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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是81-90 订阅

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Enhancing Customer Segmentation and Behavior Prediction Through machine learning and Medium Gaussian SVM 4th

Enhancing Customer Segmentation and Behavior Prediction Thro...

引用

4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Pandyan, Uma Maheswari Usha, Gandhimathi Alias S. Manoj, S., V Chandru, M. Abinandan, S. Velammal Coll Engn & Technol Dept ECE Madurai 625009 Tamil Nadu India

ISBN: (纸本)9783031837951;9783031837968

Customer segmentation and prediction, driven by machine learning algorithms, plays a vital role in today's fast-paced and data-driven market. This paper focuses on applying machine learning techniques, specifically the k-means clustering algorithm and Medium Gaussian Support Vector machine(SVM) regression learning, for customer segmentation and prediction. The process starts with data col-lection and cleaning to ensure the reliability of the data clarity gained. The k-means clustering algorithm plays a central role in identifying patterns within customer data, revealing natural groupings that may not be apparent through traditional analysis methods. Using machine learning algorithms, with a focus on k-means clustering, the segmentation process is refined, allowing for the discovery of complex patterns in customer behavior. This, in turn, enables the creation of personalized interactions through predefined customer segments. Furthermore, the segmented customer information is analyzed using different regression techniques to predict segmented customers better. Experimental results show that the proposed approach achieves the lowest mean average error of 15.723 with a model size of 12KB. The advantages of this approach are substantial, including the development of tailored marketing strategies, personalized recommendations, and the optimization of resource allocation. This understanding allows companies to create customized approaches for each group, leading to happier customers, more people recommending their services or products, and a better overall image for the brand.

关键词： Customer segmentation machine learning Medium Gaussian SVM

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Joint Graph Augmentation and Adaptive Synthetic Sampling for Imbalanced Node Classification 13th

Joint Graph Augmentation and Adaptive Synthetic Sampling for...

引用

13th international conference on Natural Language Processing and Chinese computing

作者： Lu, Guangquan Chen, Wanxin Han, Yadan Tang, Jiamin Huang, Faliang Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Nanning Normal Univ Guangxi Key Lab Human Machine Interact & Intellig Nanning Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Graph representation learning is a very important part of the field of machine learning. It focuses on learning and extracting representations of nodes and edges in graph data to better analyze features and relationships within the graph structure. Graph neural networks (GNNs) are widely used to deal with node classification problems and has achieved state-of-the-art performance. However, most of the existing methods are performed under the condition that the number of node classes is balanced. In fact, in real-life scenarios, the number of samples of some classes will be significantly less than that of other classes. This leads to insufficient information representation of the minority class graph, thereby affecting the classification performance of GNNs. This paper proposes a new framework GraphAdasyn for imbalanced node classification to address this problem. The model inputs a hybrid view to utilize global and local topology information, expends an over-sampling method that is generalized to multiple-class imbalanced problems in the graph. This method enables adaptively synthesize the number of minority class samples based on the density distribution and is flexibly applicable to various datasets. Through numerous experiments results indicate that proposed framework demonstrates superior performance compared to all baseline methods in imbalanced node classification tasks.

关键词： Graph representation learning Imbalanced node classification Graph neural network

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Research on Multi-Core Cache data Prefetching Strategy based on Deep learning 24

Research on Multi-Core Cache Data Prefetching Strategy based...

引用

7th international conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Qiao, Xinyu Zhang, Jianxun Lin, Binghui College of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798400717437

Currently, the research prospect of deep learning applied to data prefetching in multi-core architectures are very broad. In order to give full play to the performance of parallel computing in multi-core processor systems, based on an in-depth discussion of the research of multi-core cache prefetching neural network model based on deep learning, a new deep learning model is constructed by introducing Convolutional Neural Network (Text CNN) and CBAM (Convolutional Block Attention Module) attention layer and utilizing Long Short-Term Memory (LSTM) parallel computing. The model is able to operate efficiently in multi-core architectures and effectively predict data access patterns to improve the accuracy and efficiency of data prefetching, thus optimizing the performance of the overall system. In order to evaluate the performance improvement of the improved model in cache prefetching in a multi-core processor system, multiple sets of experiments were conducted for comparative analysis. Experimental results compared with Voyager, the improved model improves the prefetching page and offset accuracies by 5.5% and 5.3%, respectively, and the overall accuracy is improved by 5.8%, the loss rate is reduced by 1.5%, the coverage rate is improved by 8.4%, and the F1 score value is improved by 0.07, which indicates that the improved model achieves a significant enhancement in cache prefetching effectiveness. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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MuSS: Multimodal Satellite Service for Unsupervised Land-Cover Classification 22nd

MuSS: Multimodal Satellite Service for Unsupervised Land-Cov...

