[AISWorld] Newly published papers of JCSE (Mar. 2021)

[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 4,000 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 15, Number 1, March 2021

 

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. 15, no. 1, March 2021

 

[Paper One]

- Title: A Knowledge Extraction Pipeline between Supervised and Unsupervised
Machine Learning Using Gaussian Mixture Models for Anomaly Detection

- Authors: Reda Chefira and Said Rakrak

- Keyword: Classification; Clustering; Association rules; Knowledge
extraction; Swarm intelligence

 

- Abstract

This paper presents a new approach to design a decision support model with
suitability across various contexts, and in particular for the Internet of
Things. It provides an anomaly detection-learning model that is adapted to
the patient's medical condition. A highly balanced artificial intelligence
based on a Gaussian mixture model and association rules leverages the
knowledge acquired through cross-referencing supervised and unsupervised
machine learning. This process ensures an unsupervised cluster-based model,
to accurately classify medical inputs according to their risk level and
provide a knowledge extraction bridge between the supervised and
unsupervised aspects of the data, thereby enhancing the medical
decision-making process to be data-driven and therefore case-specific. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 15, no. 1, pp.1-17
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=371&page_url=Cur
rent_Issues> 

 

[Paper Two]

- Title: A Review of Vision-Based Techniques Applied to Detecting
Human-Object Interactions in Still Images

- Authors: Sunaina, Ramanpreet Kaur, and Dharam Veer Sharma

- Keyword: Human-object interactions; Action recognition; Visual
relationships; Deep learning; Hand crafted; Computer vision

 

- Abstract

Due to the rising demand for automatic interpretation of visual
relationships in several domains, human-object interaction (HOI) detection
and recognition have also gained more attention from researchers over the
last decade. This survey paper concentrates on human-centric interactions,
which can be categorized as human-to-human and human-to-objects.

Although an extensive amount of research work has been done in this area,
real-world constraints like the domain of possible interactions make the
research a challenging task. This paper provides an analysis of conventional
hand-crafted representation-based methods and recent deep learning-based
methods, ongoing advancements taking place in the field of HOI recognition
and detection, and challenges faced by the researchers. Moreover, we present
a detailed picture of publicly available datasets for HOI evaluations. At
the end, the future scope of the study is discussed.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 15, no. 1, pp.18-33
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=372&page_url=Cur
rent_Issues> 

 

[Paper Three]

- Title: Automated Detection of Age-Related Macular Degeneration from OCT
Images Using Multipath CNN

- Authors: Anju Thomas, P. M. Harikrishnan, Adithya K. Krishna, P.
Palanisamy, and Varun P. Gopi

- Keyword: Age-related macular degeneration; Multipath CNN; Sigmoid; Macular
region

 

- Abstract

Age-related macular degeneration (AMD) is an eye disorder that can have
harmful effects on older people. AMD affects the macula, which is the core
portion of the retina. Hence, early diagnosis is necessary to prevent vision
loss in the elderly. To this end, this paper proposes a novel multipath
convolutional neural network (CNN) architecture for the accurate diagnosis
of AMD. The architecture proposed is a multipath CNN with five convolutional
layers used to classify AMD or normal images. The multipath convolution
layer enables many global structures to be generated with a large filter
kernel. In this proposed network, the sigmoid function is used as the
classifier. The proposed CNN network is trained on the Mendeley dataset and
evaluated on four datasets-the Mendeley, OCTID, Duke, and SD-OCT Noor
datasets- and it achieved accuracies of 99.60%, 99.61%, 96.67%, and 93.87%,
respectively. Although the proposed model is only trained on the Mendeley
dataset, it achieves good detection accuracy when evaluated with other
datasets. This indicates that the proposed model has the capacity to detect
AMD. These results demonstrate the efficiency of the proposed algorithm in
detecting AMD compared to other approaches. The proposed CNN can be applied
in real-time due to its reduced complexity and learnable parameters. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 15, no. 1, pp.34-46
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=373&page_url=Cur
rent_Issues> 

 

[Paper Four]

- Title: A Method to Measure the Degree of the Favorite Location Visiting of
Mobile Objects

- Authors: Dong Yun Choi and Ha Yoon Song

- Keyword: Human location preference; Location visiting frequency inside
area; Rank of location visiting frequency; Rank of area visiting frequency;
Location visiting duration inside area; Rank of location visiting duration;
Rank of area visiting duration; Positioning data analytics

 

- Abstract

To understand the mobility of humans or things, it is necessary to measure
the degrees of location visits in everyday mobility. In this paper, we
discuss measures that can present human preferences to certain locations
based on location data and analysis. From raw positioning data and the
concept of location clusters, which are sets of positioning data
representing location areas, several measures can be deduced. First, the
location point and location area can be separated because visiting a pin
point location is different from visiting a certain area. Second, the number
of visits to a location and the duration of a visit to a location have
different meanings. Third, the rank of the location visited is sometimes
more meaningful than the absolute counts. In consideration of these aspects,
we established six basic measures and two derived measures. The actual
calculation of each measure requires raw positioning data to be processed.
The raw positioning data were collected by volunteers over several years of
their everyday lives. All measures for multiple volunteers were generated
and analyzed for verification. The processing of raw positioning data to
generate measures requires a vast number of calculations, like big data
processing. As a solution, we implemented a generation process using the
programming language R; GPGPU technology was utilized to derive numerical
results within areas on able time limit with considerable speed-ups, because
an undesirably large amount of time was required to process measures with
CPU-only machines. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 15, no. 1, pp.47-57
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=374&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).