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

共 33936 条记录，以下是1181-1190 订阅

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data-Centric Operational Design Domain Characterization for machine learning-Based Aeronautical Products 1

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42nd international conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Kaakai, Fateh Adibhatla, Sridhar (Shreeder) Pai, Ganesh Escorihuela, Emmanuelle Thales 3 Ave Charles Lindbergh F-94628 Rungis France Rockdale Syst LLC Cincinnati OH 45246 USA NASA KBR Ames Res Ctr Moffett Field CA 94401 USA Airbus Operat SAS 316 Route Bayonne F-31060 Toulouse France

ISBN: (数字)9783031409233

ISBN: (纸本)9783031409226;9783031409233

We give a first rigorous characterization of Operational Design Domains (ODDs) for machine learning (ML)-based aeronautical products. Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, together with a categorization of the data that ML-based applications can encounter in operation, whilst identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: the requirements necessary to drive the design of ML Models (MLMs);the potsince operating outside the flightential effects on MLMs and higher levels of the system hierarchy;the learning assurance processes that may be needed, and system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope.

关键词： Aeronautical products Development assurance machine learning Operational design domain System safety

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2022 international conference on Intelligent Equipment and data Processing, IEDP 2022

2022 International Conference on Intelligent Equipment and D...

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2022 international conference on Intelligent Equipment and data Processing, IEDP 2022

The proceedings contain 22 papers. The topics discussed include: Anderson-darling test for ground radar environment sensing;machine vision-based hook defect detection;fuse welding and tin integration automatic machining device;deep drawing process design and quality optimization of mirror micromotor shell;design and implementation of protocol conversion of intelligent power distribution equipment for new power system;research on modulation recognition of underwater acoustic communication signal based on deep learning;bit-serial logical operation processor based on shift registers and mealy machine;design and optimization of a 4-bit absolute-value detector using half adder and comparator;design and implementation of electrical engineering experiment based on the Arduino platform;continuous-cooling-transformation diagrams for boron-low carbon micro alloy hot rolled enamel steel;research on intrusion identification of hazardous construction areas based on machine vision;a method of airport runway dataset construction for the visual detection algorithm;and design of 3D inspection system of switch station with distributed photovoltaic connected to distribution network.

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Improving Relevance in a Recommendation System to Suggest Charities without Explicit User Profiles Using Dual-Autoencoders 7

Improving Relevance in a Recommendation System to Suggest Ch...

引用

7th international conference on data Science and machine learning Applications (CDMA)

作者： Adames, Pablo Mokhasi, Sourabh Pauchard, Yves Moshirpour, Mohammed Rostoker, Camilo Univ Calgary Calgary AB Canada Benevity Inc Calgary AB Canada

ISBN: (纸本)9781665410144

This work explores the effect of the quality of inferred user profiles on the accuracy of charitable recommendations when using an item-based collaborative filter algorithm. A gap was identified in the literature with respect to the application of charitable recommendation systems in the absence of rich user profiles. This paper introduces an approach to generate relevant recommendations when neither user profiles nor feedback on donation preferences is available. The discovery of user preferences is achieved via the construction of implicit ratings computed from custom feature engineering, while the sparsity of item and user ratings was addressed with a dimension reduction strategy based on dual-autoencoders from a commercial machine learning platform. Our analysis shows the magnitude and sensitivity of the relationship between the relevance of the recommendations and the average number of donations per user. Raw data for this research was provided by a leading online donation platform and contains 24 million anonymous donations to 165 thousand unique causes from over 1.2 million users. We find that the most effective way to increase the relevance of recommendations by a factor of 2 at any top-k value is to train the collaborative filter with users that have at least 50 donations in the data set. As a result, the training set for the collaborative filter is restricted to 8% of the original users, 70% of the companies, 49% of the causes, and 70% of the original countries where users making donations reside.

关键词： Training Dimensionality reduction Sensitivity machine learning algorithms Density measurement Collaboration Filtering algorithms

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Byzantine-Robust learning on Heterogeneous data via Gradient Splitting 40

Byzantine-Robust Learning on Heterogeneous Data via Gradient...

引用

40th international conference on machine learning

作者： Liu, Yuchen Chen, Chen Lyu, Lingjuan Wu, Fangzhao Wu, Sai Chen, Gang Zhejiang Univ Key Lab Intelligent Comp Based Big Data Zhejiang Hangzhou Peoples R China Sony AI Tokyo Japan Microsoft Corp Redmond WA 98052 USA

Federated learning has exhibited vulnerabilities to Byzantine attacks, where the Byzantine attackers can send arbitrary gradients to a central server to destroy the convergence and performance of the global model. A wealth of robust AGgregation Rules (AGRs) have been proposed to defend against Byzantine attacks. However, Byzantine clients can still circumvent robust AGRs when data is non-Identically and Independently Distributed (non-IID). In this paper, we first reveal the root causes of performance degradation of current robust AGRs in non-IID settings: the curse of dimensionality and gradient heterogeneity. In order to address this issue, we propose GAS, a GrAdient Splitting approach that can successfully adapt existing robust AGRs to non-IID settings. We also provide a detailed convergence analysis when the existing robust AGRs are combined with GAS. Experiments on various real-world datasets verify the efficacy of our proposed GAS. The implementation code is provided in https://***/YuchenLiu-a/byzantine-gas.

