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Gestational weight gain prediction using privacy preserving federated learning 43

Gestational weight gain prediction using privacy preserving ...

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43rd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Puri, Chetanya Dolui, Koustabh Kooijman, Gerben Masculo, Felipe Van Sambeek, Shannon Den Boer, Sebastiaan Michiels, Sam Hallez, Hans Luca, Stijn Vanrumste, Bart Katholieke Univ Leuven Dept Elect Engn ESAT eMedia Lab Leuven Belgium Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS Leuven Belgium Katholieke Univ Leuven imec DistriNet Leuven Belgium Philips Res Eindhoven Netherlands Univ Ghent Dept Data Anal & Math Modelling Ghent Belgium

ISBN: (纸本)9781728111797

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data. Federated learning can help mitigate this problem by sending local copies of trained models instead of raw data and aggregate them at the central server. In this paper, we present a privacy preserving federated learning approach where the participating users collaboratively learn and update the global model. Furthermore, we show that this model updation can be done incrementally without having the need to store the local updates eternally. Our proposed model achieves a mean absolute error of 4.455 kgs whilst preserving privacy against 2.572 kgs achieved in a centralised approach utilising individual training data until day 140. Clinical relevance- Privacy preserving training of machine learning algorithm for early gestational weight gain prediction with minor tradeoff to performance.

关键词： Training Pregnancy Privacy data privacy Machine learning algorithms Training data Predictive models

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Survey on data Mining Techniques, Process and algorithms

Survey on Data Mining Techniques, Process and Algorithms

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2020 international Virtual conference on Bridging Innovative Trends in Mathematics, Engineering and Technology, BITMET 2020

作者： Vijayalakshmi, S. Nivethithaa, K.K. Department of Computer Science Sri Ramakrishna College of Arts and Science for Women Tamil Nadu Coimbatore India

The term "data Mining"is refers to the extraction of patterns and knowledge from large amounts of raw data and often defined as finding hidden information in a database. It insinuate analyzing data patterns in large volume of data using one or more software. data mining involves effective data collection and warehousing as well as computer processing. © Published under licence by IOP Publishing Ltd.

关键词： data mining

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SPCPFS: a pseudo-label filtering strategy with fusion of perplexity and confidence 5

SPCPFS: a pseudo-label filtering strategy with fusion of per...

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5th international conference on Machine Learning and Natural Language processing (MLNLP)

作者： Liu, Zhiqiang Ma, Zhiqiang Wang, Jiatai Li, Jingyi Sun, Jiaqi Bao, Caijilahu Inner Mongolia Univ Technol Coll Data Sci & Applicat Hohhot Peoples R China

ISBN: (纸本)9781450399067

In the pseudo-label filtering for semi-supervised Mongolian speech recognition, the correctness of word combinations and the correctness of speech and word correspondences in the self-training set cannot be guaranteed simultaneously. To solve this problem, we propose a pseudo-label filtering strategy with fusion of perplexity and confidence, which is called sentence perplexity confidence. The strategy simultaneously evaluates the semantic relations of pseudo-labels and the correspondence between pseudo-labels and acoustic features of unlabeled speech, which improves the accuracy of the self-training set and thus the performance of the target speech recognition model output by semi-supervised training. We conducted ablation experiments and comparison experiments of sentence perplexity confidence on Mongolian datasets IMUT-MC and IMUT-MC-SMI. The experimental results show that the sentence perplexity confidence is ahead of the sentence-level confidence and perplexity in terms of accuracy improvement ability of the self-training set, and the output target speech recognition models reach 14.7% and 16.1% for WER and SER respectively.

关键词： Semi-supervised training Pseudo-label filtering Confidence Perplexity Mongolian speech recognition

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Recent Gas/Odor Sensor Array Signal processing Trend for Model Nose: A Mini Review

Recent Gas/Odor Sensor Array Signal Processing Trend for Mod...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Shiv Nath Chaudhri Department of Computer Science and Design Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The study of merging artificial intelligence and the processing of gas and odor sensors' data is known as artificial olfaction or model nose. Gas/odor sensor data is formed by sensor response modalities that produce signature patterns that represent a particular response of a gas/odor sensor. It can be difficult to analyze the particular objective by using drift-affected gas and odor sensor data and trying to gain hidden insight from the model nose. The classification and quantification of gas and odor sensor signals is significantly challenging in model nose tasks and plays a vital role in careful sensor signal processing. The application of data-driven tactics for utilizing the outcomes of the model nose for higher performance is quite challenging. The gas/odor sensor signal processing aims to provide salient features as per the application area, in addition to achieving high accuracy. The recent sensor signal processing methods for the model nose were reviewed. The dataset, response modalities employed, trade-offs associated with each technique, and classification and quantification techniques were all covered in the article. At the end, it is demonstrated that there is a significant challenge and an open research area regarding the improvement of sensor signal processing techniques, such as raising accuracy and sensitivity values and being practical in using model noses efficiently.

