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Covid-19 Diagnosis via Voice Using Online Sequential Extreme learning machine

Covid-19 Diagnosis via Voice Using Online Sequential Extreme...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Xiong, Ling Liu, Junxiu Su, Min Zhang, Shunsheng Wu, Guopei Shu, Haiping Jiang, Bingxiong Guangxi Normal Univ Sch Elect Engn Guilin Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

With the worldwide spreading of Coronavirus disease 2019 (Covid-19) pandemic, besides the traditional diagnosing approach, Artificial Intelligence provides additional support for the pre-diagnosis of Covid-19 by using data such as patients' images, and sounds, etc. Being able to recognize Covid-19 positive patients quickly and correctly is the key to preventing the expansion of the disease. However, the existing Covid-19 diagnosis models still face challenges due to the complex network structure and additional medical examination. It takes much time to return a diagnosis result. In this paper, a diagnostic model is proposed as an early work for Covid-19 diagnosis using sound samples. The features of sound signals are expressed by Mel Frequency Cepstral Coefficients, which are input into the Online Sequential Extreme learning machine for normal/abnormal detection. data from an open-source database were used to train the proposed model, the experiments show that using vowel pronunciations the model can achieve an accuracy of 96.4% on average, with about 10 times faster for testing than the Support Vector machine.

关键词： Covid-19 Diagnosis Mel Frequency Cepstral Coefficient Online Sequential Extreme learning machine Coswara

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Graph Neural Network-Based Representation learning for Medical Time Series 1

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32nd international conference on Artificial Neural Networks (ICANN)

作者： Zheng, Zhuzi Guo, Changchun Chen, Jianyong Li, Jianqiang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518000 Peoples R China Shenzhen Pingshan Peoples Hosp Shenzhen 518000 Peoples R China

ISBN: (数字)9783031442230

ISBN: (纸本)9783031442223;9783031442230

The ability to analyze and predict medical time series data is crucial for enhancing healthcare decision-making and improving patient outcomes. Currently, the algorithms used for classification and prediction of medical time series data are limited in their capabilities and may not be reliable enough to meet the demands of practical applications. The purpose of this paper is to promote the representation learning of complex data primarily comprised of medical time series, in order to facilitate various downstream tasks. Under the framework of graph neural networks (GNN), we present indegree regularized neural message passing to reflect the dependencies between different sequences. Our approach also leverages representation learning to convert multivariate time series (MTS) and static features into nodes of GNN. Moreover, we propose a dynamic loss function to encourage the consistent learning of sensor dependency graphs across models. Based on these proposals, our method can effectively capture not only the temporal dependencies among variables, but also the multidimensional dependencies among MTS and static features. We classify time series on two medical challenge and a human activity datasets. The results show that our approach can significantly improve downstream task performance across various metrics. Code is available at https://***/Zzzoptimus/GICG.

关键词： Graph neural network Medical time series Regularized neural message passing Time series classification

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MetAug: Contrastive learning via Meta Feature Augmentation 39

MetAug: Contrastive Learning via Meta Feature Augmentation

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39th international conference on machine learning (ICML)

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Xiong, Hui Chinese Acad Sci Inst Software Sci & Technol Integrated Informat Syst Lab Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzho Guangzhou Guangdong Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Thrust Artificial Intelligence Guangzhou Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci Engn Hong Kong Peoples R China

What matters for contrastive learning? We argue that contrastive learning heavily relies on informative features, or "hard" (positive or negative) features. Early works include more informative features by applying complex data augmentations and large batch size or memory bank, and recent works design elaborate sampling approaches to explore informative features. The key challenge toward exploring such features is that the source multi-view data is generated by applying random data augmentations, making it infeasible to always add useful information in the augmented data. Consequently, the informativeness of features learned from such augmented data is limited. In response, we propose to directly augment the features in latent space, thereby learning discriminative representations without a large amount of input data. We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder. However, insufficient input data may lead the encoder to learn collapsed features and therefore malfunction the augmentation generator. A new margin-injected regularization is further added in the objective function to avoid the encoder learning a degenerate mapping. To contrast all features in one gradient back-propagation step, we adopt the proposed optimization-driven unified contrastive loss instead of the conventional contrastive loss. Empirically, our method achieves state-of-the-art results on several benchmark datasets.

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Bipartite Graph-based Discriminative Feature learning for Multi-View Clustering 22

Bipartite Graph-based Discriminative Feature Learning for Mu...

