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Label Noise Filtering Based on data Distribution and Relative Density

Label Noise Filtering Based on Data Distribution and Relativ...

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Symposium of Image, Signal processing, and Artificial Vision (STSIVA)

作者： Hang Liu Shuai Bian Chi Liu Yong Qiu Daizhi Lang Zhiwen Liu Jianjun Lei Chongqing Meiansen Technology Co. Ltd. Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Label noise is a non-negligible problem in supervised and semi-supervised machine learning. Current methods to deal with label noise are mainly algorithm-level robust modeling and data-level noise filtering. However, these methods suffer from poor or inefficient filtration. In this paper, a label noise filtering method based on data distribution and relative density (DD-RDF) is proposed. Firstly, according to the sample distribution of the data set, the regions where each sample is located and divided. Secondly, different filtering rules are adopted for noise removal in different areas. Compared with the data Distribution Filtering (DDF) algorithm, the DD-RDF algorithm adopts relative density filtering rules to increase neighborhood information in the low-density mixed label area, which makes the algorithm more robust. Experimental results on 12 UCI standard multi-classification data sets show that the noise filtering effect of the DD-RDF algorithm is better than that of the comparison algorithms under different noise ratios.

关键词： Machine learning algorithms Filtering Filtration Machine learning Classification algorithms Informatics Standards

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Self-organizing algorithm for pilot modeling the reaction of society to the phenomenon of the Black Swan

Self-organizing algorithm for pilot modeling the reaction of...

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international conference on Application of Information and Communication Technologies (AICT)

作者： Michail Artemenko Vladimir Budanov Nicoly Korenevskiy Southwest State University Kursk Russia Southwest state university Kursk Russia

ISBN: (数字)9781728173863

ISBN: (纸本)9781728173870

The work is devoted to pilot modeling of the reaction of society to technogenic and biological sudden environmental influences. For the description, the methodology of the socio-technical landscape is used, which reflects the development of certain social practices in the digital and technical environments. At the bifurcation points of the evolution of the socio-technical landscape, the phenomena of the "Black Swan" and "Boomerang Blow" arise - a powerful impulse effect on the landscape. There is an urgent problem of predicting reaction reactions. The aim of the work is the development and pilot study of a self-organizing algorithm of structural-parametric identification of transients. Impacts of a technical and biological nature are considered as objects of research: the development of the use of artificial intelligence and computer technology in medicine and the spread of COVID 19. models for countries: UK, Russia and Uzbekistan are given. It is shown that the reaction of the sociotechnical landscape is acceptable described by homogeneous differential equations of the second degree with a complex exponentially harmonic function on the right side. The most adequate mathematical models are given. This work was supported by the Russian Science Foundation grant # 19-18-00504.

关键词： COVID-19 Biological system modeling Differential equations Prediction algorithms Harmonic analysis data models Mathematical model

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Doctor for Machines: A Failure Pattern Analysis Solution for Industry 4.0

Doctor for Machines: A Failure Pattern Analysis Solution for...

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IEEE international conference on Big data

作者： Dhaval Patel Nianjun Zhou Shrey Shrivastava Jayant Kalagnanam AI Applications IBM Research NY USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The success of IoT depends on our ability to solve challenging problems which were previously infeasible. One of the most critical challenges in IoT space is the preventive maintenance in industrial manufacturing processes to maximize equipment availability and durability. Traditionally, preventive management only follows less cost-effective strategies, say time or usage based management. With large amount sensor data harvested from IoT, we can develop much more intelligent predictive maintenance based on accurate machinery failure prediction. In this paper, we develop a framework named DoM (Doctor for Machines) to produce the best predictive model for several oil and gas industry engagements. Our framework is built in the form of pipeline that allows us to generate multiple models simultaneously with parallel computing. We configure the modeling process by assigned different machine learning tasks as such sampling, feature extraction, modeling, and post-processing into the pipeline. The pipeline forms a machine learning graph workflow. With the automation of execution all the tasks in the workflow, the client can easily choose a best predictive model fitting their failure tolerance. We have successfully applied DoM to six data sets, and identified valuables insights on the best practices of creating predictive model. Our clients have successfully achieved financial benefits by applying DoM to develop predictive maintenance schemes to their expensive equipment.

关键词： Computational modeling Biological system modeling Pipelines Medical services Machine learning Predictive models data models

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Actionable Knowledge Extraction Framework for COVID-19

Actionable Knowledge Extraction Framework for COVID-19

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IEEE international conference on Big data

作者： Mohammad Masum Hossain Shahriar Hisham M. Haddad Sheikh Ahamed Sweta Sneha Mohammad Rahman Alfredo Cuzzocrea Analytics and Data Science Institute Kennesaw State University Kennesaw USA Kennesaw State University Marietta USA Marquette University USA Kennesaw State University USA Florida International University USA University of Calabria Italy

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research dataset (CORD-19) containing over 51,000 scholarly articles, including over 40,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. Medical professional including physicians frequently seek answers to specific questions to improve guidelines and decisions. The huge resource of medical literature is important sources to generate new insights that can help medical communities to provide relevant knowledge and overall fight against the infectious disease. There are ongoing attempts to develop intelligent systems to automatically extract relevant knowledge from many unstructured documents. In this paper, we propose an efficient question answering framework based on automatically analyzing thousands of articles to generate both long text answers (sections/ paragraphs) in response to the questions that are posed by medical communities. In the process of developing the framework, we explored natural language processing techniques like query expansion, data preprocessing, and vector space models early. We show the initial results of an example query answering for the incubation period.

