[AISWorld] Newly published papers of JCSE (Sep. 2023)

[JCSE]

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 17, Number 3, September 2023

 

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. 17, no. 3, September 2023

 

[Paper One]

- Title: A Study on the Recognition of English Pronunciation Features in
Teaching by Machine Learning Algorithms

- Authors: Wei Xiong

- Keyword: Machine learning; English pronunciation; Feature recognition;
Pronunciation error; Support vector machine

 

- Abstract

A better understanding of students' English pronunciation features would be
a useful guide for teaching spoken English. This paper first analyzed the
English pronunciation features and extracted Mel-frequency cepstral
coefficients (MFCC) features from the pronunciation signal. Then, the
support vector machine (SVM) method was used to identify the cases of
incorrect and correct pronunciation. To further improve the recognition
effect, deep features were extracted using deep brief network (DBN) as the
input of the SVM, and the parameters of both DBN and SVM were optimized by
the sparrow search algorithm (SSA). Experiments were conducted on the
dataset. The results showed that the MFCC-SSA-SVM algorithm had better
recognition performance than the MFCC-SVM algorithm. The DBN-SVM algorithm
had higher recognition correctness and accuracy than the MFCC-SSA-SVM
algorithm, while the SSA-DBN-SVM method had 88.07% correctness and 85.49%
accuracy, indicating the best performance. The results demonstrated the
reliability of the proposed method for English pronunciation feature
recognition; therefore, it can be applied in practical spoken language
teaching.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 3, pp.93-99
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=440&page_url=Cur
rent_Issues> 

 

[Paper Two]

- Title: Exploration of Key Point Localization Neural Network Architectures
for Y-Maze Behavior Test Automation

- Authors: Gwanghee Lee, Sangjun Moon, Dasom Choi, Gayeon Kim, and Kyoungson
Jhang

- Keyword: Deep learning; Computer vision; key point detection; Y-maze
behavior test

 

- Abstract

The Y-maze behavioral test is a pivotal tool for assessing the memory and
exploratory tendencies of mice in novel environments. A significant aspect
of this test involves the continuous tracking and pinpointing of the mouse's
location, a task that can be labor-intensive for human researchers. This
study introduced an automated solution to this challenge through
camera-based image processing. We argued that key point localization
techniques are more effective than object detection methods, given that only
a single mouse is involved in the test. Through an experimental comparison
of eight distinct neural network architectures, we identified the most
effective structures for localizing key points such as the mouse's nose,
body center, and tail base. Our models were designed to predict not only the
mouse key points but also the reference points of the Y-maze device, aiming
to streamline the analysis process and minimize human intervention. The
approach involves the generation of a heatmap using a deep learning neural
network structure, followed by the extraction of the key points' central
location from the heatmap using a soft argmax function. The findings of this
study provide a practical guide for experimenters in the selection and
application of neural network architectures for Y-maze behavioral testing.

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 3, pp.100-108
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=441&page_url=Cur
rent_Issues> 

 

[Paper Three]

- Title: On Counting Monotone Polygons and Holes in a Point Set

- Authors: Sang Won Bae

- Keyword: Erdös–Szekeres problem; Counting problem; Monotone polygon;
Discrete geometry; Computational geometry; Algorithm

 

- Abstract

In this paper, we studied the problem of counting the number of monotone
polygons in a given set S of n points in general position in the plane. A
simple polygon is said to be monotone when any vertical line intersects its
boundary at most twice. To our best knowledge, this counting problem remains
unsolved and no nontrivial algorithm is known so far. As a research step
forward to tackle the problem, we define a subclass of monotone polygons and
present, for the first time, efficient algorithms that exactly count them. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 3, pp.109-116
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=442&page_url=Cur
rent_Issues> 

 

[Paper Four]

- Title: Segmentation and Rigid Registration of Liver Dynamic Computed
Tomography Images for Diagnostic Assessment of Fatty Liver Disease

- Authors: Kyoyeong Koo, Jeongjin Lee, Jiwon Hwang, Taeyong Park, Heeryeol
Jeong, Seungwoo Khang, Jongmyoung Lee, Hyuk Kwon, Seungwon Na, Sunyoung Lee,
Kyoung Won Kim, and Kyung Won Kim

