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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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Factors affecting the use of Zoom app in online lectures during Covid-19 pandemic

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AIP Conference Proceedings 2023年第1期2714卷

作者： Sukri Syafrudin Kartika Handayani Ranu Agastya Nugraha Siti Lestari Lindung Parningotan Manik Computer Science Graduate Program University of Nusa Mandiri Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Jakarta Indonesia

The rapid global spread of the Covid-19 led to the utilization of online tools for various lecturing activities. Online learning is a change mandated on lecturers and students to achieve significant educational goals, irrespective of time and location. This research investigates factors affecting the use of the videoconferencing app, Zoom in supporting the online lectures. This study is quantitative research with the unified theory of acceptance and the use of technology (UTAUT) model to analyze online learning software acceptance. The research subjects consist of students and lecturers from the University of Bina Sarana Informatika (UBSI) Indonesia. The result shows that the performance and effort expectancies, as well as the social factors, affect users’ attitude towards the software use and their behavioral intention. Also, the facilitating conditions affect their attitude with no significant effect on intention.

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HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

arXiv

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arXiv 2024年

作者： Wisnu, Dyah A.M.G. Rini, Stefano Zezario, Ryandhimas E. Wang, Hsin-Min Tsao, Yu Taiwan International Graduate Program—Social Network and Human Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

This paper introduces HAAQI-Net, a non-intrusive deep learning-based music audio quality assessment model for hearing aid users. Unlike traditional methods like the Hearing Aid Audio Quality Index (HAAQI) that require intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. By utilizing a Bidirectional Long Short-Term Memory (BLSTM) architecture with attention mechanisms and features extracted from the pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. Experimental results demonstrate HAAQI-Net’s effectiveness, achieving a Linear Correlation Coefficient (LCC) of 0.9368, a Spearman’s Rank Correlation Coefficient (SRCC) of 0.9486, and a Mean Squared Error (MSE) of 0.0064 and inference time significantly reduces from 62.52 to 2.54 seconds. To address computational overhead, a knowledge distillation strategy was applied, reducing parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores like the Mean Opinion Score (MOS) through fine-tuning. This adaptation significantly improved prediction accuracy, validated through statistical analysis. Furthermore, the robustness of HAAQI-Net was evaluated under varying Sound Pressure Level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a scalable solution for music audio quality assessment in hearing aid applications, contributing to efficient and accurate models in audio signal processing and hearing aid technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Age at menarche by birth cohort: A pooled analysis of half a million women in Asia

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Public Health 2024年 237卷 130-134页