关键词： machine learning

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Student Dropout Prediction Using machine learning 9

Student Dropout Prediction Using Machine Learning

引用

9th international conference on Business and Industrial Research, ICBIR 2024

作者： Kuntintara, Wichukorn Warabuntaweesuk, Piya Rattapasakorn, Sountaree School of Engineering Bangkok University Bangkok Thailand

ISBN: (纸本)9798350383010

Student dropout is a critical challenge for universities worldwide. To address this concern, Bangkok University implemented additional caring, complemented by a machine learning model developed to predict freshman dropout probability. This research investigates the factors contributing to student dropout, including demographic characteristics, academic performance, and financial status. Utilizing data from the 2021st academic year, we perform a comparative analysis of multiple classifier algorithms to identify the most effective model for dropout prediction. The Extreme Gradient Boosting (XGBoost) algorithm emerges as the top performer based on its precision-recall curve (AUC-PR) score, highlighting the significance of features like RegS2 (second semester registration status) and CumCredit_S1 (median first-semester credits earned). The combined oversampling and undersampling technique are used to address this imbalance and improve the performance of the XGBoost algorithm with fine-tuned parameters. To implementation on 2022nd students, tuned classifier achieve precision up to 84.96% and recall up to 86.84%. It can assist advisers to identify at-risk students from many variables, not only GPA. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Kubeflow Distributed machine learning

Research on Kubeflow Distributed Machine Learning

引用

2022 IET international conference on engineering Technologies and Applications, IET-ICETA 2022

作者： Wong, Wing-Kwong Lin, Wei-Chin Chen, Yu-Bin Nation Yunlin University of Science & Technology Dept. Electronic Engineering Yunlin Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

As the accuracy of machine learning improves, the depth of the model will become deeper and the amount of input data will become larger and larger, and calculation will be too large to complete with one computer. Also, multiple computers are required for gaining more speed. To achieve distributed machine learning, Kubeflow and Kubernetes can be better managed through a graphical interface. This research used a cluster and the Docker virtual containers, With Jupyter Notebook to do distributed training with TensorFlow, this study compare the impact of the difference in the computing speeds of the devices on the overall computing time while maintaining the same accuracy and analyze what speed can be achieved with different devices working together. © 2022 IEEE.

关键词： machine learning

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RBF Neural Networks Model-Free Feedforward Control of Piezoelectric Actuator 9

RBF Neural Networks Model-Free Feedforward Control of Piezoe...

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9th international conference on Automation, Control and Robotics engineering (CACRE)

作者： He, Tao Wang, Yinbing Lin, Zhicheng Guo, Kai Li, Duo Huang, Tao Chongqing Univ Coll Mech & Vehicle Engn Chongqing Peoples R China Shandong Univ Dept Mech Engn Jinan Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

ISBN: (纸本)9798350350319;9798350350302

Piezoelectric actuator is widely utilized in ultraprecision position fields. However, it is challenged by difficulty modeling dynamic properties, such as hysteresis, creep, vibration, etc. Therefore, this paper presents a model-free feedforward control strategy of Radial Basis Functions Neural Network (RBFNN) for piezoelectric actuator. RBFNN is used to estimate the inverse dynamic model of the piezoelectric actuator, and avoiding the difficult modeling and impossible to invert problems. This feedforward controller has model-free advantage that it does not depend on the specific physics-based model and is only data-driven by references and control signals. The parameters of RBFNN are training by offline methods and online learning strategy especially. The offline learning method can compensate the trained trajectory with high precision, but for the untrained trajectory, the compensation accuracy is insufficient. For unknown target trajectories, the online learning strategy is proposed, which utilizes current data for parameter iteration and updating to improve control accuracy. Finally, simulation verify that the proposed model-free feedforward control strategy is effective. Especially the online learning strategy has the adaptive ability and generalization ability for unknown trajectory targets.

关键词： difficulty modeling radial basis function neural network feedforward compensation online learning

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Evaluation of Normalization Algorithms for Breast Mammogram Mass Segmentation 2

Evaluation of Normalization Algorithms for Breast Mammogram ...

