Medicine Has Gone to the Dogs: Deep Learning and Robotic Olfaction to Mimic Working Dogs

Stark, Emily; Hoover, Stephen; DeCesare, Alexandra; Barenholtz, Elan

doi:10.1109/mts.2018.2876216

Cited by 3 publications

(1 citation statement)

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“…In addition to seeing everything, Jeremy Bentham's panopticon can now smell everything as well (cf. Stark et al 2018a). The consensus over this seems to be that such a regime of odors should be regarded as an outgrowth of other biometric activities and should accordingly be opposed.…”

Section: Deep Learningmentioning

confidence: 99%

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Rieger¹

2019

The Democratization of Artificial Intelligence

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Three years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain-a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats.

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Section: Deep Learningmentioning

confidence: 99%

Reduction and Participation

Rieger¹

2019

The Democratization of Artificial Intelligence

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Improving the Detection of Explosives in a MOX Chemical Sensors Array With LSTM Networks

Torres-Tello

Guaman

2020

IEEE Sensors J.

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Evaluation and Analysis of College Students’ Mental Health from the Perspective of Deep Learning

Wang

2022

Wireless Communications and Mobile Computing

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College students are easily affected by the outside world, which leads to mental health problems, so it is particularly important to accurately evaluate and analyze the mental health status of college students. At present, the evaluation and analysis model of college students’ mental health is inaccurate and inefficient, which cannot analyze the mental health problems of college students. In order to evaluate and analyze the mental health problems of college students more accurately, this paper designs an evaluation and analysis model of college students’ mental health from the perspective of in-depth learning. The accuracy of model evaluation and analysis is improved, and a better comparison result is obtained. Firstly, the BP neural network model was compared with the logistic model and ARIMA model, and the results showed that the accuracy of the BP neural network model was more than 70% in five comparisons and was higher than that of the logistic model and ARIMA models. Second, the BP deep learning method is compared with several conventional methods (KNN, MF, NCF, and DMF) in the comparison phase of the model. The RMSE, MAE, and MAPE of the BP method are lower than those of the other four traditional methods. Finally, in the comparative experiment, the precision and AUC of the BP model are improved by 2%, and the three indicators of precision, recall, and F1 are also higher than those of other models. Through the specific evaluation of the five indicators of the four college students, from the five indicators of psychological adaptability, frustration, emotional stability, temperament, and personality, the mental health of the four college students is better.

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Medicine Has Gone to the Dogs: Deep Learning and Robotic Olfaction to Mimic Working Dogs

Cited by 3 publications

References 20 publications

Reduction and Participation

Reduction and Participation

Improving the Detection of Explosives in a MOX Chemical Sensors Array With LSTM Networks

Evaluation and Analysis of College Students’ Mental Health from the Perspective of Deep Learning

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