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2023 IEEE International Conference on computer, information, and Telecommunication systems, CITS 2023

作者： Nadeem, Adnan Ashraf, Muhammad Mehmood, Amir Siddiqui, Muhammad Shoaib Almoamari, Hani Abbasi, Qammer H. Islamic University of Madinah Faculty of Computer and Information Systems Madinah42351 Saudi Arabia Federal Urdu University of Arts Science and Technology Department of Physics Karachi75300 Pakistan Sir Syed University of Engineering and Technology Department of Computer Science and Information Technology Karachi75300 Pakistan James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798350336092

The Kingdom of Saudi Arabia is one of the world's leading fresh date producers. Identifying damaged date palm trees on the farm is important for saving water and stopping the spread of pest which has caused the damage. In this paper, we aim to evaluate the performance of IoT-based thermal image camera input for the automatic detection of damaged trees in a Date Tree farm. The thermal images will be obtained by an IoTbased thermal camera attached to UAV. Then the temperature histogram vector will be generated which will be fed to the SVM for identification of the damaged tree. The system will raise the alarm along with the coordinates of the damaged tree for the farmer to proactively deal with the issue. The initial results of the tested scenario are encouraging for its detailed implementation in the future. © 2023 IEEE.

关键词： Cameras

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Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

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computers, Materials & Continua 2023年第2期74卷 3977-3996页

作者： Billel Meghni Afaf Benamor Oussama Hachana Ahmad Taher Azar Amira Boulmaiz Salah Saad El-Sayed M.El-kenawy Nashwa Ahmad Kamal Suliman Mohamed Fati Naglaa K.Bahgaat Department of Electrical Engineering Badji Mokhtar UniversityLSEM LaboratoryAnnaba23000Algeria Department of Electrical Engineering Biskra UniversityLGEB LaboratoryBiskra07000Algeria Department of Drilling and Rig Mechanics Ksadi Merbah UniversityOuargla30000Algeria College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt Department of Electronics University of Badji MokhtarLERICA LaboratoryAnnaba23000Algeria Delta Higher Institute for Engineering&Technology(DHIET) Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Faculty of Engineering Cairo UniversityGiza12613Egypt Department of Communications and Electronics Engineering Faculty of EngineeringCanadian International College(CIC)ElShiekh ZayedEgypt

The integration of wind turbines(WTs)in variable speed drive systems belongs to the main factors causing lowstability in electrical ***,in order to avoid this issue,WTs hybridization with a storage system is a *** paper investigates WT system operating at variable *** system contains of a permanent magnet synchronous generator(PMSG)supported by a battery storage system(BSS).To enhance the quality of active and reactive power injected into the network,direct power control(DPC)scheme utilizing space-vector modulation(SVM)technique based on proportional-integral(PI)control is ***,to improve the rendition of this method(DPC-SVM-PI),the rooted tree optimization technique(RTO)algorithm-based controller parameter identification is used to achieve PI optimal *** compare the performance ofRTO-based controllers,they were implemented and tested along with some other popular controllers under different working *** obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion(THD),overshoot percentage,settling time,rise time,average active power value,overall efficiency,and active power steadystate error.

关键词： Rooted tree optimization(RTO)method direct power control(DPC) wind turbine(WT) proportional integral(PI) PMSG

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UAV-Assisted Data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wu, Guoquan Nie, Jiangtian Tang, Jianhang Zhang, Yang Xiao, Jiang Xiong, Zehui Yanshan University School of Information Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore Guizhou University State Key Laboratory of Public Big Data China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Huazhong University of Science and Technology School of Computer Science and Technology China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

ISBN: (纸本)9798350345384

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively. © 2023 IEEE.

关键词： Resource allocation

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SANS: Spatial-Aware Neural Solver for Plane Geometry Problem 27th

SANS: Spatial-Aware Neural Solver for Plane Geometry Proble...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lin, Zi-Hao Xiao, Shun-Xin Chen, Zi-Rong Li, Jian-Min Wang, Da-Han Zhang, Xu-Yao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Geometry problem solving (GPS) is an important research direction in artificial intelligence. Previous studies have demonstrated the effectiveness of neural solvers in GPS. However, they are deficiencies in accurately representing spatial relationships of geometric primitives within visually rich geometric diagrams. This paper presents a novel neural solver termed spatial-aware neural solver (SANS) that can perceive spatial relationships between geometric primitives. SANS includes two new modules: multimodal dual-branch spatial awareness pre-trained language module and point-primitive spatial-aware attention module. The pre-training module employs a dual-branch visual-textual point-matching strategy to align visual and textual points, and utilizes semantic structure pre-training to model global relationships. Additionally, the point-primitive spatial awareness attention module enhances the model’s ability to perceive spatial relationships between geometric primitives by accounting for the relative positions of points. Experiments show that SANS achieves 81.5 and 74.1 of accuracy on the Geometry3K and PGPS9K datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

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computers, Materials & Continua 2022年第6期71卷 5441-5457页

作者： Mahmoud Ragab Ehab Bahauden Ashary Maha Farouk S.Sabir Adel A.Bahaddad Romany F.Mansour Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.

