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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Ming-Hui Lai Don-Gey Liu Ching-Hwa Cheng Chung-Yu Lai Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Electronic Engineering Feng Chia University Taichung Taiwan Department of Medical Equipment Development and Application Hungkuang University Taichung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Spinocerebellar ataxia is a neurological disorder characterized by impaired neural coordination, leading to unstable gait, coordination difficulties, and other motor abnormalities. This disease significantly impacts patients' daily lives, making it challenging for them to maintain balance, walk smoothly, and even perform fine motor skills. The causes of spinocerebellar ataxia are diverse, including genetic factors, brain injuries, or certain diseases. Many patients with spinocerebellar ataxia require specialized medical care and ongoing monitoring to manage its effects. This study aims to utilize Human Posture Estimation to detect the gait patterns of patients with spinocerebellar ataxia and integrate it with a deep learning model to distinguish between normal and affected individuals. The outcomes offer physicians an objective diagnostic tool and facilitate early detection and intervention for patients with impaired gait, thereby supporting the enhancement or preservation of their quality of life. The current prediction accuracy for the risk of ataxia gait is 80%, with an F1 score of 78.26%. It is necessary to conduct a detailed quantitative assessment by analyzing the gait of patients with spinocerebellar ataxia. Quantitative evaluation of the data allows for an objective assessment of the patient's gait, which helps healthcare professionals to more accurately understand and assess the gait status of patients with spinocerebellar ataxia.

关键词： Deep learning Neurological diseases Accuracy Medical services Motors Feature extraction Skeleton

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Analyzing Significance of Facial Features for Detection of Parkinson's Disease via Static Images

Analyzing Significance of Facial Features for Detection of P...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Chuang-Chien Chiu Shoou-Jeng Yeh Yen-Chi Sun Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Section of Neurology and Neurophysiology Cheng-Ching General Hospital Taichung Taiwan Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Parkinson's disease (PD) is a neurodegenerative disorder often characterized by motor symptoms such as hypomimia, and a reduction in facial expression. We explored the significance of facial features in identifying PD through static images, focusing on detecting hypomimia. A dataset comprised of PD patients and healthy controls' static facial images were collected and analyzed. Facial feature detection was conducted using OpenCV and Dlib libraries for the precise localization of key landmarks. In the subsequent feature extraction, parameters such as symmetry, eye aspect ratio, mouth corner distance, muscle tension, and mouth aperture ratio were used. Through the standardization of these features, meaningful comparisons across diverse images were made. By analyzing hypomimia-related features alongside traditional facial parameters, the results provide a comprehensive understanding of the role of facial expressions in PD diagnosis. The findings suggested that examining facial features in static images enhances the accuracy of PD detection and understanding of the nuanced motor symptoms associated with the disease.

关键词： Accuracy Parkinson's disease Sociology Mouth Standardization Muscles Motors

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Creating Mathematical Models to Help Formulate Company Strategies for Knowledge Management System Implementation

Creating Mathematical Models to Help Formulate Company Strat...

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International Conference on Information Management and Technology (ICIMTech)

作者： Wahyu Sardjono Widhilaga Gia Perdana Dewi Sagita Pranata Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Post Graduate Program of Management Science State University of Jakarta Jakarta Indonesia Department of Informatics Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster UK

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This research aims to build a mathematical model to formulate the problems of implementing knowledge management systems in companies that often face obstacles in achieving the desired objectives and goals. With increasing competition in the business sector, organizations or organizations realize the importance of utilizing the knowledge assets that reside in each individual and organization. If this knowledge can be managed optimally, it can become a competitive advantage for the company due to the emergence of innovative ideas by the concepts of knowledge management theory. By implementing optimal knowledge management (KM), companies can design innovative solutions to improve business operations and increase overall revenue. The factor analysis method will be used to find the determining factors for the success of the implementation of knowledge management systems (KMS) and the regression analysis method will also be used to form a mathematical model of several new factors which are formed as independent variables with the current level of community understanding of KMS as the dependent variable. The research results provide insight into strategies to improve success factors to successfully facilitate KMS implementation. This study contributes to existing knowledge management by providing insight into dynamics that go beyond the technical aspects of KMS, ultimately developing strategies for more effective and efficient utilization of knowledge and organizational growth and supporting competitive advantage for companies.

关键词： Performance evaluation Knowledge acquisition Collaboration Companies Knowledge management Mathematical models Regression analysis Information management Problem-solving System implementation

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Bibliometric Analysis: Research Trends in Leaf Image Segmentation and Classification

Bibliometric Analysis: Research Trends in Leaf Image Segment...

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Artificial Intelligence and Mechatronics Systems (AIMS), International Conference on

作者： Eka Puji Agustini Rahmad Gernowo Kurnianingsih Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro Universitty Semarang Indonesia

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: the trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights “Extraction” as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject.