作者： Abe, Sarah K. Nishio, Marisa Huang, Hsi-Lan Leung, Chi Yan Islam, Md Rashedul Rahman, Md Shafiur Saito, Eiko Shin, Aesun Merritt, Melissa A. Choi, Ji-Yeob Katagiri, Ryoko Mohammadi, Zahra Shu, Xiao-Ou Wakai, Kenji Sawada, Norie Ideno, Yuki Tamakoshi, Akiko Seow, Wei Jie Koh, Woon-Puay Sakata, Ritsu Hozawa, Atsushi Kim, Jeongseon Nagata, Chisato Sugawara, Yumi Park, Sue K. Kweon, Sun-Seog Azizi, Fereidoun Malekzadeh, Reza Moy, Foong-Ming Pourfarzi, Farhad Gao, Yu-Tang Kubo, Yoko Hirabayashi, Mayo Nagai, Kazue Kimura, Takashi Yuan, Jian-Min Kanemura, Seiki Wada, Keiko Kang, Daehee Shin, Min-Ho Khalili, Davood Poustchi, Hossein Rezaianzadeh, Abbas Mansour-Ghanaei, Fariborz Najafi, Farid Mohebbi, Iraj Boffetta, Paolo Lee, Jung Eun Matsuo, Keitaro Rothman, Nathaniel Qiao, You-Lin Zheng, Wei Inoue, Manami Division of Prevention National Cancer Center Institute for Cancer Control Japan Hitotsubashi Institute for Advanced Study Hitotsubashi University Japan Research Center for Child Mental Development Hamamatsu University School of Medicine Japan Sustainable Society Design Center Graduate School of Frontier Sciences the University of Tokyo Japan Department of Preventive Medicine Seoul National University College of Medicine Seoul South Korea Cancer Research Institute Seoul National University Seoul South Korea The Daffodil Centre The University of Sydney a joint venture with Cancer Council NSW Sydney NSW Australia Department of Biomedical Sciences Seoul National University Graduate School Seoul South Korea Graduate School of Informatics Faculty of Informatics Chiba University Japan Liver and Pancreatobiliary Diseases Research Center Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Division of Epidemiology Department of Medicine Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center Vanderbilt University Medical Center Nashville TN United States Department of Preventive Medicine Nagoya University School of Medicine Nagoya Japan Division of Cohort Research National Cancer Center Institute for Cancer Control Japan Gunma University Center for Food Science and Wellness Maebashi Japan Department of Public Health Hokkaido University Faculty of Medicine Sapporo Japan Saw Swee Hock School of Public Health and Department of Medicine National University of Singapore and National University Health System Singapore Healthy Longevity Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Institute for Clinical Sciences Agency for Science Technology and Research (A∗STAR) Singapore Department of Epidemiology Radiation Effects Research Foundation Hiroshima Japan Division of Epidemiology Department of Public Health and Forensic Medicine Tohoku University Graduate School

Objectives: To evaluate changes in the age at menarche in Asian populations. Study design: Retrospective cohort study. Methods: We included 548,830 women from six countries in Asia. The data were sourced from 20 cohorts participating in the Asia Cohort Consortium (ACC) and two additional cohort studies: Japan Multi-institutional Collaborative Cohorts (J-MICC), and Japan Nurse Health Study (JNHS) with data on age at menarche. Joinpoint regression was used to evaluate changes in age at menarche by birth year and by country. Results: The study includes data from cohorts in six Asian countries namely, China, Iran, Japan, Korea, Malaysia and Singapore. Birth cohorts ranged from 1873 to 1995. The mean age of menarche was 14.0 years with a standard deviation (SD) of 1.4 years, ranged from 12.6 to 15.5 years. Over 100 years age at menarche showed an overall decrease in all six countries. China showed a mixed pattern of decrease, increase, and subsequent decrease from 1926 to 1960. Iran and Malaysia experienced a sharp decline between about 1985 and 1990, with APC values of −4.48 and −1.24, respectively, while Japan, South Korea, and Singapore exhibited a nearly linear decline since the 1980s, notably with an APC of −3.41 in Singapore from 1993 to 1995. Conclusions: Overall, we observed a declining age at menarche, while the pace of the change differed by country. Additional long-term observation is needed to examine the contributing factors of differences in trend across Asian countries. The study could serve as a tool to strengthen global health campaigns. © 2024 The Royal Society for Public Health

关键词： Age Asia Birth cohort Menarche

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems

Implementation of Face Patterns and Smile Recognition for In...

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International Conference of Computer and informatics Engineering (IC2IE)

作者： Miftakhurrokhmat Teddy Suparyanto Gregorius Natanael Elwirehardja Shofiyati Nur Karimah Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Informatics Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Students “attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students” attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification.

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Applying an Agile Science Roadmap to Integrate and Evaluate Ethical Frameworks Throughout the Lifecycle and Use of Artificial Intelligence Tools in the Intensive Care Unit

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Critical Care Nursing Clinics of North America 2025年第2期37卷 347-363页

