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共 11147 条记录，以下是451-460 订阅

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A Meta-analysis of machine learning for the Diagnosis of Covid-19 Disease

A Meta-analysis of Machine Learning for the Diagnosis of Cov...

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2022 IEEE international conference on data science and Information System, ICDSIS 2022

作者： Gowda, Mona N Singh, Dalwinder Professional University Computer Science and Engineering Lovely Punjab Phagwara India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Coronavirus 2019 has wreaked havoc on people's lives all across the globe. The number of positive cases is increasing, and the Asian country is now one of the most severely impacted. This article examines machine learning models that are more accurate at predicting covid. Based on the data from China, regression-based, decision tree-based, naive Bayes, and random forest-based models were developed and verified on a sample from India. A data-driven strategy with better precision, such as the one used here, is beneficial for the government and public to respond in a proactive manner. This study reveals that the suggested framework has superior capabilities in detecting COVID-19. © 2022 IEEE.

关键词： Generative adversarial networks

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Neural Networks with Variational Quantum Circuits 1

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9th EAI international conference on Industrial Networks and Intelligent Systems, INISCOM 2023

作者： Rizvi, Syed Muhammad Abuzar Ulum, Muhammad Shohibul Asif, Naema Shin, Hyundong Department of Electronics and Information Convergence Engineering Kyung Hee University Yongin17104 Korea Republic of

ISBN: (数字)9783031473593

ISBN: (纸本)9783031473586

The field of machine learning is an interdisciplinary area that aims to extract useful information from data through mathematical means. Integrating quantum computing with machine learning has led to exciting new avenues of research, where quantum mechanics principles are applied to enhance and optimize classical machine learning algorithms. In this study, we explore hybrid quantum-classical neural networks with an approach that combines both classical and quantum computing. We achieve this by implementing a variational quantum circuit as the output layer of a classical convolutional neural network. We use this hybrid neural network to classify images of digits from the MNIST dataset. Using this approach, we were able to classify images with high accuracy. Furthermore, due to its flexibility, this hybrid algorithm can be adapted to explore the potential of quantum computing especially in the era of noisy intermediate-scale quantum devices. © 2023, © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Heat Flow Prediction in Songliao Basin Based on Deep Neural Network 10

Heat Flow Prediction in Songliao Basin Based on Deep Neural ...

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10th international conference on Environmental and Engineering Geophysics, ICEEG 2023

作者： Bai, Lige Li, Jing Zeng, Zhaofa College of Geo-exploration Science and Technology Jilin University Changchun China

The heat flow is the key data for accurately predicting the contribution of underground heat, but due to the high cost of measurement, the spatial difference is huge, and the heat flow information in some areas is little known. This paper uses deep neural network technology to perform machine learning on a large number of relevant geological and geophysical features and heat flow measurements on a global scale. In addition, the model uncertainty quantification process is introduced, and the reliability of the prediction results is discussed based on the global sensitivity analysis and correlation calculation of the deep learning method. Finally, for the distribution map of heat flow and absolute difference in the Songliao Basin in the study area, the measured data error is less than 11%, and the predicted absolute difference is less than ±20 mW/m2. The results prove the accuracy of the method and the abnormal high heat flow in the central region of the basin. The existence of, further combined with the results of geophysical surveys, provides evidence for the analysis of regional thermal causes. © Published under licence by IOP Publishing Ltd.

关键词： Heat transfer

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A Novel and Efficient Multi-scale Spatio-Temporal Residual Network for Multi-class Intrusion Detection 6th

A Novel and Efficient Multi-scale Spatio-Temporal Residual ...

引用

6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Li, Nan Gao, Zhaojian Ye, Jiabin Tang, Wei Che, Xun Chen, Yadang Nanjing Big Data Group Co. Ltd. Nanjing210003 China School of Computer Science and Engineering Southeast University Nanjing210096 China Nanjing Zhixun Interconnection Technology Co. Nanjing211100 China Hangzhou SecLead Information Technology Co. Ltd. Hangzhou310056 China Jiangsu Ruizhihe Information Technology Co. Wuxi214434 China School of Computer Science and Cyberspace Security Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

With the development of network devices, the network traffic presents high-dimensional, enormous as well as complex characteristics, and the network threats and attacks continue to intensify. Existing network intrusion detection models tend to disregard the extraction and learning of the temporal features of data, which will greatly affect the accuracy of network intrusion detection models. To address the shortcomings of the single structure and the inability to comprehensively learn features, we propose a novel Transformer-based network intrusion detection method, which integrates the degree of importance of traffic features and the fact that intrusion detection data has temporal and spatial characteristics. Specifically, firstly, we adopt the Transformer to perform feature extraction and construct global correlations on the input data, after that, we utilize the improved Inception to extract multi-scale features and weight the spatial features at different scales using self-attention module, in addition to that, BiGRU is employed to enhance the temporal features. Finally, the proposed model is validated on the publicly available CIC-IDS-2017 and CIC-DDoS-2019 datasets, which verifies that the proposed method outperforms the existing state-of-the-art models in terms of performance by four evaluation metrics, and also shows significant performance improvement in binary as well as multi-class classification tasks compared to other state-of-the-art methods, which proves the efficiency and effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Research on a Fast Forecast Model of Ocean Wave Heights Based on Deep learning Algorithms 34th

Research on a Fast Forecast Model of Ocean Wave Heights Base...

