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Trends and Applications in Knowledge Discovery and Data Mining

Overview of attention for book
Cover of 'Trends and Applications in Knowledge Discovery and Data Mining'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Identification of Harvesting Year of Barley Seeds Using Near-Infrared Hyperspectral Imaging Combined with Convolutional Neural Network
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    Chapter 2 Plant Leaf Disease Segmentation Using Compressed UNet Architecture
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    Chapter 3 Hierarchical Topic Model for Tensor Data and Extraction of Weekly and Daily Patterns from Activity Monitor Records
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    Chapter 4 Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images
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    Chapter 5 Unsupervised Noise Reduction for Nanochannel Measurement Using Noise2Noise Deep Learning
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    Chapter 6 Classification Bandits: Classification Using Expected Rewards as Imperfect Discriminators
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    Chapter 7 Overview and Insights from Scope Detection of the Peer Review Articles Shared Tasks 2021
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    Chapter 8 Scholarly Text Classification with Sentence BERT and Entity Embeddings
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    Chapter 9 Domain Identification of Scientific Articles Using Transfer Learning and Ensembles
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    Chapter 10 Identifying Topics of Scientific Articles with BERT-Based Approaches and Topic Modeling
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    Chapter 11 Using Transformer Based Ensemble Learning to Classify Scientific Articles
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    Chapter 12 1st International Workshop on Data Assessment and Readiness for AI
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    Chapter 13 Cooperative Monitoring of Malicious Activity in Stock Exchanges
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    Chapter 14 Data-Debugging Through Interactive Visual Explanations
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    Chapter 15 Data Augmentation for Fairness in Personal Knowledge Base Population
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    Chapter 16 ROC Bot: Towards Designing Virtual Command Centre for Energy Management
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    Chapter 17 Digitize-PID: Automatic Digitization of Piping and Instrumentation Diagrams
Attention for Chapter 4: Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

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3 Mendeley
Summary X
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Chapter title
Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images
Chapter number 4
Book title
Trends and Applications in Knowledge Discovery and Data Mining
Published in
Lecture notes in computer science, May 2021
DOI 10.1007/978-3-030-75015-2_4
Book ISBNs
978-3-03-075014-5, 978-3-03-075015-2
Authors

Ryosuke Kaneko, Hiromichi Nagao, Shin-ichi Ito, Kazushige Obara, Hiroshi Tsuruoka, Kaneko, Ryosuke, Nagao, Hiromichi, Ito, Shin-ichi, Obara, Kazushige, Tsuruoka, Hiroshi

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 67%
Researcher 1 33%
Readers by discipline Count As %
Linguistics 1 33%
Earth and Planetary Sciences 1 33%
Economics, Econometrics and Finance 1 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 August 2021.
All research outputs
#14,554,120
of 23,308,124 outputs
Outputs from Lecture notes in computer science
#4,348
of 8,160 outputs
Outputs of similar age
#227,464
of 438,811 outputs
Outputs of similar age from Lecture notes in computer science
#8
of 14 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,160 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,811 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.

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