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. 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

 

[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

 

[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

 

[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

 

 

[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 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, 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, office@kiise.org (phone: +82-2-588-9240; FAX: +82-2-521-1352).