[AISWorld] Newly published papers of JCSE (Dec. 2016)

[office at kiise.org]

Dear Colleague:

 

We are pleased to announce the release of a new issue of Journal of
Computing Science and Engineering (JCSE), published by the Korean Institute
of Information Scientists and Engineers (KIISE). KIISE is the largest
organization for computer scientists in Korea with over 4000 active members.


 

Journal of Computing Science and Engineering (JCSE) is a peer-reviewed
quarterly journal that publishes high-quality papers on all aspects of
computing science and engineering. JCSE aims to foster communication between
academia and industry within the rapidly evolving field of Computing Science
and Engineering. The journal is intended to promote problem-oriented
research that fuses academic and industrial expertise. The journal focuses
on emerging computer and information technologies including, but not limited
to, embedded computing, ubiquitous computing, convergence computing, green
computing, smart and intelligent computing, and human computing. JCSE
publishes original research contributions, surveys, and experimental studies
with scientific advances.

 

Please take a look at our new issue posted at http://jcse.kiise.org
<http://jcse.kiise.org/> . All the papers can be downloaded from the Web
page.

 

The contents of the latest issue of Journal of Computing Science and
Engineering (JCSE)

Official Publication of the Korean Institute of Information Scientists and
Engineers

Volume 10, Number 4, December 2016

 

pISSN: 1976-4677

eISSN: 2093-8020

 

* JCSE web page: http://jcse.kiise.org

* e-submission: http://mc.manuscriptcentral.com/jcse

 

Editor in Chief: Insup Lee (University of Pennsylvania)

Il-Yeol Song (Drexel University) 

Jong C. Park (KAIST)

Taewhan Kim (Seoul National University)

 

 

JCSE, vol. 10, no. 4, December 2016

 

[Paper One]

- Title: Use of Word Clustering to Improve Emotion Recognition from Short
Text

- Authors: Shuai Yuan, Huan Huang, and Linjing Wu

- Keyword: Emotion recognition; Affective computing; Word clustering

 

- Abstract

Emotion recognition is an important component of affective computing, and is
significant in the implementation of natural and friendly human-computer
interaction. An effective approach to recognizing emotion from text is based
on a machine learning technique, which deals with emotion recognition as a
classification problem. However, in emotion recognition, the texts involved
are usually very short, leaving a very large, sparse feature space, which
decreases the performance of emotion classification. This paper proposes to
resolve the problem of feature sparseness, and largely improve the emotion
recognition performance from short texts by doing the following:
representing short texts with word cluster features, offering a novel word
clustering algorithm, and using a new feature weighting scheme. Emotion
classification experiments were performed with different features and
weighting schemes on a publicly available dataset. The experimental results
suggest that the word cluster features and the proposed weighting scheme can
partly resolve problems with feature sparseness and emotion recognition
performance.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 10, no. 4, pp.103-110
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=270&page_url=Cur
rent_Issues> 

 

[Paper Two]

- Title: An ADHD Diagnostic Approach Based on Binary-Coded Genetic Algorithm
and Extreme Learning Machine

- Authors: Vasily Sachnev and Sundaram Suresh

- Keyword: Attention deficit hyperactivity disorder; ADHD-200; Hippocampus;
Binary-coded genetic algorithm

 

- Abstract

An accurate approach for diagnosis of attention deficit hyperactivity
disorder (ADHD) is presented in this paper. The presented technique
efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and
typically developing control (TDC) by using only structural magnetic
resonance imaging (MRI). The research examines structural MRI of the
hippocampus from the ADHD-200 database. Each available MRI has been
processed by a region-of-interest (ROI) to build a set of features for
further analysis. The presented ADHD diagnostic approach unifies feature
selection and classification techniques. The feature selection technique
based on the proposed binary-coded genetic algorithm searches for an optimal
subset of features extracted from the hippocampus. The classification
technique uses a chosen optimal subset of features for accurate
classification of three subtypes of ADHD and TDC. In this study, the famous
Extreme Learning Machine is used as a classification technique. Experimental
results clearly indicate that the presented BCGAELM (binary-coded genetic
algorithm coupled with Extreme Learning Machine) efficiently classifies TDC
and three subtypes of ADHD and outperforms existing techniques.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 10, no. 4, pp.111-117
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=271> 

