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parallel classification model of arrhythmia based on DenseNet-BiLSTM

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2021年第4期41卷 1548-1560页

作者： Gan, Yi Shi, Jun-cheng He, Wei-ming Sun, Fu-jia Univ Shanghai Sci & Technol Sch Mech Engn Shanghai Peoples R China Chuo Univ Fac Sci & Engn Tokyo Japan

In order to improve the classification performance of the model for different kinds of arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is researched and proposed. Firstly, the model adopts a parallel structure. After wavelet denoising and heartbeat segmentation of ECG signals, this model can simultaneously capture the waveform features of small-scale heartbeat and large-scale heartbeat containing RR interval;Then, based on deep learning, Densely connected convolutional network (Dense-Net) is applied to improve the model's ability to extract local features of ECG signals, and bidirectional long short-term memory network (BiLSTM) is introduced to improve the performance of the model in extracting time series features of ECG signals;Finally, weighted cross entropy loss function is used to alleviate the class imbalance of arrhythmia, and Soft-max function is applied to achieve 4 classifications of arrhythmia. Experiments based on MIT-BIH arrhythmia database show that under the intra-patient paradigm, training time for each epoch, overall accuracy, F-1 and specificity are 42 s, 99.44%, 95.89% and 99.32%, respectively;Under the inter-patient paradigm, training time for each epoch, overall accuracy, F-1 and specificity are 23 s, 92.37%, 63.49% and 94.51%, respectively. Compared with other classification models, the model proposed in this paper has a good classification effect and is expected to be used in clinical auxiliary diagnosis. (C) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Arrhythmia parallel classification DenseNet BiLSTM Weighted cross entropy loss function

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parallel classification Method of Library Borrowing Data Based on Bayes 3

Parallel Classification Method of Library Borrowing Data Bas...

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Yi, Cheng Liu, Jieling Library of Wuhan University of Technology Wuhan430200 China

ISBN: (纸本)9798350341546

Based on the parallel classification of library borrowing data, the dynamic management and service ability of library borrowing information are improved, and a parallel classification method of library borrowing data based on Bayesian is proposed. According to the borrowing reports of user groups and individuals, a sampling model of library borrowing data is constructed, and a comprehensive borrowing index analysis model for users' personal construction system is established. Bayesian classification model is used to realize the feature mining and classification of borrowing data, and the correlation between different types of literature resources is mined according to the results of spectral feature extraction. Combined with the borrowing intensity between users with different attributes and different book classifications, Naive Bayesian classification algorithm is used to fully explore the internal relationship between fields. Based on the causal relationship between influencing factors, a dynamic model of parallel classification of library borrowing data is constructed, and the library borrowing data is matched according to certain rules. After weighted calculation, the reading behavior is quantified in the form of comprehensive index to realize parallel classification of library borrowing data. The test results show that the parallel classification of library borrowing data by this method improves the mining efficiency and accuracy of borrowing data, and the parallel processing efficiency is high. © 2023 IEEE.

关键词： Bayesian Borrowing data dig Library parallel classification

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Design of Human Resource Big Data parallel classification System Based on Social Information Cognitive Model 5th

Design of Human Resource Big Data Parallel Classification Sy...

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5th EAI International Conference on Advanced Hybrid Information Processing (ADHIP)

作者： Sun, Bo Zhang, Cai-ming China Univ Lab Relat Sch Lab Relat & Human Resource Beijing 100048 Peoples R China

ISBN: (纸本)9783030945510;9783030945503

In order to improve the classification function and operation performance of human resources, a new big data parallel classification system is designed. The parallel processor is installed to optimize the analog-to-digital converter, human resource data storage and wireless communication network. This paper constructs a social information cognitive model, under which human resource data can be obtained in real time, and preprocessed by data cleaning, Chinese word segmentation and stop word elimination. Human resource data features are extracted, and the similarity between the extracted data features and standard data features is calculated to realize the parallel classification function of human resource big data. Through the system test experiment, the conclusion is drawn: compared with the traditional classification system, the recall rate and accuracy rate of the design system are increased by 5.5% and 3.5% respectively, and it has more advantages in classification speed.

关键词： Social information cognitive model Human resources Resource big data parallel classification

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A Cloud-Based Data Mining Framework for Improved Clinical Diagnosis through parallel classification 1

A Cloud-Based Data Mining Framework for Improved Clinical Di...

