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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Atikah Rahayu Mandasari Erna Budhiarti Nababan Herman Mawengkang Master of Informatics Program Universitas Sumatera Utara Medan Indonesia Faculty Of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages.

关键词： Long short term memory Training Vectors Accuracy Machine translation Vocabulary Predictive models Numerical models Decoding Context modeling

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE Engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Prevalence of refractive errors in Hungary reveals three-fold increase in myopia

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International Journal of Ophthalmology(English edition) 2022年第7期15卷 1174-1179页

作者： János Németh TennóDaiki Gergely Dankovics István Barna Hans Limburg Zoltán Zsolt Nagy Department of Ophthalmology Semmelweis UniversityBudapest 1085Hungary Comprehensive Health Test Program of Hungary Szentendre 2000Hungary Department of Media and Education Informatics Eötvös Loránd UniversityBudapest 1053Hungary 1st Department of Internal Medicine Semmelweis UniversityBudapest 1083Hungary Health Information Services Grootebroek 1613LCThe Netherlands

AIM:To examine the prevalence and composition of refractive errors in ***:Nationwide cross-sectional data collected between 2014 and 2019 were analysed from the Comprehensive Health Screening program of Hungary,which provided spectacle dioptric power and autorefractometry data for 68227 people(35850 women and 32377 men).Their age distribution,18-99y,was similar to the national demographic ***:Of the total population,16.50%of the refractive errors exhibited hyperopia,40.05%emmetropia,and 43.45%*** was 3 times more frequent(58.7%)in younger ages(18-35y of age)compared to older age groups(19.4%of those 56-70y of age;P<0.001).High myopia showed a low prevalence(0.21%),and an increase parallel with ageing(r=0.716;P=0.009).CONCLUSION:Myopia is the most frequent refractive error in *** prevalence of myopia is especially increased,up to 2-3 times,in the younger age *** actions need to be taken to reduce the onset of myopia and its associated consequences.

关键词： refractive errors myopia hyperopia emmetropia Hungary

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Domain adaptation in practice: Lessons from a real-world information extraction pipeline 2

Domain adaptation in practice: Lessons from a real-world inf...

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2nd Workshop on Domain Adaptation for NLP, Adapt-NLP 2021

作者： Miller, Timothy Laparra, Egoitz Bethard, Steven Computational Health Informatics Program Boston Children’s Hospital Department of Pediatrics Harvard Medical School United States School of Information University of Arizona United States

ISBN: (纸本)9781954085084

Advances in transfer learning and domain adaptation have raised hopes that once-challenging NLP tasks are ready to be put to use for sophisticated information extraction needs. In this work, we describe an effort to do just that – combining state-of-the-art neural methods for negation detection, document time relation extraction, and aspectual link prediction, with the eventual goal of extracting drug timelines from electronic health record text. We train on the THYME colon cancer corpus and test on both the THYME brain cancer corpus and an internal corpus, and show that performance of the combined systems is unacceptable despite good performance of individual systems. Although domain adaptation shows improvements on each individual system, the model selection problem is a barrier to improving overall pipeline performance. © 2021 Association for Computational Linguistics

关键词： Information retrieval

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The global landscape of cancer stigma research and practice: perspectives, measures, interventions, and actions for change

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Journal of the National Cancer Institute. Monographs 2024年第63期2024卷 1-3页

作者： Kathryn Heley Vidya Vedham Robin C Vanderpool Health Communication and Informatics Research Branch Behavioral Research Program Division of Cancer Control and Population Sciences National Cancer Institute Rockville MD USA. Center for Global Health Office of the Director National Cancer Institute Rockville MD USA.

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First Order Piecewise Collocation Solution of Fredholm Integral Equation Second Type Using SOR Iteration 7th

First Order Piecewise Collocation Solution of Fredholm Integ...

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7th International Conference on Computational Science and Technology, ICCST 2020

作者： Mohamad, N.S. Sulaiman, J. Saudi, A. Zainal, N.F.A. Mathematics with Economics Program Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9789813340688

We evaluate the first-order approximation solution piecewise by first-order polynomial collocation with Quadrature scheme on second-type Fredholm integral equations. This discretization derived the formulation to solve the first order piecewise approximation equation in which the linear system was built. The SOR method was described as a linear solver in which its formulation was constructed and applied in this study. In order to obtain the approximation solutions, the combination of SOR iterative method with the first-order piecewise polynomial by collocation with quadrature scheme has shown that performance of SOR method is superior than Jacobi method in terms of number of iterations and time of completion. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fredholm integral equations

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Analyzing the Effectiveness of Smartphone Applications on Disease Management and Healthcare Systems