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Evaluation of Household Waste Image Identification Using Convolutional Neural Network

SSRN

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SSRN 2022年

作者： Sunardi Yudhana, Anton Fahmi, Miftahuddin Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Master Program of Informatics Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Waste identification has been widely carried out using machine learning with various methods. One method that is often used is the Convolutional Neural Network (CNN). Waste identification has a function as machine learning that helps and accelerates the waste management process, especially the waste sorting process. This research was conducted by collecting data by downloading a dataset from Kaggle. The data were then be labeled in a folder according to their type. The dataset folder was entered into a Python machine learning system equipped with the TensorFlow module. The data was preprocessed so that it became simplified and the machine could read the data. The data was trained to be able to distinguish between the types of waste. After that, the CNN method was implemented in machine learning so that the machine could classify the waste image. The results of the CNN implementation show that the system can classify images with the greatest accuracy of 99.83% with a validation accuracy of 76.1% and the smallest loss percentage of about 0.04% at epoch 18. The results show that there was overfitting in the dataset classification process caused by the average accuracy value of the training data and validation data being too large, around 20%. © 2022, The Authors. All rights reserved.

关键词： Waste management

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Analysis of Intention and Use of Audit Tools Linked Archive System (ATLAS) at Public Accounting Firms 7

Analysis of Intention and Use of Audit Tools Linked Archive ...

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

作者： Widuri, Rindang Hidayatullah Novianti, Intan BINUS Graduate Program - Master of Accounting Bina Nusantara University Accounting Department Jakarta11480 Indonesia Informatics and Business Institute Darmajaya Faculty of Economics and Business Accounting Department Lampung Bandar Lampung35141 Indonesia PT. Global Tiket Network Finance Department Jakarta10230 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The goal of this study is to explore the influence of performance expectation, effort expectation, social influence, and facilitating conditions on the intention to continue using Audit Tools and Linked Archive System (ATLAS) by the external auditor at a public accounting firm in Indonesia. This study adopted the Unified Theory of Acceptance and Use of Technology. This study collected data from 126 external auditors using the purposive sampling method. The empirical data indicate that performance expectations, effort expectations, and social influence have a substantial impact on continuation intent. Moreover, facilitating conditions have no appreciable positive impact. This study suggests that an accounting firm needs to provide better IT support and improve the quality of information about the parties that can be contacted if there are difficulties and obstacles in using ATLAS. © 2022 IEEE.

关键词： Economic and social effects

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Cluster Dominance Analysis of Strength Training Motion Characteristics

Cluster Dominance Analysis of Strength Training Motion Chara...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Hapnes Toba Elizabeth Wianto Maya Malinda Agung Wijaya Al Halim Chien-Hsu Chen Master Program in Computer Science Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Visual Communication Design Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Management Universitas Kristen Maranatha Bandung Indonesia Bachelor Program in Informatics Universitas Kristen Maranatha Bandung Indonesia Industrial Design Department National Cheng Kung University Tainan Taiwan

This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.

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Computer Science and Engineering (IC2SE), International Conference on

作者： Anggit Dwi Hartanto Yoga Pristyanto Andy Saputra Universitas AMIKOM Yogyakarta Indonesia Master Program of Informatics Universitas AMIKOM Yogyakarta Indonesia

ISBN: (纸本)9781665400473

Plagiarism action often occurs in the world of education. One example of plagiarism in education, especially in the university environment, is the frequent occurrence of similarities between theses made by students. One solution is to develop an application that can detect similarities in thesis documents that can be used by all groups, especially to facilitate students. Natural Language Processing (NLP) is one of the fields that can be a solution in developing document similarity detection applications. In this study, testing focused on Indonesian documents. In addition, this study aims to build a system or application to detect the similarity of Indonesian documents using the Rabin-Karp algorithm with the Cosine Similarity method as a Distance-Based Similarity Measure and using the Stemming technique. The experiment results using 15 documents of scientific publications in Indonesian show that the Rabin-Karp algorithm with Cosine Similarity can be used to detect the similarity of published manuscripts, especially in Indonesian. With the fastest processing time of seconds and the longest around. seconds, this algorithm quickly detects document similarities, especially in documents. Further research is recommended to optimize from the algorithm side so that the processing time is faster with a much larger number of words.

关键词： Computer science Plagiarism Education Natural language processing Optimization Testing

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Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach

Minimizing the Number of Stunting Prevalence Using the Eucli...

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International Conference of Computer Science and Information Technology

作者： Muhammad Zarlis Tanty Oktavia Relita Buaton Ferda Ernawan Kevin Andrian Department BINUS Graduate Program - Master of Information System Management Information System Management Bina Nusantara University Jakarta Indonesia Informatics Engineering STMIK Kaputama Binjai Indonesia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city.

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