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检索条件"任意字段=2014 International Conference on Data and Software Engineering, ICODSE 2014"

共 4537 条记录，以下是71-80 订阅

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Promoting Open data Utilization in Infrastructure Management Through an Integrated System of data Processing, Cloud Storage, and Visualization

Promoting Open Data Utilization in Infrastructure Management...

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international Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Zhenlong Zhang Kei Kawamura Yiming Su Hisao Emoto Shuji Sawamura C. K. Nguyen Graduate School of Sciences and Technology for Innovation Yamaguchi University Ube Japan Social Systems and Civil Engineering Faculty of Engineering Tottori University Tottori Japan Department of construction Yamaguchi Prefecture Government Yamaguchi Japan MienTrung University of Civil Engineering 195 Ha Huy Tap Tuy Hoa Phu Yen Vietnam

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

During the rapid economic growth of Japan in the 1960s, the country's infrastructure development progressed swiftly. Since 2014, the Japanese government has mandated regular inspections of major urban infrastructure. This policy has resulted in the annual accumulation of substantial amounts of infrastructure inspection data. Two years prior to implementing this policy, the Japanese government also launched an open data initiative to encourage industry developers to utilize open data. However, this study observes that inconsistencies in data formatting and standards across different regions pose significant challenges to the utilization of this data. To address the obstacles hindering the use of open data, this study proposes an integrated system(Service Link Platform) for effectively managing and utilizing periodic transportation infrastructure inspection data which encompasses data collection, data format processing, cloud storage and transmission, and data utilization. This article will layout the manner in which the system employs a method for distributing data in JSON (JavaScript Object Notation) format to publicly manage bridge inspection data and ensure compatibility of data formats.

关键词： Cloud computing Roads Government Standards organizations data visualization Transportation Inspection Personnel Open data software development management

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Mathematical Model for Scheduling Problems: A Compatibility Test on University Course Timetabling Problem 1

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8th international conference on Computational Science and Technology, ICCST 2021

作者： Aizam, Nur Aidya Hanum Ismail, Zuliyana Farahi Yen, Candy Loh Siew Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia

ISBN: (数字)9789811685156

ISBN: (纸本)9789811685149

The study presents the application of a general mathematical model previously developed for another set of timetabling problem which is the University Course Timetabling (UCT). The aim of this study is to extend the validation process of the formulated model using a standard Mixed Integer Linear Programming (MILP). A real public university course timetabling problem was used as our case study. All data was gathered and analysed before embedded to the general model and solved optimally using the AIMMS mathematical software with CPLEX as the solver to a personal laptop of 2.20 GHz and 4.95 GB RAM. The data consists of 27 programmes, 449 core courses, 59 rooms and 70 time slots. An optimal standard university course timetable was produced within a few minutes of CPU time. Optimality denotes that the courses were assigned to the preferred slots and rooms, while fulfilling requirements such as the university’s policies, and other demands of all parties involved. With the timetable produced, it is proven of the capability of the mathematical model developed in solving timetabling problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling

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Application of mT5 and Semantic Role Labeling for Aspect-Based Sentiment Analysis in Political Opinion

Application of mT5 and Semantic Role Labeling for Aspect-Bas...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), international conference on

作者： Jinpeng Yu Zijun Li Na Liu Weixuan Kong Zijun Huang Xuefeng Li College of Electronic and Information Engineering Tongji University Shanghai P.R. China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai P.R. China College Of Software Engineering Tongji University Shanghai P.R. China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

As aspect-based sentiment analysis (ABSA) extends its application across various domains, effectively utilizing this technology in political opinion analysis poses significant challenges, particularly due to limited training data in sensitive areas. To address this, we constructed two datasets: a 7,334-sample three-class sentiment analysis dataset from political news via web scraping, and a high-quality dataset of 100 manually annotated news sentences designed for testing aspect-level sentiment computation. “This paper proposes a sentiment classification method based on the mT5 model, combined with Named Entity Recognition (NER) and Semantic Role Labeling (SRL). Experimental results show that our method performs comparably to existing approaches on the SemEval-2014 (Rest14) dataset, while achieving 84% accuracy in aspect entity recognition and 69% overall recognition accuracy on our custom dataset. These findings suggest that the proposed method can significantly enhance model performance in scenarios with limited and sensitive training data.

