检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Kiyani, Anum Tanveer Ali, Kamran Lasebae, Aboubaker Alam, Amjad Khoshkholghi, Mohammad Ali Alkhayyat, Ahmed Faculty of Computer and Data Science York St John University United Kingdom Faculty of Science and Technology Middlesex University London United Kingdom The Islamic University Iraq

Authentication methods employing PINS and passwords are extensively used in user authentication schemes. However, these techniques are highly susceptible to different types of security attacks i.e., masquerade attacks etc. Moreover, these techniques tend to authenticate the user once at the start of session or sometimes after regular interval of time which can lead to security loopholes. In this aspect, a wide range of research is currently in proceeding to formulate the methods which can authenticate the users continuously using such inherent characteristics of users that are directly related to individual itself. In this research, a continuous user authentication method has been proposed which can authenticate the user on each action by distinguishing the normal behaviour of user from abnormal actions. The proposed method uses the mouse dynamics, a behavioural biometric modality, which depicts the mouse usage behaviour of user. This study implements the four different methodologies, based on baseline and deep neural networks, in order to improve the system performance. Each of the methodology incorporates the proposed recurrent confidence model which enables the system to validate user’s identity based on each action. The system performance has been evaluated based on normalized portion of genuine and imposter actions where ideal system requires the former as high as possible by avoiding the false lockout while latter ought to be as low as possible by rapidly detecting the imposter user. © 2023, The Authors. All rights reserved.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

ESTIMATING PHYSICAL INFORMATION CONSISTENCY OF CHANNEL data AUGMENTATION FOR REMOTE SENSING IMAGES

arXiv

引用

arXiv 2024年

作者： Burgert, Tom Demir, Begüm BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany

The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification. In particular, channel transformations (e.g., solarize, grayscale, brightness adjustments) are integrated into data augmentation pipelines for remote sensing (RS) image classification tasks. However, contradicting beliefs exist about their proper applications to RS images. A common point of critique is that the application of channel augmentation techniques may lead to physically inconsistent spectral data (i.e., pixel signatures). To shed light on the open debate, we propose an approach to estimate whether a channel augmentation technique affects the physical information of RS images. To this end, the proposed approach estimates a score that measures the alignment of a pixel signature within a time series that can be naturally subject to deviations caused by factors such as acquisition conditions or phenological states of vegetation. We compare the scores associated with original and augmented pixel signatures to evaluate the physical consistency. Experimental results on a multi-label image classification task show that channel augmentations yielding a score that exceeds the expected deviation of original pixel signatures can not improve the performance of a baseline model trained without augmentation. © 2024, CC BY.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms for Marine Waste Recognition

YOLOv8 vs. YOLOv9: Evaluating Object Detection Algorithms fo...

引用

IEEE International Symposium on Consumer Electronics (ISCE)

作者： Achmad Zahir Wajdi Prasetyo Wibowo Shiori Sasaki Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Faculty of Data Science Musashino University Tokyo Japan

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Marine debris, the majority of which is composed of plastic (61 % to 87 %), is a significant environmental issue facing the world. Between 4.8 million and 12.7 million metric tons of plastic are thought to have entered the ocean in 2010 alone, increasing the estimated total amount of particles in the water to at least 5 trillion. Global plastic production, which reached 348 million tons in 2017, is expected to double in the next 20 years. To overcome this challenge, effective waste detection methods are needed, one of which is by utilizing computer vision, specifically the You Only Look Once (YOLO) algorithm. YOLOv9, as the latest version, offers improved object detection performance compared to its predecessor, YOLOv8. This research aims to evaluate the performance and efficiency of YOLOv9 and YOLOv8 in detecting marine waste. Using the Trash Annotations in Context dataset (TACO), We train both models and conduct tests to evaluate their accuracy and efficiency. Experimental results show that YOLOv9 has higher precision at 45% but lower recall at 6%, compared to YOLOv8, which has a precision of 33 % and a recall of 15 %. Overall, YOLOv8 shows better performance with higher mAP50 at 13% and mAP50-95 values at 10%, compared to YOLOv9, which has mAP50 at 4% and mAP50-95 values at 3%.

