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*** Enhancing the Modernization Public Sector Organizations by Deploying Natural Language Processing to Make Their digital Content CLEARER to Those with Cognitive Disabilities

*** Enhancing the Modernization Public Sector Organizations ...

引用

2023 Annual Conference of the Spanish Association for Natural Language Processing: Projects and System Demonstrations, SEPLN-PD 2023

作者： Moreda, Paloma Botella, Beatriz Espinosa-Zaragoza, Isabel Lloret, Elena Martin, Tania J. Martínez, Patricio Suárez, Armando Palomar, Manuel GPLSI Research Group Dept. of Software and Computing Systems Universidad de Alicante CENID Centro de Inteligencia Digital San Vicente Alicante03690 Spain

The *** project (TED2021-130707B-I00) researches how natural language processing technology can support the authoring of accessible content in Spanish for people with cognitive disabilities. Our main objective is to research, implement, deploy, evaluate, and ultimately provide robust technologies for natural language processing to support the authoring of accessible Spanish content for public sector organisations (at local, regional and national level) that is intelligible to people with cognitive disability, thereby widening their inclusion and empowerment in Europe. It is expected to impact positively the quality of life of people with cognitive disabilities, facilitating their access to educational, vocational, cultural, and social opportunities in public sector organisations © 2023 Copyright for this paper by its authors.

关键词： Natural language processing systems

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DWA-based Dynamic Obstacle Avoidance Planning Method for USVs

DWA-based Dynamic Obstacle Avoidance Planning Method for USV...

引用

International Conference on Computer Science and Network Technology (ICCSNT)

作者： Kexin Luo Shiyou Chen Yu Wang dept. of Systems Engneering Wuhan Digital Engineering Institute Wuhan China dept. of Computing Environment Wuhan Digital Engineering Institute Wuhan China

To solve the problem of environment perceived instability in the process of autonomous navigation of USVs, a local planning method based on DYNAMIC WINDOWS APPROACH is proposed for the known static obstacles and unknown dynamic obstacles collision avoidance planning in the navigation area of USVs, and combined with the Velocity Obstacle method, the velocity obstacle region is incorporated in the dynamic window to further optimize the speed constrained It also sets the fusion to start distance and limit distance in the evaluation function to take into account the safety and economy of the planning and improves the efficiency of the dynamic window method to evaluate the predicted trajectory for finding the best. The simulation results verify that the improved DWA can achieve automatic obstacle avoidance in complex environments, and ensure the safety of obstacle avoidance navigation while the planning path is more reasonable and smooth, avoiding the USVs from bypassing outside the dense obstacle cluster, and the number of iterations and running time are reduced by more than 15%. The research results have more obvious scientific value and practical significance to the development of autonomous obstacle avoidance technology for unmanned vehicles.

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Enhancing Arabic Language Teaching through Artificial Intelligence: Assessing Effectiveness and Educational Implications 3

Enhancing Arabic Language Teaching through Artificial Intell...

引用

3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Sa'idah, Mandrasi Amira Diantoro, Karno Mahmudah, Umi Dolan, Ellen Santoso, Nesti Anggraini Raihana Putri Junaedi, Sausan University of Darussalam Gontor Dept. of Arabic Education Indonesia Stmik Mercusuar Dept. of Informatics Engineering West Java Indonesia Queensland University Dept. of School of Cultural and Language Studies in Education Brisbane Australia University of Raharja Dept. of Information Systems Tangerang Indonesia Pandawan Sejahtera Indonesia Dept. of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350367492

This study investigates the impact of integrating Artificial Intelligence (AI) in Arabic language teaching using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. Questionnaires from teachers and students at institutions using AI in Arabic instruction were analyzed to understand the relationships between AI usage, teacher training, student involvement, and learning outcomes. The primary goal is to assess AI's influence on student learning, teacher effectiveness, and student engagement. Findings show that AI significantly improves student learning outcomes (path coefficient = 0.45, p © 2024 IEEE.

