the supply chain. T...
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ISBN:
(数字)9798350313352
ISBN:
(纸本)9798350313369
This paper presents a study focused on the analysis of phytosanitary production. The objective is to predict pro-duction per unit, in order to optimize production efficiency, as an essential key in the supply chain. The data for this study encompasses production data, product names, and input param-eters (electricity usage, water consumption, and raw materials), with production as the target variable. The machine learning technique applied for forecasting is the K-Nearest Neighbors regression (KNN-R) algorithm, known for its robust performance, especially on small datasets. This results in a predictive model with a mean absolute error (MAE) of 0.052 and a mean squared error (MSE) of 0.0057. This research provides valuable insights for agricultural and food companies seeking to improve their production forecasting methods and enhance the efficiency of agricultural product manufacturing processes.
The goal of this paper is to develop a deep learning model for predicting citrus yield. The data used consists of two sources: (1) field data that includes information on fertilization and phytosanitary treatment prod...
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Underground prospecting operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. This can result in vehicle-to-vehicle collisions,...
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ISBN:
(纸本)9781450396943
Underground prospecting operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. This can result in vehicle-to-vehicle collisions, as well as vehicle-to-pedestrian or structural-element collisions, resulting in accidents. In this article, we discuss an anti-collision system for pedestrian identification in deep mines under the premise that we are looking to prevent collisions with moving machinery. This study presents the findings from testing an image processing module and sensory system based on deep learnig in the context of "smart connected mine" project.
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