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Full-Text Articles in Engineering

Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven A. Freeman, Gretchen A. Mosher Aug 2019

Evaluating Machine Learning Performance In Predicting Injury Severity In Agribusiness Industries, Fatemeh Davoudi Kakhki, Steven A. Freeman, Gretchen A. Mosher

Agricultural and Biosystems Engineering Publications

Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines ...


Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar Jul 2019

Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar

Biological Systems Engineering--Dissertations, Theses, and Student Research

Mid-late season weeds are those that escape the early season herbicide applications and those that emerge late in the season. They might not affect the crop yield, but if uncontrolled, will produce a large number of seeds causing problems in the subsequent years. In this study, high-resolution aerial imagery of mid-season weeds in soybean fields was captured using an unmanned aerial vehicle (UAV) and the performance of two different automated weed detection approaches – patch-based classification and object detection was studied for site-specific weed management. For the patch-based classification approach, several conventional machine learning models on Haralick texture features were compared ...


Intelligent Software Tools For Recruiting, Swatee B. Kulkarni, Xiangdong Che Jul 2019

Intelligent Software Tools For Recruiting, Swatee B. Kulkarni, Xiangdong Che

Journal of International Technology and Information Management

In this paper, we outline how recruiting and talent acquisition gained importance within HRM field, then give a brief introduction to the newest tools used by the professionals for recruiting and lastly, describe the Artificial Intelligence-based tools that have started playing an increasingly important role. We also provide further research suggestions for using artificial intelligence-based tools to make recruiting more efficient and cost-effective.


Weld Penetration Identification Based On Convolutional Neural Network, Chao Li Jan 2019

Weld Penetration Identification Based On Convolutional Neural Network, Chao Li

Theses and Dissertations--Electrical and Computer Engineering

Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms.

A GTAW welding system is firstly built. Project a dot-structured laser pattern onto the weld pool surface during welding process, the reflected laser pattern ...