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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

arXiv

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arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： John Raphael G. Cuevas John Kenneth R. Rili Anny Neolicia G. Santillan Vivien A. Agustin Mary Grace N. Gonzales Criselle J. Centeno Richard C. Regala Department of Information Technology College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Associate Dean of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Faculty of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Director of Graduate Program of College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Computer Lab Administrator of College of Engineering and Technology Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design.

关键词： Solid modeling Three-dimensional displays Layout Decision making Clustering algorithms Real-time systems User experience Mobile applications Augmented reality Optimization

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Improvement of Jakarta's air quality during large scale social restriction 2

Improvement of Jakarta's air quality during large scale soci...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Rahutomo, R. Purwandari, K. Sigalingging, J.W.C. Pardamean, B. Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather Data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Mapping of Smart Economy Research Themes: A Nine-Year Review 8

Mapping of Smart Economy Research Themes: A Nine-Year Review

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Purnomo, Agung Dian Sano, Albert Verasius Nindito, Hendro Madyatmadja, Evaristus Didik Sianipar, Corinthias P. M. Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Graduate School of Global Environmental Studies Kyoto University Department of Global Ecology Kyoto606-8502 Japan

ISBN: (纸本)9781665416979

Research on the smart economy as a part of a smart city continues to develop but is limited to one country and/or one field. From a bibliometric review, this study aims to visually study mapping and research trends in the field of the smart economy on an international scale. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyze search results function on Scopus. This study analyzed 125 scientific documents published from 2011 to 2020. According to the research, the Peter the Great St. Petersburg Polytechnic University, Russia, and Mboup, G. had the most active individual scientists and in smart economy research. computer science and Advances in 21st Century Human Settlements were the most studied and disseminated outlet of smart economy research. There was one category map of collaborative researchers from around the world. Based on the identification of a collection of knowledge generated from nine years of publication, this research proposes a grouping of smart economy research themes: Economy, Application of smart cities, Smart technology, Smart cities character, Economy of urban, Education and Smart environment, abbreviated as EASSEES research themes. © 2021 IEEE.

关键词： Mapping

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of computer Science and Information Technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Assistive technology with device for self-inflating bag in neonatology

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Research on Biomedical Engineering 2025年第2期41卷

作者： Marcelino, Ana Carolina Rodovanski, Giovana Pascoali Bonetti, Karoline Panes, Caio Rabinovich de Almeida Martins, Fernando Moran, Cristiane Aparecida Araranguá Brazil Graduate Program in Neurosciences and Behavior Department of Experimental Psychology of the Institute of Psychology-University of São Paulo Sao Paulo Brazil Araranguá Brazil Computer Engineering Instituto Mauá de Tecnologia São Caetano do Sul Brazil Electronic Engineering and Computing with Emphasis on Electronic Devices and Systems at Instituto Tecnológico de Aeronáutica–ITA São Caetano do Sul Brazil Department of Physiotheraphy and at the Postgraduate Program in Rehabilitation Science of the Federal University of Santa Catarina-UFCS Ararangua Brazil

Purpose: The objectives of this study were to create a device with technological development of the self-inflating bag (SIB) equipment to measure the variables tidal volume, peak inspiratory pressure, and inspiratory flow and to evaluate the knowledge of undergraduate students in handling the SIB. To reduce the risks of mortality, length of hospital stay, and infections, SIB is a resource used to ventilate the patient through the inflation of the device, but it is not capable of measuring the ventilatory parameters. Excess ventilation and incorrect handling of the bag can cause lung injury in newborns. Therefore, measuring these parameters can prevent lung injury resulting from inadequate ventilation. Method: This is a cross-sectional and experimental study, with the development of a device SIB that allows the evaluation of variables of neonatal respiratory mechanics that were tested with an artificial lung. It was approved by the Research Ethics Committee and the research was developed in three stages: the first represents the search for scientific articles for the consensus of neonatal ventilatory variables for the construction of the SIB parameters;the second stage depicts the technological development of a device capable of monitoring such variables through the SIB. In the third stage, the content validation of the device with the handling of the SIB was carried out by students of medicine and physiotherapy courses of Brazil. Results: In this work, a device that analyzes the values of the main ventilatory parameters in neonatology was developed and to generate a safety range for the use of the device, curves with a tolerance of 10% up and down were created. All electronic components were coupled to the equipment, with a display that transmits the parameter values. The web page can be used on a cell phone, tablet, computer, or SmartTv, as long as it is connected to the "RespiratorIoT_AP" network. Among the 29 participants in the study, 8 said they were familiar w

关键词： Students

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Transformative Sport Science Using Social Media as a Collaboration Tool 7

Transformative Sport Science Using Social Media as a Collabo...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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