关键词： Contrastive Learning

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Predicting Shear Capacity of RC Beams Strengthened with NSM FRP Using Neural Networks

Predicting Shear Capacity of RC Beams Strengthened with NSM ...

引用

2023 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2023

作者： Guler, Ozan Ahmed, Husham Abbas, Ali Sharif, Mhd Saeed School of Architecture Computing and Engineering Uel Dept. of Engineering and Construction London United Kingdom Jacobs Uk Ltd Principal- Civil/Structural Engineering London United Kingdom School of Architecture Computing and Engineering Uel Department Computer Science and Digital Technologies London United Kingdom

ISBN: (纸本)9798350307771

This research aims to predict the shear capacity of NSM FRP beams using artificial neural network approach. The study investigates the key considerations and the necessary analysis for this prediction. NSM FRP beams are reinforced concrete beams that are strengthened with near-surface mounted (NSM) fiber-reinforced polymer (FRP) composites. Accurately predicting their shear capacity is important for ensuring their safety and reliability in real-world applications. The neural network method is a machine learning approach that is increasingly used in engineering analysis and design. The study explores how this method can be used to predict the shear capacity of NSM FRP beams and what factors should be taken into account in this analysis. The research also discusses the analytical approach required for this prediction, highlighting the necessary steps for obtaining accurate results. Overall, this study provides valuable insights into the use of the neural network for predicting the shear capacity of NSM FRP beams. The findings can help inform future research and practical applications in the field of structural engineering, contributing to the development of safer and more reliable structures. © 2023 IEEE.

关键词： Forecasting

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Cirrhosis Prediction in Chronic Liver Disease Patients Using Machine Learning Techniques 3

Cirrhosis Prediction in Chronic Liver Disease Patients Using...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 IEEE.

关键词： Support vector machines

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Transient stability improvement of Croatian power system using FACTS 11

Transient stability improvement of Croatian power system usi...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Šešok, Anica Pavić, Ivica University of Zagreb Faculty of Electrical Engineering and Computing Dept. of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9798350399660

In the last twenty years, the share of renewable energy sources (RES) in total electricity production has increased significantly, which has led to certain problems in power system operation. In systems with a significant share of RES, connected to the transmission network via power electronics devices, the inertial response of the system is reduced. Due to the reduced inertia of the power system, synchronous generators are more sensitive to their disturbed states and as a result, can easily lose stability. Loss of stability of one generator can lead to failure of other system elements, which may ultimately result in partial or even complete breakdown of the power system. Recently, a large increase in production from renewable sources has been recorded in the Croatian power system, therefore this paper investigates their impact on the system's transient stability and the possibilities of its improvement using Flexible AC Transmission systems (FACTS). © 2022 IEEE.

关键词： Flexible AC transmission systems

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A Comparative Study of Hough Transform and PCA for Bolt Orientation Detection 22

A Comparative Study of Hough Transform and PCA for Bolt Orie...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Gambale, Antonio Coleman, Sonya Kerr, Dermot Vance, Philip Kerr, Emmett Fermuller, Cornelia Aloimonos, Yiannis School of Computing Engineering & Intelligent Systems Ulster University Londonderry United Kingdom Dept. of Electronic & Mechanical Engineering Atlantic Technological University Letterkenny Ireland Institute for Advanced Computer Studies University of Maryland United States

