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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33943 条记录，以下是601-610 订阅

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Latency Analysis of Multi-Exit Split Federated learning via Testbed Implementation 15

Latency Analysis of Multi-Exit Split Federated Learning via ...

引用

15th international conference on Information and Communication Technology Convergence, ICTC 2024

作者： Lim, Hyelee Rizwan, Atif Choi, Minseok Kyung Hee University Department of Electronic Engineering Yongin Korea Republic of

ISBN: (纸本)9798350364637

Federated learning (FL) offers a decentralized approach to training machine learning models across distributed devices, addressing data privacy concerns and reducing communication overhead inherent in centralized learning methods. However, as deep neural networks (DNNs) increase in complexity, the memory limitations of client devices pose significant challenges, particularly in real-time applications. To overcome these challenges, Split learning (SL) has been proposed, which partitions DNN models between client devices and a central server, thereby reducing the burden on clients while maintaining data privacy. This paper extends SL by integrating a multi-exit strategy, allowing for early inference on less complex data samples to further enhance inference speed and efficiency. We validate our approach using a real-world testbed that replicates actual communication environments, contrasting it with existing methods under network conditions. Our results demonstrate that the Multi-Exit Federated Split learning (ME-FedSL) algorithm not only reduces training and inference times but also improves the feasibility of deploying complex DNN models in federated learning environments, particularly in scenarios with bandwidth constraints. © 2024 IEEE.

关键词： Federated learning

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machine learning and Deep learning based Stock Market Prediction considering Covid-19 as a Feature

Machine Learning and Deep Learning based Stock Market Predic...

引用

2022 OPJU international Technology conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Mondal, Bhaskar Dandapat, Asimkiran National Institute of Technology Patna Department of Computer Science and Engineering Bihar 800005 India O P Jindal University Department of Computer Science and Engineering Chhattisgarh Raigarh India

ISBN: (纸本)9781665492942

India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.

关键词： COVID-19

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Conversion from multi-spectral data into SAR data with Deep Convolution Neural Architecture using Generative Adversarial Network

Conversion from multi-spectral data into SAR data with Deep ...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Gupta, Anishi Jain, Sambhav Kumar DRDL DRDO Hyderabad500058 India

Synthetic Aperture Radar (SAR) is a high-resolution radar extensively used for aerial and terrestrial images in numerous applications. The SAR images are all weather and day/night capable. It is extensively used in defense/military systems for surveillance, target detection and target tracking. In current times, the need for deep learning algorithms has increased due to its capability of generating SAR images, as it is very intensive and expensive task for the acquisition and formation of SAR images through sensors. Thus, alternative methods are required to produce SAR image data. In this paper, generative adversarial networks (GAN) are used to generate SAR images from a limited pre-existing dataset. This paper presents a novel way of translating optical images to SAR images, for the generation of paired, unpaired data set. Due to the unavailability of an open-source paired, unpaired image dataset, a synthetic paired image dataset is created for proving the concepts of CycleGAN and Pix-to-Pix algorithm by fine tuning the algorithms using generated datasets. The paper tried tackling this problem in two ways, both paired and unpaired image-to-image translation. © 2023 The Authors. Published by Elsevier B.V.

关键词： Synthetic aperture radar

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Research on Student's Devotion Evaluation Method of Online Cooperative learning

Research on Student's Devotion Evaluation Method of Online C...

引用

2022 international conference on machine learning and Knowledge engineering, MLKE 2022

作者： Xing, Zhiqiang Fan, Yue North China University of Technology School of Information Science and Technology Beijing China

ISBN: (纸本)9781665495677

Online cooperative learning is a structured teaching strategy in which team members jointly complete knowledge construction or solution acquisition through online discussion. The process evaluation of cooperative learning mainly about how well a student focus on the discussion. However, the evaluation mostly focuses on subjective evaluation, lacking quantitative and objective evaluation. This paper develops a video-based online cooperative learning quantitative evaluation method. Firstly, Considering that there are few online cooperative learning video data sets at present, we have established the online cooperative learning Video data set through classroom practice(100 students, 8000minutes), secondly, we proposed an artificial intelligence strategy combining intensive trajectory tracking, feature map extraction, VLAD feature encoding, and linear classifiers to evaluate students' devotion in the online discussion. 76.21% accuracy was obtained on the self-built data set, which laid a good foundation for subsequent online cooperative learning research. © 2022 IEEE.

关键词： Students

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SVMPT: A Hybrid Approach to Sparse and Irregular Clinical data learning with Selective Variable-wise Message Passing and Transformer 24

SVMPT: A Hybrid Approach to Sparse and Irregular Clinical Da...

引用

15th conference on Bioinformatics Computational Biology and Health Informatics

作者： Chen, Rongqin Wu, Dan Hou, Leong U. Li, Ye Univ Macau Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798400713026

Electronic Health Records (EHRs) contain a wide range of patient data, presenting both opportunities and challenges for analysis. The complexity and sparsity of these multivariate time series data, characterised by high dimensionality and non-uniform sampling, pose difficulties for conventional time series techniques. To address these challenges, we present SVMPT, a novel two-stage machine learning model that combines selective variable-wise message passing with a Transformer architecture. SVMPT uses attention mechanisms to dynamically update data representations, capture inter-variable dependencies, and synthesise missing values. SVMPT delivers state-of-the-art results in the PhysioNet Mortality Prediction Challenge 2012 and the Sepsis Early Prediction Challenge 2019, demonstrating its effectiveness in processing irregular and sparse EHR data for downstream tasks. The introduction of SVMPT contributes to the development of advanced machine learning techniques for complex EHR data analysis.

