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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Murali Dhar, M.S. Mahaveerakannan, R. Ravindaran, F. Murthyraju, Kommisetti Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Tamil Nadu Coimbatore India Shri Vishnu Engineering College for Women Department of Electronics and Communication Engineering Andha Pradesh Bhimavaram India

ISBN: (纸本)9798350313987

Underwater Wireless Sensor Networks (UWSN) have drawn increased interest from researchers in recent years due to advancements in underwater tracking, the implementation of multiple uses, and marine monitoring. Both researchers and business are drawn to the UWSN field. It is also susceptible to hostile attacks and threats because of the harsh undersea surroundings, its own capabilities, and the open acoustic channel. These features make it simple for attackers to intercept information across the source and destination. Many studies have been done to secure UWSN from malicious attacks, but the majority of them rely on a threshold that is determined before deployment or a training set of data. Because there is no proven security foundation, it is difficult and challenging for UWSN to identify harmful attacks. To identify harmful attacks in a UWSN, we support vector machines in this research. SVM produces good results and requires significantly less time to train than neural networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues. © 2023 IEEE.

关键词： malicious attacks security support vector machine underwater wireless sensor network (UWSN)

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Estimating Uncertainty in Deep Learning Methods and Applications

Estimating Uncertainty in Deep Learning Methods and Applicat...

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International Conference on computer and Applications (ICCA)

作者： Abdulrahman Alqarafi Hina Batool Tahir Abbas Jamshaid Iqbal Janjua Sadaqat Ali Ramay Munir Ahmed College of Computer Science and Engineering Taibah University Medina Saudi Arabia Department of Computer Science TIMES Institute Multan Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan College of Informatics Korea University Seoul Republic of Korea

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Uncertainty estimation in deep learning has emerged as a crucial area of research due to its significance in enhancing model reliability and decision-making in critical applications. This article explores various methods and applications of uncertainty estimation in deep learning, aiming to provide insights into its importance, methods, and potential impact. Through a comprehensive literature review and analysis, the study identifies key findings regarding the effectiveness, limitations, and ethical considerations associated with uncertainty estimation techniques. The results reveal the diverse range of methodologies employed, including Bayesian approaches, ensemble methods, and Monte Carlo sampling, each with its strengths and drawbacks. Furthermore, the article discusses the implications of uncertainty estimation in deep learning for fields such as healthcare, autonomous systems, and safety-critical domains. Overall, this study underscores the significance of uncertainty estimation in deep learning and provides valuable insights for researchers and practitioners in the field.

关键词： Deep learning Ethics Uncertainty Monte Carlo methods Decision making Estimation Medical services Reliability Artificial intelligence Systematic literature review

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Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method 9th

Support Vector Machine for Satellite Images Classification U...

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9th International Conference on Computing and informatics, ICOCI 2023

作者： Mansor, Nur Suhaili Awang, Hapini Malami, Sarkin Tudu Shehu Zolkafli, Amirulikhsan Taiye, Mohammed Ahmed Maulana, Hanhan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Sokoto852101 Nigeria Department of Cultural Sciences Faculty of Arts and Humanities Linnaeus University Vaxjo351 95 Sweden Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Bandung40134 Indonesia

ISBN: (纸本)9789819995882

Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Si

关键词： Image classification

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Application of Blockchain Technologies to Improve the Quality of Microgrid Network Management

Application of Blockchain Technologies to Improve the Qualit...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Volodymyr Khilenko Halina Bielokha Sergii Denysiuk Ivan Kotuliak Michal Ries Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovakia National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

The use of blockchain technologies and Smart contracts to improve the quality of Microgrid network management is considered. The necessity of using a special mathematical apparatus for system analysis is noted, taking into account the nonlinear nature of dynamic processes in the network and, in particular, the presence of sharply changes in the load. The advantages and disadvantages of the approach known in the literature are analyzed, when the search for the optimal power distribution among generators to ensure the minimum costs of the Microgrid is carried out on the basis of nonlinear programming methods. Graphs of transient processes in the network are given and their corresponding analysis is carried out.