 

[Paper Three]

- Title: An Improved Sample Balanced Genetic Algorithm and Extreme Learning
Machine for Accurate Alzheimer Disease Diagnosis

- Authors: Vasily Sachnev and Sundaram Suresh

- Keyword: Alzheimer disease; OASIS; Improved samples balanced genetic
algorithm; Extreme Learning Machine

 

- Abstract

An improved sample balanced genetic algorithm and Extreme Learning Machine
(iSBGA-ELM) was designed for accurate diagnosis of Alzheimer disease (AD)
and identification of biomarkers associated with AD in this paper. The
proposed AD diagnosis approach uses a set of magnetic resonance imaging
scans in Open Access Series of Imaging Studies (OASIS) public database to
build an efficient AD classifier. The approach contains two steps: "voxels
selection" based on an iSBGA and "AD classification" based on the ELM. In
the first step, the proposed iSBGA searches for a robust subset of voxels
with promising properties for further AD diagnosis. The robust subset of
voxels chosen by iSBGA is then used to build an AD classifier based on the
ELM. A robust subset of voxels keeps a high generalization performance of AD
classification in various scenarios and highlights the importance of the
chosen voxels for AD research. The AD classifier with maximum classification
accuracy is created using an optimal subset of robust voxels. It represents
the final AD diagnosis approach. Experiments with the proposed iSBGA-ELM
using OASIS data set showed an average testing accuracy of 87%. Experiments
clearly indicated the proposed iSBGA-ELM was efficient for AD diagnosis. It
showed improvements over existing techniques.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 10, no. 4, pp.118-127
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=272> 

 

[Paper Four]

- Title: Company Name Discrimination in Tweets using Topic Signatures
Extracted from News Corpus

- Authors: Beomseok Hong, Yanggon Kim, and Sang Ho Lee

- Keyword: Twitter; Tweet; Word sense discrimination; Topic signature

 

- Abstract

It is impossible for any human being to analyze the more than 500 million
tweets that are generated per day. Lexical ambiguities on Twitter make it
difficult to retrieve the desired data and relevant topics. Most of the
solutions for the word sense disambiguation problem rely on knowledge base
systems. Unfortunately, it is expensive and time-consuming to manually
create a knowledge base system, resulting in a knowledge acquisition
bottleneck. To solve the knowledgeacquisition bottleneck, a topic signature
is used to disambiguate words. In this paper, we evaluate the effectiveness
of various features of newspapers on the topic signature extraction for word
sense discrimination in tweets. Based on our results, topic signatures
obtained from a snippet feature exhibit higher accuracy in discriminating
company names than those from the article body. We conclude that topic
signatures extracted from news articles improve the accuracy of word sense
discrimination in the automated analysis of tweets.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 10, no. 4, pp.128-136
<http://jcse.kiise.org/PublishedPaper/topic_abstract.asp?idx=273> 

 

 

 

[Call For Papers]

Journal of Computing Science and Engineering (JCSE), published by the Korean
Institute of Information Scientists and Engineers (KIISE) is devoted to the
timely dissemination of novel results and discussions on all aspects of
computing science and engineering, divided into Foundations, Software &
Applications, and Systems & Architecture. Papers are solicited in all areas
of computing science and engineering. See JCSE home page at
http://jcse.kiise.org <http://jcse.kiise.org/>  for the subareas.

The journal publishes regularly submitted papers, invited papers, selected
best papers from reputable conferences and workshops, and thematic issues
that address hot research topics. Potential authors are invited to submit
their manuscripts electronically, prepared in PDF files, through
<http://mc.manuscriptcentral.com/jcse> http://mc.manuscriptcentral.com/jcse,
where ScholarOne is used for on-line submission and review. Authors are
especially encouraged to submit papers of around 10 but not more than 30
double-spaced pages in twelve point type. The corresponding author's full
postal and e-mail addresses, telephone and FAX numbers as well as current
affiliation information must be given on the manuscript. Further inquiries
are welcome at JCSE Editorial Office,  <mailto:office at kiise.org>
office at kiise.org (phone: +82-2-588-9240; FAX: +82-2-521-1352).