检索结果-内蒙古大学图书馆

computing and Informatics (ICCI), International Conference on

作者： Nada E. Elshami Ahmad Salah Heba Mohsen Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computers and Informatics Zagazig University Zagazig Egypt

Human Pose Estimation is a computer vision technique utilized in various fields such as healthcare, security, and sports to detect the pose of single or multi-person utilizing various machine learning and deep learning methodologies. These methodologies can be applied in a variety of ways, including top-down and bottom-up approaches. Furthermore, this technique can be used with images or videos, as well as in 2D or 3D domains. This paper delves into recent advancements in deep learning methods used for estimating the pose, covering commonly employed datasets, evaluation metrics, and the challenges encountered in this field. It aims to assist new researchers in getting familiar with this technique based on work published between 2019 and 2023, with a spotlight on 2D and omitting 3D.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases

Machine Learning Models for Brain Signal Classification: A F...

引用

Cyber Resilience (ICCR), International Conference on

作者： Ali M Ali Shadi Nashwan Ahmad Al-Qerem Ammar Almomani Mahmoud Al Sakhnini Amjad Aldweesh Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities.

关键词： Neurological diseases Machine learning algorithms Scalability Epilepsy Transforms Feature extraction Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision 5

Artificial Intelligence for Climate Smart Forestry: A Forwar...

引用

5th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023

作者： Luo, Feng Liu, Ling Wang, G. Geoff Kumar, Vijay Ashton, Mark S. Abernethy, Jacob Afghah, Fatemeh Browning, Matthew H. E. M Coyle, David Dames, Philip O'Halloran, Tom Hays, James Heisl, Patrick Jiang, Chenfanfu Khanal, Puskar Krovi, Venkat Narayan Kuebbing, Sara Li, Nianyi Liang, JingJing Liu, Ninghao McNulty, Steve Oswalt, Christopher M. Pederson, Neil Terzopoulos, Demetri Woodall, Christopher W. Wu, Yongkai Yang, Jian Yang, Yin Zhao, Liang School of Computing Clemson University ClemsonSC United States College of Computing Georgia Institute of Technology AtlantaGA United States Clemson University Department of Forestry and Environmental Conservation ClemsonSC United States School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States The Forest School Yale University New HavenCT United States Clemson University Department of Electrical and Computer Engineering ClemsonSC United States Clemson University Department of Parks Recreation and Tourism Management ClemsonSC United States Temple University Department of Mechanical Engineering PhiladelphiaPA United States University of California Department of Mathematics Los AngelesCA United States Clemson University Department of Automotive Engineering ClemsonSC United States Purdue University Department of Forestry and Natural Resources West LafayetteIN United States School of Computing University of Georgia AthensGA United States Northern Research Station Usda Forestry Service DurhamNH United States South Research Station Usda Forestry Service KnoxvilleTN United States Harvard University The Harvard Forest PetershamMA United States University of California Computer Science Department Los AngelesCA United States University of Kentucky Department of Forestry and Natural Resources LexingtonKY United States School of Computing University of Utah Salt Lake CityUT United States Emory University Department of Computer Science AtlantaGA United States

ISBN: (纸本)9798350323832

Forests and forest ecosystems are vital to our social, economic, and environmental well-being. However, climate change and climate-driven disturbances (CDDs) are undermining the health and resilience of forests worldwide and pose significant uncertainty to sustainable forest management. Climate-smart forestry (CSF) remains a grand challenge in practice due to our limited knowledge of how forests respond to climate change and our abilities to collect related information to empower decision making. Rapid advances in artificial intelligence (AI) can offer a timely opportunity to address the challenges in CSF. We argue that the AI-enabled, next-generation CSF can be achievable through synergistically coordinated and transdisciplinary efforts that develop and advance foundational and use-inspired AI technologies that can lead to building next-generation forest decision support systems. © 2023 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