关键词： YOLO Waste management Measurement Accuracy Oceans Production Plastics Tuning Monitoring Context modeling

来源：评论

学校读者我要写书评

暂无评论

TRANSFORMER-BASED FEDERATED LEARNING FOR MULTI-LABEL REMOTE SENSING IMAGE CLASSIFICATION

arXiv

引用

arXiv 2024年

作者： Büyüktaş, Barış Weitzel, Kenneth Völkers, Sebastian Zailskas, Felix Demir, Begüm BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany

Federated learning (FL) aims to collaboratively learn deep learning model parameters from decentralized data archives (i.e., clients) without accessing training data on clients. However, the training data across clients might be not independent and identically distributed (non-IID), which may result in difficulty in achieving optimal model convergence. In this work, we investigate the capability of state-of-the-art transformer architectures (which are MLP-Mixer, ConvMixer, PoolFormer) to address the challenges related to non-IID training data across various clients in the context of FL for multi-label classification (MLC) problems in remote sensing (RS). The considered transformer architectures are compared among themselves and with the ResNet-50 architecture in terms of their: 1) robustness to training data heterogeneity;2) local training complexity;and 3) aggregation complexity under different non-IID levels. The experimental results obtained on the BigEarthNet-S2 benchmark archive demonstrate that the considered architectures increase the generalization ability with the cost of higher local training and aggregation complexities. On the basis of our analysis, some guidelines are derived for a proper selection of transformer architecture in the context of FL for RS MLC. The code of this work is publicly available at https://***/rsim/FL-Transformer. © 2024, CC BY.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Two Factors that Influence Our Selection of Digital Avatars: Gender Performativity and Historical Culture 5th

Two Factors that Influence Our Selection of Digital Avatars:...

引用

5th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Liu, Shutang Li, Yongyu Li, Minggui Guan, Yin Jiang, Hang Jiang, Lei Faculty of Humanities and Arts Macau University of Science and Technology 999078 China Academic Affairs Office Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China Culture and Creation Department Fuzhou Polytechnic Fuzhou China

ISBN: (纸本)9789819908479

In this study, 74 college student volunteers were recruited as experimental participants, and 108 game 3D images of the Arena of Valor game were used as the selected samples. The digital avatar is presented to the subject through AR technology. Then, the participants were asked to choose five digital avatar that they would probably go on to use in the future metaverse. Finally, after excluding the effects of game familiarity and other socio-statistical variables, it was found that the gender of the selected Digital avatar was positively correlated with the actual participants’ gender, and it was further found that females were more gender specific and more aggregated in terms of Gender Expression than males, while males were less gender specific and more dispersed. This study found that although there was certain influence of historical culture on the selection aspect of the digital avatar, it was not significant. Local game characters and Japanese game characters have equally important influence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Incivility and Contentiousness Spillover between COVID-19 and Climate science Engagement∗

arXiv

引用

arXiv 2025年

作者： Narimanzadeh, Hasti Badie-Modiri, Arash Smirnova, Iuliia Chen, Ted Hsuan Yun Department of Computer Science Aalto University Finland Department of Network and Data Science Central European University Austria Faculty of Social Sciences University of Helsinki Finland Department of Environmental Science and Policy George Mason University United States