关键词： Electronic health records (EHRs) Multivariate time series Irregular and sparse data Attention mechanism Transformer Selective variable-wise message passing

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Traffic Congestion Control System Using machine learning

Traffic Congestion Control System Using Machine Learning

引用

2022 international conference on machine learning, Big data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Rohilla, Charu Kumar, Krishan Rohilla, Neha Manav Rachna International Institute of Research & Studies Faculty of Engineering & Technology Department of Computer Science & Engineering Faridabad India Vaish Arya Kanya Mahavidyyalaya Department of Computer Applications Bahadurgarh India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

The aim of this paper is to introduce the traffic congestion control system by using machine learning technology. machine learning is the property by which a computer system act without being explicitly programmed. It can be supervised, unsupervised & reinforcement learning. Traffic congestion is a critical problem in many areas around the world. Many policies have been implemented to reduce traffic congestion This paper will discuss various work done on traffic congestion control & proposed new traffic congestion control system. For the system implementation Python programming will be used. © 2022 IEEE.

关键词： Traffic congestion

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Research on Virtual Subsystem Architecture of Intelligent data Platform 7

Research on Virtual Subsystem Architecture of Intelligent Da...

引用

7th international conference on Robotics and Automation engineering (ICRAE)

作者： Gao, Lu Yang, MingChuan Qian, Bing China Telecom Corp Ltd Beijing Res Inst Beijing Peoples R China

ISBN: (纸本)9781665489188

This paper introduces the current development of distributed machine learning, federated learning and big data platform, then proposes a virtual subsystem architecture of intelligent data platform based on algorithm model, data model, resource model and security model. At the same time, the technical details of resource model scheduling, the definition of security model, the construction principle of virtual subsystem are stated. In addition, this paper also introduces the practical application of the architecture from two aspects: subsystem construction and data processing flow.

关键词： intelligent data platform virtual subsystem distributed machine learning

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Deep learning Based Detection of Toxic Mushrooms in Karnataka 2

Deep Learning Based Detection of Toxic Mushrooms in Karnatak...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Subramani, Sivakannan Imran, F.A. Ttm, Abhishek Karthik, Sanjay M. Yaswanth, J. Department of Advanced Computing St Joseph's University Bangalore560027 India

Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image classification

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Application of Neighborhood Components Analysis to Process and Survey data to Predict Student learning of Statistics 22

Application of Neighborhood Components Analysis to Process a...

引用

22nd IEEE international conference on Advanced learning Technologies (ICALT)

作者： Lu, Yikai Ober, Teresa M. Liu, Cheng Cheng, Ying Univ Notre Dame Dept Psychol Notre Dame IN USA

ISBN: (数字)9781665495196

ISBN: (纸本)9781665495196

machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature extraction by neighborhood components analysis (NCA). Our dataset comprises 287 features from both process data indicators (i.e., derived from log data of an online statistics learning platform) and self-report data from high school students enrolled in Advanced Placement (AP) Statistics (N=733). As a label for prediction, we use students' scores on the AP Statistics exam. We evaluated the performance of machine learning classifiers with a given feature extraction method by evaluation criteria including F1 scores, the area under the receiver operating characteristic curve (AUC), and Cohen's Kappas. We find that NCA effectively reduces the dimensionality of training datasets, stabilizes machine learning predictions, and produces interpretable scores. However, interpreting the NCA weights of features, while feasible, is not very straightforward compared to linear regression. Future research should consider developing guidelines to interpret NCA weights.

关键词： predictive analytics machine learning neighborhood components analysis interpretability Advanced Placement

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Identifying Risk Factors for Depression and Anxiety Among Rural Children in China: A machine learning Approach 1

Identifying Risk Factors for Depression and Anxiety Among Ru...

引用

1st IEEE international conference on data Intelligence and Innovative Application, DIIA 2024

作者： Lin, Yufeng Yang, Shengbin College of Education Guangxi Vocational Normal University Nanning China

ISBN: (纸本)9798331504434

To identify potential risk factors for depression and anxiety among rural Chinese children, this study tested and compared the performance of six machine learning algorithms: Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors(KNN), and Light Gradient Boosting machine (LightGBM), based on a psychological health dataset for rural children in China. The results indicated that Random Forest and LightGBM outperformed the other algorithms regarding predictive accuracy and precision. Specifically, these two algorithms demonstrated a significant advantage over the other four machine learning methods. In further analysis, we evaluated the predictive power of 22 potential factors associated with the risk of depression and anxiety-such as peer relationships, paternal involvement, children's behavioral issues, and parental relationships-using the feature importance of both Random Forest and LightGBM. The results revealed that this combined model effectively distinguished between high-risk and low-risk factors for depression and anxiety among rural Chinese children. This highlights the model's significant application value in the prevention of depression and anxiety in this population. © 2024 IEEE.

关键词： Adversarial machine learning

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