首页|Recent Findings from Al-Iraqia University Highlight Research in Machine Learning (Machine Learning Versus Deep Learning for Contact Detection in Human-Robot Col laboration)
Recent Findings from Al-Iraqia University Highlight Research in Machine Learning (Machine Learning Versus Deep Learning for Contact Detection in Human-Robot Col laboration)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from Baghdad, Iraq, by New sRx correspondents, research stated, “Due to the rapid progression of Human-Robo t Collaboration (HRC), ensuring safe interactions between humans and robots, con tact detecting systems must be dependable and efficient.” Our news reporters obtained a quote from the research from Al-Iraqia University: “In this research, various models are tested using a contact detection dataset that includes non-contact motions, intentional interactions, and accidental coll isions among others. K-Nearest Neighbors (KNN), Bagging, and Long Short-Term Mem ory (LSTM) networks are evaluated on their ability to classify different types o f contacts. According to the findings of the experiment, it is clear that KNN an d Bagging are reasonably accurate, but LSTM has surpassed both by achieving high er accuracy levels besides being better at handling temporal dependencies which are inherent in sensor data collected from dynamic human-robot interactions. The results have shown that when it comes to such kind of contact detection dataset s, long short-term memory (LSTM) and other deep learning models are superior to other methods. These results show that HRC systems can be made safer and more ef fective by using more sophisticated neural networks.”
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