scholarly journals Application of the DeepSense Deep Learning Framework to Determination of Activity Context from Smartphone Data

2019 ◽  
Vol 63 (1) ◽  
pp. 792-796
Author(s):  
Bethany K. Bracken ◽  
Shashank Manjunath ◽  
Stan German ◽  
Camille Monnier ◽  
Mike Farry
Keyword(s):  
Deep Learning ◽  
Feature Learning ◽  
Sensor Data ◽  
Learning Framework ◽  
User Behaviors ◽  
Single User ◽  
Determination Of Activity ◽  
Injury Status

Current methods of assessing health are infrequent, costly, and require advanced medical equipment. 92% of US adults carry mobile phones, and 77% carry smartphones with advanced sensors (Smith, 2017). Smartphone apps are already being used to identify disease (e.g., skin cancer), but these apps require active participation by the user (e.g., uploading images). The goal of this research is to develop algorithms that enable continuous and real-time assessment of individuals by leveraging data that is passively and unobtrusively captured by cellphone sensors. Our first step to accomplish this is to identify the activity context in which the device is used as this affects the accuracy and reliability of sensor data for measuring and inferring a user’s health; data should be interpreted differently when the user is walking or running versus on a plane or bus. To do this, we use DeepSense, a deep learning approach to feature learning first developed by (Yao, Hu, Zhao, Zhang, & Abdelzaher, 2017). Here we present six experiments validating our model on: (1) a baseline implementation of DeepSense on the same data used by Yao et al., (2017) achieving a balanced accuracy (BA) of 95% over the six main contexts; (2) its ability to classify context using a different publically-available dataset (the ExtraSensory dataset) using the same 70/30 train/test split used by Vaizman et al. (2018), with a BA of 75%; (3) its ability to achieve improved classification when training on a single user, with a BA of 78%; (4) its ability to achieve accurate classification of a new user with a BA of 63%; (5) its improvement to 70% BA for new users when we considered phone placement to remove confounding information, and (6) its ability to accurately classify contexts over all 51 contexts collected by Vaizman et al, achieving a BA of 80% on 9 contexts, 75% on 12, and 70% on 17. We are now working to improve these results by adding other sensors available through smartphone data collection included in the ExtraSensory dataset (e.g., microphone). This will allow us to more accurately assess minor deviations in user behaviors that could indicate changes in health or injury status by accurately accounting for irrelevant, inaccurate, or misleading readings due to contextual effects that may confound interpretation.

Fast Image Search with Pixel-Based Deep Learning Framework via Efficient Compact Binary Code and Addictive Latent Layer

2017 ◽  
Vol 32 (03) ◽  
pp. 1859004 ◽  
Author(s):  
Jun Yi Li ◽  
Jian Hua Li
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Feature Learning ◽  
Nearest Neighbor Search ◽  
Feature Representation ◽  
Image Feature ◽  
Retrieval Algorithm ◽  
Image Search ◽  
Learning Framework ◽  
Compact Binary

As we know, the nearest neighbor search is a good and effective method for good-sized image search. This paper mainly introduced how to learn an outstanding image feature representation form and a series of compact binary Hash coding functions under deep learning framework. Our concept is that binary codes can be obtained using a hidden layer to present some latent concepts dominating the class labels with usable data labels. Our method is effective in obtaining hash codes and image representations, so it is suitable for good-sized dataset. It is demonstrated in our experiment that the performances of the proposed algorithms were then verified on three different databases, MNIST, CIFAR-10 and Caltech-101. The experimental results reveal that two-proposed image Hash retrieval algorithm based on pixel-level automatic feature learning show higher search accuracy than the other algorithms; moreover, these two algorithms were proved to be more favorable in scalability and generality.

Classification of remote sensed images using random forests and deep learning framework

2016 ◽  
Author(s):  
S. Piramanayagam ◽  
W. Schwartzkopf ◽  
F. W. Koehler ◽  
E. Saber
Keyword(s):  
Deep Learning ◽  
Random Forests ◽  
Learning Framework

scholarly journals APPLICATION OF DEEP LEARNING OF MULTI-TEMPORAL SENTINEL-1 IMAGES FOR THE CLASSIFICATION OF COASTAL VEGETATION ZONE OF THE DANUBE DELTA

2018 ◽  
Vol XLII-3 ◽  
pp. 1311-1318 ◽  
Author(s):  
S. Niculescu ◽  
D. Ienco ◽  
J. Hanganu
Keyword(s):  
Deep Learning ◽  
Temporal Characteristic ◽  
Sensor Data ◽  
Coastal Vegetation ◽  
Danube Delta ◽  
Time Dimension ◽  
Vegetation Zone ◽  
Multi Temporal ◽  
Deep Learning Network

Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.

scholarly journals DeepRank: A deep learning framework for data mining 3D protein-protein interfaces

2021 ◽  
Author(s):  
Nicolas Renaud ◽  
Cunliang Geng ◽  
Sonja Georgievska ◽  
Francesco Ambrosetti ◽  
Lars Ridder ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Data Sets ◽  
Biological Processes ◽  
Learning Framework ◽  
Fundamental Information ◽  
Protein Interfaces

AbstractThree-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance.We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression.We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive or outperforms state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

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