JARVIS-Leaderboard:a large scale benchmark of materials design methods

引用

npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

来源：评论

学校读者我要写书评

暂无评论

The future of E-commerce in the publishing industry 2

The future of E-commerce in the publishing industry

引用

2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Aseh, Khairi Al-Qudah, Omar Mohammed Ali Abdullah Yusoff, M. Hafiz Pathmanathan, P. Ravindran Latiff, Nurul Adilah Abdul Jusoh, Julaily Aida Abu-Ulbeh, Waheeb El-Ebiary, Yousef A. Baker Al Moaiad, Yazeed International Institute of Applied Science Swiss School of Management Netherlands Irbid National University Faculty of Financial and Business Science Head of E-Marketing Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia UMT Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer Science and Informatics Cybersecurity Department Jordan MEDIU Faculty of Computer and Information Technology Malaysia

ISBN: (纸本)9781665432221

In comparison to conventional book-printed content, electronic "publishing"has allowed users to import files to either a personal computer or an e-book reader to read text in a digital format. Reduced printing costs and reduced time to market by streamlined drafting, proofing, and printing methods are the benefits of the publishing industry over the conventional printed book (p-book). It prevents binding, distribution and shipping. The intent of this paper is to explore the strategic use of e-commerce in the transformation of the publishing industry. The first part of the paper discusses the strategic use of e-commerce/Web-Based Business in the transition from a single distribution channel mass producer of printed books to a multiple distribution channel that will include e-books and print on demand technology in the publishing industry. Emphasis is put on management techniques in the global marketplace for the use of emerging information technologies. The second section of this paper addresses the transfer in competitive influence between the different levels of the supply chain of the publishing industry, including the latest trends in electronic publishing from the stakeholders' point of view. Expected to serve as the base for development within the digitized print industry. As e-publishing becomes popular and moves towards maturity, to allow a "mainstream"status, common standards must emerge and combine. © 2021 IEEE.

关键词： Electronic publishing

来源：评论

学校读者我要写书评

暂无评论

Concurrent Pipeline Rendering Scheme based on GPU multi-queue and partitioning images

Concurrent Pipeline Rendering Scheme based on GPU multi-queu...

引用

2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

来源：评论

学校读者我要写书评

暂无评论

On developing deep learning models with particle swarm optimization in the presence of poisoning attacks

On developing deep learning models with particle swarm optim...

引用

Information Technology (ACIT)

作者： Majdi Maabreh Omar Darwish Ola Karajeh Yahya Tashtoush Department of Computer Information Systems The Hashemite University Zarqa Jordan Information Security and Applied Computing Department Eastern Michigan University MI USA Department of Computer Science Virginia Polytechnic Institute and State University VA USA Department of Computer Science Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Deep learning (DL) has demonstrated several successes in a variety of fields, particularly in the era of big data. The process of training a deep learning model entails selecting the ideal learning parameters such as the number of hidden layers and neurons. Particle Swarm Optimization (PSO) is one useful nature-inspired algorithm to set those two influential learning parameters. In this study, two different datasets are used to study and evaluate the particle swarm optimizer with deep learning on datasets of different concentrations of poisoning attack, where some adversarial samples were crafted by attackers to ruin the learning process. The results showed that particle swarm optimization could find settings for deep learning with the existence of poisoned data that maximizes the model accuracy on the unseen testing dataset, and could also offer better recommendations compared to those recommended on all benign samples. This may introduce a concern that optimizers might conceal the existence of data poisoning, which may lead to unreliable learning in the advanced stages of upgrading the model on updated datasets.

关键词： Deep learning Training Neurons Big Data Data models Safety Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

G-Adaptivity: optimised graph-based mesh relocation for finite element methods

arXiv

引用

arXiv 2024年

作者： Rowbottom, James Maierhofer, Georg Deveney, Teo Mueller, Eike Paganini, Alberto Schratz, Katharina Liò, Pietro Schönlieb, Carola-Bibiane Budd, Chris Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Mathematical Institute University of Oxford United Kingdom Department of Computer Science University of Bath United Kingdom Department of Mathematical Sciences University of Bath United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Laboratoire Jacques-Louis Lions Sorbonne Université France Department of Computer Science and Technology University of Cambridge United Kingdom

