scholarly journals Convolutional Neural Network Classification of Telematics Car Driving Data

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6 ◽  
Author(s):  
Guangyuan Gao ◽  
Mario Wüthrich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Location Data ◽  
Neural Network Classification ◽  
Feature Information ◽  
Car Driving

The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks.

Image Classification of Time Series Based on Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Wenjie Cao ◽  
Cheng Zhang ◽  
Zhenzhen Xiong ◽  
Ting Wang ◽  
Junchao Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Deep Convolutional Neural Network

Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

2021 ◽  
Author(s):  
Luke Gundry ◽  
Gareth Kennedy ◽  
Alan Bond ◽  
Jie Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reaction Mechanisms ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Neural Network Classification ◽  
Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

An Artificial Neural Network Classification of Prescription Nonadherence

2020 ◽  
pp. 487-501
Author(s):  
Steven Walczak ◽  
Senanu R. Okuboyejo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Healthcare Providers ◽  
Classification Model ◽  
Survey Method ◽  
Neural Network Classification ◽  
Treatment Plans ◽  
Ann Models ◽  
Artificial Neural

This study investigates the use of artificial neural networks (ANNs) to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.

scholarly journals Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4017 ◽  
Author(s):  
Davor Kolar ◽  
Dragutin Lisjak ◽  
Michał Pająk ◽  
Danijel Pavković
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Input Matrix ◽  
Accelerometer Signal ◽  
Rotary Machinery ◽  
Signal Input

Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.

scholarly journals A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

2019 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Bingxin Liu ◽  
Ying Li ◽  
Guannan Li ◽  
Anling Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Oil Film ◽  
One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks

2021 ◽  
Vol 24 ◽  
Author(s):  
Qingyu Tian ◽  
Mao Ding ◽  
Hui Yang ◽  
Caibin Yue ◽  
Yue Zhong ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Drug Target ◽  
Channel Model ◽  
Search Space ◽  
Topological Features ◽  
Latent Features ◽  
Chemical Background

Background: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative, but also more challenging, to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance. Method: We have improved the Graph DTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in Graph DTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then input into a 2-layer FC network, and a valuable binding affinity is output. Result: We use the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model shows better performance by comparing it with the experimental results of Graph DTA. Conclusion: In this paper, we altered the Graph DTA model to predict drug-target affinity. It represents the drug as a graph, and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

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