[AISWorld] Newly published papers of JCSE (Dec. 2017)

[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 11, Number 4, December 2017

 

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. 11, no. 4, December 2017

 

[Paper One]

- Title: Identifying Post-translational Modification Crosstalks for Breast
Cancer

- Authors: Chi-Hua Tung, Pei-Wei Shueng, Yen-Wei Chu, Chi-Wei Chen, and
Chian-Ying Chen

- Keyword: Post-translational modification; Crosstalk; Sequence analysis;
Breast cancer

 

- Abstract

Post-translational modifications (PTMs) of proteins play substantial roles
in the gene regulation of cell physiological functions and in the generation
of major diseases. However, the majority of existing studies only explored a
certain PTM of proteins, while very few have investigated the PTMs of two or
more domains and the effects of their interactions. In this study, after
collecting data regarding a large number of breast cancer-related and
validated PTMs, a sequence and domain analysis of breast cancer proteins was
carried out using bioinformatics methods. Then, protein-protein interaction
network-related tools were applied in order to determine the crosstalks
between the PTMs of the proteins. Finally, statistical and functional
analyses were conducted to identify more modification sites of domains and
proteins that may interact with at least two or more PTMs. In addition to
exploring the associations between the interactive effects of PTMs, the
present study also provides important information that would allow
biologists to further explore the regulatory pathways of biological
functions and related diseases.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 11, no. 4, pp.111-120
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=294&page_url=Cur
rent_Issues> 

 

[Paper Two]

- Title: Feature Selection via Embedded Learning Based on Tangent Space
Alignment for Microarray Data

- Authors: Xiucai Ye and Tetsuya Sakurai

- Keyword: Unsupervised feature selection; Embedded learning; Sparse
regression; Tangent space alignment; Microarray gene expression data

 

- Abstract

Feature selection has been widely established as an efficient technique for
microarray data analysis. Feature selection aims to search for the most
important feature/gene subset of a given dataset according to its relevance
to the current target. Unsupervised feature selection is considered to be
challenging due to the lack of label information. In this paper, we propose
a novel method for unsupervised feature selection, which incorporates
embedded learning and l2,1-norm sparse regression into a framework to select
genes in microarray data analysis. Local tangent space alignment is applied
during embedded learning to preserve the local data structure. The l2,1-norm
sparse regression acts as a constraint to aid in learning the gene weights
correlatively, by which the proposed method optimizes for selecting the
informative genes which better capture the interesting natural classes of
samples. We provide an effective algorithm to solve the optimization problem
in our method. Finally, to validate the efficacy of the proposed method, we
evaluate the proposed method on real microarray gene expression datasets.
The experimental results demonstrate that the proposed method obtains quite
promising performance.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 11, no. 4, pp.121-129
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=295&page_url=Cur
rent_Issues> 

 

[Paper Three]

- Title: Feature Selection Based on Bi-objective Differential Evolution

- Authors: Sunanda Das, Chi-Chang Chang, Asit Kumar Das, and Arka Ghosh

- Keyword: Feature selection; Rough set theory; Differential evolution;
Classification

 

- Abstract

Feature selection is one of the most challenging problems of pattern
recognition and data mining. In this paper, a feature selection algorithm
based on an improved version of binary differential evolution is proposed.
The method simultaneously optimizes two feature selection criteria, namely,
set approximation accuracy of rough set theory and relational algebra based
derived score, in order to select the most relevant feature subset from an
entire feature set. Superiority of the proposed method over other
state-of-the-art methods is confirmed by experimental results, which is
conducted over seven publicly available benchmark datasets of different
characteristics such as a low number of objects with a high number of
features, and a high number of objects with a low number of features.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 11, no. 4, pp.130-141
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=296&page_url=Cur
rent_Issues> 

 

[Paper Four]

- Title: Use of Information Technologies to Explore Correlations between
Climatic Factors and Spontaneous Intracerebral Hemorrhage in Different Age
Groups

- Authors: Hsien-Wei Ting, Chien-Lung Chan, Ren-Hao Pan, Robert K. Lai, and
Ting-Ying Chien

- Keyword: Big data; Climatic factors; Decision tree; Random forest;
Spontaneous intracerebral hemorrhage

 

- Abstract

Spontaneous intracerebral hemorrhage (sICH) has a high mortality rate.
Research has demonstrated that sICH occurrence is related to weather
conditions; therefore, this study used the decision tree method to explore
the impact of climatic risk factors on sICH at different ages. The Taiwan
National Health Insurance Research Database (NHIRD) and other open-access
data were used in this study. The inclusion criterion was a first-attack
sICH. The decision tree algorithm and random forest were implemented in R
programming language. We defined a high risk of sICH as more than the
average number of cases daily, and the younger, middle-aged and older groups
were calculated as having 0.77, 2.26 and 2.60 cases per day, respectively.
In total, 22,684 sICH cases were included in this study; 3,102 patients were
younger (<44 years, younger group), 9,089 were middle-aged (45-64 years,
middle group), and 10,457 were older (>65 years, older group). The risk of
sICH in the younger group was not correlated with temperature, wind speed or
humidity. The middle group had two decision nodes: a higher risk if the
maximum temperature was >19Celsius(probability = 63.7%), and if the maximum
temperature was <19Celsius in addition to a wind speed <2.788 (m/s)
(probability = 60.9%). The older group had a higher risk if the average
temperature was >23.933Celsius(probability = 60.7%). This study demonstrated
that the sICH incidence in the younger patients was not significantly
correlated with weather factors; that in the middle-aged sICH patients was
highly-correlated with the apparent temperature; and that in the older sICH
patients was highly-correlated with the mean ambient temperature. "Warm"
cold ambient temperatures resulted in a higher risk of sICH, especially in
the older patients.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 11, no. 4, pp.142-151
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=297&page_url=Cur
rent_Issues> 

[Paper Five]

- Title: Selective Encryption Algorithm for 3D Printing Model Based on
Clustering and DCT Domain

- Authors: Giao N. Pham, Ki-Ryong Kwon, Eung-Joo Lee, and Suk-Hwan Lee

- Keyword: 3D printing data; 3D printing security; Selective encryption;
DCT; Clustering

 

- Abstract

Three-dimensional (3D) printing is applied to many areas of life, but 3D
printing models are stolen by pirates and distributed without any permission
from the original providers. Moreover, some special models and anti-weapon
models in 3D printing must be secured from the unauthorized user. Therefore,
3D printing models must be encrypted before being stored and transmitted to
ensure access and to prevent illegal copying. This paper presents a
selective encryption algorithm for 3D printing models based on clustering
and the frequency domain of discrete cosine transform. All facets are
extracted from 3D printing model, divided into groups by the clustering
algorithm, and all vertices of facets in each group are transformed to the
frequency domain of a discrete cosine transform. The proposed algorithm is
based on encrypting the selected coefficients in the frequency domain of
discrete cosine transform to generate the encrypted 3D printing model.
Experimental results verified that the proposed algorithm is very effective
for 3D printing models. The entire 3D printing model is altered after the
encryption process. The decrypting error is approximated to be zero. The
proposed algorithm provides a better method and more security than previous
methods.

 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 11, no. 4, pp.152-159
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=298&page_url=Cur
rent_Issues> 

 

 

[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).