作者： Lindroth, Heidi Sahajwani, Juhi Hudson, Mark Heier, Laura Gonzalez, Andrew A. Bhattacharyya, Anirban Zheng, Zhi Boustani, Malaz Herasevich, Vitaly McGowan, Michelle Barry, Barbara Division of Nursing Research Department of Nursing Mayo Clinic Rochester MN United States Center for Aging Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Center for Health Innovation and Implementation Science School of Medicine Indiana University Indianapolis IN United States Department of Critical Care Medicine Mayo Clinic Jacksonville FL United States School of Psychology and Counselling The Open University Milton Keynes United Kingdom Department of Graduate Nursing Viterbo University La Crosse WI United States Department of Neurosurgery Mayo Clinic Rochester MN United States Center for Health Services Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Surgical Outcomes and Quality Improvement Center Department of Surgery School of Medicine Indiana University Indianapolis IN United States Department of Electrical Engineering University of Notre Dame Notre Dame IN United States Department of Anesthesiology and Perioperative Medicine Mayo Clinic Rochester MN United States Biomedical Ethics Departments of Quantitative Health Sciences and Artificial Intelligence and Informatics Biomedical Ethics Research Program Mayo Clinic Rochester MN United States Kern Center Mayo Clinic Rochester MN United States

This article summarizes existing ethical frameworks for healthcare artificial intelligence (AI) and ambient sensing technology, such as computer vision, and examines their application to improve patient outcomes in the intensive care unit (ICU). Integrating ethical considerations such as privacy, fairness, and autonomy into the lifecycle of an AI tool is necessary to fully harvest AI's potential to deliver safe, high-quality, personalized, and low-cost healthcare services that provide positive experiences for patients, families, and clinicians while improving health outcomes. Drawing from agile science, the article proposes a practical roadmap for clinician and researcher use to identify, integrate, and monitor ethical considerations throughout the lifecycle of an AI tool with the intention to improve patient care. A use case illustrates the application of the agile science-informed roadmap that details the development of a passive digital marker for delirium severity.

关键词： Agile science AI tools Artificial intelligence Computer vision Delirium severity Ethical frameworks for health care AI ICU Passive digital marker

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Spatially differentiated life cycle assessment of Thailand's transport: The implications from country-specific factors and alternative technologies

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Sustainable Production and Consumption 2025年 57卷 319-340页

作者： Vitoon Chotanapund Shabbir H. Gheewala Vladimir Strezov Nazmul Huda Jitti Mungkalasiri Trakarn Prapaspongsa Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand School of Natural Sciences Faculty of Science and Engineering Macquarie University NSW 2109 Australia The Joint Graduate School of Energy and Environment (JGSEE) King Mongkut's University of Technology Thonburi Bangkok Thailand Center of Excellence on Energy Technology and Environment Ministry of Higher Education Science Research and Innovation Ministry of Education Bangkok Thailand School of Engineering Faculty of Science and Engineering Macquarie University NSW 2109 Australia Technology and Informatics Institute for Sustainability (TIIS) National Metal and Materials Technology Center (MTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

This study proposed country-specific characterisation factors (CFs) for Thailand by modifying the Thai Spatially Differentiated Life Cycle Impact Assessment (ThaiSD) method and introducing monetary conversion factors to express environmental impacts in Thai Baht. Five impact categories were fully parameterised, including fine particulate matter formation (PMF), human toxicity (both cancer and non-cancer) (HT c and HT nc ), freshwater ecotoxicity (FET), and water scarcity (WS). Other spatialised CFs were selected from regionalisation models in ReCiPe2016 and IMPACT World+. When comparing the use of country-specific CFs with global average, the impact scores for PMF, HT c , HT nc , FET, photochemical ozone formation and terrestrial acidification demonstrated notably different levels, ranging from ±25 to ±50 %. The developed method was then applied to assess environmental impacts and costs of Thailand's transport sector, encompassing freight and passenger transport across roadway, railway, waterway, and aviation. Climate change (CC) was significantly attributed to human health and ecosystem quality impacts, while the major contributor to resource scarcity impact was fossil resource scarcity (FS). In some scenarios, non-exhaust emissions, particularly from freight trucks, accounted for 40–60 % of the total PMF impacts. Environmental costs of freight and passenger transport in Thailand were 0.08–3.64 Thai Baht per tonne-kilometre and 0.01–0.81 Thai Baht per passenger-kilometre, respectively. Among transport modes, trains were found to be the most environmentally favourable option for both passenger and freight transport, while aviation had the highest environmental impact for freight transport. In contrast, passenger aviation had a comparable environmental burden to passenger cars due to optimised occupancy rates. Despite the efficiency of modern internal combustion engine vehicles (ICEVs), particularly Euro 5 and 6, employing blended biodiesel was less effective in comp