关键词： COVID-19 Infectious diseases Pandemics Medical services Big data Natural language processing data mining

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End-to-end relation extraction based on part of speech syntax tree

End-to-end relation extraction based on part of speech synta...

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international conference on Machine Learning, Big data and Business Intelligence (MLBDBI)

作者： Chengpeng Wang Yuanli Wang Junchang Mo Shuo Wang School of Information Engineering Wuhan University of Technology Wuhan China Liuzhou Wuling Automobile Industry Co. Ltd Liuzhou China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

Recently, deep learning has achieved richer research results on entity relationship extraction tasks. Existing methods mainly focus on the character features of the sequence input, without considering that the input structural features may learn meaningless subsequences. In this paper, we propose an end-to-end joint extraction model based on syntactic tree structure, which can learn character features and sentence structure features at the same time. In the decoding process, we use a tree structure to learn sentence structure features based on character parts of speech. We test our models in two public datasets and our model outperforms the baseline method significantly.

关键词： Deep learning Syntactics Feature extraction Decoding data mining Task analysis Speech processing

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Trend-guided Small Hydropower System Power Prediction Based on Extreme Learning Machine

Trend-guided Small Hydropower System Power Prediction Based ...

引用

Power and Energy Engineering (ICPEE), international conference on

作者： Chunjie Lian Hua Wei Shengchao Qin Zongsheng Li Guangxi Key Laboratory of Power System Optimization and Energy Technology (Guangxi University) Nanning China

ISBN: (数字)9781728191256

ISBN: (纸本)9781728191263

A trend-guided extreme learning machine prediction model (TG-ELM) is proposed, which enriches the physical mechanism of the input and output of the traditional extreme learning machine (ELM), and solves the problems that a large amount of raw data in the traditional hydropower prediction model depends on a single data normalization process, which causes the prediction accuracy of the entire prediction model to decrease. The model first smoothed and repaired abnormal output data of the small hydropower group, and then extracted the trend of power change from the processed power curve, and used this trend as a new feature input for the extreme learning machine model to provide a correct and unique prediction trend for the prediction of the extreme learning machine. The model is applied to the power generation forecast of small hydropower groups in Guangxi, which greatly improves the accuracy of the power generation forecast and shortens the calculation time of the forecast. Based on the historical data of the power generation of the small hydropower group in Guangxi in August 2019 for simulation analysis, the root mean square error and average absolute error of the prediction are 4.16 WM and 6.11%, respectively. Compared with prediction methods such as backpropagation neural network and support vector machine, the proposed prediction model has distinct advantages and is more suitable for industrial applications.

关键词： Hydroelectric power generation Prediction algorithms Market research Predictive models Neural networks Smoothing methods data models

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Forensic Gender Discrimination in Malaysian Population Using Machine Learning Methods

Forensic Gender Discrimination in Malaysian Population Using...

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international conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Loong Chuen Lee Muhammad Yusuf Adam Siti Norfaraan Abd Sanih Nur Izzati Bohari Forensic Science Program FSK Universiti Kebangsaan Malaysia Bangi Malaysia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (数字)9781728196732

ISBN: (纸本)9781728196749

Latent fingerprint is one of the most encountered evidence in a crime scene and is useful for personal identification. In forensic investigation, reliability of the identification process is greatly affected by the visibility of the minutiae features. This is because the matching of known and unknown fingerprints achieved according to the types and locations of the minutiae features. However, most of the fingerprints recovered from the crime scene are of low quality, i.e. incomplete print or minutiae features. Under such circumstances, forensic analyst can try to determine gender of donor of the latent fingerprint. This piece of information could help to narrow down the scope of searching of suspect. Forensic discrimination of gender based on ridge counts has been proposed for a few decades ago. Despite multiple works have reported the use of machine learning methods in gender classification using fingerprints, the fingerprint features were mainly extracted from the images. In this work, the diagonal ridge counts were calculated manually within a well-defined region, i.e. 25 centimetres squared. This approach is more relevant to a real crime scene investigation. This work employed two well-known machine learning algorithms, i.e. naïve Bayes (NB) and Classification and Regression Trees (CART) algorithms, in discriminating gender based on the ridge counts. The performances of predictive models have been assessed by ethnicity and finger digit via bootstrapping without replacement approach. Results showed one-digit samples can perform as good as the five-digit or ten-digit samples. Comparing to the global predictive model, ethnicity-specific models of Indian and Malay subjects, respectively, showed better improvement. Moreover, by considering all five digits of a particular hand as input data, NB tended to outperform CART; whilst the relative performances reversed when only one digit was considered as input data. In conclusion, fingerprint ridge counts can be a potentia