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International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Lokeswari, Y. V. Jacob, Shomona Gracia SSN Coll Engn Dept Comp Sci & Engn Madras 603110 Tamil Nadu India

ISBN: (纸本)9781467392235

Healthcare Organizations have been dealing with rapidly growing Electronic Health Records (EHRs) and digital images. Maintaining high volumes of medical data leads to scalability issue. Cloud computing provides scalable resources on demand which includes computing and storage as a service. In this paper, the authors propose a model that would enable mining meaningful medical information from a community cloud that connects multiple health organizations. These health organizations work for a common purpose and store health records in cloud. Storing EHRs in cloud makes data maintenance easier. In order to mine cloud-based medical data, classification a data mining technique is applied on EHRs to diagnose chronic diseases based on patients' symptoms. classification algorithms like Decision Tress Induction, k-NN classifier and Naive Bayesian classifier are run in parallel across multiple processors (Virtual machines) in cloud. It is believed that Ensemble methods like Bagging or Boosting can be applied to improve accuracy of each classifier in predicting clinical outcomes.

关键词： Terms Electronic Health Records (EHR) Community Cloud parallel classification Ensemble methods

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GEL: A Platform-Independent Reasoner for parallel classification with OWL EL Ontologies Using Graph Representation

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2017年第1期26卷

作者： Zhou, Zhangquan Qi, Guilin Southeast Univ Sch Comp Sci & Engn Nanjing 086211189 Jiangsu Peoples R China

In the community of Semantic Web, the state-of-the-art reasoners are all implemented on specific platforms. As the Web Ontology Language (OWL) has been widely used in different real-world applications, it is required that the task of reasoning can be flexibly adapted to different platforms. In this paper, we take the first effort to give a platform independent approach for parallel classification of the description logic OWL EL, which is a tractable fragment of OWL 2. classification, which is the task of computing a subsumption hierarchy between concepts, plays an important role in many applications of OWL EL. The current classification methods rely on the specific parallel computation platforms and can hardly achieve a tradeoff between efficiency and scalability. To develop a platform-independent approach for performing classification in OWL EL, we first give a novel and well defined graph formalism gEL for representing EL ontologies and present a group of sound and complete rules for ontology classification on a gEL graph. Based on this formalism, we design a parallel classification algorithm, which can be implemented on different parallel computation platforms, and we also show the correctness of the algorithm. We implement a system, which can easily switch between multi-core and a distributed cluster. Finally, we conduct experiments on several real-world OWL EL ontologies. The experimental results show that our system outperforms two stateof-the-art EL reasoning systems, and has a linear scalability on the extensions of GO ontologies.

关键词： OWL 2 EL description logics parallel classification graph platform independence

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Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data

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GENES & GENOMICS 2019年第11期41卷 1301-1313页

作者： Venkataramana, Lokeswari Jacob, Shomona Gracia Ramadoss, Rajavel Saisuma, Dodda Haritha, Dommaraju Manoja, Kunthipuram Sri Sivasubramaniya Nadar Coll Engn Dept CSE Chennai Tamil Nadu India Sri Sivasubramaniya Nadar Coll Engn Dept ECE Chennai Tamil Nadu India

Background Data mining techniques are used to mine unknown knowledge from huge data. Microarray gene expression (MGE) data plays a major role in predicting type of cancer. But as MGE data is huge in volume, applying traditional data mining approaches is time consuming. Hence parallel programming frameworks like Hadoop, Spark and Mahout are necessary to ease the task of computation. Objective Not all the gene expressions are necessary in prediction, it is very essential to select important genes for improving classification accuracy. So feature selection algorithms are parallelized and executed on Spark framework to eliminate unnecessary genes and identify only predictive genes in very less time without affecting prediction accuracy. Methods parallelized hybrid feature selection (HFS) method is proposed to serve the purpose. This method includes parallelized correlation feature subset selection followed by rank-based feature selection methods. The selected subset of genes is evaluated using parallel classification algorithms. The accuracy values obtained are compared with existing rank-weight feature selection, parallelized recursive feature selection methods and also with the values obtained by executing parallelized HFS on DistributedWekaSpark. Results The classification accuracy obtained with the proposed parallelized HFS method is 97% and 79% for gastric cancer and childhood leukemia respectively. The proposed parallelized HFS method produced similar to 4% to similar to 15% improvement in classification accuracy when compared with previous methods. Conclusion The results reveal the fact that the proposed parallelized feature selection algorithm is scalable to growing medical data and predicts cancer sub-types in lesser time with higher accuracy.

关键词： parallelized hybrid feature selection Correlation feature subset selection Rank-based methods parallel classification Spark DistributedWekaSpark

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An adaptive spatio-spectral domain correlation parallel framework for hyperspectral image classification 14

An adaptive spatio-spectral domain correlation parallel fram...