Affective polarization and its accompanying cleavage-based sorting drives incivility and contentiousness around climate change and other science-related issues. Looking at the COVID-19 period, we study cross-domain spillover of incivility and contentiousness in public engagements with climate change and climate science on Twitter and Reddit. We find strong evidence of the signatures of affective polarization surrounding COVID-19 spilling into the climate change domain. Across different social media systems, COVID-19 content is associated with incivility and contentiousness in climate discussions. These patterns of increased antagonism were responsive to pandemic events that made the link between science and public policy more salient. We also show that the observed spillover activated along pre-pandemic political cleavages, specifically anti-internationalist populist beliefs, that linked climate policy opposition to vaccine hesitancy. Our findings highlight the dangers of entrenched cross-domain polarization manifesting as spillover of antagonistic behavior. © 2025, CC BY.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Assessing the Efficiency of AI for Multivariate data Analysis 3

Assessing the Efficiency of AI for Multivariate Data Analysi...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Jambi Ratna Raja Chaturvedi, Mukesh Anandhasilambarasan, D. Ashok, V. Nawadkar, Ashwini R. Va, Manoj Genba Sopanrao Moze College of Engineering Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Computer Science Aligarh India

ISBN: (纸本)9798350319125

This study aimed to evaluate the efficiency of synthetic Intelligence (AI) for multivariate records analysis. Multivariate statistics evaluation is a complicated venture that historically requires considerable manual labor and information in information science and machine mastering. AI can offer an extra green and accurate method to the venture. Consequently, this study employed a ramification of AI-based total methods to identify patterns and generate insights from the dataset. Traditional and ML algorithms were used to generate models based on the records. The models have been evaluated based on their metrics - accuracy, precision, and keep in mind. In the end, the effects from the models were analyzed to identify trends and institutions among the variables. The findings concluded that AI may want to provide a robust and accurate technique to perform multivariate records analysis. However, further research is needed to develop novel tactics to utilize AI for multivariate statistics evaluation with increased accuracy and precision. © 2023 IEEE.

关键词： considerable multivariate ramification statistics evaluation synthetic

来源：评论

学校读者我要写书评

暂无评论

Feature Selection in Associative Classification-A Review and Comparative Analysis

Feature Selection in Associative Classification-A Review and...

引用

Information Technology (ACIT)

作者： Raed Alazaidah Ahmad Al-qerem Mais Haj Qasem Ala'a Al-shaikh Nabeel Almilli MohammadNoor Injadat Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

Feature selection is one of the most important and significant steps that highly affects the final accuracy of any classification model. It aims to determine the most significant features that help in accurately predicting the class label for any new case. This research aims to survey and identify the best feature selection method that suits algorithms belong to Associative Classification (AC). Hence, nine different feature selection methods have been evaluated and compared using five AC based algorithms with respect to Accuracy metric. Results revealed that ABB-IEP and ABB-LIU methods show the best performance with datasets that contain nominal features only, while Relief feature selection method showed the best Accuracy with datasets that comprise Real and Integer features.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

引用

Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Advances in Glaucoma Detection through Machine Learning Technology 1

Advances in Glaucoma Detection through Machine Learning Tech...

引用

1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350330915

In the event that glaucoma is not treated promptly, blindness may become permanent. It ranks second among the factors that might lead to blindness in the eyes. One example of a recent imaging technology that looks within the eye is the fundus camera. Glaucoma is diagnosed using a variety of techniques, such as the retinal nerve fibre layer analyzer, The Topcon image net technique and optical coherence tomography. Despite its expensive cost and the dearth of available research in this field, the optic cup to disc ratio is used to diagnose glaucoma. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The beaker slowly gets better and inhabits most of the judgement as the glaucoma becomes worse. If glaucoma is not pickled trendy a timely manner, blindness may become irreversible. It is the second most common cause of ocular blindness. A contemporary imaging tool called the fundus camera is utilised to view within the eye. Among the methods used to diagnose glaucoma include the retinal nerve fibre layer analyzer, optical coherence tomography, and the Topcon image net approach. Despite its high cost and paucity of research, glaucoma is diagnosed using the optic cup to discus relation. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The cup expands as glaucoma progresses until it finally occupies the majority of the eye. © 2023 IEEE.

关键词： CNN ConvNets Glaucoma machine learning MATLAB Rim-One

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：