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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Padeli Lukita, Chandra Fahrurroji Purnama, Suryari Rizky, Agung Fazri, Muhammad Faizal University of Raharja Department of Computer Science Tangerang Indonesia University of Catur Insan Cendekia Department of Management Cirebon Indonesia University of Bina Bangsa Department of Informatics Engineering Serang Indonesia University of Esa Unggul Department of Management Jakarta Indonesia Pandawan Sejahtera Indonesia Department of Informatics Engineering Tangerang Indonesia Bosindo Department of Business Intelligence Tangerang Indonesia

ISBN: (纸本)9798350367492

Gamification, the implementation of game mechanics in non-game settings, is the primary approach driving current advancements in education. Educational gamification has arisen as a possible way to address the issues posed by traditional learning methods, which are generally seen as incompatible with contemporary human features and behavior. Concurrently, blockchain technology has emerged as a highly popular subject and has experienced significant growth in recent years. Blockchain, which has its origins in diverse global communities and companies, serves as an infrastructure technology that is gaining prominence in several industries, such as industry and education. An inherent benefit of blockchain technology is its capacity to function autonomously, eliminating the necessity for third-party involvement and ensuring a heightened level of security, transparency, and integrity. Regrettably, the current body of research on the integration of blockchain technology in the realm of educational gamification remains scarce. Hence, this article seeks to analyze and address the primary challenges associated with education by utilizing educational gamification facilitated by the blockchain system. A mixed-methods approach was employed, involving quantitative surveys and qualitative interviews with 500 participants and 20 key informants. Findings indicate enhanced user engagement and motivation, with increased satisfaction and active participation. The study provides a first gamification model using blockchain, serving as a foundation for future research and development in this field. © 2024 IEEE.

关键词： Decentralized finance

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Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Process (HDP) Models on News Data 5

Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Pr...

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5th IEEE International Conference on computer and informatics engineering, IC2IE 2022

作者： Lubis, Arif Ridho Prayudani, Santi Fatmi, Yulia Nugroho, Okvi Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Sma Muhammadiyah 2 Medan Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

News has become a very important need in modern society. Almost every level of society needs information such as news. Online news gets the attention of writers because there are a lot of characteristics of the data available and can be used for analysis so as to get insights and current trends, such as on 4 news portals that are most frequently accessed by the public such as ***, ***, ***, and ***. Related research in this field about Trend Analysis and Topic modeling on news media data in Indonesia then discusses news data collection methods and data collection techniques news pre-processing text, and 2 Topic Modeling algorithms applied in this research, namely Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Process (HDP). In this study, the author has succeeded in extracting topics from news data from media in Indonesia. Various topics show the effectiveness of a learning model producing topics from the LSI and HDP models. This research has implemented natural language preprocessing (NLP) to process news data so that it can be processed and used as data from modeling a topic. This study finds topics based on the model of coherence and LSI and HDP contained in the news data with a total of 29,437. The accuracy obtained is 87% compared to the model from LDA-KNN which can determine the topic with an accuracy of 72%. Hence, the Latent Semantic Indexing (LSI) model and Hierarchical Dirichlet Process (HDP) have better accuracy than the LDA-KNN model. © 2022 IEEE.

关键词： Semantics

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Designing Secured Cafe Network with Security Awareness Domain and Resource (SADAR) by Simulation using Cisco Packet Tracer 22

Designing Secured Cafe Network with Security Awareness Domai...

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10th International Conference on computer and Communications Management, ICCCM 2022

作者： Lubis, Muharman Lubis, Arif Ridho Department of Information System Telkom University Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Indonesia

ISBN: (纸本)9781450396349

computer networking has a huge part of life today, which its participation not only in one side of life but in almost all aspects of life, especially in the fields of education, organization and business. The purpose of a computer network related to connecting every device for providing information and data services through the borderless concept of anytime, anywhere and anyone. On the other hand, the use of a network in the business sector is very critical and fundamental to assist the implementation in the respect of accelerate growth and reducing cost. In this case, the network in the cafe not only become the secondary service to the customer but also enable the business to be sustained, which many customers browse and utilize Internet during their visit, also cafe's effort to broaden the range of its business. Despite, the accessibility become widespread to the network, but at some point, the demand for Internet access in cafe such as for gaming, accompanying and streaming remain huge. Thus, this study want to design a computer network using the Cisco Packet Tracer software through the concept of SADAR (security awareness domain and resource) in order to increase security compliance. This study also utilized and configure VLAN and DHCP connected to gigabit Ethernet as standard, while the testing will used PING and TRACEROUTE in order for mapping as desired. © 2022 ACM.

