scholarly journals Application of Deep Learning Approaches for Enhancing Mastcam Images

2020 ◽  
Author(s):  
Ying Qu ◽  
Hairong Qi ◽  
Chiman Kwan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Network Models ◽  
Learning Approaches ◽  
Neural Network Models ◽  
Mars Rover ◽  
The Right

There are two mast cameras (Mastcam) onboard the Mars rover Curiosity. Both Mastcams are multispectral imagers with nine bands in each. The right Mastcam has three times higher resolution than the left. In this chapter, we apply some recently developed deep neural network models to enhance the left Mastcam images with help from the right Mastcam images. Actual Mastcam images were used to demonstrate the performance of the proposed algorithms.

scholarly journals Comparing Sample-Wise Learnability across Deep Neural Network Models

2019 ◽  
Vol 33 ◽  
pp. 9961-9962
Author(s):  
Seung-Geon Lee ◽  
Jaedeok Kim ◽  
Hyun-Joo Jung ◽  
Yoonsuck Choe
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Importance Sampling ◽  
Deep Neural Network ◽  
Network Models ◽  
Relative Importance ◽  
Learning Context ◽  
Training Set ◽  
Neural Network Models ◽  
The Given

Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind of focus on individual samples invokes the concept of samplewise learnability: How easy is it to correctly learn each sample (cf. PAC learnability)? In this paper, we approach the sample-wise learnability problem within a deep learning context. We propose a measure of the learnability of a sample with a given deep neural network (DNN) model. The basic idea is to train the given model on the training set, and for each sample, aggregate the hits and misses over the entire training epochs. Our experiments show that the samplewise learnability measure collected this way is highly linearly correlated across different DNN models (ResNet-20, VGG-16, and MobileNet), suggesting that such a measure can provide deep general insights on the data’s properties. We expect our method to help develop better curricula for training, and help us better understand the data itself.

scholarly journals Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

2021 ◽  
Vol 21 (3) ◽  
pp. 175-188
Author(s):  
Sumaiya Thaseen Ikram ◽  
Aswani Kumar Cherukuri ◽  
Babu Poorva ◽  
Pamidi Sai Ushasree ◽  
Yishuo Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Network Models ◽  
Neural Network Models ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Techniques

Abstract Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature extraction is also automated in these techniques. In this paper, an ensemble of different Deep Neural Network (DNN) models like MultiLayer Perceptron (MLP), BackPropagation Network (BPN) and Long Short Term Memory (LSTM) are stacked to build a robust anomaly detection model. The performance of the ensemble model is analysed on different datasets, namely UNSW-NB15 and a campus generated dataset named VIT_SPARC20. Other types of traffic, namely unencrypted normal traffic, normal encrypted traffic, encrypted and unencrypted malicious traffic, are captured in the VIT_SPARC20 dataset. Encrypted normal and malicious traffic of VIT_SPARC20 is categorised by the deep learning models without decrypting its contents, thus preserving the confidentiality and integrity of the data transmitted. XGBoost integrates the results of each deep learning model to achieve higher accuracy. From experimental analysis, it is inferred that UNSW_ NB results in a maximal accuracy of 99.5%. The performance of VIT_SPARC20 in terms of accuracy, precision and recall are 99.4%. 98% and 97%, respectively.

scholarly journals Medication-rights detection using incident reports: A natural language processing and deep neural network approach

2019 ◽  
Vol 26 (3) ◽  
pp. 1777-1794
Author(s):  
Zoie Shui-Yee Wong ◽  
HY So ◽  
Belinda SC Kwok ◽  
Mavis WS Lai ◽  
David TF Sun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Network ◽  
Detection System ◽  
Network Models ◽  
Incident Reporting ◽  
Neural Network Models ◽  
The Right

Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using natural language processing and deep neural networks to automate medication-incident identification using free-text incident reports. We assessed the performance of deep neural network models in classifying the Advanced Incident Reporting System reports and compared the models’ performance with that of other common classification methods (including logistic regression, support vector machines and the decision-tree method). We also evaluated the effects on prediction outcomes of several deep neural network model settings, including number of layers, number of neurons and activation regularisation functions. The accuracy of the models was measured at 0.9 or above across model settings and algorithms. The average values obtained for accuracy and area under the curve were 0.940 (standard deviation: 0.011) and 0.911 (standard deviation: 0.019), respectively. It is shown that deep neural network models were more accurate than the other classifiers across all of the tested class labels (including wrong patient, wrong drug, wrong time, wrong dose and wrong route). The deep neural network method outperformed other binary classifiers and our default base case model, and parameter arguments setting generally performed well for the five medication-rights datasets. The medication-rights detection system developed in this study successfully uses a natural language processing and deep-learning approach to classify patient-safety incidents using the Advanced Incident Reporting System reports, which may be transferable to other mandatory and voluntary incident reporting systems worldwide.