关键词： Life cycle assessment Thai Spatially Differentiated Life Cycle Impact Assessment Freight transport Passenger transport Transport technologies

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Insights into the binding interactions of Macitentan to bovine serum Albumin: A Spectroscopic, electrochemical and computational study

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Journal of Molecular Liquids 2025年 429卷

作者： Sadak, Selenay Dindar, Çiğdem Kanbeş Kabir, Md.Zahirul Rehman, Fazal Mohamad, Saharuddin B. Yildirim, Sercan Uslu, Bengi Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara06560 Turkey Ankara University The Graduate School of Health Sciences Ankara06110 Turkey Biochemistry Program Institute of Biological Sciences Faculty of Science University Malaya Kuala Lumpur Malaysia Faculty of Science Bioinformatics Programme Institute of Biological Sciences Malaysia Centre of Research for Computational Sciences and Informatics for Biology Bioindustry Environment Agriculture and Healthcare University of Malaya Kuala Lumpur Malaysia Karadeniz Technical University Faculty of Pharmacy Department of Analytical Chemistry Trabzon Turkey

Macitentan (MACI) is an endothelial receptor antagonist used to treat pulmonary arterial hypertension (PAH). The pharmacokinetics and therapeutic efficacy of MACI are significantly influenced by its interaction with plasma proteins, especially bovine serum albumin (BSA). In this study, spectroscopic (fluorescence and UV–visible absorption spectroscopy), electrochemical, and computational techniques were applied to explore the binding properties and interaction mechanisms between MACI and BSA. The results indicated that the MACI quenched BSA fluorescence through a static mechanism. The interaction possessed a moderate binding affinity with Ka values ranging from 1.27 × 104 to 7.25 × 104 M−1 at different temperatures. Hydrophobic forces and hydrogen bonds were identified as critical factors in stabilizing the complex based on thermodynamic parameters. The local environment of tryptophan residues in BSA was found to be altered following the addition of MACI, as revealed by synchronous and three-dimensional fluorescence spectra. The spectroscopic studies were supported by electrochemical analyses, which indicated the formation of an electro-inactive complex between MACI and BSA. From the molecular docking experiments, the most likely binding site for MACI was found to be site I of BSA. Several hydrogen bonds and hydrophobic contacts were observed during the interaction of MACI with site I of BSA. These findings provided detailed insight to understand the binding properties of MACI to BSA, which may be beneficial for medical and pharmaceutical applications in the future. © 2025 Elsevier B.V.

关键词： Hydrogen bonds

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Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Contour Detection from Ultrasound Kidney Images with A Coars...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Tao Gu, Yidong Xu, Yanqing Wang, Caishan Zhang, Lei Cai, Jing School of Future Science and Engineering Soochow University Suzhou China UT Southwestern Medical Center Department of Radiation Oncology DallasTX United States The Affiliated Suzhou Hospital of Nanjing Medical University Department of Medical Ultrasound Suzhou Municipal Hospital Jiangsu Suzhou China University of Texas Southwestern Medical Center McDermott Center for Human Growth and Development DallasTX United States The Second Affiliated Hospital of Soochow University Department of Ultrasound Suzhou China Duke Kunshan University Graduate Program of Medical Physics and Data Science Research Center Jiangsu Kunshan China The Hong Kong Polytechnic University Department of Health Technology and Informatics Hong Kong Hong Kong

ISBN: (纸本)9798350337020

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation. © 2023 IEEE.

关键词： Artificial intelligence Explainable mathematical model Ultrasound kidney segmentation

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