关键词： Network management

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An Attention-Based Deep Network for Plant Disease Classification

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Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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Macular Holes Detection Using Deep Learning on Optical Coherence Tomography Images

Macular Holes Detection Using Deep Learning on Optical Coher...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hung, Che-Lun Lin, Keng-Hung Lee, Yu-Kai Lin, ChunHsien Tsai, Yin-Te Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan Taichung Veterans General Hospital Department of Ophthalmology Taiwan Providence University Department of Computer Science and Information Engineering Taiwan Providence University Department of Computer Science and Communication Engineering Taiwan

ISBN: (纸本)9798350337488

Macular holes (MHs) can be either idiopathic or secondary to a result of concurrent or previous pathology such as ocular inflammation, trauma or surgery. Idiopathic macular holes may eventually impair the life quality and self-care capability of patients. In clinical, the optical coherence tomography (OCT) images of macular holes can be divided into 4 stages based upon the size of macular hole. The size of macular hole is inversely influencing surgical success rate and visual outcomes. Minimizing human judgment errors in the classification of MHs and enhancing the overall quality of classification are critical objectives. In this paper, we develop a deep learning algorithm to detect MHs on OCT images and also propose an automatic algorithm to measure the size of MH. The MH detection algorithm demonstrates exceptional accuracy, while the measurement algorithm offers superior efficiency when compared to the conventional caliper-based method utilized with spectral-domain OCT devices - a time-consuming procedure for ophthalmologists. These advancements promise to expedite the diagnostic process and facilitate to rapidly recognize the stage 2 MHs from stage 3 or 4 cases. © 2023 IEEE.

关键词： Deep Learning Macular Holes OCT images

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Mobile Device Facial Beauty Prediction using Convolutional Neural Network as Makeup Reference

Mobile Device Facial Beauty Prediction using Convolutional N...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Luthfi, Muhammad Rachmadi, Reza Fuad Purnama, I Ketut Eddy Nugroho, Supeno Mardi Susiki Institut Teknologi Sepuluh Nopember Faculty of Electrical and Intellignet Informatics Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Beauty or good looks is one aspect of human attraction. Highly attractive people have an 8 to 12 % higher success in negotiations. One of the things that can be used to increase attractiveness is makeup or cosmetics. Makeup can be used alone or through the services of a Makeup Artist (MUA). MUA services are often used during important and once-in-A-lifetime events such as weddings. The difference in beauty before and after using this makeup cannot be measured but only seen and assessed manually. One application of machine learning in this field is Facial Beauty Predictions (FBP). FBP measures the value of a person's facial beauty or good looks. This paper investigated and implemented FBP on mobile devices, which makes it easier to assess anytime and anywhere. We investigated FBP models using five CNN architectures, including EfficientNetB0, VGG-16, ShuffleNet, MobileNet, and Inception. After evaluation, we choose MobileNetV2 CNN architecture as the primary model for mobile application FBP. Experiments on the FBP dataset show that MobileNetV2 achieves Pearson Correlation 0.7893 with only 10.5 MB for model file size. The model implemented for mobile devices works well in 13 different tests. © 2022 IEEE.

关键词： Forecasting

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Enhance Low-Resolution Face Recognition Accuracy Using Median and Mean Filtering Techniques Based on Convolutional Neural Networks (CNN) 18

Enhance Low-Resolution Face Recognition Accuracy Using Media...

