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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Olesia Galai Dmitrii Kurdiumov Alena Rusak Faculty of Software Engineering and Computer Systems ITMO University Saint-Petersburg Russia

Medical notes contain valuable information about patient conditions, treatments, and progress. Extracting symptoms from these unstructured notes is crucial for clinical research, population health analysis, and decision support systems. Traditional manual methods are time-consuming, but recent advances in natural language processing (NLP) and machine learning offer automated solutions. This article presents a novel approach that combines NLP techniques, such as conditional random fields (CRF) and transformer-based architectures. The proposed method demonstrates effective symptom extraction from medical notes, overcoming challenges such as varied terminologies and linguistic nuances. The study utilizes a dataset of Russian medical records, transforming it into a tabular format for training and employing unique tokenization algorithms for different models. Among the evaluated models, RuBERT achieved the highest accuracy of 91%, indicating its strong performance on the test dataset. SBERT exhibited the highest precision and F1 score, suggesting its effectiveness in accurately identifying specific sequence labels.

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Collection: The Aegean Ro-Boat Race 2023

IEEE Data Descriptions

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IEEE Data Descriptions 2024年 1卷 87-94页

作者： Alexandros Troupiotis-Kapeliaris Nikolaos Sapountzis Giannis Spiliopoulos Thomas Kogias Piyabhum Chaysri Bernardo AntÓnio Correia Gabriel Miguel Filipe Santos Silva Elias Xidias Konstantinos Blekas João Tasso de Figueiredo Borges de Sousa Dimitris Zissis Department of Product and Systems Engineering University of the Aegean Syros Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece Department of Electrical and Computer Engineering of the Faculty of Engineering University of Porto Porto Portugal

In this article, we introduce a publicly available real-world dataset collected during the Aegean Ro-Boat Race 2023, which took place at the University of the Aegean in Syros, Greece. The Aegean Ro-Boat Race represents an international competition at the university level, challenging teams to innovate and develop autonomous marine robotic systems capable of performing in unknown dynamic maritime environments under real-world conditions. The 2023 competition featured three primary mission tasks, each designed to test different aspects of the robotic systems: 1) high-speed performance for evaluating the speed and agility of the autonomous vessels; 2) collision avoidance for assessing the systems’ ability to detect and avoid obstacles in real-time; and 3) endurance for testing the operational longevity and efficiency of the robotic systems over extended periods. In total, seven teams registered for the competition, with five of them being from Greece and two from the countries of Portugal and Latvia. Due to several technical difficulties, three vessels were able to complete all races, and data were recorded during their entire participation. The spatiotemporal data for the “Aegean Ro-Boat Race” was gathered through an onboard data logging system that continuously monitored various sensors, including global positioning system (GPS), for all vessels during the entire competition. The dataset includes positional reports from the vessels during all three races (totaling over 6500 records), the positions of the external track and obstacle buoys, together with a file regarding the weather conditions during the race day. IEEE SOCIETY/COUNCIL computer Society (CS), Aerospace and Electronic systems Society (AESS), Signal Processing Society (SPS), Oceanic engineering Society (OES), Intelligent Transportation systems Society (ITSS) DATA TYPE/LOCATION CSV; Syros, Greece DATA DOI/PID 10.5281/zenodo.13318421

关键词： Robots Collision avoidance Boats Navigation Trajectory Testing Spatiotemporal phenomena Drones Technological innovation Sea measurements

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Mobile Robots through Task-Based Human Instructions using Incremental Curriculum Learning

Mobile Robots through Task-Based Human Instructions using In...

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IEEE Conference on Cybernetics and Intelligent systems

作者： Muhammad A. Muttaqien Ayanori Yorozu Akihisa Ohya Faculty of Systems and Information Engineering Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper explores the integration of incremental curriculum learning (ICL) with deep reinforcement learning (DRL) techniques to facilitate mobile robot navigation through task-based human instruction. By adopting a curriculum that mirrors the progressive complexity encountered in human learning, our approach systematically enhances robots’ ability to interpret and execute complex instructions. We explore the principles of DRL and its synergy with ICL, demonstrating how this combination not only improves training efficiency but also equips mobile robots with the generalization capability required for navigating through dynamic indoor environments. Empirical results indicate that robots trained with our ICL-enhanced DRL framework outperform those trained without curriculum learning, highlighting the benefits of structured learning progressions in robotic training.

