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IEEE/SICE International Symposium on System Integration

作者： Shijian Li Daisuke Chugo Satoshi Muramatsu Sho Yokota Jin-Hua She Hiroshi Hashimoto Uemura Takashi Hiroaki Kamishina Yoshiharu Hata Takayuki Yamada Takahiro Uchida Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan School of Information Science and Technology Tokai University Hiratsuka Japan Faculty of Science and Engineering Toyo University Kawagoe Japan School of Engineering Tokyo University of Technology Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Tokyo Japan KyotoAR Animal Advanced Medical Center Kyoto Japan Rinrei Co. Ltd. Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The objective of this paper is to identify the stresses placed on the musculoskeletal system when small dogs walk on floors with low friction coefficients. In many households, and small dogs in particular are often kept indoors. According to veterinary experience, it is known that small dogs kept indoors that walk with their lower limbs slipping cause a great deal of stress on the musculoskeletal system, and that the incidence of joint disease in the lower limbs of such small dogs is high. In this study, we measured the body movements of small dogs walking on slippery floors using a three-dimensional motion capture system and force plate system. Furthermore, by inputting these measurements into the musculoskeletal simulation model we have developed to simulate the musculoskeletal system of dogs, we derived the load on the musculoskeletal system of small dogs on slippery and non-slippery floors. As a result, it was identified that walking on a slippery floor causes a strong load on the lower limb muscles of small dogs.

关键词： Legged locomotion Limbs Friction Dogs System integration Muscles Object recognition Floors Stress Paints

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A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks

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Scientific Reports 2025年第1期15卷 1-14页

作者： Jeong, ByeongChang Lee, Yeon Ju Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University 2511 Sejong-ro Sejong 30019 South Korea Department of Applied Mathematics Korea University Sejong South Korea

The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter estimation. To address this, we proposed a novel model that integrates traditional mathematical modeling with deep learning which has shown improved predicted power across diverse fields. The proposed model includes a simple artificial neural network (ANN) for regional disease incidences, and a graph convolutional neural network (GCN) to capture spread to adjacent regions. GCNs are a recent deep learning algorithm designed to learn spatial relationship from graph-structured data. We applied the model to COVID-19 incidences in Spain to evaluate its performance. It achieved a 0.9679 correlation with the test data, outperforming previous models with fewer parameters. By leveraging the efficient training methods of deep learning, the model simplifies parameter estimation while maintaining alignment with the mathematical framework to ensure interpretability. The proposed model may allow the more robust and insightful analyses by leveraging the generalization power of deep learning and theoretical foundations of the mathematical models. © The Author(s) 2025.

关键词： Artificial neural networks COVID-19 Deep learning Graph convolutional neural network

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An approach to parameter optimizer for music mood classification in machine learning

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AIP Conference Proceedings 2025年第1期3223卷

作者： Nunik Destria Arianti T. Thoyyibah Achmad Udin Zailani Neny Rosmawarni Rinna Rachmatika Achmad Lutfi Fuadi Information System University of Nusaputra Sukabumi Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten Indonesia Faculty of Information Technology Veterans National Development University Indonesia

Music is a blend of the human voice and instruments that bring beauty to the listener. Many people like music to create an atmosphere or atmosphere. In the field of computer science, with a lot of data, music is an object that can be studied both through lyrics, audio, biographies and others. This research focuses on the mood of Indonesian music in the 70s and 80s. The data set used consists of text data and audio data. The mood tested consisted of sad, happy and neutral. This area of interest was investigated using the Crisp-DM method derived from business understanding, data understanding, data preparation, modelling, evaluation, deployment. In building the Confusion Matrix, this study uses several parameters, namely epoch, dimensions, set size, learning rate and others. The value generated by the confusion matrix contains a good 85 % value.