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30th ACM international conference on Multimedia (MM)

作者： Yan, Weiqing Xu, Jindong Liu, Jinglei Yue, Guanghui Tang, Chang Yantai Univ Sch Comp & Control Engn Yantai Peoples R China Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Shenzhen Peoples R China China Univ Geosci Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781450392037

Multi-view clustering is an important technique in machine learning research. Existing methods have improved in clustering performance, most of them learn graph structure depending on all samples, which are high complexity. Bipartite graph-based multi-view clustering can obtain clustering result by establishing the relationship between the sample points and small anchor points, which improve the efficiency of clustering. Most bipartite graph-based clustering methods only focus on topological graph structure learning depending on sample nodes, ignore the influence of node features. In this paper, we propose bipartite graph-based discriminative feature learning for multi-view clustering, which combines bipartite graph learning and discriminative feature learning to a unified framework. Specifically, the bipartite graph learning is proposed via multi-view subspace representation with manifold regularization terms. Meanwhile, our feature learning utilizes data pseudo-labels obtained by fused bipartite graph to seek projection direction, which make the same label be closer and make data points with different labels be far away from each other. At last, the proposed manifold regularization terms establish the relationship between constructed bipartite graph and new data representation. By leveraging the interactions between structure learning and discriminative feature learning, we are able to select more informative features and capture more accurate structure of data for clustering. Extensive experimental results on different scale datasets demonstrate our method achieves better or comparable clustering performance than the results of state-of-the-art methods.

关键词： Multi-view clustering Subspace Clustering Feature Selection

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4th international conference on machine learning, Optimization, and data science, LOD 2018

4th International Conference on Machine Learning, Optimizati...

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4th international conference on machine learning, Optimization, and data science, LOD 2018

ISBN: (纸本)9783030137083

The proceedings contain 46 papers. The special focus in this conference is on machine learning, Optimization, and data science. The topics include: Adaptive dimensionality reduction in multiobjective optimization with multiextremal criteria;REFINE: Representation learning from diffusion events;augmented design-space exploration by nonlinear dimensionality reduction methods;classification and survival prediction in diffuse large B-cell lymphoma by gene expression profiling;learning consistent tree-augmented dynamic bayesian networks;designing ships using constrained multi-objective efficient global optimization;a new approach to measuring distances in dense graphs;ant colony optimization for markov blanket-based feature selection. Application for precision medicine;Average performance analysis of the stochastic gradient method for online PCA;simple learning with a teacher via biased regularized least squares;Improving traditional dual ascent algorithm for the uncapacitated multiple allocation hub location problem: A RAMP approach;supervised learning approach for surface-mount device production;crawling in Rogue’s dungeons with (partitioned) A3C;decision of neural networks hyperparameters with a population-based algorithm;strong duality of the kantorovich-rubinstein mass transshipment problem in metric spaces;evolutionary construction of convolutional neural networks;improving clinical subjects clustering by learning and optimizing feature weights;a framework to automatically extract funding information from text;Speeding up budgeted stochastic gradient descent SVM training with precomputed golden section search;an unsupervised learning classifier with competitive error performance;feature based multivariate data imputation;A GRASP/VND heuristic for the max cut-clique problem;a modelica-based simulation method for black-box optimal control problems with level-set dynamic programming;a clonal selection algorithm for multiobjective energy reduction multi-depot vehicle r

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Comparison of machine learning Algorithms for Sequential dataset Prediction 5th

Comparison of Machine Learning Algorithms for Sequential Dat...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Ma, Zhuang Shen, Tao Sun, Zhichao Xu, Kaining Guo, Xingsheng School of EE University of Jinan Jinan250022 China Inspur Electronic Information Industry Co. Ltd. Langchao Rd. 1036 Jinan250101 China Jinan Lingsheng Info Tech. Co. Ltd. Huaiyin District Jinan250000 China

ISBN: (纸本)9783031505799

Accurate traffic flow prediction can provide basis for traffic control and travel planning. Accurate prediction is very important to the control and management of traffic flow in large cities. However, on the one hand, the traffic flow data information has the complicated interior space design relevance of discrete systems, that is, different kinds of connection relevance between different pavement nodes. On the other hand, it has dynamic duration correlation, that is, the spatial correlation of road nodes will change with time. At the same time, traffic flow data information generally shows a certain periodicity, but there are also some anomalies and specificity. According to XGBoost algorithm of artificial intelligence algorithm, LSTM optimization calculation method is created and compared. Based on the practical exploration of data information, it can be concluded that LSTM digital model can clearly predict traffic flow, and LSTM is better than traditional equipment learning model. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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ADASYN: Oversampling Techniques for COVID-19 Prediction

ADASYN: Oversampling Techniques for COVID-19 Prediction

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mary, L. William Raj, S. Albert Antony SRM Institute of Science and Technology Department of Computer Applications Kattankulathur Chennai India

