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Multi-disciplinary Trends in Artificial Intelligence

Overview of attention for book
Cover of 'Multi-disciplinary Trends in Artificial Intelligence'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 BoWT: A Hybrid Text Representation Model for Improving Text Categorization Based on AdaBoost.MH
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    Chapter 2 An Improved Teaching-Learning Based Optimization for Optimization of Flatness of a Strip During a Coiling Process
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    Chapter 3 An Entailment Procedure for Kleene Answer Set Programs
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    Chapter 4 An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection
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    Chapter 5 WSCOVER: A Tool for Automatic Composition and Verification of Web Services Using Heuristic-Guided Model Checking and Logic-Based Clustering
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    Chapter 6 An Improvement of Pattern-Based Information Extraction Using Intuitionistic Fuzzy Sets
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    Chapter 7 Shape Optimization in Product Design Using Interactive Genetic Algorithm Integrated with Multi-objective Optimization
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    Chapter 8 A Hierarchical Learning Approach for Finding Multiple Vehicle Number Plate Under Cluttered Background
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    Chapter 9 Multi-disciplinary Trends in Artificial Intelligence
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    Chapter 10 Causal Basis for Probabilistic Belief Change: Distance vs. Closeness
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    Chapter 11 A Scalable Spatial Anisotropic Interpolation Approach for Object Removal from Images Using Elastic Net Regularization
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    Chapter 12 Identification of Relevant and Redundant Automatic Metrics for MT Evaluation
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    Chapter 13 Bankruptcy Prediction Using Memetic Algorithm
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    Chapter 14 Crowd Simulation in 3D Virtual Environments
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    Chapter 15 Filter-Based Feature Selection Using Two Criterion Functions and Evolutionary Fuzzification
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    Chapter 16 Resolving the Manufacturing Cell Design Problem Using the Flower Pollination Algorithm
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    Chapter 17 A New Multiple Objective Cuckoo Search for University Course Timetabling Problem
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    Chapter 18 Application of Genetic Algorithm for Quantifying the Affect of Breakdown Maintenance on Machine Layout
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    Chapter 19 A Random Forest-Based Self-training Algorithm for Study Status Prediction at the Program Level: minSemi-RF
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    Chapter 20 Multi-disciplinary Trends in Artificial Intelligence
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    Chapter 21 A Comparison of Domain Experts and Crowdsourcing Regarding Concept Relevance Evaluation in Ontology Learning
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    Chapter 22 Evolutionary Analysis and Computing of the Financial Safety Net
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    Chapter 23 Learning to Navigate in a 3D Environment
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    Chapter 24 Analysis of Similarity/Dissimilarity of DNA Sequences Based on Pulse Coupled Neural Network
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    Chapter 25 Computing Sentiment Scores of Adjective Phrases for Vietnamese
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    Chapter 26 Sentiment Analysis Using Anaphoric Coreference Resolution and Ontology Inference
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    Chapter 27 A GA-SSO Based Intelligent Channel Assignment Approach for MR-MC Wireless Sensors Networks
Attention for Chapter 20: Multi-disciplinary Trends in Artificial Intelligence
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6 X users

Citations

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Chapter title
Multi-disciplinary Trends in Artificial Intelligence
Chapter number 20
Book title
Multi-disciplinary Trends in Artificial Intelligence
Published in
Lecture notes in computer science, January 2017
DOI 10.1007/978-3-319-49397-8_20
Book ISBNs
978-3-31-949396-1, 978-3-31-949397-8
Authors

Paul Weng

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 25%
Student > Bachelor 2 25%
Student > Ph. D. Student 1 13%
Unknown 3 38%
Readers by discipline Count As %
Computer Science 4 50%
Decision Sciences 1 13%
Engineering 1 13%
Unknown 2 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 May 2017.
All research outputs
#14,671,229
of 24,998,746 outputs
Outputs from Lecture notes in computer science
#4,055
of 8,155 outputs
Outputs of similar age
#219,902
of 432,362 outputs
Outputs of similar age from Lecture notes in computer science
#92
of 148 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,155 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 50% of its peers.
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 432,362 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.