ISBN: (纸本)9798331527471

In the fields of manufacturing and robotics, accurately determining the orientation of manufacturing components, such as bolts, is a critical yet challenging problem due to the limitations of existing detection methods. This study introduces a novel methodology for addressing this issue, leveraging traditional computer vision techniques, by proposing a streamlined approach that exploits the inherent geometric properties of bolts for orientation detection. Two methods are presented to ascertain the initial axis angle of the bolt: the Progressive Probabilistic Hough Transform (PPHT) and Principal Component Analysis (PCA). These methods are used in conjunction with a novel tip direction detection approach. The results of the study demonstrate consistent accuracy in angle determination, with PPHT and PCA both achieving angular deviations below ±0.5◦ in the simple dataset, and PCA showing enhanced robustness in the dataset degraded by shadows, with a maximum error under ±1.5◦. This research not only reaffirms the viability of fundamental computer vision techniques in modern robotic applications but also sets a precedent for simple, generalisable, and reliable orientation detection solutions. These methods effectively bridge the gap between highly specialised machine learning systems, which often require tailored, complex models and extensive training data, and more universally applicable, straightforward approaches. © 2024 IEEE.

关键词： Bolts

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An Algorithm for Mining Track Law Based on Adaptive Clustering

An Algorithm for Mining Track Law Based on Adaptive Clusteri...

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2021 IEEE International Conference on Unmanned systems, ICUS 2021

作者： Zhang, Shanqiang Liu, Hao Chen, Wei Wang, Yizhi Wuhan Digital Engineering Institute Dept. of Systems Engineering Wuhan China Wuhan Digital Engineering Dept. of Systems Engineering Wuhan China

ISBN: (纸本)9780738146577

In many sailing trajectories, the curve can be approximated, and the target often has a certain law of activity. So how to use unsupervised learning to mine the law of the target trajectory in the route is of great significance to situational awareness. More importantly, the law of the mining activity should be suitable for different scenarios, so the adaptiveness of the target trajectory mining algorithm should be considered. In this regard, firstly, it is based on the distance formula between the vectors to consider the similarity between the tracks, and the multi-dimensional distance calculation method is used to calculate the distance relationship between the tracks. The multi-dimensional calculation method uses longitude, latitude, altitude, and speed. The distance calculation method of attributes such as, heading, etc. under different influence factors, and then use the improved density-based clustering algorithm to mine the law of the route, and finally display the mined fitting track in the interface to represent the characteristic track. The experimental results show that the mining effect of the adaptive clustering mining algorithm is better than the fixed value of the parameters. The track accuracy and recall rate that can be mined by the adaptive method are significantly higher than the previous algorithms, and it can be extended to different scenarios. © 2021 IEEE.

关键词： Unsupervised learning

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An Intelligent Battery Management System for an Electric Vehicle Powered by Solar PV Array 59

An Intelligent Battery Management System for an Electric Veh...

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59th International Universities Power engineering Conference, UPEC 2024

作者： Challoob, Ali Falih Bin Rahmat, Nur Azzammudin Ramachandaramurthy, Vigna K. Saeed, Nagham Humaidi, Amjad Jaleel Institute of Power Engineering Universiti Tenaga Nasional Strategic Planning Centre Malaysia School of Computing and Engineering University of West London London United Kingdom University of Technology Control and Systems Eng. Dept. Baghdad Iraq

ISBN: (纸本)9798350379730

Implementing a Battery Management System (BMS) in Electric Vehicles (EVs) offers several benefits that enhance the efficiency, security, and durability of the vehicle's battery system. The BMS oversees and controls the performance of each cell in the battery pack, guaranteeing that they function within predetermined voltage and temperature thresholds to maintain safety. In this paper, an intelligent BMS for an EV model powered by a solar PV array was proposed using a fuzzy logic (FL) controller. The proposed method was applied to EVs consisting of four Lithium-ion batteries. The simulation results are achieved using MATLAB/Simulink software. The estimation of the state of charge (SOC) for each battery was used as input to the FL which controls the discharging/ charging mode for each battery via power semiconductor switches. Three different scenarios are used to verify the suggested method. A full control FL operation was obtained, and the batteries were protected successfully against overcharging and over-discharging. © 2024 IEEE.

关键词： MATLAB

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting 17

An Image Analysis-Based Automated Method using Deep Learning...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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