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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Kurnia, Dian Ade Risnanto, Slmaet Sugiyatno Kusuma, Ajeng Nisa Naufal, Yogacitra Bin Mohd, Othman Sekolah Tinggi Manajemen Informatika Dan Komputer Ikmi Departemen of Computer Science Cirebon Indonesia Unversitas Sangga Buana Faculty of Engineering Departemen of Informatics Engineering Bandung Indonesia Universitas Bhayangkara Jakarta Raya Fakulti Informatics Jakarta Indonesia Unversitas Sangga Buana Bandung Indonesia Universiti Teknikal Malaysia Fakulti Teknologi Maklumat Dan Komunikasi Melaka Malaysia

ISBN: (纸本)9798331527686

The aim of this study is to evaluate the effectiveness of median and mean filtering techniques in enhancing the performance of deep learning-based face recognition systems on low-resolution images. The main challenge in face recognition lies in the low quality of images due to various factors such as lighting, distance, and camera resolution. Median filtering replaces each pixel with the median value of its neighbors, effectively removing salt-and-pepper noise, while mean filtering replaces each pixel with the average value of its neighbors, resulting in smoother images but potentially blurring important details. The results show that median filtering is more effective in preserving edge details and reducing speckle noise, whereas mean filtering excels at producing overall smoother images. The combination of these techniques is expected to enhance the accuracy of face recognition on low-resolution images, contributing to the development of more reliable and precise face recognition systems © 2024 IEEE.

关键词： Wiener filtering

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Graph Convolutional Networks for Predicting Mechanical Characteristics of 3D Lattice Structures 13th

Graph Convolutional Networks for Predicting Mechanical Chara...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Oleka, Valentine Zahedi, Seyyed Mohsen Taherkhani, Aboozar Baserinia, Reza Zahedi, S. Abolfazl Yang, Shengxiang School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Engineering and Sustainable Development De Montfort University Leicester United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom

ISBN: (纸本)9783031579189

Recent advancements in deep learning methods encouraged researchers to apply them to process 3D objects. Initially, convolutional neural networks which have shown their ability in the processing of 2D images were used for 3D object processing. These methods need a complex process to convert 3D objects to 2D images. This conversion leads to increased computation cost and possible information loss during the transformation. This research introduces a Graph Convolutional Network approach for predicting mechanical properties of custom-designed 3D lattice structures for tissue engineering applications. Seventeen scaffold geometrics were generated for training while eight were used for testing. Unlike traditional preprocessing into images, this methodology reduces preprocessing by leveraging GCNs to directly process 3D geometrics in graph form. The experimental results show the efficiency of our proposed method in predicting 3D lattice structures. © IFIP International Federation for Information Processing 2024.

关键词： Convolution

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Novel replaceable EEG electrode system

Novel replaceable EEG electrode system

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作者： Warsito, Indhika Fauzhan Fiedler, Patrique Komosar, Milana Haueisen, Jens Institute of Biomedical Engineering and Informatics Faculty of Computer Science and Automation Technische Universität Ilmenau Ilmenau Germany

Due to the direct contact between electrode and scalp, dry EEG electrodes are exposed to increased mechanical wear compared to conventional gel-based electrodes. However, state-of-the-art commercial cap systems commonly use permanently fixated electrodes which can lead to downtime of the EEG cap during professional repair and replacement as well as reduced overall lifetime. An easily replaceable EEG electrode would furthermore improve hygiene, especially for newborn and infant applications. We propose a novel replaceable electrode system, consisting of an electrode holder, a snap top, a contact ring fixated inside the electrode holder, and a replaceable electrode. The production process consists of 3D printing, silicone molding, resin casting, and electroless plating. The replaceable electrode system is integrated into a multichannel EEG cap system. A verification study is conducted with 30 volunteers. The operators experienced that the new electrode holder eases adjustment of the electrode to have proper contact with the scalp. During the study, defective electrodes can be replaced without a soldering process. Furthermore, all electrodes stayed in the holder and did not fall off the cap for the whole session. In conclusion, the novel replaceable electrode system is suitable for EEG measurements. © 2022 The Author(s), published by De Gruyter.

关键词： Electroencephalography

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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