- Keyword: Fatty liver; Liver CT imaging; Segmentation; Rigid registration;
Diagnosis

 

- Abstract

This study presents a method for diagnosing fatty liver disease by using
time-difference liver computed tomography (CT) images of the same patient to
perform segmentation and rigid registration on liver regions, excluding the
vascular regions. The proposed method comprises three main steps. First, the
liver region is segmented in the precontrast phase, and the liver and liver
vessel regions are segmented in the portal phase. Second, rigid registration
is performed between the liver regions to align the liver positions affected
by the patient's posture or breathing. Finally, fatty liver diagnosis is
performed with the average Hounsfield unit (HU) value calculated using only
the area removed from the vessel area segmented in the portal phase after
registration in the precontrast liver area. The mean distance error between
the points corresponding to the liver boundary was 3.136 mm and the mean
error between the anatomic landmarks was 4.166 mm. A fatty liver diagnosis
was confirmed in a total of 18 cases, and the results were identical to the
histology results. This technique may be valuable in clinically diagnosing
fatty liver using liver CT imaging, which is widely available and more
commonly used than abdominal magnetic resonance. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 3, pp.117-126
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=443&page_url=Cur
rent_Issues> 

 

[Paper Five]

- Title: An Efficient Autism Detection Using Structural Magnetic Resonance
Imaging Based on Selective Binary Coded Genetic Algorithm

- Authors: Vasily Sachnev and B. S. Mahanand

- Keyword: Autism spectrum disorder; Structural magnetic resonance imaging;
Voxel-based morphometry; Genetic algorithm; Extreme learning machine

 

- Abstract

In this work, an efficient machine learning technique for autism diagnosis
using structural magnetic resonance imaging (MRI) is proposed. The proposed
technique employs the voxel-based morphometry (VBM) approach to extract a
set of 989 relevant features from MRI. These features are used to train an
efficient extreme learning machine (ELM) classifier to identify autism
spectrum disorder (ASD) and healthy controls. The proposed selective binary
coded genetic algorithm (sBCGA) found a subset of significant VBM features.
The selected subset of features was used to build a final ELM classifier
with maximum overall accuracy. The proposed sBCGA uses a selective
sample-balanced crossover designed to improve the classification of ASD and
healthy controls. The proposed sBCGA has been extensively tested, and the
experiment results clearly indicated better accuracy compared to existing
methods. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 3, pp.127-134
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=444&page_url=Cur
rent_Issues> 

 

[Paper Six]

- Title: An Efficient Attention Deficit Hyperactivity Disorder (ADHD)
Diagnostic Technique Based on Multi-Regional Brain Magnetic Resonance
Imaging

- Authors: Vasily Sachnev and B. S. Mahanand

- Keyword: Attention deficit hyperactivity disorder; ADHD-200; MRI; Voxels
selection method; Extreme learning machine

 

- Abstract

In this paper, an efficient technique for the diagnosis of attention deficit
hyperactivity disorder (ADHD) was proposed. The proposed method used
features/voxels extracted from structural magnetic resonance imaging (MRI)
scans of seven brain regions and efficiently classified three subtypes of
ADHD: ADHD-C, ADHD-H, and ADHD-I, as well as the typically developing
control (TDC). Training and testing data for experiments were obtained from
ADHD-200 database, and 41,721 features/voxels were extracted from sMRI by
using region-of-interest (ROI). The proposed ADHD diagnostic technique built
an efficient ADHD classifier in two steps. In the first step, a proposed
regional voxels selection method (rVSM) selected an optimal set of
features/voxels from seven brain regions available in ADHD-200, i.e., the
Amygdala, Caudate, Cerebellar Vermis, Corpus Callosum, Hippocampus,
Striatum, and Thalamus. In the second step, voxels/features selected by rVSM
were used together to form unified set of voxels. The unified set of voxels
was used by a multiregion voxels selection method to train an efficient
classifier using the extreme learning machine (ELM). Finally, the proposed
method selected a unique set of voxels from the seven brain regions and
built a final ELM classifier with maximum accuracy. Experiments clearly
indicated that the proposed method produced better results compared to
existing methods. 

To obtain a copy of the entire article, click on the link below.
JCSE, vol. 17, no. 4, pp.135-143
<http://jcse.kiise.org/PublishedPaper/year_abstract.asp?idx=445&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).