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检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

共 5741 条记录，以下是661-670 订阅

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Multi-objective Particle Swarm Optimization Based Enhanced Fuzzy C-Means Algorithm for the Segmentation of MRI data 3rd

Multi-objective Particle Swarm Optimization Based Enhanced F...

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3rd international conference on VLSI, Communication and Signal processing, VCAS 2020

作者： Singh, Munendra Asha, C.S. Sharma, Neeraj Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Varanasi India

ISBN: (纸本)9789811627606

Fuzzy c-means algorithm and its variants are popular for the segmentation of magnetic resonance imaging (MRI) data. The enhanced fuzzy c-means approach is one among them that comprises weighted local spatial data. However, the quantity of spatial data added with input MRI image differs and that depends on the noise content and sequence of MRI. Hence, the value of weight factor needs to be chosen appropriately and automatically to attain the accurate segmentation results. In this perspective, the current work focuses to generate optimum weight values and presents an optimized enhanced fuzzy c-means algorithm for MRI data. The proposed method utilizes the multi-objective particle swarm optimization to control the weight parameter that leads to maximum segmentation accuracy. The new approach is tested and validated on a standard simulated BrainWeb MRI dataset. The outcome shows that the proposed approach is flexible and robust to noise content as compared to the conventional algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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ASTC: An Adaptive Gradient Compression Scheme for Communication-Efficient Edge Computing 23

ASTC: An Adaptive Gradient Compression Scheme for Communicat...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wu, Jun Mao, Yingchi Xu, Xuesong School of Computer and Information Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China

ISBN: (纸本)9781665494571

In edge computing, though distributed training of Deep Neural Network (DNN) is expected to exchange massive gradients between the parameter servers and workers, the high communication cost constrains the training speed. To break this limitation, the gradient compression algorithms employ the ultimate compression ratio at the expense of the accuracy of the trained model. Therefore, new gradient compression techniques are necessary to ensure both communication efficiency and model accuracy. This paper introduced a novel technique - an Adaptive Sparse Ternary Gradient Compression (ASTC) scheme to enhance the communication efficiency and training accuracy during the DNN training. Specifically, ASTC firstly establishes the model compression selection criterion by the amount of gradients to compress the network layer that meets the criterion. Secondly, ASTC evaluates the gradients' importance based on entropy to adaptively perform sparse compression. Finally, ASTC performs ternary quantization compression and a lossless code scheme to further reduce the communication cost. Numerical results demonstrate that the average iteration time of ASTC can be reduced largely compared to the state-of-art benchmark schemes. Furthermore, ASTC improves an average of about 1.9% in training accuracy. © 2021 IEEE.

关键词： Training Adaptation models Costs Quantization (signal) Smart cities Computational modeling Benchmark testing

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MFENet: Multi-Feature Extraction Net for Remote Sensing Semantic Segmentation 7

MFENet: Multi-Feature Extraction Net for Remote Sensing Sema...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Zhang, Chao Lu, Xingshun Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

In this paper, we tackle the remote sensing semantic segmentation task by capturing feature information across multiple scales, all channels, and global locations. Different from previous works that simply use U-net to extract multi-scale features, we further improve U-net and propose a Multi-Feature Extraction Network (MFE-Unet). Specifically, we propose the MFE module, which uses both dilated convolution module and two attention modules. Dilated convolution is used to enhance U-net's ability to represent multi-scale information. The two attention modules refer to the channel attention module and the pixel attention module. Channel attention maps all channels centrally, assigns weights uniformly, and adaptively adjusts the importance of each channel's information. Pixel attention treats features at each location as the same individual, and similar features will be associated together to further improve feature representation. We conducted multiple sets of experiments on the "AI+"remote sensing image dataset. Experiments show that our network is sufficient against several advanced models. © 2022 IEEE.

关键词： Convolution

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Formalization and processing of data Requirements for the Development of Next Generation Railway Traffic Management Systems 4th

Formalization and Processing of Data Requirements for the De...

