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ICT Innovations 2019. Big Data Processing and Mining

Overview of attention for book
ICT Innovations 2019. Big Data Processing and Mining
Springer International Publishing

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Automatic Text Generation in Macedonian Using Recurrent Neural Networks
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    Chapter 2 Detection of Toy Soldiers Taken from a Bird’s Perspective Using Convolutional Neural Networks
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    Chapter 3 Prediction of Student Success Through Analysis of Moodle Logs: Case Study
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    Chapter 4 Multidimensional Sensor Data Prediction
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    Chapter 5 Five Years Later: How Effective Is the MAC Randomization in Practice? The No-at-All Attack
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    Chapter 6 Improvement of the Binary Varshamov Bound
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    Chapter 7 Electrical Energy Consumption Prediction Using Machine Learning
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    Chapter 8 Diatom Ecological Modelling with Weighted Pattern Tree Algorithm by Using Polygonal and Gaussian Membership Functions
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    Chapter 9 A Study of Different Models for Subreddit Recommendation Based on User-Community Interaction
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    Chapter 10 Application of Hierarchical Bayesian Model in Ophtalmological Study
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    Chapter 11 Friendship Paradox and Hashtag Embedding in the Instagram Social Network
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    Chapter 12 An In-Depth Analysis of Personality Prediction
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    Chapter 13 Ski Injury Predictions with Explanations
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    Chapter 14 Performance Evaluation of Word and Sentence Embeddings for Finance Headlines Sentiment Analysis
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    Chapter 15 A Hybrid Model for Financial Portfolio Optimization Based on LS-SVM and a Clustering Algorithm
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    Chapter 16 Protein Secondary Structure Graphs as Predictors for Protein Function
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    Chapter 17 Exploring the Attention Mechanism in Deep Models: A Case Study on Sentiment Analysis
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    Chapter 18 Image Augmentation with Neural Style Transfer
Overall attention for this book and its chapters
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1 tweeter

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Title
ICT Innovations 2019. Big Data Processing and Mining
Published by
Springer International Publishing, October 2019
DOI 10.1007/978-3-030-33110-8
ISBNs
978-3-03-033109-2, 978-3-03-033110-8
Editors

Gievska, Sonja, Madjarov, Gjorgji

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 40%
Student > Doctoral Student 1 20%
Student > Bachelor 1 20%
Unknown 1 20%
Readers by discipline Count As %
Engineering 2 40%
Computer Science 1 20%
Nursing and Health Professions 1 20%
Unknown 1 20%