scholarly journals Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach

2019 ◽  
Vol 49 ◽  
pp. 120-129 ◽  
Author(s):  
Filipe Rodrigues ◽  
Ioulia Markou ◽  
Francisco C. Pereira
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Approach ◽  
Demand Prediction ◽  
Textual Data

scholarly journals Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

2021 ◽  
Vol 3 (3) ◽  
pp. 234-248
Author(s):  
N. Bhalaji
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Deep Learning ◽  
Extreme Points ◽  
Learning Approach ◽  
Load Estimation ◽  
Learning Method ◽  
Network Computing ◽  
Noise Interference

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

2020 ◽  
Vol 12 (2) ◽  
pp. 21-34
Author(s):  
Mostefai Abdelkader
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Learning Approach ◽  
Probabilistic Representation ◽  
Effective Manner ◽  
Textual Data ◽  
Positive Class ◽  
Negative Class ◽  
Deep Learning Model

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.

A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin

2020 ◽  
Vol 13 (3) ◽  
pp. 915-927 ◽  
Author(s):  
Dostdar Hussain ◽  
Tahir Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
River Basin ◽  
Time Series Prediction ◽  
Learning Approach

scholarly journals A deep learning approach for anomaly detection with industrial time series data: a refrigerators manufacturing case study

2019 ◽  
Vol 38 ◽  
pp. 233-240 ◽  
Author(s):  
Mattia Carletti ◽  
Chiara Masiero ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Series Data ◽  
Learning Approach

scholarly journals SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data

2019 ◽  
Vol 6 (4) ◽  
pp. 6618-6628 ◽  
Author(s):  
Yi-Fan Zhang ◽  
Peter J. Thorburn ◽  
Wei Xiang ◽  
Peter Fitch
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Sensor Data ◽  
Learning Approach

scholarly journals Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

2021 ◽  
Vol 13 (22) ◽  
pp. 4599
Author(s):  
Félix Quinton ◽  
Loic Landrieu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Rotation ◽  
Large Scale ◽  
State Of The Art ◽  
Crop Rotations ◽  
Learning Approach ◽  
Type Mapping ◽  
Current State ◽  
Crop Type

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

scholarly journals DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1991-2005 ◽  
Author(s):  
Mohsin Munir ◽  
Shoaib Ahmed Siddiqui ◽  
Andreas Dengel ◽  
Sheraz Ahmed
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Learning Approach ◽  
Unsupervised Anomaly Detection
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