关键词： Medical data classification feature selection qkm classifier ltsa optimization

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DARIJA-C: Towards a Moroccan DARIJA Speech recognition and speech-to-text Translation Corpus 1

DARIJA-C: Towards a Moroccan DARIJA Speech recognition and s...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Labied, Maria Belangour, Abdessamad Banane, Mouad Laboratory of Information Technology and Modeling LTIM Hassan II University Ben M'Sik Faculty of Sciences Casablanca Morocco Laboratory of Artificial Intelligence & Complex Systems Engineering Faculty of Legal Economic and Social Sciences Hassan II University Casablanca Morocco

ISBN: (纸本)9781665472753

This paper introduces an automated collection of a speech corpus for the Moroccan Arabic dialect 'Darija' (DARIJA-C) which is intended for speech-to-text translation from Moroccan Darija into classical Arabic language. The DARIJA-C corpus is designed for Moroccan Darija automatic speech-to-text translation purposes. Nevertheless, it can be useful for automatic speech recognition of this dialect. To address both scale and sustainability, the DARIJA-C project uses crowdsourcing to collect and validate speech transcriptions and translations. By providing an automatic web platform for recording speech, along with their corresponding translation by distinct unknown speakers. The first versions of the Darija-C dataset will include only the translation of Moroccan Darija speech to classical Arabic. In later versions, we will include the translation of Moroccan Darija into other international languages such as French, and English,... The goal of this work is to build the largest crowdsourced corpus of Darija speech, which to our knowledge will be the first corpus for Moroccan Darija Speech-to-text translation. © 2023 IEEE.

关键词： Crowdsourcing

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Dual Fusion Network for Hyperspectral Semantic Segmentation 12th

Dual Fusion Network for Hyperspectral Semantic Segmentation

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12th International Conference on Image and Graphics, ICIG 2023

作者： Ding, Xuan Gu, Shuo Yang, Jian PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031463075

With the development of imaging technology, it becomes increasingly easy to obtain hyperspectral images (HSI) containing rich spectral information. The application of hyperspectral images in autonomous driving is expected to become a reality. Hyperspectral images have higher dimensions and more information than RGB images, which brings the challenge of training with limited samples. It is not easy to train satisfactory semantic segmentation models directly from hyperspectral images. In this paper, we propose a hyperspectral dual-fusion (hyperDF) network that can fully utilize the pre-training knowledge of the RGB modality to improve the performance of hyperspectral semantic segmentation. Specifically, we generate pseudo-color images from hyperspectral images to simulate RGB signals in order to make better use of the pre-trained RGB semantic segmentation models. The pseudo-color and hyperspectral images are processed in a dual encoder structure and then fused through a channel-wise attention fusion module. Finally, a multi-scale decoder injected with low-level features is used to predict the semantic segmentation results. Experimental results on the Hyperspectral City V2.0 dataset show that our method achieves state-of-the-art results, with a mIoU of 51.68%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantic Segmentation

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Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis 37

Robust-MSA: Understanding the Impact of Modality Noise on Mu...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Mao, Huisheng Zhang, Baozheng Xu, Hua Yuan, Ziqi Liu, Yihe State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

ISBN: (纸本)9781577358800

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sentiment analysis

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Hyper-Convergence Storage Framework for EcoCloud Correlates

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computers, Materials & Continua 2022年第1期70卷 1573-1584页

作者： Nadia Tabassum Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer Science University of Veterinary and Animal SciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

Cloud computing is an emerging domain that is capturing global users from all walks of life—the corporate sector,government sector,and social arena as *** cloud providers have offered multiple services and facilities to this audience and the number of providers is increasing very *** enormous pace is generating the requirement of a comprehensive ecosystem that shall provide a seamless and customized user environment not only to enhance the user experience but also to improve security,availability,accessibility,and *** technology is providing robust solutions to many of our problems,the cloud platform is one of *** is worth mentioning that these solutions are also amplifying the complexity and need of sustenance of these rapid *** with cloud computing,new entrants as cloud service providers,resellers,tech-support,hardware manufacturers,and software developers appear on a daily *** actors playing their role in the growth and sustenance of the cloud *** objective is to use convergence for cloud services,software-defined networks,network function virtualization for infrastructure,cognition for pattern development,and knowledge *** order to gear up these processes,machine learning to induce intelligence to maintain ecosystem growth,to monitor performance,and to become able to make decisions for the sustenance of the *** may be programmed to“superficially”imitate most business applications and create large numbers using lightweight workload generators that merely stress the *** today’s current IT environment,when many enterprises use the cloud to service some of their application demands,a different performance testing technique that assesses more than the storage is *** and storage are merged into a single building block with HCI(Hyper-converged infrastructure),resulting in a huge pool of compute and storage resources when clustered with other building ***

关键词： Virtual cloud software define network network function virtualization hyper-convergence virtualization

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