关键词： Sentiment analysis Analytical models Accuracy Computational modeling Semantics Training data Named entity recognition data models Labeling Testing

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Research project of an automatic wireless monitoring system of buildings, road and industrial infrastructures 11

Research project of an automatic wireless monitoring system ...

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11th international conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2022

作者： De Stefano, A. Cardoni, A. Kather, M. Domaneschi, M. Cimellaro, G.P. Fenoglio, T.M. Fantinuoli, G.L. Morino, A. Taverna, G. Putetto, C. Flex Patch System Srl Corso Giacomo Matteotti 36 Turin10121 Italy Department of Structural Geotechnical and Building Engineering Politecnico di Torino Corso Duca degli Abruzzi 24 Turin10129 Italy GD Test Srl Corso Casale 239 Turin10132 Italy Effegi Elettronica Via Pancalieri 37 Vigone10067 Italy Carbonteam Srl Via Armando Sabatini 15 Saluzzo12037 Italy

An automatic wireless monitoring system of buildings, road infrastructures (e.g., bridges, viaducts, tunnels) and industrial infrastructures (e.g., silos, tanks, pipelines) of new conception is presented in the present contribution. The system is based on networks of multifunctional sensors integrated in a textile base of composite material easily applicable on the surfaces of the structural elements to be monitored. The sensors are equipped with innovative features that allow to measure physical quantities in dynamic field acquiring a large amount of data. The system is managed by a computer platform that allows to collect and process the data operating automatic modal analysis for the structural assessment. The final interpretation of the results is developed with the help of a software based on algorithms developed using different approaches (e.g. Machine Learning). This solution allows the analysis of the structures with prescriptive and predictive purposes to determine the state of safety in relation to natural and man-made hazards. © 2022 international conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Structural health monitoring

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Body Area Wireless Sensor Network Communication with High data Rate using Defected Ground Plane Antennas

Body Area Wireless Sensor Network Communication with High Da...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： S. Palanivel Rajan R. Sripathi D. R. Suryaraj G. Thirumal Electronics and Communication Engineering M.Kumarasamy College of Engineering Karur India

This paperwork explains the feasibility of the textile denim material being used as a substrate in Body Area Wireless Sensor Networks (BA-WSN) for the enhancement of data rate. To get a high data rate in sensor networks, the bandwidth of the antenna needed to be increased. Moreover, the substrates which are used in the are expensive. Hence low-cost Fire Retardant FR4 is used. A simple 55 × 50 mm2 monopole Logo antenna was developed for the enhancement of bandwidth using shaped loop shaped defective ground structure at Wireless Local Area Network (WLAN) band 2.45GHz using Computer Simulation Technology (CST) 2014 version software. The bandwidth of the antenna is responsible for the data rate of transmission and hence the main objective the work is focused on the wider bandwidth operation at WLAN frequency. A performance study was done with and without case of loop shaped defective structure. Thus, comparing the conventional FR4 board, defective FR4 board yield large bandwidth for Wireless Sensor Networks with high data rate.

关键词： Wireless communication Temperature sensors Wireless sensor networks Wireless LAN Temperature Transmitting antennas Bandwidth

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Exploratory data Analysis and Predicting Indian Crime Against Women Using Machine Learning Approaches