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Classification machine learning with chromagram and word embedding feature

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AIP Conference Proceedings 2025年第1期3223卷

作者： Thoyyibah Ines Heidiani Ikasari Perani Rosyani Wasis Haryono Sofa Sofiana Neny Rosmawarni Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten South Tangerang Indonesia Faculty of Information Technology Veterans National Development University Jakarta Jakarta Indonesia

Indonesia is a country that is rich in artistic diversity. This art can be a song. In addition to traditional songs, Indonesia is rich in songs from various genres. Some of the genres used by the author are pop, ballad, dangdut. Song or music has become an interesting area of research in computer science. Music recognition is a computational problem in the field of computer vision. Music introduction aims to classify music into several categories such as genre, mood, or music generation. Although in the last ten years a number of studies have been published, the problem of music recognition is still a difficult problem to solve by computers. In addition, it is necessary to explore several features that represent music, including audio spectrograms, chromagrams, and song lyrics. The author processes the lyrics and audio features in the form of a chromagram using many songs. Music can be a collection of data that can be processed by machine learning. The data used for classification is song data in the form of datasets. The author uses his own dataset or manual methods in collecting and processing datasets. The song is downloaded from YouTube after that it is converted into mp3 and cut. The author processed 1181 songs from the 70’s and 80’s era which only took the chorus part. Lyrics in chorus form are saved in excel. The audio chorus is saved in .wav. The results of the Mood classification used by the author in music consist of 3, namely happy, sad and neutral which are processed using machine learning. Using 2 experiments on Chromagram and word embedding feature has a higher value of 81%.

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Reproducing ATT&CK Techniques and Lifecycles to Train Machine Learning Classifier

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IEEE Network 2025年

作者： Yudha, Fietyata Lin, Ying-Dar Lai, Yuan-Cheng Sudyana, Didik Hwang, Ren-Hung EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu City300 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City300 Taiwan The Department of Information Management National Taiwan University of Science and Technology Tapei106 Taiwan The Computer and Network Center National Cheng Kung University Tainan701 Taiwan The College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan711 Taiwan

he MITRE ATT&CK framework categorizes threat actor behaviors into a sequence of techniques called the attack lifecycle. Based on this, Our work introduces a dual-labeled dataset that is accurately labeled with distinct techniques and lifecycles defined by ATT&CK. The dataset offered a more thorough perspective than previous datasets that employed binary or attack classification. It encompassed 17 distinct techniques throughout five lifecycles and was generated through an automated method that guarantees reproducibility and learnability. Reproducibility guarantees the dataset’s consistency, whereas learnability signifies its use in training machine learning models. Our analysis produced a positive result. The dataset achieved a Pearson correlation coefficient of 0.7 for reproducibility. Regarding distinguishing classes, it exhibits an average AUC-ROC score of 0.92 for techniques and 0.82 for lifecycles. The model training yielded an average F1 score of 0.95 for technique classification and 0.9 for lifecycle classification, but only for the traffic dataset. © 1986-2012 IEEE.

关键词： Adversarial machine learning

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Pani/MoS₂ composite for an efficient catalyst for photocatalytic reduction and adsorption of chromium Cr (VI)

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Inorganic Chemistry Communications 2025年 178卷

作者： Sharma, Krishna Prasad Shin, Miyeon Awasthi, Ganesh Prasad Gwak, Nayeong Kim, Sung Hoon Yu, Changho Division of Convergence Technology Engineering Jeonbuk National University Jeonbuk State Jeonju 54896 South Korea Department of Energy Storage/Conversion Engineering (BK21 FOUR) Jeonbuk National University Jeonbuk State Jeonju 54896 South Korea Department of Electronics and Information Engineering Korea University Sejong 30019 South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart City Convergence Technology Korea University Sejong 30019 South Korea

The development and use of a cost-effective, eco-friendly catalyst for the efficient removal of Cr (VI) from wastewater are critical. In response, we have created a Pani/MoS2 composite for adsorption, utilizing a bina... 详细信息