We present a novel, and effective, approach to achieve optimal mesh relocation in finite element methods (FEMs). The cost and accuracy of FEMs is critically dependent on the choice of mesh points. Mesh relocation (r-adaptivity) seeks to optimise the mesh geometry to obtain the best solution accuracy at given computational budget. Classical r-adaptivity relies on the solution of a separate nonlinear "meshing" PDE to determine mesh point locations. This incurs significant cost at remeshing, and relies on estimates that relate interpolation- and FEM-error. Recent machine learning approaches have focused on the construction of fast surrogates for such classical methods. Instead, our new approach trains a graph neural network (GNN) to determine mesh point locations by directly minimising the FE solution error from the PDE system Firedrake to achieve higher solution accuracy. Our GNN architecture closely aligns the mesh solution space to that of classical meshing methodologies, thus replacing classical estimates for optimality with a learnable strategy. This allows for rapid and robust training and results in an extremely efficient and effective GNN approach to online r-adaptivity. Our method outperforms both classical, and prior ML, approaches to r-adaptive meshing. In particular, it achieves lower FE solution error, whilst retaining the significant speed-up over classical methods observed in prior ML work. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Feature enhancement model with up sampling based cyber threat attack detection and classification on imbalanced dataset in Industrial Internet of Things

引用

Alexandria Engineering Journal 2025年 128卷 247-258页

作者： Hayam Alamro Fahd N. Al-Wesabi Sultan Alahmari Jawhara Aljabri Shouki A. Ebad Asma Alshuhail Fouad Shoie Alallah Abdulrhman Alshareef Mahir Mohammed Sharif Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia King Abdul Aziz City for Science and Technology (KACST) Cybersecurity Institute Riyadh Saudi Arabia Department of Computer Science University College in Umluj University of Tabuk Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia

With the fast growth and utilization of artificial intelligence (AI) models, the Industrial Internet of Things (IIoT) has remarkably progressed in locating industrial communication and enhancing industrial methods quickly. In Industry 5.0, the hyper-automation method is a technical trend that navigates manufacturing objects to intellectual devices of IIoT, smart, agile software, cloud computing, smart robotics, and embedded modules by reliability and higher intricacy. Machine learning (ML) and deep learning (DL) methods were established for identifying anomalies by understanding the usual behaviour methods of the cyber threat attack and identifying and detecting deviations. This study proposes a Feature Enhancement Model with a White Shark Optimizer-based Cyber Threat Attack Detection and Classification (FEWSO-CTADC) technique on an Imbalanced Dataset in an IIoT environment. The primary focus of the FEWSO-CTADC technique is to enhance the automatic classification and detection of cyber threats in the IIoT environment. Initially, the FEWSO-CTADC technique implements a data preprocessing model to scale the raw information into a uniform format. Next, the SMOTE technique is used to manage the imbalanced class distribution in the attack recognition database. Moreover, the WSO-based feature subset selection is accomplished to select the superior set of features. Finally, the FEWSO-CTADC method utilizes the stacked auto-encoder (SAE) method for attack classification and recognition. Extensive experiments were conducted to demonstrate the improved performance of the FEWSO-CTADC approach, and the results were compared across various methods. The performance validation of the FEWSO-CTADC approach exhibited a superior value of 99.20 % over recent techniques under diverse metrics.

关键词： Cyber threat Stacked auto-encoder White shark optimizer Feature enhancement model Industrial Internet of Things

来源：评论

学校读者我要写书评

暂无评论

The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule

The Advanced Encryption Standard with a Dynamic S-Box Using ...

引用

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Abdelrahman Altigani Muhammad Rukunuddin Ghalib Huwaida T. Elshoush Muawia A. Elsadig Nashat Alrefai Shiraz Naserelden Computer Information Science Higher Colleges of Technology Al Ain UAE School of Engineering & Computing De Monfort University Dubai UAE Computer Science Department Faculty of Mathematical Sciences and Informatics University of Khartoum Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam KSA Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam KSA Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption.

关键词： Ciphers Schedules Heuristic algorithms Dynamics Force Dynamic scheduling Encryption computer security Time complexity Standards

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：