关键词： Training Visualization Mechatronics Navigation Random access memory Deep reinforcement learning Indoor environment

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Game-Theoretic Sectoral Demand Response Procurement in Multi-Energy Microgrid Planning

Game-Theoretic Sectoral Demand Response Procurement in Multi...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Mohseni, Soheil Brent, Alan C Sustainable Energy Systems School of Engineering and Computer Science Faculty of Engineering Victoria University of Wellington Wellington6140 New Zealand

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

Multi-energy community microgrids (MGs) have been recognized as key enablers for harnessing distributed demand-side flexibility resources, especially when integrating storage. However, the literature on demand response-integrated community energy system design and dispatch optimization has, thus far, failed to concurrently maximize the flexibility potential in several energy carriers, thereby neglecting the potentially significant improvement opportunities of the associated business cases. In response, this paper introduces a novel Nash bargaining-based cooperative game approach for the optimal aggregator-mediated demand response scheduling of multi-energy community MGs serving electricity and thermal loads, as well as hydrogen as a transportation fuel. More specifically, the proposed approach systematically and effectively characterizes how the players share the resulting surplus from demand-side management in an equitable manner under the assumption that the interests of the players - the MG operator, sectoral demand response aggregators, and small-scale end-users - are neither completely opposed nor completely coincident. The proposed approach is then integrated into a meta-heuristic-based long-term MG planning method. A case study for a community-based residential users' aggregation scheme in Aotearoa-New Zealand demonstrates the effectiveness of the method in reducing the total discounted system cost of a multi-energy MG by 14% (equating to US1.9m) and 31% (US5.4m) respectively compared to the cases where: (i) the actors' behaviors are characterized using non-cooperative game theory under self-interestedness assumptions;and (ii) no demand response programs are implemented. © 2022 IEEE.

关键词： Heuristic methods

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A Practical computer Architecture Education with RISC-V and TL-Verilog 32

A Practical Computer Architecture Education with RISC-V and ...

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32nd International Scientific Conference Electronics, ET 2023

作者： Minev, Petar Kukenska, Valentina Varbov, Ilian Dinev, Matyo Technical University of Gabrovo Faculty of Electrical Engineering and Electronics Department of Computer Systems and Technologies 4 H. Dimirat str. Gabrovo5300 Bulgaria

ISBN: (纸本)9798350302004

The report presents a hands-on learning approach that can be implemented in the computer Architectures labs. A model of a pipelined microarchitecture RISC-V processor core developed using the high-level hardware description language TL-Verilog is described. Basic steps for implementation and visualization of the model are proposed. The necessity of its application in the educational process of students with different training profiles is substantiated. © 2023 IEEE.

关键词： computer hardware description languages

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A New Classification Method for Drone-Based Crops in Smart Farming

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第9期16卷 164-174页

作者： Al-Rami, Bandar Alheeti, Khattab M. Ali Aldosari, Waleed M. Alshahrani, Saeed Matar Al-Abrez, Shahad Mahdi Dean of Faculty of Computer Studies Arab open University Riyadh Saudi Arabia College of Computer Sciences and Information Technology Computer Networking Systems Department University of Anbar Ramadi Iraq Department of Computer Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Faculty of Computer Science and Information Technology Saudi Electronic University Riyadh Saudi Arabia

During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for in-creasing the quality and quantity of the product. On the other hand, drones showed a major potential for enhancing imagery systems and re-mote sensing usage for many different applications such as crop classification, crop health monitoring and weed management. In this paper, an intelligent method for classifying crops is proposed to use a transfer learning approach based on a number of drone images. Moreover, the Convolution-al Neural Network (CNN) method is used as a classifier to improve efficiency for obtaining more accurate results in the training and testing phases. Various metrics are measured to evaluate the efficiency of the proposed model such as accuracy rate of detection, error rate and confusing matrix. It is found to be proven from the experimental results that the proposed method presents more efficient results with an accuracy detection rate of 92.93% © 2022. International Journal of Interactive Mobile *** Rights Reserved.

关键词： Drones

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Analysis and Assessment of Wind Energy Potential of Almukalla in Yemen

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computers, Materials & Continua 2022年第8期72卷 3113-3129页

作者： Murad A.A.Almekhlafi Fahd N.Al-Wesabi Majdy M.Eltahir Anwer Mustafa Hilal Amin M.El-Kustaban Abdelwahed Motwakel Ishfaq Yaseen Manar Ahmed Hamza Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayelAseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayelAseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Electronic Department Faculty of EngineeringUniversity of Science and TechnologySana’aYemen

Energy is an essential element for any civilized country’s social and economic development,but the use of fossil fuels and nonrenewable energy forms has many negative impacts on the environment and the *** Republic of Yemen has very good potential to use renewable ***,we find few studies on renewable wind energy in *** the lack of a similar analysis for the coastal city,this research newly investigates wind energy’s potential near the Almukalla area by analyzing wind ***,evaluation,model identification,determination of available energy density,computing the capacity factors for several wind turbines and calculation of wind energy were extracted at three heights of 15,30,and *** wind speeds were obtained only for the currently available data of five recent years,2005–*** study involves a preliminary assessment of Almukalla’s wind energy potential to provide a primary base and useful insights for wind engineers and *** research aims to provide useful assessment of the potential of wind energy in Almukalla for developing wind energy and an efficient wind *** Weibull distribution shows a perfect approximation for estimating the intensity of Yemen’s wind *** on both theWeibullmodel and the results of the annual wind speed data analysis for the study site in Mukalla,the capacity factor for many turbines was also calculated,and the best suitable turbine was *** to the International Wind Energy Rating criteria,Almukalla falls under Category 7,which is,rated“Superb”most of the year.

