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  • Regular paper March 31, 2024

    0 91 13

    Development of Energy-sensitive Cluster Formation and Cluster Head Selection Technique for Large and Randomly Deployed WSNs

    Sagun Subedi and Sang Il Lee *

    Journal of information and communication convergence engineering 2024; 22(1): 1-6

    Abstract : Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

  • Regular paper March 31, 2024

    0 123 12

    U-Net-based Recommender Systems for Political Election System using Collaborative Filtering Algorithms

    Nidhi Asthana 1 and Haewon Byeon 2*

    Journal of information and communication convergence engineering 2024; 22(1): 7-13

    Abstract : User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people’s access to the democratic process, the concept of “e-democracy” applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

  • Regular paper March 31, 2024

    0 113 16

    Wide-Bandwidth Wilkinson Power Divider for Three-Way Output Ports Integrated with Defected Ground Structure

    Sreyrong Chhit , Jae Bok Lee , Dal Ahn , and Youna Jang

    Journal of information and communication convergence engineering 2024; 22(1): 14-22

    Abstract : This study presents the design of a Wilkinson power divider for three-way output ports (WPD3OP), which incorporates a defected ground structure (DGS). An asymmetric power divider is integrated into the output ports of the conventional Wilkinson power divider (WPD), establishing a three-way output port configuration. The DGS introduces periodic or irregular patterns into the ground plane to suppress unwanted electromagnetic wave propagation, and its incorporation can enhance the performance of the power divider, in terms of the power-division ratio, isolation, and bandwidth, by reducing spurious resonances. The proposed design algorithm for an asymmetric power divider for three-way output ports is analyzed via circuit simulations using High-Frequency Simulation Software (HFSS). The results verify the validity of the proposed method. The analysis of the WPD3OP integrated with DGS certifies the achievement of a center frequency of 2 GHz. This confirmation is supported by schematic ideal design simulation results and measurements encompassing insertion losses, return losses, and isolation.

  • Regular paper March 31, 2024

    0 100 13

    Measuring Acceptance Levels of Webcast-Based E-Learning to Improve Remote Learning Quality Using Technology Acceptance Model

    Satmintareja , Wahyul Amien Syafei , and Aton Yulianto

    Journal of information and communication convergence engineering 2024; 22(1): 23-32

    Abstract : This study aims to improve the quality of distance learning by developing webcast-based e-learning media and integrating it into an e-learning platform for functional job training purposes at the National Research and Innovation Agency, Indonesia. This study uses a Technology Acceptance Model (TAM) to assess and predict user perceptions of information systems using webcast platforms as an alternative to conventional applications. The research method was an online survey using Google Forms. Data collected from 136 respondents involved in practical job training were analyzed using structural equation modeling to test the technology acceptance model. The results showed that the proposed model effectively explained the variables associated with the adoption of web-based e-learning during the COVID-19 pandemic in Indonesia for participants engaged in functional job training. These findings suggest that users’ perceptions of ease of use, usefulness, benefits, attitudes, intentions, and webcast usage significantly contribute to the acceptance and use of a more effective and efficient webcast-based e-learning platform.

  • Regular paper March 31, 2024

    0 83 16

    Construction of Text Summarization Corpus in Economics Domain and Baseline Models

    Sawittree Jumpathong , Akkharawoot Takhom , Prachya Boonkwan , Vipas Sutantayawalee, Peerachet Porkaew, Sitthaa Phaholphinyo, Charun Phrombut, Khemarath Choke-mangmi, Saran Yamasathien, Nattachai Tretasayuth, Kasidis Kanwatchara, Atiwat Aiemleuk, and Thepchai Supnithi

    Journal of information and communication convergence engineering 2024; 22(1): 33-43

    Abstract : Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language.

  • Regular paper March 31, 2024

    0 76 16

    Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N Recommendations

    Siva S and Shilpa Chaudhari

    Journal of information and communication convergence engineering 2024; 22(1): 44-55

    Abstract : High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

  • Regular paper March 31, 2024

    0 62 11

    Similar Image Retrieval Technique based on Semantics through Automatic Labeling Extraction of Personalized Images

    Jung-Hee Seo *, Member, KIICE

    Journal of information and communication convergence engineering 2024; 22(1): 56-63

    Abstract : Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

  • Regular paper March 31, 2024

    0 61 14

    Prospect Analysis for Utilization of Virtual Assets using Blockchain Technology

    Jeongkyu Hong *, Member, KIICE

    Journal of information and communication convergence engineering 2024; 22(1): 64-69

    Abstract : Blockchain is a decentralized network in which data blocks are linked. Through a decentralized peer-to-peer network, users can create shared databases, resulting in a trustworthy and aggregated database known as a blockchain that enhances reliability and security. The distributed nature of the blockchain enables data to be stored on multiple nodes, eliminating the need for a central server or platform. This disintermediation significantly reduces the transaction and administrative costs. The blockchain is particularly valuable in applications where reliability and stability are critical because it establishes an open database that ensures data integrity, making it virtually impossible to tamper with or falsify data. This study explores the diverse applications of the blockchain technology in virtual assets, such as cryptocurrency, decentralized finance, central bank digital currency, nonfungible tokens, and metaverses. In addition, it analyzes the potential prospects and developments driven by these innovative technologies.

  • Regular paper March 31, 2024

    0 57 12

    Single-Image Dehazing based on Scene Brightness for Perspective Preservation

    Young-Su Chung and Nam-Ho Kim , Member, KIICE

    Journal of information and communication convergence engineering 2024; 22(1): 70-79

    Abstract : Bad weather conditions such as haze lead to a significant lack of visibility in images, which can affect the functioning and reliability of image processing systems. Accordingly, various single-image dehazing (SID) methods have recently been proposed. Existing SID methods have introduced effective visibility improvement algorithms, but they do not reflect the image’s perspective, and thus have limitations that distort the sky area and nearby objects. This study proposes a new SID method that reflects the sense of space by defining the correlation between image brightness and haze. The proposed method defines the haze intensity by calculating the airlight brightness deviation and sets the weight factor of the depth map by classifying images based on the defined haze intensity into images with a large sense of space, images with high intensity, and general images. Consequently, it emphasizes the contrast of nearby images where haze is present and naturally smooths the sky region to preserve the image’s perspective.

  • Regular paper March 31, 2024

    0 54 11

    Manchu Script Letters Dataset Creation and Labeling

    Aaron Daniel Snowberger and Choong Ho Lee *, Member, KIICE

    Journal of information and communication convergence engineering 2024; 22(1): 80-87

    Abstract : The Manchu language holds historical significance, but a complete dataset of Manchu script letters for training optical character recognition machine-learning models is currently unavailable. Therefore, this paper describes the process of creating a robust dataset of extracted Manchu script letters. Rather than performing automatic letter segmentation based on whitespace or the thickness of the central word stem, an image of the Manchu script was manually inspected, and one copy of the desired letter was selected as a region of interest. This selected region of interest was used as a template to match all other occurrences of the same letter within the Manchu script image. Although the dataset in this study contained only 4,000 images of five Manchu script letters, these letters were collected from twenty-eight writing styles. A full dataset of Manchu letters is expected to be obtained through this process. The collected dataset was normalized and trained using a simple convolutional neural network to verify its effectiveness.

Mar 31, 2024 Vol.22 No.1, pp. 1~87

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Journal of Information and Communication Convergence Engineering Jouranl of information and
communication convergence engineering
(J. Inf. Commun. Converg. Eng.)

eISSN 2234-8883
pISSN 2234-8255