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4th international conference on Reliability, Safety, and Security of Railway Systems (RSSRail)

作者： Magnien, Airy Cecchetti, Gabriele Ruscelli, Anna Lina Hyde, Paul Liu, Jin Wegele, Stefan SNCF Paris France UIC Paris France Scuola Super Sant Anna Pisa Italy Newcastle Univ Sch Engn Newcastle Upon Tyne Tyne & Wear England Siemens Munich Germany

ISBN: (纸本)9783031058134;9783031058141

Railway Traffic Management Systems (TMSs) handle data from multiple railway subsystems, including Rail Business Services (such as interlocking, RBC, maintenance service, etc.) and external services (such as passenger information systems, weather forecast, etc.). In turn, the data from these subsystems are described in several models or ontologies contributed by various organizations or projects which are in a process of converging or federation. The challenge of the Shift2Rail OPTIMA project, which is implementing a communication platform for virtual testing of new applications for railway TMS, is to allow the exchange of data between different services or users and to support new traffic management applications, enabling access to a large number of disparate data sources. In this paper, the core activities of the OPTIMA project related to the formulation and standardization of a common data model are described. A new Common data Model is developed based on standardized data structures to enable the seamless exchange of large amounts of data between different and heterogeneous sources and consumers of data, that contributes to the building of next generation of a more effective and efficient railway TMS suitable to offer precise and real-time traffic information to railway operators and other end users.

关键词： Railway Traffic Management System Common data Model Model transformation Context-free grammar

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New Energy Station Monitoring Equipment Rule Verification Model Based on data Envelopment Analysis

New Energy Station Monitoring Equipment Rule Verification Mo...

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2020 international conference on Smart Grid and Energy Engineering, SGEE 2020

作者： Zhu, Dongge Ma, Yiming Li, Yongliang Yan, Zhenhua Liu, Jia Ma, Rui Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Ningxia750002 China

According to the functional and performance requirements of the new energy station monitoring equipment, an operating rule verification model based on data envelopment analysis is proposed, and the online automatic discovery of equipment and safe operation rules for key links are designed. The equipment adopts data envelopment analysis method to read real-time data of new energy station monitoring equipment, optimizes the automatic discovery function of key sections, and tests the operating parameters of wind turbines and wind farms online through remote testers, formulates monitoring rules, and forms data requirements, Use algorithms to acquire and process data, and build models. Realize the discrete processing and rule mining of key links and safety operating procedures to ensure the safe operation of new energy station monitoring equipment. Finally, the experimental results show that the equipment can quickly and automatically generate new energy station monitoring equipment safe operation knowledge and improve new energy Safety of station monitoring equipment. © Published under licence by IOP Publishing Ltd.

关键词： data envelopment analysis

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Predicting COVID-19 Transmission in Southern California with Machine Learning Methods

Predicting COVID-19 Transmission in Southern California with...

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IEEE international conference on Big data Analysis (ICBDA)

作者： Han Li Ran Wei Wenyu Wang Nanpeng Yu School of Computer Science and Engineering University of California Riverside Riverside USA School of Public Policy University of California Riverside Riverside USA Department of Electrical and Computer Engineering University of California Riverside Riverside USA

ISBN: (数字)9798350352962

ISBN: (纸本)9798350352979

The COVID-19 pandemic has posed significant chal-lenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficul-ties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. These findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region.

关键词： COVID-19 Analytical models Pandemics Sociology Predictive models data models Public healthcare

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Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis 8

Heart Disease Risk Prediction using Machine Learning with Pr...

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8th international conference on Intelligent and Advanced Systems (ICIAS) / World Engineering, Science and Technology Congress

作者： Reddy, Karna Vishnu Vardhana Elamvazuthi, Irraivan Abd Aziz, Azrina Paramasivam, Sivajothi Chua, Hui Na Univ Teknol Petronas Dept Elect & Elect Engn Seri Iskandar 32610 Perak Malaysia UOWMKDU Univ Coll Sch Engn Shah Alam Selangor Malaysia Sunway Univ Dept Comp & Informat Syst Subang Jaya Selangor Malaysia

ISBN: (纸本)9781728176666

Cardiovascular diseases (CVDs) are killing about 17.9 million people every year. Early prediction can help people to change their lifestyles and to endure proper medical treatment if necessary. The data available in the healthcare sector is very useful to predict whether a patient will have a disease or not in the future. In this research, several machine learning algorithms such as Decision Tree (DT), Discriminant Analysis (DA), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Ensemble were trained on Cleveland heart disease dataset. The performance of the algorithms was evaluated using 10-fold cross-validation without and with Principal Component Analysis (PCA). LR provided the highest accuracy of 85.8% with PCA by keeping 9 components and Ensemble classifiers and attained an accuracy of 83.8% using a Bagged tree with PCA by keeping 10 components.

关键词： Heart disease data Pre-processing Machine Learning Principal Component Analysis

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Proceedings of the 46th international conference on Very Large data Bases, VLDB December 2019

Proceedings of the 46th International Conference on Very Lar...