The development and use of a cost-effective, eco-friendly catalyst for the efficient removal of Cr (VI) from wastewater are critical. In response, we have created a Pani/MoS₂ composite for adsorption, utilizing a binary organic–inorganic material produced through a combination of in-situ oxidative polymerization and hydrothermal reactions. This study highlights the significance of using a bifunctional organic–inorganic composite for Cr (VI) removal. The hydrothermally prepared Pani/MoS₂ material serves as a bifunctional catalyst, facilitating both adsorption and photocatalytic reduction. Comprehensive characterization, photocatalysis, and adsorption analyses of the Pani/MoS₂ composite provide valuable insights into its adsorption and reduction mechanisms. The results showed a reduction efficiency of 67.03% after 80 min and a removal efficiency of 73.60% at pH 3 after 24 h. Additionally, Pani/MoS₂ exhibited an adsorption capacity of 29.44 mg/g after 24 h. The hybrid organic–inorganic composite contains multiple adsorption sites, resulting in enhanced adsorption capacity. The adsorption process involves a variety of reactions, including electrostatic interactions, complexation reactions, physical and chemical adsorption, reduction reactions, and photocatalytic reduction, which incorporates simultaneous oxidation and reduction through electron-hole pairs. A study on the stability and the effect of radical scavengers demonstrated the comparable stability of Pani/MoS₂ and the role of radical interference during Cr (VI) adsorption. © 2025 Elsevier B.V.

关键词： Adsorption Composite Molybdenum disulfide Photocatalytic reduction Polyaniline

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Driver fatigue monitoring system using video face images & physiological information

Driver fatigue monitoring system using video face images & p...

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4th Biomedical Engineering International Conference, BMEiCON-2011

作者： Anumas, Sasiporn Kim, Soo-Chan Graduate Program in Bio and Information Technology Hankyong National University Anseong Korea Republic of

ISBN: (纸本)9781457721908

This paper is focused on driving impairment, due to fatigue of a driver. Both face features and physiological signal were used to monitor the fatigue. Analyzing the images from the video, we applied Active Shape Models (ASM) algorithm to get the changes on the face, such as degrees of eye closure, eye closure duration, blink frequency, degrees of mount opening, yawn duration. We analyzed Heart Rate Variability to evaluate the driver's level of fatigue using photoplethysmography signal. These parameters were combined using a fuzzy classifier to infer the level of driver fatigue. The system has been tested with different environment both night and day by simulation in the laboratory and with different users. © 2011 IEEE.

关键词： Physiology

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Frameworks, methodologies and specification tools for the enterprise architecture application in healthcare systems: A systematic literature review 22

Frameworks, methodologies and specification tools for the en...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Da L. Junior, Silvano H. Silva, Francisco I. C. Albuquerque, Gustavo S. G. De Medeiros, Francisco P. A. Lira, Heremita B. Information Technology Graduate Program Federal Institute of Paraiba João Pessoa Brazil

ISBN: (纸本)9781728162676

Although there are many works on the Enterprise Architecture (EA) application in healthcare systems, the literature lacks studies that provide a systematic approach to this topic specifically. This work presents a deep and broad Systematic Literature Review (SLR) to select studies demonstrating current EA practices in healthcare systems. The researchers established an SLR protocol returning 280 primary studies after the first step of the Data Selection and a consolidated inclusion of 46 articles after the second step. It was assessed the level of disagreement during the team's evaluations using Cohen's Kappa. This SLR revealed essential aspects of state-of-the-art EA application in healthcare systems, such as the most used frameworks, methodologies, and specification tools, and the criteria for their choice. © 2021 IEEE.

关键词： Specifications

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A New Credit Scoring Model to Reduce Potential Predatory Lending: A Design Science Approach 30th

A New Credit Scoring Model to Reduce Potential Predatory Len...

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Advances in Systems Engineering - Proceedings of the 30th International Conference on Systems Engineering, ICSEng 2023

作者： Zakowska, Anna Center for Information Systems and Technology Program Claremont Graduate University ClaremontCA United States

ISBN: (纸本)9783031405785

This research examines the potential impact of using a new credit scoring model that incorporates factors beyond the traditional FICO credit score model to address the issue of predatory lending and financial inclusion for "credit invisible," "credit unscorable," "unbankable," and "underbankable" individuals. The lack of credit history for such borrowers creates a challenge for lenders to assess their creditworthiness, leading to the use of unfair lending practices. The research analyzed a dataset containing credit reports and loan information for 1000 applicants to investigate the shortcoming of the FICO score and the impact of new attributes in a new credit scoring model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks programming Predictive models Quantum circuit Physics Long short term memory

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