Analyzing the Effectiveness of Smartphone Applications on Di...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Rabei Raad Ali Wisam Subhi Al-Dayyeni Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Omar M. Jihad Department of Computer Engineering Technology Northern Technical University Mosul Iraq School of Information Technologies and Engineering ADA University Baku Azerbaijan Study Program in Informatics Engineering Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mobile cloud computing plays a major role. The taxonomy of mobile computing comprises operational aspects, end-user issues, and service quality and mobility management. The use of smartphone technologies and applications has become a highly significant approach to improving healthcare management. Mobile health services such as mobile pathology, mobile neurosurgery, cancer treatment, and behavioral/psychological disorders are gaining significance where smartphone applications are being used. Portability, flexibility, and convenience are major characteristics of mobile computing that have helped patients and doctors to develop better relationships through coordination and communication. The relationship between smartphone technologies and applications and healthcare management can be understood in several broad aspects. This article aims to analyze the current and future implications of these technologies on healthcare and disease management systems.

关键词： Pathology Cloud computing Taxonomy Cancer treatment Medical services Benchmark testing Neurosurgery Information and communication technology Mobile computing Diseases

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest

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IEEE Access 2025年 13卷 84713-84726页

作者： Dias, Jean A. C. Guimarães, Pedro H. Do V. Pereira, Williane G. S. Tamasauskas, Leonardo De O. Gomes, Marivan S. Correa, Alan B. S. Figueiredo, Karla Costa, Gilson Costa, Gabriel Brito Costa, Fernando A. R. Seruffo, Marcos C. Da R. Operational Research Laboratory Belém66075-110 Brazil School of Technology Amazonas 69850-000 Brazil Belém66075-110 Brazil Institute of Mathematics and Statistics Department of Informatics and Computer Science Rio de Janeiro20550-013 Brazil Institute of Biological and Health Sciences II Department of Genetics Rio de Janeiro20550-013 Brazil Santarém68035-110 Brazil Federal University of Rio Grande do Norte Climate Sciences Post-Graduate Program Rio Grande do Norte Natal59078-970 Brazil Pará Santarém68035-110 Brazil Post-Graduate Program in Forest Science Technology and Innovation-PPGCTIF Pará Santarém68035-110 Brazil Pará Santarém68035-110 Brazil Pará Belém66075-110 Brazil Pará Castanhal68746-630 Brazil

This study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a machine learning technique (Long Short-Term Memory - LSTM). Applied to GPP data from the PE-QFR site, the hybrid model achieved significantly better error metrics, with RMSE of 1.69 gC/m2/day, MAE of 1.35 gC/m2/day, and MAPE of 0.20%, compared to the standalone LSTM (RMSE of 2.16 gC/m2/day, MAE of 1.78 gC/m2/day, and MAPE of 0.27%). Furthermore, the hybrid model showed stronger agreement with the observed data, with correlation coefficient r =0.62 and R2 =0.39 , whereas the LSTM alone yielded r =0.26 and R2 = –0.002. The STL decomposition allowed effective separation of trend, seasonality, and residual components, enabling tailored modeling of each, which contributed to the improved predictive performance. These results demonstrate the advantage of hybrid approaches in capturing the nonlinear and seasonal patterns of GPP, supporting enhanced environmental monitoring and more informed climate change mitigation strategies in the Amazon. © 2013 IEEE.

关键词： Carbon sequestration

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Secure Multi-Point Coordinated Beamforming using Deep Learning in 5G and Beyond Networks

Secure Multi-Point Coordinated Beamforming using Deep Learni...

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IEEE International Workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Utku Ozmat Mehmet Akif Yazici Mehmet Fatih Demirkol Informatics Institute Istanbul Technical University Istanbul Türkiye 5G Program Management Department Turk Telekom Istanbul Türkiye Prorize LLC GA USA

In 5G and beyond networks, a critical need exists for a rapid, energy-efficient, and secure beam selection process. This study introduces a secure multi-point coordinated beamforming approach based on deep learning. It prioritizes beam pairs between a transmitter and a legitimate user, with the goal of optimizing the user’s signal strength while ensuring that the eavesdropper’s signal strength remains below a predefined threshold. Instead of exhaustive search, the method focuses on a limited set of top-performing beam pairs, resulting in reduced communication overhead and energy consumption. The scheme’s performance is assessed using statistical systemlevel variables. Numerical results indicate a 75% reduction in signaling overhead, with 87.41% accuracy in selecting the best beam pair and achieving 99.62% of the desired signal strength. In terms of security, the method enhances secure communication probability by 70.4%, compared to the system without security constraints.

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