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Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)

作者： Mohammad Daboor Faten Kharbat Fathi mueen Azzuni Iyad Sultan Mohammad Odeh Nursing Department King Hussein Cancer center Amman Jordan College of Engineering Al Ain University Abu Dhabi UAE Pediatric Department Kanad Hospital AlAin Abu Dhabi UAE Department of Pediatrics King Hussein Cancer Center Amman Jordan Cancer Care Informatics Program King Hussein Cancer Center (KHCC) Amman Jordan College of Art Technology and Environment University of the West of England Bristol UK Global Academy for Digital Health Bristol UK

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

This paper presents the development and validation of a Unified Modeling Language (UML) Activity Diagram for diagnosing Central Line-Associated Bloodstream Infections (CLABSIs) based on guidelines from the Centers for Disease Control and Prevention (CDC) and the National Healthcare Safety Network (NHSN). The activity diagram translates complex guidelines into an accessible visual model, refined through iterative feedback from domain experts in healthcare and software engineering. This tool aims to enhance understanding, adherence, and education regarding CLABSI diagnosis, ultimately improving diagnostic accuracy and consistency. Additionally, the diagram serves as a foundation for developing software applications to automate the detection and diagnosis of CLABSIs, further enhancing clinical efficiency and patient outcomes. By providing a clear and structured process model, this study contributes significantly to the field of healthcare informatics and patient safety.

关键词： Training Visualization Unified modeling language Medical services Software Safety Standards Guidelines Software engineering Software development management

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Numerical Simulation of Spear Motion as Game Items

Numerical Simulation of Spear Motion as Game Items

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International Symposium on Nanoscience and Nanotechnology in Life Sciences 2017, ISNNLS 2017

作者： Muhima, R.R. Mardi, S. Hariadi, M. Puspitasari, I. Informatics Engineering Department ITATS Arief Rachman Hakim 100 Surabaya Indonesia Electrical Engineering Department Sepuluh Nopember Institute of Technology Indonesia Information System Study Program Faculty of Science and Technology Universitas Airlangga Jl. Mulyorejo Surabaya60115 Indonesia

Game development in 3D is mostly done by characters, items and the environment. Game items such as character weapons modeled in 3D will be the attraction of a game. In this paper, spear motion as a game items is modeled in 3D. Nonlinear Equations Six Degrees of Freedom (6 DOF) are used for mathematical models of spear motion. The parameters studied in the motion model are: geometry, mass and aerodynamics. Spear aerodynamic parameters were analyzed using the Datcom method. Numerical simulation of mathematical models of spear motion with variations in the initial velocity of the throw and the direction of the throw. From the results of numerical simulation, the maximum range R = 131.7 m at the initial velocity V0 = 40 m/s, the direction of throw (angle θ0 = 35 deg, angle φ0 = 10 deg, ψ0 = 0 deg). And the maximum height Hmax = 12.18 m is achieved at the initial velocity V0 = 20 m/s, direction of throw (angle θ0 = 35 deg, angle φ0 = 40 deg, angle ψ0 = 0 deg). © 2020 Published under licence by IOP Publishing Ltd.

关键词： Aerodynamics

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An aerodynamic analysis of energy saving car based on computational fluid dynamic using solidworks

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AIP Conference Proceedings 2024年第1期3222卷

作者： R. F. Naryanto A. Setiyawan A. Roziqin S. Maulana F. B. Darsono I. G. M. Andika M. K. Delimayanti R. A. Widodo Mechanical Engineering Department Universitas Negeri Semarang Semarang Indonesia Automotive Engineering Education Universitas Negeri Semarang Semarang Indonesia Computer and Informatics Engineering Department Politeknik Negeri Jakarta Depok Indonesia Master Program in Applied Electrical Engineering Politeknik Negeri Jakarta Depok Indonesia

Improved transportation increased the amount of fuel consumption required. Transportation in Indonesia is the largest oil-consuming sector, with a percentage of 40.1% of the total. Vehicle manufacturers are required to produce a vehicle that has a high level of fuel efficiency. This study aims to determine the aerodynamics of energy-efficient vehicle bodies based on computational fluid dynamics (CFD). This research was conducted using SolidWorks to implement the model. This study examined the variations in airflow velocity at 25, 40, 55, and 70 km/hour. It can be found that the coefficient of drag (Cd) values of the latest body designs with variations in airflow velocity were increased value. The calculation results of the coefficient of drag values were 0.149, 0.154, 0.162, and 0.162, respectively, and the average value was 0.156. In this research, we have found that the energy-saving car body can reduce the drag coefficient compared with the previous study.