关键词： Accuracy Nose Signal processing Sensor phenomena and characterization data models Sensor systems Sensors Intelligent sensors Gas detectors Sensor arrays

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A Hyperspectral Band Selection Network Combining Siamese Network and Local-Global Attention 13

A Hyperspectral Band Selection Network Combining Siamese Net...

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13th international conference on Information Technology in Medicine and Education, ITME 2023

作者： An, Yuan Chen, Guangjian Huang, Dehua Zheng, Huiqin Wang, Qin Jian, Cairen Weng, Qian College of Computer and Data Science Fuzhou University Fuzhou China Fujian Geologic Surveying and Mapping Institute Fuzhou China Tan Kah Kee College Xiamen University Zhangzhou China

ISBN: (纸本)9798350319156

Hyperspectral images can provide dozens to hundreds of continuous spectral bands, greatly enhancing the richness of information. However, the redundancy among adjacent bands leads to increased data processing complexity. Despite the recent introduction of numerous band selection methods, there has been limited focus on incorporating context information from the entire spectral range into this task. Furthermore, researchers have primarily concentrated on band informativeness and sparse representation for reconstructing all bands, often overlooking the separability of classes in downstream tasks. To address these challenges, we propose a hyperspectral band selection network combining Siamese Network Local-Global Attention (SLGA). This approach first segments the hyperspectral image into homogeneous regions and constructs sample pairs based on a random elimination strategy. Next, it utilizes the Local-Global Attention (L-GA) mechanism to obtain band weights that capture both local and global spectral structures. These reweighted bands are then fed into a twin network to obtain their high-dimensional representations, compute loss values, and update network parameters. Finally, extensive classification experiments using SVM, KNN, and LDA classifiers are conducted on the Indian Pines and Botswana hyperspectral image datasets. The results from these experiments on benchmark datasets demonstrate that the proposed SLGA method performs exceptionally well, outperforming state-of-the-art algorithms. © 2023 IEEE.

关键词： Classification (of information)

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th international conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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Cleaning Method of Equipment Fault database Based on ISOMAP Equidistant Mapping Clustering

Cleaning Method of Equipment Fault Database Based on ISOMAP ...

引用

9th international conference on Condition Monitoring and Diagnosis (CMD)

作者： Zhou Jiayu Hou Huijuan Sheng Gehao Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (数字)9784886864314

ISBN: (纸本)9784886864314

With the development of artificial intelligence algorithms, monitoring data of power equipment is widely used in early fault diagnosis of equipment. Since the original monitoring data may contain some abnormal "dirty" data, which will seriously affect the accuracy of intelligent diagnosis of equipment faults, it is necessary to clean the equipment fault database. In this paper, the linear model and the proximity model are combined, and a data cleaning method based on ISOMAP(isometric feature mapping) equidistant map clustering is constructed. Firstly, the ISOMAP analysis method is used to extract the features of the original data, and on this basis, the DBSCAN(density-based spatial clustering of applications with noise) density clustering analysis is carried out. This method fully considers the nonlinear relationship and clustering characteristics between the characteristic quantities of power equipment, removes outliers, and improves data quality. This paper takes the cleaning of the fault database of the dissolved gas concentration in the oil of the transformer as an example. The results show that the fault diagnosis learning network trained by the cleaned database can improve the diagnosis accuracy by 17%. Compared with other data cleaning algorithm models, the classification and recognition rate of the method proposed in this paper is improved by 5%.

关键词： troubleshooting online monitoring dimensionality reduction clustering data cleaning

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Lossless Compression Scheme for Efficient GNSS data Transmission on IoT Devices

Lossless Compression Scheme for Efficient GNSS Data Transmis...

