'Convolutional neural network' presentation slideshows

Machine Learning in High Energy Physics

Machine Learning in High Energy Physics. Satyaki Bhattacharya Saha Institute for Nuclear Physics. Machine Learning in High Energy Physics. Satyaki Bhattacharya Saha Institute of Nuclear Physics. Why machine learning ?. We are always looking for a faint signal against a large background

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Lecture: Deep Convolutional Neural Networks

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Lecture 9: Machine Learning Overview AP Computer Science Principles

Lecture 9: Machine Learning Overview AP Computer Science Principles. Machine Learning. Machine learning(statistical learning, artificial intelligence) is a large set of tools and algorithms that are used to process and understand data. Two types of machine learning:

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Convolutional neural network for Image recognition

Convolutional neural network for Image recognition. Reference http://cs231n.github.io/convolutional-networks/. By Yunzhe Xue. Dense neural network and Convolutional neural network. A simple CNN structure. CONV: Convolutional kernel layer RELU: Activation function

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GRS - Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People

Recognition languages are developed for the better communication of the challenged people. The recognition signs include the combination of various with hand gestures, movement, arms and facial expressions to convey the words thought. The languages used in sign are rich and complex as equal as to languages that are spoken. As the technological world is growing rapidly, the sign languages for human are made to recognised by systems in order to improve the accuracy and the multiply the various sign languages with newer forms. In order to improve the accuracy in detecting the input sign, a model has been proposed. The proposed model consists of three phases a training phase, a testing phase and a storage output phase. A gesture is extracted from the given input picture. The extracted image is processed to remove the background noise data with the help of threshold pixel image value. After the removal of noise from the image and the filtered image to trained model is tested with a user input and then the detection accuracy is measured. A total of 50 sign gestures were loaded into the training model. The trained model accuracy is measured and then the output is extracted in the form of the mentioned language symbol. The detection mechanism of the proposed model is compared with the other detection methods such as Hidden Markov Model HMM , Convolutional Neural Networks CNN and Support Vector Machine SVM . The classification is done by means of a Support Vector Machine SVM which classifies at a higher accuracy. The accuracy obtained was 99 percent in comparison with the other detection methods. D. Anbarasan | R. Aravind | K. Alice "GRS Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: https://www.ijtsrd.com/papers/ijtsrd9638.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/9638/grs-u2013-gesture-based-recognition-system-for-indian-sign-language-recognition-system-for-deaf-and-dumb-people/d-anbarasan

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