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Optimizing CLABSI Prediction: Data and Process Modeling Approach for Enhanced Model Performance

Optimizing CLABSI Prediction: Data and Process Modeling Appr...

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Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)

作者： Mohammad Daboor Faten Kharbat Fathi Mueen Azzuni Iyad Sultan Mohammad Odeh Nursing Department King Hussein Cancer center Amman Jordan College of Engineering Al Ain University Abu Dhabi UAE Pediatric Department Kanad Hospital AlAin Abu Dhabi UAE Department of Pediatrics King Hussein Cancer Center Amman Jordan Cancer Care Informatics Program King Hussein Cancer Center (KHCC) Amman Jordan Jordan College of Art Technology and Environment University of the West of England Bristol UK Global Academy for Digital Health Bristol UK

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

Central Line-Associated Bloodstream Infections (CLABSIs) are important Healthcare-Associated Infections (HAI) resulting in increased morbidity, mortality, and medical care costs. Data relevance, quality, and appropriateness are crucial characteristics for any effective and successful predictive model including CLABSI prediction. This study tackles the problem of finding out which among many possible features might be most representative or relevant to predict CLABSls by proposing a new objective method of selecting features; we integrate text mining techniques with process modeling thus making it possible for us to know objectively which amongst them are significant ones that should always reflect in any dataset used for CLABSI prediction. What our approach does is to enhance the strength and accuracy of predictive models by providing verified sets of features as shown by results achieved herein. Also, this research brings into focus such points like having validated data as well process models which may play a role in determining the readiness levels of CLABSI dataset at hospital settings.

关键词： Text mining Costs Accuracy Process modeling Hospitals Predictive models Data models

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Clinical applications of artificial intelligence in diabetes management: A bibliometric analysis and comprehensive review

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informatics in Medicine Unlocked 2024年 50卷

作者： Daza, Alfredo Olivos-López, Ander J. Chumbirayco Pizarro, Margarita Abad Escalante, Karol Moira Chavez Ortiz, Patricia Gladys Montes Apaza, Rousell Dario Ruiz-Baca, Jesús Sánchez-Chávez, Juan Pablo Faculty of Engineering Professional School of Systems Engineering and Informatics Universidad Nacional del Santa Nuevo Chimbote Peru Department of Health Sciences Professional School of Human Medicine Universidad Nacional del Santa Nuevo Chimbote Peru Humanistic Training Program Universidad César Vallejo Lima Peru Faculty of Health Sciences School of Nursing Universidad César Vallejo Lima Peru Academic Department of Engineering Sciences Professional School of Renewable Energy Engineering Universidad Nacional de Juliaca Juliaca Peru Faculty of Science Professional School of Human Medicine Universidad Nacional del Santa Nuevo Chimbote Peru

Background: Diabetes is one of the most common pathologies today and has become a constant problem in public health worldwide. Objective: The purpose of this study is to deepen the knowledge of the most applied type of diabetes, the methods to test the performance of the model, the most used techniques with better accuracy, the metrics and the programming languages used in the detection of diabetes. To give a more holistic view of this area of health, this article presents a systematic review of literature (SLR) to analyze artificial intelligence techniques in the detection of diabetes. Methods: A comprehensive search of five databases: MDPI, ScienceDirect, Scopus, Taylor and Francis, and Web of Science using keywords was carried out, which identified 70 articles between 2017 and 2023. This article followed the PRISMA methodology, considering inclusion and exclusion criteria, with the aim of synthesizing the findings related to various aspects: types of diabetes, methods to test model performance, techniques, metrics and programming languages;In addition, new challenges and future work were proposed. Results: The type of diabetes most applied was Type-2 Diabetes Mellitus, the methods to test model performance most used were cross validation with 10 and 5 k-folds;on the other hand, the most efficient technique was Random Forest, the same that obtained the best accuracy, the essential metrics to determine the effectiveness of the model were Accuracy and F1-Score and finally the most used programming language to develop the models was Python. Conclusions: This research provides scientific evidence on how machine learning and deep learning techniques can improve diabetes detection. Therefore, this article seeks to contribute to the field of health, specifically to the detection, management and treatment of diabetes using artificial intelligence algorithms as a competitive advantage. It also provides information to make more accurate clinical decisions, for which we iden