scholarly journals Exploring Methods for Predicting Important Utterances Contributing to Meeting Summarization

2019 ◽  
Vol 3 (3) ◽  
pp. 50
Author(s):  
Nihei ◽  
Nakano
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
User Study ◽  
Prediction Models ◽  
Network Models ◽  
Feature Model ◽  
Feature Models ◽  
Neural Network Models ◽  
F Measure

Meeting minutes are useful, but creating meeting summaries are a time consuming task. Aiming at supporting such task, this paper proposes prediction models for important utterances that should be included in the meeting summary by using multimodal and multiparty features. We will tackle this issue from two approaches: Handcrafted feature models and deep neural network models. The best handcrafted feature model achieved 0.707 in F-measure, and the best deep-learning based verbal and nonverbal model (V-NV model) achieved 0.827 in F-measure. Based on the V-NV model, we implemented a meeting browser, and conducted a user study. The results showed that the proposed meeting browser better contributes to the understanding of the content of the discussion and the participant roles in the discussion than the conventional text-based browser.

scholarly journals IUCNN - deep learning approaches to approximate species' extinction risk

2021 ◽  
Author(s):  
Alexander Zizka ◽  
Tobias Andermann ◽  
Daniele Silvestro
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Extinction Risk ◽  
Class Imbalance ◽  
Network Models ◽  
Training Data ◽  
Assessment Process ◽  
Learning Approaches ◽  
Neural Network Models

Aim: The global Red List (RL) from the International Union for the Conservation of Nature is the most comprehensive global quantification of extinction risk, and widely used in applied conservation as well as in biogeographic and ecological research. Yet, due to the time-consuming assessment process, the RL is biased taxonomically and geographically, which limits its application on large scales, in particular for understudied areas such as the tropics, or understudied taxa, such as most plants and invertebrates. Here we present IUCNN, an R-package implementing deep learning models to predict species RL status from publicly available geographic occurrence records (and other traits if available). Innovation: We implement a user-friendly workflow to train and validate neural network models, and subsequently use them to predict species RL status. IUCNN contains functions to address specific issues related to the RL framework, including a regression-based approach to account for the ordinal nature of RL categories and class imbalance in the training data, a Bayesian approach for improved uncertainty quantification, and a target accuracy threshold approach that limits predictions to only those species whose RL status can be predicted with high confidence. Most analyses can be run with few lines of code, without prior knowledge of neural network models. We demonstrate the use of IUCNN on an empirical dataset of ~14,000 orchid species, for which IUCNN models can predict extinction risk within minutes, while outperforming comparable methods. Main conclusions: IUCNN harnesses innovative methodology to estimate the RL status of large numbers of species. By providing estimates of the number and identity of threatened species in custom geographic or taxonomic datasets, IUCNN enables large-scale analyses on the extinction risk of species so far not well represented on the official RL.

An evaluation of deep neural network models for music classification using spectrograms

2021 ◽  
Author(s):  
Jingxian Li ◽  
Lixin Han ◽  
Xiaoshuang Li ◽  
Jun Zhu ◽  
Baohua Yuan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Music Classification

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

Feature extraction-based image steganalysis using deep learning

2021 ◽  
pp. 188-198
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Secure Communication ◽  
Information Technologies ◽  
Network Models ◽  
Error Rates ◽  
Multimedia Data ◽  
Secret Message ◽  
Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

Application of Deep Neural Network Models in Drug Discovery Programs

ChemMedChem ◽  
2021 ◽  
Author(s):  
Christoph Grebner ◽  
Hans Matter ◽  
Daniel Kofink ◽  
Jan Wenzel ◽  
Friedemann Schmidt ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Deep Neural Network ◽  
Network Models ◽  
Neural Network Models
Sign in / Sign up

Export Citation Format

Share Document