Advanced Ambulatory Operating Stretcher Learned by Means of Convulational Neural Network (CNN)

Talpur, Sariena; Khoso, Nazaruddin

doi:10.14738/jbemi.53.4660

Cited by 2 publications

(2 citation statements)

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“…They obtained an accuracy of 78.20% by using augmented datasets. [24] Presented a self-operating stretcher developed using CNN transfer learning that solves the problem of transporting patients from long distances or short distances. The stretcher is trained to detect and identify the objects in its path.…”

Section: B Cnn Application In Classification Problemsmentioning

confidence: 99%

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A Convolutional Neural Network for Automatic Identification and Classification of Fall Army Worm Moth

Chulu¹,

Phiri²,

Nkunika³

et al. 2019

IJACSA

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To combat the problem caused by the Fall Army Worm in the country there is a need to come up with robust early warning and monitoring systems as the current manual system is labor intensive and time consuming. The automation of the identification and classification of the insect is one of the novel methods that can be undertaken. Therefore this paper presents the results of training a Convolutional Neural Network model using Google's Tensorflow Deep Learning Framework for the identification and classification of the Fall Army worm moth. Due to lack of enough training dataset and good computing power, we used transfer learning, which is the process of reusing a model trained on one task as a starting point for a model on a second task. Googles pre-trained InceptionV3 model was used as the underlying model. Data was collected from four sources namely the field, Lab setup, by crawling the internet and using Data Augmentation. We Present results of the best three trials in terms of training accuracy after several attempts to get the best metrics in terms of learning rate and training steps. The best model gave a prediction average accuracy of 82% and a 32% average prediction accuracy on false positives. The results shows that it is possible to automate the identification and classification of the Fall Army worm Moth using Convolutional Neural Networks.

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Section: B Cnn Application In Classification Problemsmentioning

confidence: 99%

“…The stretcher is trained to detect and identify the objects in its path. Authors in [17,23,24] showed that CNN are also applicable in medicine and are helping to solve many challenges that the field faces. [25] Applied CNN to the automatic classification of mosquitoes in the field.…”

Section: B Cnn Application In Classification Problemsmentioning

confidence: 99%