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On biased compression for distributed learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 12974-13023页

作者： Aleksandr Beznosikov Samuel Horváth Peter Richtárik Mher Safaryan Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA and Skolkovo Institute of Science and Technology Moscow Russia and School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow Russia Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal KSA

In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased compressors often show superior performance in practice when compared to the much more studied and understood unbiased compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate $O\left( \delta L \exp[-\frac{\mu K}{\delta L}] + \frac{(C + \delta D)}{K\mu}\right)$, where δ ≥1 is a compression parameter which grows when more compression is applied, L and µ are the smoothness and strong convexity constants, C captures stochastic gradient noise (C = 0 if full gradients are computed on each node) and D captures the variance of the gradients at the optimum (D = 0 for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance.

关键词： compression operators biased compressors distributed learning linear convergence error feedback

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A Novel Denoising Autoencoder Method for Surface Defect Detection of Screw Products 12

A Novel Denoising Autoencoder Method for Surface Defect Dete...

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2023 12th International Conference on Engineering Mathematics and Physics, ICEMP 2023

作者： Chen, J.W. Lin, W.J. Hung, C.L. Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Mold and Die Engineering National Kaohsiung University of Science and Technology Kaohsung807618 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan

Defect detection is an important aspect of assessing the surface quality of screw products. A defective screw greatly affects the mechanism of screw product. Recently, unsupervised learning has been widely used for defect detection in industrial applications. In most cases, anomaly networks are unable to reconstruct abnormal images into satisfactory normal images, which results in poor defect detection performance. In this paper, a denoising autoencoder is used to enhance the capability of reconstructing defect screw images. By using this technique, the model can efficiently extract more features during reconstruction. Compared to the results without noise, the IoU can be increased by over 11%. The paper also develops an intelligent screw detection system for realistic industrial applications. Consequently, the proposed scheme is well suited to industrial defect detection scenarios since the models require only normal samples for training. © Published under licence by IOP Publishing Ltd.

关键词： Screws

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Onto-ikea: A knowledge retrieval framework based on ikea ontology

Onto-ikea: A knowledge retrieval framework based on ikea ont...

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Joint Ontology Workshops 2021 Episode VII: The Bolzano Summer of Knowledge, JOWO 2021

作者： Vassiliades, Alexandros Zarkadas, Nikos Bassiliades, Nick Patkos, Theodore Aristotle University of Thessaloniki School of Informatics Thessaloniki Greece Foundation for Research and Technology Institute of Computer Science Heraklion Greece

The evolution of Linked Open Data (LOD) has encouraged developers to create more and more context related ontologies. This advance is extremely important because Artificial Intelligence (AI) applications can access domain specific information that is in machine understandable format. In this paper, we present a knowledge retrieval framework which is based on our custom made IKEA ontology. The ontology is created with information from the IKEA dataset that we also created. The dataset contains information about household objects found in the IKEA website, relations between objects, and features such as dimensions, and material for the objects. Moreover, we provide a query interface that the user can use to address queries to the ontology. © 2021 CEUR-WS. All rights reserved.

关键词： Ontology

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Designing a Low-cost High-end Android-based Wireless Board for the EMULSION IoT Platform 3

Designing a Low-cost High-end Android-based Wireless Board f...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Ganchev, Ivan Ji, Zhanlin O'Droma, Mairtin Department of Computer Systems University of Plovdiv 'Paisii Hilendarski' Plovdiv Bulgaria Institute of Mathematics and Informatics Sofia Bulgaria Limerick Ireland North China University of Science and Technology College of Artificial Intelligence China

ISBN: (纸本)9789463968058

This paper proposes an innovative generic wireless low-cost high-end Android-based board for use by the EMULSION IoT platform, which is being elaborated as IoT-service and IoT-system prototyping ready. Based on the AllWinner A33 quad core Cortex-A7 CPU with a Mali400MP2 GPU and 512MB RAM, the board supports a 5MP CMOS sensor camera, 1080p video, MIDP display, and Hi-Fi audio. The paper reveals the basics of the design of this proprietary board. © 2022 URSI.

关键词： Costs

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Intelligent System for Recommendation of Mobile Services to Consumers 3

Intelligent System for Recommendation of Mobile Services to ...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

ISBN: (纸本)9789463968058

This paper presents an intelligent system for recommendation of services to mobile users (consumers) by considering the current context. The system builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers' choices for getting the best 'cost / quality of experience (QoE)' ratio when utilizing different mobile services. © 2022 URSI.

关键词： Intelligent systems

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