Exploratory Data Analysis and Predicting Indian Crime Agains...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： N. Srinivasa Gupta B. Valarmathi K. Santhi T.Chellatamilan Sameeksha Nanda Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Crime forecasting is a critical endeavor aimed at preventing future criminal actions, specifically the rising rate of violence against women in India. This study proposes the model to predict crimes committed against women using data from the National Crime Records Bureau's (NCRB) monthly crime report. The dataset encompasses district and state-wise crime activities from 2001 to 2014. The proposed work objectives are identifying factors influencing these crimes and developing predictive models using machine learning (ML). The study employs Exploratory data Analysis (EDA), to enhance the predictive power of the models. The suggested study uses XGBoost, Random Forest (RF), and Neural Network (NN). The neural network algorithm outperforms the Random Forest and XGBoost techniques among these three. The Random Forest algorithm has the worst performance. After comparing the accuracies of various methods, such as XGBoost (92%), and Random Forest (89.84%) classifiers using imbalanced datasets (without employing Synthetic Minority Oversampling Technique (SMOTE) technique), it was found that the neural network algorithm (95.81%) delivers the highest accuracy. This study looks at model accuracies and finds that, after applying the SMOTE algorithm, neural network algorithms performed best, with an accuracy of 96.76%, outperforming the accuracies of the remaining models, which included the Random Forest (93.08%) and XG-Boost (95%).

关键词： Radio frequency Technological innovation Accuracy Machine learning algorithms data analysis Predictive models Prediction algorithms data models Classification algorithms Random forests

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The software Architecture and development approach for the ASTRI Mini-Array gamma-ray air-Cherenkov experiment at the Observatorio del Teide 7

The Software Architecture and development approach for the A...

引用

conference on software and Cyberinfrastructure for Astronomy VII

作者： Bulgarelli, A. Lucarelli, F. Tosti, G. Conforti, V Parmiggiani, N. Schwarz, J. H. Antonelli, L. A. Baroncelli, L. Bigongiari, C. Capalbi, M. Cardillo, M. Castillo, G. A. R. Catalano, O. Compagnino, A. A. Corpora, M. Costa, A. Crestan, S. Cusumano, G. D'Ai, A. Fioretti, V Gallozzi, S. Germani, S. Gianotti, F. Giordano, V Giuliani, A. Bruno, P. G. Green, J. G. Grillo, A. Incardona, F. Iovenitti, S. La Palombara, N. La Parola, V Landoni, M. Lombardi, S. Maccarone, M. C. Millul, R. Mineo, T. Mollica, D. Pagliaro, A. Pareschi, G. Pastore, V Perri, M. Pintore, F. Romano, P. Russo, F. Sangiorgi, P. Saturni, F. G. Sciacca, E. Scuderi, S. Tacchini, A. Testa, V Trifoglio, M. Tutone, A. Vercellone, S. INAF OAS Bologna Via P Gobetti 93-3 I-40129 Bologna Italy INAF OAR Roma Via Frascati 33 I-00078 Rome Italy ASI SSDC Roma Via Politecn Snc I-00133 Rome Italy Univ Perugia Dept Fis & Geol Via A Pascoli I-06123 Perugia Italy INAF OA Brera Via Brera 28 I-20121 Milan Italy INAF IASF Palermo Via Ugo La Malfa 153 I-90146 Palermo Italy INAF IAPS Roma Via Fosso del Cavaliere 100 I-00133 Rome Italy INAF OACT Catania Via Santa Sofia 78 I-95123 Catania Italy INAF IASF Milano Via Alfonso Corti 12 I-20133 Milan Italy

ISBN: (纸本)9781510653603;9781510653597

The ASTRI Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics (INAF) and devoted to the imaging of atmospheric Cherenkov light for very-high gamma-ray astronomy. The project is deploying an array of 9 telescopes sensitive above 1 TeV. In this contribution, we present the architecture of the software that covers the entire life cycle of the observatory, from scheduling to remote operations and data dissemination. The high-speed networking connection available between the observatory site, at the Canary Islands, and the data Center in Rome allows for ready data availability for stereo triggering and data processing.

关键词： ASTRI Mini-Array Cherenkov telescopes software Architecture software engineering

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Real-Time Anomaly Detection Business Process for Industrial Equipment Using Internet of Things and Unsupervised Machine Learning Algorithms 23rd

Real-Time Anomaly Detection Business Process for Industrial...