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34th international Ocean and Polar Engineering conference, ISOPE 2024

作者： Tan, Haiyue Xiao, Heng Song, Zhenya Xie, Botao Engineering Research & Design Department CNOOC Research Institute Ltd Beijing China Faculty of Geographical Science Beijing Normal University Beijing China First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Ministry of Natural Resources Shandong Qingdao China

ISBN: (纸本)9781880653784

Accurate predictions of ocean waves which are one of the main environmental loads that offshore oil and gas platform structures suffer from are necessary for safety of platform operations and navigation activities. In this study, a new neural network model named Wave-CNN based on convolutional neural networks is carried out to forecast the significant wave height in the South China Sea. Reanalysis data of wind fields and significant wave height data provided by ECMWF from 2010 to 2018 are applied to train the neural network model and those data in 2019 constitute the test set. In order to improve the computational efficiency of the neural network model, Wave-CNN is optimized by distributed data parallel training mode, with the cross card synchronous BN algorithm and the Apex mixed-precision training method. The results show that this model has good performances on hourly temporal variations and spatial seasonal distributions of significant wave heights in the South China Sea and is capable to complete a 7-day forecast in seconds and a full-year forecast within 3 hours. © 2024 by the international Society of Offshore and Polar Engineers (ISOPE).

关键词： Neural network models

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The Secondary Isolated data Island: Isolated data Island Caused by Blockchain in Federated learning

The Secondary Isolated Data Island: Isolated Data Island Cau...

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2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenghan Wang, Shanshan Zhao, Chuan Cao, Xueyang Yan, Shuhao Han, Yadi Huang, Limei Chen, Zhenxiang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China University of Jinan School of Information Science and Engineering Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350337488

Compared to other fields, data in the medical field has a higher degree of sensitivity. Therefore, in order to ensure that when using medical data for machine learning, a Federated learning (FL) framework is usually used. However, the traditional centralized FL framework greatly increases the risk of leaking privacy of clients. For this reason, many researchers have developed and designed a Federated learning Based on Blockchain (FLchain) to enhance the privacy of this framework. Usually, each hospital will use an independent FLchain. But the researchers overlooked a crucial issue: the application of blockchain in FLchain can create isolated data island. Different FLchain use different blockchains, so the data between the two frameworks cannot be used to train a model at the same time. We call this problem the secondary isolated data island (SIDI). The problem leads to hospitals using FLchain architecture being unable to use each other's data for model training while ensuring information security. We first discovered and proposed the issue of SIDI, and solved it by using Federated learning Based on Blockchain with Inter-Blockchain Technology (IBT-FLchain) framework. We designed an Hashed Timelock Contract of global model aggregation (HTLC-M) in FLchain by improving the Hashed Timelock Contract (HTLC). We have demonstrated through experiments that the IBT-FLchain framework can effectively solve SIDI problem and greatly alleviate the storage cost of clients in the blockchain. © 2023 IEEE.

关键词： Blockchain Federated learning Inter-Blockchain Technology Isolated data Island

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Siamese-PointNet++: Point Cloud Classification with Siamese PointNet++

Siamese-PointNet++: Point Cloud Classification with Siamese ...

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2022 international conference on Image Processing, Computer Vision and machine learning, ICICML 2022

作者： Rao, Mengbin Yuan, Sen Tang, Ping Ge, Jianjun Information Science Academy China Electronics Technology Group Corporation Beijing100086 China Beijing100101 China

ISBN: (纸本)9781665464680

It is significant to explore the related information of point pairs to improve the classification accuracy of a point cloud. This paper proposes the Siamese PointNet++, which is end-to-end trained offline with point-set pair. More specifically, PointNet++is used to extract features from point-set pairs, and then a relation module with 1D CNN architecture is applied to compute the relation scores. We conducted extensive experiments on the test data from the 3D point cloud classification challenge of the 2019 IEEE GRSS data Fusion Contest. The inspiring experimental results demonstrate the effectiveness of the proposed framework. © 2022 IEEE.

关键词： data fusion

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Evaluating the Performance of Long Short-Term Memory for Web Attack Detection 26

Evaluating the Performance of Long Short-Term Memory for Web...