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46th international conference on Very Large data Bases, VLDB 2020

This proceedings contains 12 papers. The Proceedings of the VLDB Endowment (PVLDB) provides a high-quality publication service to the data management research community. This conference issue covers the topics which include: database indexing and optimization techniques for new hardware and computing platforms, persistent memory, new database join algorithms in the context of stream and sample-based query processing, graph databases, research work on developing new database technologies that support video query processing using neural networks, and also discuss learning individual fairness measures from data and automatic mining of factual mistakes from Wikipedia, etc. The key terms of this proceedings include persistent memory, LSM-based storage systems, bridging theory, vocabulary querying, canonical hub labeling, optimizing databases, s-t simple path enumeration, video analytics, differential indexing, neural network. © VLDB Endowment.

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Mitigation of DNS Water Torture Attacks within the data Plane via XDP-Based Naive Bayes Classifiers 10

Mitigation of DNS Water Torture Attacks within the Data Plan...

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10th IEEE international conference on Cloud Networking (IEEE CloudNet)

作者： Kostopoulos, Nikos Korentis, Stavros Kalogeras, Dimitris Maglaris, Vasilis Natl Tech Univ Athens NTUA NETMODE Lab Athens Greece

ISBN: (纸本)9781665435383

Water Torture is a DDoS attack vector that exhausts the processing resources of victim Authoritative DNS Servers. By crafting DNS requests involving names that appear once and are unknown to the victim, attackers bypass the DNS caches of intermediary Recursive DNS Servers (Resolvers), hence forwarding the entire attack traffic to the victim. As a countermeasure, machine learning algorithms have been proposed to filter attack traffic on Resolvers. Our proposed schema implements via programmable data plane methods efficient machine learning algorithms that differentiate between legitimate and DDoS attack traffic within cloud infrastructures. Specifically, we leverage on XDP to implement data plane Naive Bayes Classifier inference and effectively mitigate Water Torture attacks within data center Resolvers. DNS requests regarded as invalid by the Naive Bayes Classifier are dropped within the Linux kernel before any resources are allocated to them, while valid ones are forwarded to the user space to be resolved. Our schema was assessed via a proof of concept setup within a virtualized environment, with learning and testing performed via legitimate and malicious DNS data records with statistical properties consistent with datasets widely reported in the literature. Our experiments mainly focused on evaluating the filtering throughput of the proposed mitigation schema given the constraints imposed by XDP. We conclude that our XDP-based Naive Bayes Classifier significantly decreases the volume of attack traffic within the data plane, thus efficiently safeguarding Resolvers.

关键词： Programmable data Planes In-network Machine Learning Inference eXpress data Path (XDP) Naive Bayes Classifier Water Torture Attack

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Application of Machine Learning algorithms to Path Loss Modeling: A Review 5

Application of Machine Learning Algorithms to Path Loss Mode...

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5th international conference on Information Technology for Education and Development, ITED 2022

作者： Abdulkarim, Abubakar Faruk, Nasir Alozie, Emmanuel Sowande, Olugbenga. A. Olayinka, Imam-Fulani Yusuf Usman, Aliyu D. Adewole, Kayode S. Oloyede, Abdulkarim A. Chiroma, Haruna Garba, Salisu Imoize, Agbotiname Lucky Musa, Abdulwaheed Taura, Lawan S. Ahmadu Bello University Department of Electrical Engineering Nigeria Kampala International University Department of Electrical Telecommunications Computer Engineering Uganda Sule Lamido University Department of Physics Nigeria Directorate of ICT Sule Lamido University Nigeria University of Ilorin Department of Telecommunication Science Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Nigeria University of Ilorin Department of Computer Science Nigeria College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Sule Lamido University Department of Computer Science Nigeria University of Lagos Department of Electrical and Electronics Engineering Nigeria Kwara State University Department of Electrical and Computer Engineering Nigeria

ISBN: (纸本)9781509064229

The demand for high-speed internet services is increasing due to emerging needs such as e-commerce, e-health, education, and other high-technology applications. Wireless communication networks have now become a necessity, especially with the introduction of the 5G networks which have the potential to provide extraordinary data rates with extremely low latency. The deployment and operation of 5G and beyond networks in built-up environments would require a complex and reliable radio propagation model that guides network engineers to achieve effective coverage estimation and appropriate base station placements. The inefficiency, and sometimes inconsistencies of deterministic and empirical path loss models necessitated the need to integrate machine learning models. Recently, different machine learning-based pathloss models have been developed to overcome drawbacks associated with conventional pathloss models due to their significant learning and prediction abilities. This paper aims to review path loss models relative to machine learning-based algorithms with a focus on models developed in the last 21 years (2000 to 2021) to study their network parameters and architectures, designs, and applicability, and proffer further research directions. © 2022 IEEE.

关键词： 5G mobile communication systems

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