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Selected Papers from International Conference on Smart Media and Applications...
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Selected Papers from International Conference on Smart Media and Applications (SMA 2021)

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 11104

Special Issue Editors

Prof. Dr. Kyungbaek Kim

E-Mail Website
Guest Editor
Department of Artificial Intelligent Convergence, Chonnam National University, Gwangju, Korea
Interests: AI applied cyber physical systems; software defined infrastructure; big data platforms; blockchain; security
Prof. Dr. Jiman Hong

E-Mail Website
Guest Editor
School of Computer Science & Engineering, Soongsil University, Seoul 07027, Korea
Interests: system software; operating systems; software platforms; system security; IOT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 10th International Conference on Smart Media and Applications (SMA 2021), held in Gunsan, Republic of Korea, 9–11 September 2021, will provide an excellent forum for addressing research issues in smart media and applications. SMA 2021 focuses on innovative solutions to complex problems in all areas of industry, engineering, and sciences using advanced techniques in smart media and computer science applications. This Special Issue on “Selected Papers from SMA 2021” is expected to publish excellent papers presented at SMA 2021 on topics including smart media, smart software applications, smart information, smart services, and so forth. The main goal of the Special Issue is to discover new scientific knowledge relevant but not limited to the following topics:

- Intelligent/cloud/distributed computing and systems;
- Artificial intelligence, image/audio processing, computer graphics, HCI;
- Information processing, information security, mobile communication, IoT;
- Content convergence, games, animation, web/mobile, and smart learning;
- Design management/marketing/methodology, UI/UX;
- Media convergence, storytelling and production creation/publishing;
- E-business, ERP, social networks, smart logistics;
- Smart life/finance/agriculture, smart cities, and transformation.

Prof. Dr. Kyungbaek Kim
Prof. Dr. Jiman Hong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