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26th international conference on Computer and Information Technology, ICCIT 2023

作者： Momu, Sharmin Akter Siddiqui, Rafiur Rahman Hoque, Md.Samiul Kawsur, Mir Md American International University-Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Web threats provide a continuing and dynamic hazard to both individuals and organizations, encompassing a wide spectrum of malicious activities such as SQL injections and denial-of-service attacks. In this research, we look into how Long Short-Term Memory (LSTM) neural networks can be used to better detect malicious activity on the web. LSTM is a subset of machine learning algorithms and it's a potential tool for web attack detection because of its abilities in sequential data analysis. Experiments were conducted utilizing the CIC-DDoS2019 dataset, which comprises a diverse range of Distributed Denial of Service (DDoS) attacks and benign network traffic. The multi-layered LSTM model we have developed demonstrates effective discrimination between normal and attack occurrences in the context of binary classification and multiclass classification. In order to optimize performance, we conducted experiments where we manipulated several model parameters, such as the number of layers, activation functions, sample size, and epochs. The evaluation metrics included in our study are accuracy, precision, recall, and F1-score. We obtained an impressive accuracy rate of 99.7 % for portmap attacks, 99.9% for LDAP attacks, and 99.9% for NTP attacks. For multiclass classification, we obtained 99.9% accuracy rate. The result can be attributed to the structure of the model, the utilization of high-quality training data, and the optimization of hyperparameters. © 2023 IEEE.

关键词： binary classification CICDDoS2019 DDoS deep learning long short-term memory multi-layer lstm multiclass classification web attack detection

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Forecasting the Economic Crisis of Sri Lanka: Application of machine learning Algorithms for Time Series data 2

Forecasting the Economic Crisis of Sri Lanka: Application of...

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2nd international conference on machine learning and data Engineering, ICMLDE 2023

作者： Raj, A. Stephan Antony Kumarasankaralingam, Lakshmanan Balamurugan, M. Maheswari, B. Gowri, J. Dutta, Anurag Department of Mathematics Rathinam Group of Institutions Kovilpalayam Campus Tamil Nadu Coimbatore India Department of Mathematics Kuwait American School of Education Salmiya Kuwait Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Mathematics Dr Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Mathematics Nallamuthu Gounder Mahalingam College Tamil Nadu Pollachi India Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore Calcutta India

The economic crisis in Sri Lanka began in 2019 and reached a crisis point in 2022 when protesters stormed the presidential palace in Colombo. Acute food, fuel, and other item shortages, soaring inflation, prolonged power outages, and a collapsing economy with few job opportunities all contribute to the populace's unrest. Sri Lanka still faces severe economic difficulties, and no one can say when they will be resolved. This country has seen the most prominent financial collapse since its independence in 1948. Unexpected inflation levels, a close to complete exhaustion of foreign currency reserves, a shortage of medical aid, and a rising price of essential commodities are the results. Numerous compounding reasons, including tax cuts, printing money, a countrywide regulation to move to natural or biological agriculture, the 2019 Sri Lanka Easter bombings, and the repercussions of the COVID-19 epidemic, have been cited as the root cause of the disaster. As early as 2022, these protests in Sri Lanka might be traced back to the country's economic woes. The primary driver of the current financial crisis in Sri Lanka is inflation. This article will look into Sri Lanka's inflation rates beginning in 2021. The inflation Rate of Sri Lanka has been forecasted for the next 6 months (through September 2023) using time series data. We used numerous Time Series Forecasting Algorithms for this multi-step ahead forecast. The results generated by these time series forecasters were in accordion with each other. Thus, we put forward the normalized results from each of these forecasting paradigms to get a better prediction on the inflation Rates, that Sri Lanka is supposed to face shortly (6 months), keeping in consideration of the current scenario being unaltered. The authors hope the Sri Lankan Administration will use the paper's predictions to take charge, which could lead to positive changes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Optimal Design and Implementation of an Open-Source Emulation Platform for User-Centric Shared E-mobility Services 10th

Optimal Design and Implementation of an Open-Source Emula...

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10th international conference on machine learning, Optimization, and data science, LOD 2024

作者： Shah, Maqsood H. Ding, Yue Zhu, Shaoshu Gu, Yingqi Liu, Mingming Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland

ISBN: (纸本)9783031824807

With the rising concern over transportation emissions and pollution on a global scale, shared electric mobility services like E-cars, E-bikes, and E-scooters have emerged as promising solutions to mitigate these pressing challenges. However, existing shared E-mobility services exhibit critical design deficiencies, including insufficient service integration, imprecise energy consumption forecasting, limited scalability and geographical coverage, and a notable absence of a user-centric perspective, particularly in the context of multi-modal transportation. More importantly, there is no consolidated open-source platform which could benefit the E-mobility research community. This paper aims to bridge this gap by providing an open-source platform for shared E-mobility. The proposed platform, with an agent-in-the-loop approach and modular architecture, is tailored to diverse user preferences and offers enhanced customization. We demonstrate the viability of this platform by providing a comprehensive analysis for integrated multi-modal route-optimization in diverse scenarios of energy availability, user preferences and E-mobility tools placement for which we use modified Ant Colony Optimization algorithm so called Multi-Model Energy Constrained ACO (MMEC-ACO) and Q-learning algorithms. Our findings demonstrate that Q-learning achieves significantly better performance in terms of travel time cost for more than 90% of the instances as compared to MMEC-ACO for different scenarios including energy availability, user preference and E-mobility tools distribution. For a fixed (O, D) pair, the average execution time to achieve optimal time cost solution for MMEC-ACO is less than 2 s, while Q-learning reaches an optimal time cost in 20 s on average. For a run-time of 2 s, Q-learning still achieves a better optimal time cost with a 20% reduction over MMEC-ACO’s time cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Travel time

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