关键词： Deep learning Diabetes Machine learning Prediction

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Digital storytelling of Physics (DiS-Phy): Learning physics from home through stories 10

Digital storytelling of Physics (DiS-Phy): Learning physics ...

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10th National Physics Seminar, SNF 2021

作者： Sanjaya, L.A. Putri, E.A. Wibowo, F.C. Robby, D.K. Puspa, R.W.D. Pertiwi, W.A. Sianipar, L.K. Qoiriyah, W. Sitompul, H. Department of Physics Education Universitas Negeri Jakarta Jakarta13220 Indonesia Department of Management Education Universitas Negeri Jakarta Jakarta13220 Indonesia Kartika VIII-1 Senior High School Jakarta Indonesia Management of Islamic Education STAI AHSANTA Jelutung Kota Jambi Jambi36125 Indonesia Department of Physics Education Universitas Pelita Harapan Tangerang15811 Indonesia Informatics Engineering Study Program Faculty of Engineering Sheikh Yusuf Islamic University Tangerang Indonesia

This study aims to develop a product such as "Digital storytelling of Physics (DiS-Phy)" that can be used as a physics’ educational media for distance learning (PJJ) on magnetic field material. DiS-Phy developed as a digital storytelling media which is able to combine several learning media such as writing, pictures and learning videos in one medium. The developed DiS-Phy also equipped with scientific literacy questions. DiS-Phy can be accessed on all types of distance learning devices such as smartphones, laptops and tablets that are connected to the internet. DiS-Phy including the type of research and development (Research & Development) which refers to the ASSURE model. The result of validation test shows the percentages of 91,07%, according to subject-matter expert, 93,16%, according to media expert and 87,50% according to learning expert. DiS-Phy also tested by 4 physics teachers at senior high school in Jakarta that achieved 92,57% points that make this product has been qualified as a worthy physics’ distance learning device. Also of 35 as respondents in this study shows the percentages of 87,48% points that make this product worthy to use as an educational media. Based on the results, it can be concluded that the DiS-Phy has been qualified as a worthy physics’ distance learning device for teachers and students. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： E-learning

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Using LiDAR sensors for detection flood and landslide in West Nusa Tenggara

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AIP Conference Proceedings 2024年第1期3026卷

作者： Made Sutha Yadnya I. Wayan Sudiarta I. G. P. Wedashwara Wedarama Electrical Enginnering Enginnering Fakulty University of Mataram Mataram 80125 Indonesia Informatics Engineering Faculty of Engineering University of Mataram Mataram Indonesia Physic Study Program Faculty of Mathematics and Natural Science University of Mataram Mataram 80125 Indonesia

In this pre-research, this is an initial study using LiDAR to detect weather conditions in real time. In 5G communication, the opportunity to use an integrated IoT connection is given because the path is used, one of which is millimeter wave. This is applied to disaster conditions, namely flood and landslide detection. Data obtained briefly within one month in data processing needs to be added so that the real sensor power is obtained in the outdoor position because the sensor is used as needed. LiDAR has problems when used in rainy conditions which form polarization scattering or light scattering is formed by moving raindrops, especially when the rain is getting heavier. The position of LiDAR in this study is able to detect sunny conditions with an available level of 97.99% and rain outage is still 0.3%, this in heavy rainy conditions needs further research.