ISBN: (纸本)9798331512965

In 2019, the highly contagious COVID-19 virus emerged and rapidly spread, causing a global health crisis characterized by inflammatory respiratory symptoms. The primary target of this disease is the respiratory system. Research has identified bats as the reservoir for the virus, but the intermediate host remains unknown. There is ongoing concern about various coronavirus variants, with the Omicron variant previously dominating globally and now attention shifting to a new variant, BA.2.86, informally known as Pirola, which is being closely monitored worldwide. However, the inherent class imbalance in COVID-19 datasets poses a significant obstacle to the effectiveness of these models. This paper proposes a novel approach combining outlier detection and Adaptive Synthetic Minority Oversampling Technique (ADASYNA) to address this issue. The Outlier-ADASYNA method identifies outliers within the minority class (COVID-19 cases) and synthesizes informative samples to rebalance the dataset. The proposed approach is evaluated using data from 5,400 patient records for both training and testing. The study employs ADASYN to effectively mitigate dataset imbalances and outstanding traditional oversampling techniques. This paper presents a comparative analysis of machine learning techniques, evaluating their performance using diverse metrics. The classification algorithm Random Forest attains the highest accuracy percentage of 90%, and RF Oversampling 92% surpassing numerous classification algorithms in predictive performance. © 2025 IEEE.

关键词： COVID-19

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Traffic Flow Forecasting Using Attention Enabled Bi-LSTM and GRU Hybrid Model 29th

Traffic Flow Forecasting Using Attention Enabled Bi-LSTM and...

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29th international conference on Neural Information Processing

作者： Chauhan, Nisha Singh Kumar, Neetesh Indian Inst Technol Roorkee Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)9789819916474;9789819916481

In the past few years, machine learning (ML) techniques have been seen to provide a range of Intelligent Transportation Systems (ITS) related solutions. Avoiding traffic jams is one of the most challenging problems to solve globally. In this paper, the use of ML techniques to forecast traffic flow to avoid congestion and redirect vehicles to reduce travel time, waiting time, pollution, and fuel consumption are explored. Deep hybrid models, in particular, have become effective ways to predict traffic flow. The temporal and periodic aspects of the traffic data cannot be effectively captured by the layered architecture used by the majority of hybrid models. Therefore, this work proposes a hybrid deep learning model of two distinct modules to extract the temporal and periodic characteristics from the traffic data. In order to learn the temporal properties of the traffic data, first module makes use of a Bi-LSTM whereas the second module is developed using GRU to extract the periodic properties of the traffic data. Additionally, a novel attention mechanism named confined attention is added to the first module to enhance the performance of the model by concentrating solely on the most recent pertinent data in the traffic flow sequence. The performance of the proposed model is evaluated using a publicly available real-world data set and compared with many existing methodologies for traffic flow prediction. As a result, a drop in the average value of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) for all of the prediction horizons is observed, which is ranged from 9.2%-29%, 4.1%-54.2%, and 5.9%-20.6%, respectively.

关键词： Traffic flow prediction Deep learning Hybrid models Bi-LSTM GRU Attention

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Withdrawals Prediction in Virtual learning Environments with Deep Self-paced learning 17th

Withdrawals Prediction in Virtual Learning Environments wit...

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17th international conference on Computer science and Education, ICCSE 2022

作者： Li, Xianyue Xu, Wei Xie, Shuo School of Marxism Shanghai University of Political Science and Law Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9789819924486

Withdrawals prediction in virtual learning environments aims to predict student dropout by modeling student behaviour when utilizing e-learning platforms. Classic machine learning approaches lack sufficient expression ability. Deep learning methods are inclined to get stuck in the local minimum. In addition, there is not any public source code platform to comprehensively compare all the baselines. In this paper, we propose a new Withdrawals Prediction method in virtual learning environments with Deep Self-Paced learning (WPDSPL) to deal with these two problems. Specifically, WPDSPL overcomes the bad local minimum problem by introducing self-paced learning into LSTM to gradually add data from easy ones to more complex ones during the training procedure. In addition, we deal with the inconvenient comparison problem by releasing the source code to comprehensively compare all the baselines. Comprehensive experiments demonstrate the superiority of our proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Intelligence-Based Recommendation System for Critical Stroke Management in Intensive Care Units

Intelligence-Based Recommendation System for Critical Stroke...

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

作者： Garcıa Terriza, Luis Risco-Martın, Jose L. Ayala, Jose L. Reig Rosello, Gemma Department of Computer Architecture and Automation Universidad Complutense de Madrid Madrid28040 Spain Stroke Care Unit Hospital Universitario de La Princesa Spain

This work presents an integrated recommendation system capable of providing support in healthcare critical environments such as Intensive Care Units or Stroke Care Units using machine learning techniques. The system can manage several patients by reading monitoring hemodynamic data in real-time, presenting current death risk probability, and showing recommendations that would reduce such probability and, in some cases, avoid death. This system introduces a novel method to produce recommendations based on genetic models and supervised machine learning. The interface is built upon a web application where clinicians can evaluate recommendations and straightforwardly provide feedback. © 2023 by SCITEPRESS – science and Technology Publications, Lda

关键词： Adversarial machine learning

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