引用

22nd international conference on Service Oriented computing

作者： Gacha, Yassine Besbes, Olfa Abdellatif, Takoua Univ Carthage SERCOM Lab Carthage 1054 Tunisia Mil Res Ctr Aouina 2045 Tunisia Sousse Univ SITCOM Sousse 4002 Tunisia Sousse Univ ENISO Sousse 4002 Tunisia

ISBN: (纸本)9789819608072;9789819608089

Remote sensing data encompasses multiple modalities, including multispectral images, Synthetic Aperture Radar (SAR), Digital Elevation Model (DEM), and more. Each type of data possesses unique properties and characteristics. Preparing a dataset for machine learning that includes these modalities is challenging and requires specific geospatial data expertise. In this paper, we introduce a novel service, MuSS, for multimodal remote sensing data processing and analysis. MuSS dynamically prepares datasets based on user specifications, leveraging Google Earth Engine (GEE) to seamlessly access and process diverse satellite data. Using the MuSS-prepared multimodal satellite dataset, we propose an unsupervised deep learning model for pixel-level land-cover classification. Unlike traditional methods that require labeled data, which is difficult to gather in remote sensing, our model utilizes derived indicators to accurately distinguish water and other surfaces without labeled training data. Our service is particularly beneficial for agriculture and assessing climate change impacts, yet its configurable ETL pipeline ensures broad applicability. Experimental results demonstrate that our approach significantly outperforms traditional baseline methods, highlighting the service's efficacy and versatility.

关键词： Multimodal satellite service Configurable ETL pipeline Unsupervised Deep learning framework Land-cover classification

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A Novel Approach to Handle Imbalanced Code-Mixed Languages Using SMOTE-ENN During Stratified Cross Validation 4th

A Novel Approach to Handle Imbalanced Code-Mixed Languages U...

引用

4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Ojha, Uma Yadav, Ajay Kumar Banasthali Vidyapith Dept Comp Sci Vanasthali 30422 Rajasthan India

ISBN: (纸本)9783031837920;9783031837937

Code-mixed text is often used in multi-lingual countries, where people mix more than one language in a single statement to express their thoughts freely on online media platforms. However, the existing code-mixed datasets that are available are small in size and highly skewed towards the non-sarcastic class. The imbalanced data leads to poor predictive performance such as low F1 score when they are trained on machine-learning models. Thus, in this research, we make an attempt to correctly handle imbalanced Hindi-English (Hi-Eng) code-mixed data to detect sarcasm. The proposed approach uses hybrid oversampling technique (SMOTE-ENN) during 10 fold stratified cross validation. We experimented and performed comparative analysis on our proposed approach with five machine-learning models say Multinomial Naive Bayes, Support Vector machine, Logistic Regression, Random Forest Tree and Extreme Gradient Boosting. The F1-score of the proposed approach was compared with the baseline approach. We observed that our proposed approach shows new insight into handling the imbalanced data.

关键词： machine learning Imbalanced Code-Mixed Hindi-English SMOTE-ENN SMOTE-TOMEK oversampling techniques stratified- k- fold cross validation TF-IDF XGBOOST

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Sub-Batch Update Mechanism for Component-Wise Natural Gradient Descent 24

Sub-Batch Update Mechanism for Component-Wise Natural Gradie...

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8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Van Tran, Sang Nakata, Toshiyuki Yamaguchi, Rie Shigetomi Mhd, Irvan Yoshimoto, Yoshihide Graduate School of Information Science and Technology The University of Tokyo Bunkyo Tokyo Japan

ISBN: (纸本)9798400718014

Neural networks have become a cornerstone of modern machine learning, powering advances in numerous domains. In distributed computing environments, leveraging large batch sizes is crucial for the efficient training of these networks. However, this approach often leads to reduced data stochasticity when sampling information such as the loss function gradient, adversely affecting the generalization performance of the models. We tackle this issue by introducing the Sub-Batch update mechanism, which divides the large batch into sub-batches and updates the network parameters using each sub-batch. The Sub-Batch update mechanism increases the number of nonlinear operations of calculating the neural network parameter update vector from the sampled network information in Component-Wise Natural Gradient Descent (CW-NGD) neural network training algorithm. This increases the data stochasticity, resulting in accuracy improvement in the trained neural network. Experimental results show that with Sub-Batch update mechanism applied CW-NGD produces trained networks with higher accuracy compared to Adam, SGD, across all six datasets tested, including the ImageNet dastaset. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615247

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for Parallel Processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with Parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for data-Dependent Memory Accesses;A Neural Network-Based PUF Protection Method Against machine learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated learning;dynamic Privacy Protection with Large Language Model in Social Networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a data Watermark Scheme Base on data Converted Bitmap for data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc Networks;mitigating Over-Unlearning in machine Unlearning with Synthetic data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness data Poisoning Attacks in Federated learning.

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615278