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Generalization is not a universal guarantee: Estimating similarity to training data with an ensemble out-of-distribution metric

arXiv

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arXiv 2025年

作者： Schreyer, W. Max Anderson, Christopher Thompson, Reid F. Computational Biology Program Oregon Health & Science University PortlandOR United States Department of Biomedical Engineering Oregon Health & Science University PortlandOR United States Portland VA Research Foundation PortlandOR United States Department of Radiation Medicine Oregon Health & Science University PortlandOR United States Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University PortlandOR United States Division of Hospital and Specialty Medicine VA Portland Healthcare System PortlandOR United States

Failure of machine learning models to generalize to new data is a core problem limiting the reliability of AI systems, partly due to the lack of simple and robust methods for comparing new data to the original training dataset. We propose a standardized approach for assessing data similarity in a model-agnostic manner by constructing a supervised autoencoder for generalizability estimation (SAGE). We compare points in a low-dimensional embedded latent space, defining empirical probability measures for k-Nearest Neighbors (kNN) distance, reconstruction of inputs and task-based performance. As proof of concept for classification tasks, we use MNIST and CIFAR-10 to demonstrate how an ensemble output probability score can separate deformed images from a mixture of typical test examples, and how this SAGE score is robust to transformations of increasing severity. As further proof of concept, we extend this approach to a regression task using non-imaging data (UCI Abalone). In all cases, we show that out-of-the-box model performance increases after SAGE score filtering, even when applied to data from the model’s own training and test datasets. Our out-of-distribution scoring method can be introduced during several steps of model construction and assessment, leading to future improvements in responsible deep learning implementation. Copyright © 2025, The Authors. All rights reserved.

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Natural Language Interface to Database (NLIDB) for Query with Temporal Aspect

Natural Language Interface to Database (NLIDB) for Query wit...

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2019 International Conference on Data and Software engineering, ICoDSE 2019

作者： Poetra, Dion Ajie Esterina Widagdo, Tricya Azizah, Fazat Nur Institut Teknologi Bandung Magister of Informatics Study Program Bandung Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728149929

NLIDB (Natural Language Interface to Database) was developed to simplify database access using natural language. In previous studies, NLIDB has been developed which can translate natural languages into several languages, including in Indonesian language. However, there are types of queries that cannot be handled by the system, one of them is a query with a temporal aspect. In this study, we propose a method of translating sentences in Indonesian language that have temporal aspects into SQL queries. The process of translating sentence is performed by identifying the input sentence and parsing the sentence into a parsed tree with PC- PATR as the syntax parser. The parsed tree is analyzed and mapped into parts of SQL. Then, an object search is performed to the ontology that is built from the database to get classes, attributes, operators, and values based on tokens in parts of SQL. Finally, the results of the previous process are organized into SQL queries. The NLIDB application built in this study was successful in translating imperative sentences with temporal aspects in Indonesian language into SQL queries for current state, time slice, sequenced and non-sequenced queries. © 2019 IEEE.

关键词： Database systems

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Natural Language Interface to Database (NLIDB) for Decision Support Queries

Natural Language Interface to Database (NLIDB) for Decision ...

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2019 International Conference on Data and Software engineering, ICoDSE 2019

作者： Anisyah, Ani Widagdo, Tricya Esterina Nur Azizah, Fazat Institut Teknologi Bandung Magister of Informatics Study Program Bandung Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728149929

Data access that is accurate, fast, and precise is a modern need for analysis or business decision making. NLIDB (Natural Language Interface to Database) is a system that allows users to access information from a database by entering requests that are stated in natural language. The NLIDB for Indonesian language that has been built has not been able to handle decision support data queries. The method of translating imperative sentences into SQL queries is done in two stages. The first stage is identifying the imperative sentence and parsing the sentence into a parsed tree that is assisted by the PC-PATR syntactic parser. The second stage is processing the parse tree into an SQL query. The parse tree is analyzed to look for parts of SQL, then do object searching for SQL parts to ontology. The objects found will be arranged into SQL queries. NLIDB application development is carried out to test the results of the translation of imperative sentences into SQL queries and see the application performance in translating imperative sentences into SQL queries. Application development is done with the Python programming language and MySQL database. The application that was built successfully translates imperative sentences into SQL queries for cross tab data, summary data, trend data, and top-N/bottom-N data. The application performance in carrying out the translation process is quite good, but it is necessary to improve performance for the ontology object searching process on the query generator component. Development that can be done for further research is the handling of sentence forms that have affixes and subquery handling. © 2019 IEEE.

关键词： Decision making

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