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16 pages, 1023 KiB  
Article
A Smart Evacuation Guidance System for Large Buildings
by Van-Quyet Nguyen, Huy-The Vu, Van-Hau Nguyen and Kyungbaek Kim
Electronics 2022, 11(18), 2938; https://doi.org/10.3390/electronics11182938 - 16 Sep 2022
Cited by 5 | Viewed by 2093
Abstract
In large buildings, during the situation of fire or other hazards, a smart evacuation guidance system needs to fully consider manifold aspects of hazards to guide evacuees through exit gates as fast as possible by dynamic and safe routes. In this paper, we [...] Read more.
In large buildings, during the situation of fire or other hazards, a smart evacuation guidance system needs to fully consider manifold aspects of hazards to guide evacuees through exit gates as fast as possible by dynamic and safe routes. In this paper, we propose a smart evacuation guidance system with a dynamic evacuation routing approach by using the LCDT (Length-Capacity-Density-Trustiness) weighted graph model and partial view (PV) information which represents the hazard intensity and the crowd congestion information of a group of sections/floors in the building. The proposed system is designed as a distributed system with multiple layers of computing by using smart indicators. Given such a system, we develop an efficient distributed approach, so-called LCDT&PV, to find out effective evacuation routes dynamically. We then propose an estimating congestion strategy in order to improve the efficiency of dynamic evacuation routes. To validate the proposed system, we implement a simulator to compare the proposed evacuation routing approach with baseline approaches. Experimental results show that the proposed approach reduces up to 30% of the total evacuation time compared with others. Furthermore, through the results of initial smart indicator implementation, which can interact with the simulator, we show the viability of the proposed system. Full article
(This article belongs to the Special Issue Selected Papers from International Conference on Smart Media and Applications (SMA 2021))
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15 pages, 916 KiB  
Article
Optimal Allocation of IaaS Cloud Resources through Enhanced Moth Flame Optimization (EMFO) Algorithm
by Srinivasan Thiruvenkadam, Hyung-Jin Kim and In-Ho Ra
Electronics 2022, 11(7), 1095; https://doi.org/10.3390/electronics11071095 - 30 Mar 2022
Cited by 1 | Viewed by 1490
Abstract
A new generation of computing resources is available to customers via IaaS, PaaS, and SaaS administrations, making cloud computing the most significant innovation in recent history for the general public. A virtual machine (VM) is configured, started, and maintained across numerous physical hosts [...] Read more.
A new generation of computing resources is available to customers via IaaS, PaaS, and SaaS administrations, making cloud computing the most significant innovation in recent history for the general public. A virtual machine (VM) is configured, started, and maintained across numerous physical hosts using IaaS. In many cases, cloud providers (CPs) charge utility customers who have registered their premises with the utility registration authorities. Given the opposing aims of increasing customer demand fulfillment while decreasing costs and optimizing asset efficiency, efficient VM allocation is generally considered as one of the most difficult tasks for CPs to overcome. This paper proposes the Enhanced Moth Flame Optimization (EMFO) algorithm to provide a unique strategy for assigning virtual machines to suit customer requirements. The recommended approach is applied on Amazon’s EC2 after three distinct experiments are assumed. The utility of the proposed method is further shown by the use of well-known optimization techniques for effective VM allocation. The app was created using a Java-based programming language and then run on the Netbeans IDE 12.4 platform. Full article
(This article belongs to the Special Issue Selected Papers from International Conference on Smart Media and Applications (SMA 2021))
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13 pages, 1664 KiB  
Article
The Efficacy of Shape Radiomics and Deep Features for Glioblastoma Survival Prediction by Deep Learning
by Dang-Linh Trinh, Soo-Hyung Kim, Hyung-Jeong Yang and Guee-Sang Lee
Electronics 2022, 11(7), 1038; https://doi.org/10.3390/electronics11071038 - 25 Mar 2022
Cited by 1 | Viewed by 2034
Abstract
Glioblastoma (known as glioblastoma multiforme) is one of the most aggressive brain malignancies, accounting for 48% of all primary brain tumors. For that reason, overall survival prediction plays a vital role in diagnosis and treatment planning for glioblastoma patients. The main target of [...] Read more.
Glioblastoma (known as glioblastoma multiforme) is one of the most aggressive brain malignancies, accounting for 48% of all primary brain tumors. For that reason, overall survival prediction plays a vital role in diagnosis and treatment planning for glioblastoma patients. The main target of our research is to demonstrate the effectiveness of features extracted from the combination of the whole tumor and enhancing tumor to the overall survival prediction. By the proposed method, there are two kinds of features, including shape radiomics and deep features, which is utilized for this task. Firstly, optimal shape radiomics features, consisting of sphericity, maximum 3D diameter, and surface area, are selected using the Cox proportional hazard model. Secondly, deep features are extracted by ResNet18 directly from magnetic resonance images. Finally, the combination of selected shape features, deep features, and clinical information fits the regression model for overall survival prediction. The proposed method achieves promising results, which obtained 57.1% and 97,531.8 for accuracy and mean squared error metrics, respectively. Furthermore, using selected features, the result on the mean squared error metric is slightly better than the competing methods. The experiments are conducted on the Brain Tumor Segmentation Challenge (BraTS) 2018 validation dataset. Full article
(This article belongs to the Special Issue Selected Papers from International Conference on Smart Media and Applications (SMA 2021))
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Review

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29 pages, 1230 KiB  
Review
Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
by Muhammad Morshed Alam and Sangman Moh
Electronics 2022, 11(7), 1099; https://doi.org/10.3390/electronics11071099 - 30 Mar 2022
Cited by 23 | Viewed by 4482
Abstract
A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications. UAVs must perform real-time communication among themselves and the base station via [...] Read more.
A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications. UAVs must perform real-time communication among themselves and the base station via an efficient routing protocol. However, designing an efficient multihop routing protocol for FANETs is challenging due to high mobility, dynamic topology, limited energy, and short transmission range. Recently, owing to the advantages of multi-objective optimization, Q-learning (QL)-based position-aware routing protocols have improved the performance of routing in FANETs. In his article, we provide a comprehensive review of existing QL-based position-aware routing protocols for FANETs. We rigorously address dynamic topology, mobility models, and the relationship between QL and routing in FANETs, and extensively review the existing QL-based position-aware routing protocols along with their advantages and limitations. Then, we compare the reviewed protocols qualitatively in terms of operational features, characteristics, and performance metrics. We also discuss important open issues and research challenges with potential research directions. Full article
(This article belongs to the Special Issue Selected Papers from International Conference on Smart Media and Applications (SMA 2021))
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