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14th IEEE International Conference on Signal Processing (ICSP)

作者： Wang, Xianchen Xie, Weixin Shenzhen Univ ATR Natl Key Lab Def Technol Shenzhen Peoples R China Shenzhen Polytech Sch Elect & Commun Engn Shenzhen Peoples R China

ISBN: (纸本)9781538646731

With the rapid development of hyperspectral image technology, the spectral resolution and spatial resolution of hyperspectral images are continuously increasing, which in turn leads to a rapid increase of data amount in hyperspectral images. The huge amount of data poses a big challenge to the rapid processing of hyperspectral images. This paper proposes an adaptive spatio-spectral domain correlation parallel framework for hyperspectral image classification. The experimental results show that the classification accuracy and processing speed of our proposed framework are more efficient and higher than that of existing classification algorithms for hyperspectral images.

关键词： Adaptive Spectral Domain Correlation parallel classification

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A multi-core computing approach for large-scale multi-label classification

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INTELLIGENT DATA ANALYSIS 2017年第2期21卷 329-352页

作者： Rodriguez, Juan Manuel Godoy, Daniela Mateos, Cristian Zunino, Alejandro Unicen Univ ISISTAN Res Inst Campus UnivTandil B7001BBO Tandil Buenos Aires Argentina

Large scale multi-label learning, i.e. the problem of determining the associated set of labels for an instance, is gaining relevance in recent years due to the emergence of several real-world applications. Most notably, the exponential growth of the SocialWeb where a resource can be labeled by millions of users using one or more tags, i.e. a resource can be associated to several labels at the same time. A well-known approach for multi-label classification is the Binary Relevance (BR) algorithm which trains a binary classifier for each label independently. However, the serial implementation of BR is not suitable for medium or large datasets due to the time and computational resources required for training. For example, training classifiers for mid-size datasets using MULAN implementation of BR might take several weeks. This paper discusses a parallel implementation of the MULAN BR technique that harnesses the computational power of nowadays multi-core processors. Our implementation presents a speed-up in the training phase of up to 12 times when compared to the original MULAN implementation. In addition, the cross-validation technique of MULAN had huge RAM requirements, making it unusable with large datasets. Therefore, we have overcome this limitation by using compact data structures and taking advantage of disk caching. We have also compared our implementation against scikit-learn, a popular tool for data mining and data analysis, showing significant improvements in speed-up.

关键词： Binary relevance classification multi-core programming parallel classification multi-label classification

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parallel online sequential extreme learning machine based on MapReduce

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NEUROCOMPUTING 2015年第PART A期149卷 224-232页

作者： Wang, Botao Huang, Shan Qiu, Junhao Liu, Yu Wang, Guoren Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China

In this age of big data, analyzing big data is a very challenging problem. MapReduce is a simple, scalable and fault-tolerant data processing framework that enables us to process a massive volume of data. Many machine learning algorithms have been designed based on MapReduce, but there are only a few works related to parallel extreme learning machine (ELM) which is a fast and accurate learning algorithm. Online sequential extreme learning machine (OS-ELM) is one of improved ELM algorithms to support online sequential learning efficiently. In this paper, we first analyze the dependency relationships of matrix calculations of OS-ELM, then propose a parallel online sequential extreme learning machine (POS-ELM) based on MapReduce. POS-ELM is evaluated with real and synthetic data with the maximum number of training data 1280 K and the maximum number of attributes 128. The experimental results show that the training accuracy and testing accuracy of POS-ELM are at the same level as those of OS-ELM and ELM, and it has good scalability with regard to the number of training data and the number of attributes. Compared to original ELM and OS-ELM where the capability to process large scale data is bounded by the limitation of resources within a single processing unit, POS-ELM can deal with much larger scale data. The larger the number of training data is, the higher the speedup of POS-ELM is. It can be concluded that POS-ELM has more powerful capability than both ELM and OS-ELM for large scale learning. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Extreme learning machine Large scale learning Online sequential learning Mapreduce parallel classification

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Review of Feature Selection, Dimensionality Reduction and classification for Chronic Disease Diagnosis

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IEEE ACCESS 2021年 9卷 87310-87317页

作者： Alhassan, Afnan M. Zainon, Wan Mohd Nazmee Wan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Shaqra Univ Coll Comp & Informat Technol Shaqra 11961 Saudi Arabia

The early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classification techniques to predict and diagnose chronic disease. The appropriate selection of attributes plays a crucial role in improving the classification accuracy of the diagnosis systems. Additionally, dimensionality reduction techniques effectively improve the overall performance of the machine learning algorithms. On chronic disease databases, the classification techniques deliver efficient predictive results by developing intelligent, adaptive and automated system. parallel and adaptive classification techniques are also analyzed in chronic disease diagnosis which is used to stimulate the classification procedure and to improve the computational cost and time. This survey article represents the overview of feature selection, dimensionality reduction and classification techniques and their inherent benefits and drawbacks.

关键词： Feature extraction Diseases Databases Medical diagnosis Dimensionality reduction Filtering classification algorithms Adaptive classification chronic disease dimensionality reduction feature selection parallel classification

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