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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Yamashita, Akihiro Shirakawa, Taichi Matsubayashi, Katsushi National Instituteo of Technology Tokyo College Dept. of Computer Science Tokyo Japan Graduate School of Media Design Keio University Kanagawa Japan

ISBN: (纸本)9798350373332

Braille text is widely used around the world as a means of communication for the visually impaired. Although Braille is often regarded as a tool for the visually impaired, there are instances where sighted individuals may want to verify Braille documents. For example, teachers at schools for the visually impaired may need to check homeworks or assignments written in Braille, or care workers supporting the daily lives of visually impaired people may need to verify Braille documents. To meet such social demands, optical Braille recognition (OBR) technologies have typically been developed using cameras and scanners. In particular, methods using object detection models based on Deep Learning have achieved high accuracy in recent years. Since Braille is represented by embossed dots on thick paper, the appearance of shadows varies greatly depending on the angle of the lighting. Therefore, the angle and intensity of the light significantly affect the accuracy of Braille recognition. Specifically, in the case of double-sided Braille, where Braille is embossed on both sides, it is necessary to distinguish between raised and recessed dots. Previous studies have mostly imposed constraints on the angle of illumination, such as limiting the imaging method to scanners or requiring the light source to be positioned on the top of the Braille document when using a camera. However, for practical use in daily life, it is preferable to recognize Braille documents regardless of the direction and lighting conditions. In this study, we created a dataset of Braille images captured under various angle of lighting and developed a Braille recognition model that is independent of the lighting angle by fine-tuning the object recognition model. We implemented and compared RetinaNet, which has been used in previous research, and the anchor-free model YOLOX as object detection models. As a result, we achieved a model capable of detecting Braille images with an accuracy of mAP50=O.98 or higher, reg

关键词： Deep learning

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Probabilistic Model Building Evolutionary Algorithm Using the Same Type and Size of Model for Any Binary Optimization Problems 13

Probabilistic Model Building Evolutionary Algorithm Using th...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kishimoto, Atushi Ohnishi, Kei Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka-shi Fukuoka820a-8502 Japan

ISBN: (纸本)9798350373332

We propose a new probabilistic model building evolutionary algorithm that uses the same type and size of probabilistic model for any binary optimization problems. The proposed method represents a population as a binary matrix for any binary optimization problems and solves a given problem by transforming the population in the form of matrix to better one with 512 production rules of cellular automaton. Concretely, the proposed method builds a probabilistic model for generating new solution candidates while applying the 512 production rules to the matrix representing the population. The probabilistic model consists of the 512 inverse production rules and their application probabilities. The strong point of the proposed method is to be able to transform any binary optimization problems to the same size of 512-bit production rule optimization problem. Since the proposed method treats any binary optimization problems in a unified manner, we can easily explore a robust method for various binary optimization problems as the proposed method with particular configurations. As the first evaluation of the proposed method, we apply the proposed method to the same kind but different sizes of problems. The results show that depending strongly on the bit patterns of the global optima, the proposed method with the same configurations realizes a good scalability of the number of function evaluations required for obtaining near optimum to the problem size growth. © 2024 IEEE.

关键词： Cellular automata

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Development of “Peter's First-Aid Adventure” Virtual Reality-Based Serious Game in First-Aid Education: Usability Analysis of Virtual Reality Interaction Tools

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Procedia computer science 2024年 245卷 309-319页

作者： Alexander Rainer Angeline Setiono Kevin Leonardrich Dimas Ramdhan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Incidents that threaten lives can occur unexpectedly, highlighting the importance of immediate first aid. Studies show that Virtual Reality (VR)-based Serious Games (SG) are excellent alternatives for learning, especially for physical training, as they provide immersive and realistic scenarios where users can learn and practice their skills in a safe environment. Therefore, this research proposes the design of a VR-based SG for learning first aid procedures. A total of 31 participants tested the game's usability using both VR controllers and hand tracking, playing the game with both tools in random order, and then completing the XR-GUQ and Interaction Categories questionnaire. The results indicate that controllers offer higher usability than hand tracking. However, the game can still effectively utilize both interaction tools, given their satisfactory usability ratings.