引用

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

作者： Urvioza Gohel, Bakul Kumar, Pankaj Gujarat Gandhinagar382007 India University of Petroleum and Energy Studies Dehradun Bidholi248007 India

Medical images acquired at different institutions have different data distributions due to varying scanners, imaging protocols, or patient cohorts. Thus, normalizing samples before using them to train the Deep learning (DL) models is an essential task. Scholars have used various normalization methods to normalize mammograms. However, which normalization method works best on mammograms and if it can help training generalize the DL model has not been analyzed in the literature. This paper presents a comparative analysis of commonly used normalization methods, such as scaling, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Z-score normalization on mammograms for breast mass segmentation tasks. We trained the Attention Unet model on INbreast and the subset of the Cohort of Screen-Aged Women dataset (CSAW-S). We performed cross-dataset analysis on trained models to investigate how normalization methods helped train robust models to handle diverse datasets. Our results suggest that the normalizing method of input data affects the segmentation model's performance. We achieved the best scores with the Z-score normalization closely followed by CLAHE. However, further investigation suggests that the normalization method's performance may depend on the variability of mammograms in the dataset. Results of cross-dataset analysis suggest the importance of choosing the normalization method for consistent DL model performance. To our knowledge, this study represents the first systematic analysis of the performance of prevalent normalization techniques on a diverse dataset of mammogram images. The insights gained from our experiments can guide the selection of normalization methods for improved mass segmentation accuracy. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Quantum Clustering for Cybersecurity 5

Quantum Clustering for Cybersecurity

引用

2024 international conference on Quantum Computing and engineering

作者： El Maouaki, Walid Innan, Nouhaila Marchisio, Alberto Said, Taoufik Bennai, Mohamed Shafique, Muhammad Hassan II Univ Casablanca Fac Sci Ben Msick Quantum Phys & Magnetism Team LPMC Casablanca Morocco New York Univ Abu Dhabi NYUAD Div Engn eBRAIN Lab Abu Dhabi U Arab Emirates NYUAD NYUAD Res Inst Ctr Quantum & Topol Syst CQTS Abu Dhabi U Arab Emirates

ISBN: (纸本)9798331541378

In this study, we develop a novel quantum machine learning (QML) framework to analyze cybersecurity vulnerabilities using data from the 2022 CISA Known Exploited Vulnerabilities catalog, which includes detailed information on vulnerability types, severity levels, common vulnerability scoring system (CVSS) scores, and product specifics. Our framework preprocesses this data into a quantum-compatible format, enabling clustering analysis through our advanced quantrun techniques, QCSWAPK-means and QkerneIK-means. These quantum algorithms demonstrate superior performance compared to state-of-the-art classical clustering techniques like k-means and spectral clustering, achieving Silhouette scores of 0.491, Davies-Bouldin indices below 0.745, and Calinski-Harahasz scores exceeding 884, indicating more distinct and well-separated clusters. Our framework categorizes vulnerabilities into distinct groups, reflecting varying levels of risk severity: Cluster 0, primarily consisting of critical Microsoft-related vulnerabilities;Cluster 1, featuring medium severity vulnerabilities from various enterprise software vendors and network solutions;Cluster 2, with high severity vulnerabilities from Adobe, Cisco, and Google;and Cluster 3, encompassing vulnerabilities from Microsoft and Oracle with high to medium severity. These findings highlight the potential of QML to enhance the precision of vulnerability assessments and prioritization, advancing cybersecurity practices by enabling more strategic and proactive defense mechanisms.

关键词： Quantum machine learning Quantum Clustering Cybersecurity

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A Novel machine learning Approach of Multi-omics data Prediction

A Novel Machine Learning Approach of Multi-omics Data Predic...

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2022 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Rao, B. Srinivasa Lavanya, S. Kajendran, K. Sharma, Prince Prashant Verma, Devvret Radhakrishnan, P. Manikandan, G. Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Karpagam College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering TamilNadu Chennai India GurukulaKangri Deemed to Be University Department of Pharmaceutical Sciences Uttarakhand Haridwar India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun248002 India Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai Thandalam602105 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

An integrated (combined) approach is needed in today's biomedical research in order to effectively use this data and obtain insights into natural systems. Genome, proteome, and metaproteomic data may be used to comprehend the complexities of molecular genetics utilising "machine learning"performance tracking methods derived from diverse omics sources. New biomarkers may be discovered by merging and analysing omics data using machine learning techniques. These biomarkers may aid in the proper identification of illness, the separation of patients, and the provision of tailored therapy. This study looks at a variety of "integrative machine learning or ML techniques"that are being used to obtain a better understanding of biological systems during natural bodily performance as well as when systems are *** data collection method has been used for this paper to gather relevant and factual data related to ML techniques of multi omics data prediction. © 2022 IEEE.

关键词： Biomarkers

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