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23rd international conference on Computational Science and Its Applications, ICCSA 2023

作者： Gultekin, Emrullah Aktas, Mehmet S. Computer Engineering Department Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9783031371165

Today, it is evident that the Internet of Things, real-time data processing, and artificial intelligence technologies are essential in industrial settings to enable early warning autonomous anomaly detection systems. Such systems can detect anomaly situations that could cause failures shortly after they occur, allowing necessary maintenance to be performed promptly. In this research, a software platform has been designed and developed to collect data from sensors placed on industrial equipment to monitor their condition using the required IoT infrastructure. The real-time data collected from this platform is analyzed using real-time data processing techniques. Here, a business process is introduced for instant anomaly detection using real-time clustering analysis methods. To validate the proposed business process architecture, a prototype software has been developed, and its ability to detect anomaly situations has been evaluated. The results show that the proposed business process architecture is effective in real-time anomaly detection and can successfully detect anomalies that can lead to industrial equipment failures. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Investigation soil degradation in Iraq by using geomatics techniques 3

Investigation soil degradation in Iraq by using geomatics te...

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3rd international Scientific conference of engineering Sciences and Advances Technologies, IICESAT 2021

作者： Dalhel, Fadhaa Turkey Albayati, Muthanna M. Ziboon, Abdulrazak T. Department of Civil Engineering University of Technology Baghdad Iraq

In this study Remote Sensing techniques have been used to investigate soil degradation in Iraq (alluvial plain) for the period (1976 - 2020) by using different many source of data such as satellite images (Landsat 1-5 MSS C1 Level, Landsat 4-5 TM C1 Level-1, Landsat 7, Landsat 8, and sentinel 2) and use more than one software like (ENVI 5.3, ERDAS Imagine 2015, Arc GIS 10.7, and Blender). This study focusing on determine the main degraded areas in Iraq, (alluvial plain) chosen because of spread sand dunes. To make the study soberer, ground trothing achieved to verify the real status of the area and collect the data by field visit. Bands G, B, R, and IR from LANDSAT-3 in 1976, 1996 and 2014, the same bands used from Sentinel-2 dated 2021. Unsupervised classification made for images of 1976 and 1996 then supervised classification did for images in 2014 and 2021. The classification was carried out using ERDAS Imagine V.2015 software and areas of the main land uses in the study area were calculated. The results indicate that there is a big problem in the base year 1976, this problem almost disappeared in the second station of work 1996, but it returned back after that through the results for the years 2014 and 2021. © Published under licence by IOP Publishing Ltd.

关键词： Landsat

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AI based Technologies for international Space Station and Space data 11

AI based Technologies for International Space Station and Sp...

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11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Potgantwar, Amol Bedi, Pradeep Raboaca, Maria Simona Constantin, Neagu Bogdan Verma, Chaman School of Computer Sciences and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Sandip Institute of Technology and Research Center Sandip University Nashik India Galgotias University Deapartment of Cse Greater Noida India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Iasi Department of Power Engineering Gheorghe Asachi' Technical Iasi700050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

Billions of galaxies, stars, solar systems, planets, and other undiscovered mysterious objects are present in the continuously expanding space. Humans took a giant step forward when the international Space Station was deployed into Earth's lower orbit for a better understanding of space. This research focuses of the Artificial Intelligence based technologies that are deployed in the international Space Station and potential models. The study proposes some Machine Learning models and technologies that could be deployed in the international Space Station to increase its efficiency and provide security to the crew. Powerful and trending Machine Learning/Deep Learning Algorithms like ANN and Clustering algorithms are suggested by the paper to get insights from the data gathered from the space and to promote the Industry Automation. A detailed explanation of the requirement, operation, and construction of NASA's 'Robonauts' designed for the international Space Station is discussed in the research. The paper also put light on the ATLAS, an asteroid detecting system. The methods for providing medical aid to the crew, debris and its influence, analyzing data and extracting insight from space research data using machine learning are also highlighted. Investigation on how technologies used in space exploration could be used in the ISS to improve its performance and an overview of some of the existing AI-based technologies deployed in the international Space Station (ISS) is discussed. © 2022 IEEE.

关键词： Machine learning

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