关键词： Serious Games Virtual Reality First Aid Hand Tracking Usability Health Interaction Tools Education

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Neural Network-Based Analysis of Mammography Images for Identifying Breast Cancer Histological Subtypes

Neural Network-Based Analysis of Mammography Images for Iden...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Alqaaf, Muhammad technology, Ahmad Kamal Nasution graduate school Of science And Supriyanti, Retno Asaduzzaman, Abu Ono, Naoaki Altaf-Ul-Amin, Md. Kanaya, Shigehiko Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Jenderal Soedirman University Department of Electrical Engineering Banyumas Indonesia Wichita State University Department of Electrical and Computer Engineering Wichita United States

ISBN: (纸本)9798350391213

Breast cancer (BC) is a complex disease with multiple histological subtypes that exhibits distinct biological and clinical characteristics. Accurate identification of these subtypes is crucial for the implementation of personalized treatment strategies and the improvement of patient outcomes. This study aimed to leverage the potential of neural networks and mammography data to identify specific histological subtypes of BC, with a focus on distinguishing between in situ and invasive carcinoma. We used the Digital Database for Screening Mammography (DDSM) and its curated subset, CBIS-DDSM, which provides mammography images of normal and malignant cases with verified pathological information. A convolutional neural network (CNN) architecture was designed to extract relevant features from mammography images. The model was trained using data augmentation techniques to enhance diversity and mitigate overfitting, resulting in training and validation accuracies ranging from 0.98 to 1.0. The features extracted by the trained model were then used for clustering analysis using Kmeans on PCA (KM-PCA), which identified nine well-separated clusters with minimal overlap. The clustering results were validated using a subset of mammography images with confirmed invasive carcinoma types from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Two validation approaches were incorporated to strengthen the hypothesis and further validate the results. Overall, this study demonstrates the potential of combining neural networks and mammography data to accurately identify BC histological subtypes, which could pave the way for personalized and effective treatment strategies. © 2024 IEEE.

关键词： Lung cancer

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Comparative usability analysis across different driving control scenarios in a driving simulation game

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Procedia computer science 2024年 245卷 330-336页

作者： David Juan Davis Leon Lewis Christian Oliver Muhamad Fajar Galih Dea Pratama Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

This study aims to address the gap in the evaluation of driving simulation games by further assessing and developing ProjectStir (a Driving Simulation Game) through its adaptation to two different driving control devices: a steering wheel and an arcade stick. Despite the growing popularity of simulation games, limited research has been conducted on how different control devices impact user experience and usability. Usability was evaluated through variables including Easiness of Game Control, Learnability of Gameplay, Remembrance, Challenge, Efficiency, Tension, Familiarity, and Satisfaction. Involving 30 respondents from the computer science program at BINUS University, the results revealed no significant differences in usability between the two scenarios (p<0.05), as determined by the Wilcoxon signed-rank test and paired t-test. These findings suggest that ProjectStir offers consistent usability, regardless of whether a steering wheel or an arcade stick is used for gameplay, thus providing valuable insights for future development and refinement of driving simulation games.

关键词： Game Based Leaning Serious Game Usability User Experience

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Player evaluation and learning outcomes of “NusaVenturas”: A serious game for enhancing player knowledge and interest in Indonesian traditional foods

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Procedia computer science 2024年 245卷 320-329页

作者： Vito Sebastian Sanjaya Nicholas Valenthinus Tanoto Nicholas Tandinata Dimas Ramdhan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The popularity of Indonesian traditional food has been declining due to the increasing influence of foreign cuisine. This study presents the development method that combining Game Development Life Cycle (GDLC) with Experiential Game Learning Design Framework and evaluation of a serious game "NusaVenturas" using usability, learning outcome, and Game Experience Questionnaire (GEQ) that designed to introduce, promote interest, educate, and preserve knowledge of Indonesian traditional foods. "NusaVenturas" is a unique 2D serious game that integrates puzzle-platformer elements with time-management cooking mechanics. The study involved 37 participants aged 18 to 23. Evaluation results indicate that "NusaVenturas" significantly enhances players' knowledge of Indonesian traditional foods, with statistical analysis confirming the significance of the findings.

关键词： serious games culture indonesian foods usability learning outcomes game experience questionnaire

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A Low Cost Triple-Node-Upset Self-Recoverable Latch Design for Nanoscale CMOS

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IEEE Transactions on Device and Materials Reliability 2025年第2期25卷 296-307页

作者： Bai, Na Zhang, Pengshuai Xu, Yaohua Guo, Yunhui Li, Hang Wang, Yi Wen, Xiaoqing Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Hefei230601 China National University of Defense Technology College of Computers Hunan Changsha410073 China Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka8208502 Japan

Due to the constant reduction of semiconductor feature sizes, the charge sharing effect is getting worse. The occurrence probability of triple-node upset (TNU), which has a significant impact on the circuit’s reliability, is rising in nanoscale digital circuits. This paper proposes a low overhead and TNU self-recovery latch (TNU-SR) based on the 28 nm CMOS technology to efficiently tolerate TNU and reduce the power dissipation and latency of the latch. The latch design proposed in this article adopts stacked transistors to reduce sensitive nodes and area overhead. The TNU-SR latch uses clock gating and high-speed transfer path technology, resulting in low power dissipation and low delay. For simulation results, compared with existing latches, it can be known that the TNU-SR latch performs well in terms of delay, power consumption, and area, so it is a good choice for radiation-hardened design for space applications. In addition, compared with the latest TNU self-recovery latch (HLTNURL), the TNU-SR latch proposed in this paper reduces the Power-Area-Delay Product (PADP) metric by 111.13%. © 2001-2011 IEEE.