关键词： Almukalla energy potential Rayleigh distribution Weibull distribution wind power density wind speed

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Social Web Analysis for Decision Support: A Case Study 5

Social Web Analysis for Decision Support: A Case Study

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5th International Conference on Machine Learning and Intelligent systems, MLIS 2023

作者： Freire, Manuela Antunes, Francisco Costa, João Paulo Univ Coimbra CeBER Faculty of Economics Portugal Department of Management and Economics University of Beira Interior Portugal Inescc - Computer and Systems Engineering Institute of Coimbra Portugal

ISBN: (纸本)9781643684468

In this study, we focused on analyzing customer-generated data on Facebook to explore how textual content on a social web can provide valuable information for decision support. To accomplish this goal, we used several techniques that included social network analysis (SNA), natural language processing (NLP), data mining (DM), and machine learning (ML), integrating them with artificial intelligence approaches. Our analysis aimed to harness the information generated during the Volkswagen pollutant emissions situation in a case study that was conducted using the textual content from 10,642 posts, that represented the interactions of 25,877 users over a span of twenty-two weeks. The results demonstrated that monitoring online social networks (OSNs) can significantly enhance decision-making processes and might help to mitigate potential damages to brands/businesses. By leveraging the proposed methodological approach, a set of orientations for decision-making was extracted, providing valuable guidance for brand management and reputation protection. Overall, this study highlights the importance of analyzing textual content on OSNs and leveraging advanced computational techniques to improve decision support. © 2023 The authors and IOS Press.

关键词： Machine learning

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Investigating the Determinants of Compliance Intention in Behavior Change Support systems 29

Investigating the Determinants of Compliance Intention in Be...

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29th Annual Americas Conference on Information systems: Diving into Uncharted Waters, AMCIS 2023

作者： Ekpezu, Akon Obu Oinas-Kukkonen, Harri Wiafe, Isaac Faculty of Information Technology and Electrical Engineering Oulu Advanced Research on Service and Information Systems University of Oulu Finland Department of Computer Science University of Ghana Ghana

ISBN: (纸本)9781713893592

The effectiveness of behavior change support systems (BCSS) in promoting health and well-being is unflinching. However, its long-term effectiveness is hindered by non-compliance. Research in BCSS that focuses on compliance is scarce, particularly the determinants of compliance intention are not well established. To address this gap, this study investigated the determinants of compliance intention in BCSS by extending the persuasive system design model to include competence, effectiveness, enjoyment, and persuasiveness. By surveying a sample of prospective BCSS users (N=234), partial least squares structural equation modeling was used to evaluate the proposed model. The findings suggest that the prospective users’ perceptions of primary task support and dialogue support, as well as social support and credibility support significantly influenced their perceptions of self-competence and system effectiveness respectively. These perceptions influenced enjoyment, then persuasiveness, and finally compliance intention. Future studies will validate these findings on the compliance behavior of BCSS. © 2023 29th Annual Americas Conference on Information systems, AMCIS 2023. All rights reserved.

关键词： Behavior Change Support systems Compliance Intention Persuasive systems Design Persuasive Technology Physical Activity

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A Perceptual Data Cleansing Model (SDCM) for Reducing the Dirty Data

A Perceptual Data Cleansing Model (SDCM) for Reducing the Di...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Madi, Mohammad Azmi Abdel-Wahab, Ahmed Alshanty, Mohammed Bawazeer, Shaikhan Alzahrani, Mohammed Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Department of Computer Sciences Amman Jordan Arab Open University Faculty of Computer Studies Riyadh Saudi Arabia Al-Azhar University Deprtment of Systems and Computers Engineering Cairo Egypt

ISBN: (纸本)9798350347050

Data cleansing approaches aim at revealing and reducing different types of outsourced errors. Such errors introduce a major issue as data cleansing often involves costly computations and time consumption. Data cleansing is complicated as most of the errors within the obtained data emerge in different forms, such as typos, duplicates, noncompliance with business rules, outdated data, and missing values. In this paper, the Supervised Dataset Cleaning Model (SDCM) is proposed in order to detect and reduce different types of outsourced errors that are stored in the data repository according to the supervised cleaning rules that are practiced from the previous cleaning processes. The findings indicates that the cleaning execution time can be reduced with this supervised model and the accuracy of the model is also increased when it covers the entire training rules and classified error types derived from the data warehouse. There are some expected future directions, which include: implementing full scale of SDCM and comparing the results obtained with different current methods. By comparing different data cleaning algorithms with SDCM, a knowledge gap is likely to arise in the search for further future improvements. © 2023 IEEE.

关键词： Errors

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