Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Deep Learning <s> Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU ($\approx$ 2.5 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments. Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia. <s> BIB001 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Deep Learning <s> Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first method explores various convolutional temporal feature pooling architectures, examining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improvements over previously published results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.6% vs. 88.0%) and without additional optical flow information (82.6% vs. 73.0%). <s> BIB002 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Deep Learning <s> Children with autism often experience sudden meltdowns which not only makes the moment tough for the caretakers/parents but also make the children hurt themselves physically. Studies have discovered that children with autistic spectrum disorder exhibit certain actions through which we can anticipate mutilating meltdowns in them. The objective of our project is to build a system that can recognize such kind of actions using deep learning techniques thereby, notifying the caretakers/parents so that they can get the situation under control in lesser time. Using deep learning RCNNs, we can train the system faster yet reliable because unlike all the machine learning algorithms, deep learning algorithms are more efficient and have more scope into future. We have trained a classifier on images that are gathered from videos and reliable internet sources with most predictive gestures, through which we can detect the meltdowns more precisely. We have trained a model that validated the accuracy by ~93% which is accompanied by a loss/train classifier with a minimal 0.4% loss. Functional testing was done through feeding the deep neural network with chosen actions performed by five individuals that resulted in an accuracy of ~92% in all cases, which can assure the real-time usage of the system. <s> BIB003
Deep learning uses multiple hidden layers in a neural network architecture that mimic the brain's neural connections. In the reviewed literature, deep learning has been used to study predictors of challenging behavior and analyze neuroimaging in individuals with ASD. BIB003 to identify behaviors that precede challenging behaviors, including meltdowns and self-injurious behavior, in children with ASD. The authors took advantage of the NVIDIA Deep Learning GPU Training System (DIGITS) along with the deep learning framework Caffe BIB001 ) to rapidly train a convolutional neural network (CNN). As the name suggests, CNNs have Bconvolutional^and subsampling layers, allowing the model to extract features while preserving spatial relationships  . Using video and images collected from various databases and Internet sources, the authors were able to train a recurrent CNN to identify common gestures displayed before a meltdown by children with ASD. A recurrent CNN has additional weights allowing cycles within the network (Yue-Hei BIB002 . On average, the model was trained in 30 to 60 min and tested on video recording of five individuals with 92% accuracy. Since the gestures were identified in less than 5 s on average, authors also developed an alarming mechanism to alert caregivers in real time. By using this model to train a more robust model and the addition of enhanced hardware, there is great potential for helping both children with ASD and their caregivers before meltdowns occur.