关键词： CMOS integrated circuits

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Development of educational game as a media to raise public awareness of illegal animal trafficking issue in Indonesia

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Procedia computer science 2024年 245卷 337-345页

作者： Jonathan William Khalid Ahmadi Muhamad Fajar Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Abstract The amount of illegal wildlife trade cases increases every year in Indonesia. This causes some unique Indonesian wildlife to become endangered or even extinct. Therefore, the purpose of this research is to raise public awareness to illegal wildlife trade issue by using a game as its media to reduce the number of cases of illegal wildlife trade in Indonesia. The game made in this research will be developed using the GDLC development method and 2021.3.11f1 version of Unity as its game engine. To evaluate the game that has been developed in this research, a comparation to other related games is conducted and resulted in the game that has been developed in this research is significantly better than other related games in Challenge, Learnability and Concentration aspects (p < 0.001).

关键词： Animal Game Wildlife Education Awareness

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Bridging Gender Disparities in Mental Health: A Bilingual Large Language Model for Multilingual Therapeutic Chatbots 22

Bridging Gender Disparities in Mental Health: A Bilingual La...

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22nd International Learning and technology Conference, L and T 2025

作者： Elboardy, Ahmed Tamer Mohamed, Ziad Khoriba, Ghada Arafa, Tamer Rashed, Essam A. Nile University School of Information Technology and Computer Science Giza Egypt University of Hyogo Graduate School of Information Science Kobe650-0047 Japan University of Hyogo Advanced Medical Engineering Research Institute Himeji670-0836 Japan

ISBN: (纸本)9798331532215

Significant gender disparities and a general lack of support systems compound the state of mental health in developing countries. Women, in particular, face unique challenges due to societal norms and expectations that often restrict their access to mental health services. Additionally, the cultural stigma associated with mental health issues affects both men and women but can be particularly debilitating for women who may be less likely to seek help or disclose their struggles. This study explores the application of Jais-13B, a pre-existing bilingual Large Language Model (LLM) proficient in English and Arabic, tailored to address mental health inquiries. We employed a rich dataset encompassing both languages, essential for fine-tuning and enhancing Jais-13B's responsiveness and empathetic engagement in mental health contexts. The optimization focused on improving the model's ability to deliver accurate and contextually aware responses, which is crucial for effective mental health support. Performance evaluation using the cross-entropy loss function demonstrated that Jais-13B achieved an accuracy of 70.58%, indicating its capability to produce relevant and precise answers to mental health questions. These results showcase Jais-13B's potential to enhance digital mental health interventions significantly, providing accessible care solutions across diverse linguistic landscapes. This study reaffirms the effectiveness of bilingual LLMs in mental health applications and provides insights into their practical implementation, setting a foundational benchmark for future research in multilingual therapeutic chatbots. © 2025 IEEE.

关键词： Electronic health record

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An Improved Transfer Learning-Based Model with Data Augmentation for Brain Tumor Detection 6

An Improved Transfer Learning-Based Model with Data Augmenta...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Prayogo, Rizal Dwi Hamid, Nur Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350344349

Current advances in deep learning have brought various breakthroughs in processing medical data. However, dealing with a limited number of medical datasets remains a challenge in deep learning and often leads to overfitting. To solve this research gap, here we show a new approach to improve the performance of a transfer learning-based model for brain tumor detection from 253 brain magnetic resonance imaging (MRI) sample images. The concept of transfer learning has been applied using a pretrained Inception V3combined with data augmentation. Modified layers using dropout and regularization have been additionally utilized to deal with overfitting. The proposed method shows an increase in accuracy of 6.430%, a precision of 5.531 %, a recall of 10.545%, and an F1-score of 8.040% compared to the baseline method. We show that our proposed method has been able to effectively enhance performance and reduce overfitting, even with a small number of datasets. Moreover, our proposed method outperforms state-of-The-Art brain tumor detection. © 2024 IEEE.

关键词： Tumors

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