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IEEE international conference on Electrical, Computer, and Energy Technologies (ICECET)

作者： Perez, Rafael Leithardt, Valderi R. Q. Correia, Sergio D. Polytech Inst Portalegre Technol Dept Portalegre Portugal Polytech Inst Portalegre Valoriza Portalegre Portugal ULHT COPELABS Lisbon Portugal

ISBN: (纸本)9781665442312

Wireless data transmission is one of the most energy-consuming tasks performed on embedded devices, being a crucial feature of battery-powered Internet of Things (IoT) applications. The present work proposes a new methodology to reduce the energy footprint of GNSS-based sensors by decreasing the amount of transmitted data applying a lossless compression strategy. Online trajectory data is structured through a preprocessing stage without information loss and posteriorly compressed through standard lossless algorithms. Simulations are performed considering different trajectory shapes, comparing the proposed schema with traditional compression methods without the proposed pre-processing stage. The results show that the proposed scheme can reach lower compression rates, reducing embedded IoT devices' energy footprint.

关键词： data Transmission Global Navigation Satellite System Internet of Things Lossless Compression Wireless Sensors Networks

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Machine Learning algorithms for Mouse LFP data Classification in Epilepsy

Machine Learning Algorithms for Mouse LFP Data Classificatio...

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16th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Golfidis, Antonis Vinos, Michael Vassilopoulos, Nikos Papadaki, Eirini Skaliora, Irini Cutsuridis, Vassilis Department of Biology National and Kapodistrian University of Athens Athens Greece Department of History and Philosophy of Science National and Kapodistrian University of Athens Athens Greece Center for Basic Research Biomedical Research Foundation of the Academy of Athens Athens Greece School of Computer Science University of Lincoln Lincoln United Kingdom

Successful preictal, interictal and ictal activity discrimination is extremely important for accurate seizure detection and prediction in epileptology. Here, we introduce an algorithmic pipeline applied to local field potentials (LFPs) recorded from layers II/III of the primary somatosensory cortex of young mice for the classification of endogenous (preictal), interictal, and seizure-like (ictal) activity events using time series analysis and machine learning (ML) models. Using the HCTSA time series analysis toolbox, over 4000 features were extracted from the LFPs after applying over 7700 operations. Iterative application of correlation analysis and random-forest-recursive-feature-elimination with cross validation method reduced the dimensionality of the feature space to 22 features and 27 features, in endogenous-to-interictal events discrimination, and interictal-to-ictal events discrimination, respectively. Application of nine ML algorithms on these reduced feature sets showed preictal activity can be discriminated from interictal activity by a radial basis function SVM with a 0.9914 Cohen kappa score with just 22 features, whereas interictal and seizurelike (ictal) activities can be discriminated by the same classifier with a 0.9565 Cohen kappa score with just 27 features. Our preliminary results show that ML application in cortical LFP recordings may be a promising research avenue for accurate seizure detection and prediction in focal epilepsy. © 2023 by SCITEPRESS Science and Technology Publications, Lda.

关键词： Radial basis function networks

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2nd international conference on Machine Learning and Information processing, ICMLIP 2020

2nd International Conference on Machine Learning and Informa...

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2nd international conference on Machine Learning and Information processing, ICMLIP 2020

ISBN: (纸本)9789813348585

The proceedings contain 55 papers. The special focus in this conference is on Machine Learning and Information processing. The topics include: Performance Analysis of Different models for Twitter Sentiment;electricity Forecasting Using Machine Learning: A Review;end to End Learning Human Pose Detection Using Convolutional Neural Networks;conference Paper Acceptance Prediction: Using Machine Learning;Object Identification and Tracking Using YOLO Model: A CNN-Based Approach;real-Time Hands-Free Mouse Control for Disabled;accounting Fraud Detection Using K-Means Clustering Technique;transforming the Lives of Socially Dependent to Self-dependent Using IoT;enforcement an Evidence and Quality of Query Services in the Cost-Effective Cloud;Prediction and Classification of Biased and Fake News Using NLP and Machine Learning models;a Machine Learning Approach Towards Increased Crop Yield in Agriculture;resume Screening Using Natural Language processing and Machine Learning: A Systematic Review;assessment of Osteogenic Sarcoma with Histology Images Using Deep Learning;SMDSB: Efficient Off-Chain Storage Model for data Sharing in Blockchain Environment;ray Tracing Algorithm for Scene Generation in Simulation of Photonic Mixer Device Sensors;a Study of Quality Metrics in Agile Software Development;Optimization of Ray-Tracing Algorithm for Simulation of PMD Sensors;real-Time Object Detection for Visually Challenged;reinforcement Learning: A Survey;smart Leaf Disease Detection Using Image processing;data Encryption on Cloud database Using Quantum Computing for Key Distribution;prediction and Prevention of Addiction to Social Media Using Machine Learning;analysis of Block Matching algorithms for Motion Estimation in Video data;information Retrieval Based on Telugu Cross-Language Transliteration;Success of H1-B VISA Using ANN.

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