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26th irish Machine Vision and image Processing Conference, iMViP 2024

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

ISBN: (纸本)9781837242672

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. in this work, we investigate Virtual KiTTi and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. it was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © This is an open access article published by the iET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Automobile simulators

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科学研究的第五范式——以智能驱动的材料设计为例

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engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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Chebyshev Polynomial Broad Learning System

Chebyshev Polynomial Broad Learning System

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2021 international Conference on information, Cybernetics, and Computational Social Systems, iCCSS 2021

作者： Feng, Shuang Wang, Bingshu Philip Chen, C.L. Beijing Normal University School of Applied Mathematics Zhuhai China Northwestern Polytechnical University School of Software Suzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9781665402453

The broad learning system (BLS) has been attracting more and more attention due to its excellent property in the field of machine learning. A great deal of variants and hybrid structures of BLS have also been designed and developed for better performance in some specialized tasks. in this paper, the Chebyshev polynomials are introduced into the BLS to take advantage of their powerful approximation capability, where the feature windows are replaced by a set of Chebyshev polynomials. This new variant, named Chebyshev polynomial BLS (CPBLS), has a light structure with a reduction in computational complexity since the sparse autoencoder is removed. instead, the dimension of each input sample is expended by n + 1 Chebyshev polynomials, mapping the original feature into a new feature space with higher dimension, which helps to classify the patterns in training. The proposed CPBLS is evaluated by some popular datasets from UCi and KEEL repositories, and it outperforms some representative neural networks and neuro-fuzzy models in terms of classification accuracy. The CPBLS also show some advantages over the recent developed compact fuzzy BLS (CFBLS) which indicates its great potential in future research and real-world applications. © 2021 iEEE.

关键词： Classification (of information)

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A new lightweight data security system for data security in the cloud computing

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Measurement: Sensors 2023年 29卷

作者： Mohammed, Shameer Nanthini, S. Bala Krishna, N. Srinivas, inumarthi V. Rajagopal, Manikandan Ashok Kumar, M. University of Technology and Applied Sciences Sur Oman Department of AI & DS Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Chennai India Department of CSE Mohan Babu University &SreeVidyanikethan Engineering College Andhrapradesh Tirupati517102 India Mumbai400077 India School of Business and Management Christ University Bangalore560029 India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria

in recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat information (CTi) sanitization procedure before releasing CTi for analytic strategy. in addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTi information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. in conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. © 2023 The Authors

关键词： Cryptography

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Fuzzy Clustering and Autoencoder-Based Cybersecurity for EVs in a Blockchain-Enabled Smart Cloud Environment

Fuzzy Clustering and Autoencoder-Based Cybersecurity for EVs...

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Mobile Networks and Wireless Communications (iCMNWC), iEEE international Conference on

作者： Subha B Sivakumar Nagarajan A. Thangam V. K. Somasekhar Srinivas RVS Praveen Kingshuk Das Bakshi Department of Professional Management Studies Kristu Jayanti College Kothanur India I & I Software Bethlehem USA Department of Mathematics Pondicherry University Community College Pondicherry India Department of Mathematics Mohan Babu University Tirupati India Department of Digital Engineering and Assurance Skyview Hyderabad India Department of Computer Science and Engineering Ideal Institute of Engineering Nadia India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rise of electric vehicles (EVs) has revealed several cybersecurity weaknesses, especially in data transmission inside cloud-based systems. Current cybersecurity algorithms for anomaly detection frequently struggle to manage the intricacy and magnitude of electric vehicle network data. This study presents an innovative cybersecurity system that integrates fuzzy clustering for soft anomaly detection, autoencoders for deep learning-driven feature extraction, and blockchain for secure data validation. The suggested model effectively tackles critical issues in anomaly detection, attaining an accuracy rate of 97.2%, an F1 score of 0.96, and a recall of 0.95, therefore surpassing current methodologies substantially. The results underscore the model's potential to identify and address cyber risks with enhanced precision and recall, providing a scalable and resilient solution for EV networks. Future improvements may concentrate on minimising computation time and augmenting detection efficacy in real-time systems, establishing this method as a viable option for bolstering EV cybersecurity in blockchain-integrated smart settings.

关键词： Wireless communication Accuracy Computational modeling Autoencoders Electric vehicles Feature extraction Real-time systems Blockchains Reliability Anomaly detection

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The Development of the Optical-Electronic images Segmentation Method Based on the Canny Edge Detector 18

The Development of the Optical-Electronic Images Segmentatio...

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18th iEEE international Conference on computer science and information Technologies, CSiT 2023

作者： Khudov, Hennadii Makoveichuk, Oleksandr Khudov, Vladyslav Khizhnyak, irina Kalimulin, Temir Khudov, Rostyslav Ivan Kozhedub Kharkiv National Air Force University Department of Radar Troops Tactic Kharkiv Ukraine Academician Y. Bugay International Scientific and Technical University Department of Computer Science and Software Engineering Kyiv Ukraine Kharkiv National University of Radio Electronics Department of Electronic Computers Kharkiv Ukraine V.N.Karazin Kharkiv National University Department of Theoretical and Applied Informatics Kharkiv Ukraine

ISBN: (纸本)9798350360462

The article deals with the problem associated with the segmentation of color images. The main groups of color image segmentation methods operation, are considered. The main advantages and disadvantages of each group of methods are shown. The paper suggests the use of the Canny edge detector for optical-electronic images obtained from space observation systems. it is proposed to regulate the parameters of the Canny edge detector in order to improve the segmentation result. it's determined that the value of the lower threshold should be no more than 10-20 unit, and the value of the upper threshold should be within 60-100 unit. © 2023 iEEE.

关键词： image segmentation

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Correction to: Load balancing strategy for SDN multi-controller clusters based on load prediction

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The Journal of Supercomputing 2023年第5期80卷 7120-7121页

作者： Xiao, Junbi Pan, Xingjian Liu, Jianhang Wang, Jian Zhang, Peiying Abualigah, Laith Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao China College of Science China University of Petroleum (East China) Qingdao China Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th international Conference on Advances in Computing and Data sciences, iCACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. in order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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SiMAP: A simplicial-map layer for neural networks

arXiv

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arXiv 2024年

作者： Gonzalez-Diaz, Rocio Gutiérrez-Naranjo, Miguel A. Paluzo-Hidalgo, Eduardo The Department of Applied Math I School of Engineering University of Sevilla Seville Spain The Department of Computer Science and Artificial Intelligence School of Engineering University of Sevilla Seville Spain The Department of Quantitative Methods Universidad Loyola Andalucía Campus Sevilla Dos Hermanas Seville Spain

in this paper, we present SiMAP, a novel layer integrated into deep learning models, aimed at enhancing the interpretability of the output. The SiMAP layer is an enhanced version of Simplicial-Map Neural Networks (SMNNs), an explainable neural network based on support sets and simplicial maps (functions used in topology to transform shapes while preserving their structural connectivity). The novelty of the methodology proposed in this paper is two-fold: Firstly, SiMAP layers work in combination with other deep learning architectures as an interpretable layer substituting classic dense final layers. Secondly, unlike SMNNs, the support set is based on a fixed maximal simplex, the barycentric subdivision being efficiently computed with a matrix-based multiplication algorithm. © 2024, CC BY.

关键词： Deep learning

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The Reliability of information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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international iEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. in the current world, access to information and knowledge is abundant from different sources. indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and Ai-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using Ai-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) engineering profession Education Reliability engineering information filters information age Reliability

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