关键词： Fingerprint recognition Predictive models Forensics Statistics Sociology Computational modeling data models

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Summarizer: Fuzzy Rule-Based Classification Systems for Vertical and Horizontal Big data

Summarizer: Fuzzy Rule-Based Classification Systems for Vert...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Pétala G. da S. E. Tuy Tatiane Nogueira Rios IME. Federal University of Bahia Salvador Brazil

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

The performance of Fuzzy Rule-Based Classification Systems (FRBCSs) is highly affected by the increasing number of instances and attributes present in Big data. Previously proposed approaches try to adapt FRBCSs to Big data by distributing data processing with the MapReduce paradigm, by which the data is processed in two stages: Map and Reduce. In the Map stage, the data is divided into multiple blocks and distributed among processing nodes that process each block of data independently. In the Reduce stage, the results coming from every node in the Map stage are aggregated and a final result is returned. This methodology tackles vertical high dimensionality (high number of instances), but it does not approach datasets with simultaneous vertical and horizontal high dimensionality (high number of attributes), as it is the case of text datasets. In this work, we deal with the aforementioned drawbacks by proposing Summarizer, an approach for building reduced feature spaces for horizontally high dimensional data. To this end, we carry out an empirical study that compares a well-known classifier proposed for vertical high dimensionality datasets with and without the horizontal dimensionality reduction process proposed by Summarizer. Our findings show that existing classifiers that tackles vertical Big data problems can be improved by adding the Summarizer approach to the learning process, which suggests that an unified learning algorithm for datasets with a high number of instances as well as a high number of attributes might be possible.

关键词： Big data Principal component analysis Dimensionality reduction Clustering algorithms Covariance matrices Distributed databases Task analysis

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Improving the Identification of Co-occurring Driver Pathways by Integrating Multi-Omics data

Improving the Identification of Co-occurring Driver Pathways...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Qirong Cai Jingli Wu Rui Du Zhen Zhang Ke Pan College of Computer Science Beijing Institute Of Technology Zhuhai China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

It is widely accepted that driver pathways offer important information for precision and personalized medicine of cancer treatment, hence the problems of driver pathways identification have become a research hotspot in bioinformatics. In this paper, an improved collaborative mutation driver pathways model ICMDP is proposed by integrating the somatic mutation, copy number variation and gene expression data. The model has two characteristics:(1) each individual pathway has moderate mutual exclusion and high coverage; (2) collaborative driver pathways exhibit significant common mutations in cancer samples, and the genes in collaborative driver pathways are related. Meanwhile, a parthenogenetic algorithm PA-ICMDP is proposed for solving the ICMDP model. Experiments were performed to compare algorithms PA-ICMDP, CoMDP and GAMTOC by using real biological data sets, i.e., the samples of glioblastoma and ovarian. The experimental results indicate that the PA-ICMDP algorithm can not only identify important collaborative driver pathways with higher co-occurrence mutation rates, but also detect more important driver genes such as MET, MDM2, GAB2, TERT, TBX3 and so on. In addition, the EICMDP model and the PA-EICMDP algorithm are put forward by extending the ICMDP model and the PAICMDP algorithm respectively. They can effectively identify other important pathways that collaborate with known driver pathways. The experimental results indicate that the methods presented in this paper may become suitable tools for mining driver genes and driver pathways related to cancer development.

关键词： Proteins Biological system modeling High performance computing Collaboration Tools data models Gene expression

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Predict Onset Age of Hypertension Using CatBoost and Medical Big data

Predict Onset Age of Hypertension Using CatBoost and Medical...

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international conference on Networking and Network Applications (NaNA)

作者： Huanhuan Zhao Zuchang Ma Yining Sun ChuZhou University China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

The development of IoT technology promotes the application of medical big data. Among which, early warning of chronic disease is of great significance to disease management. Hypertension is a widespread chronic disease. Preventing hypertension can effectively promote health conditions and reduce early mortality rate. In this paper, we attempt to predict the onset age of hypertension using CatBoost algorithm for supporting health management decision making based on medical big data. Firstly, the features that are highly associated with onset age of hypertension are analyzed by maximum information coefficient, and then those chosen features are used as the inputs of CatBoost algorithm to construct the prediction model of onset age of hypertension. 2363 sample data collected from a hospital in Beijing were used to verify the effectiveness of this approach. For the testing set, the RMSE was 5.38 and the MAPE was 9.42%, which outperforms linear regression model, SVM model and artificial neural network model. The experimental results show that the prediction model can predict individual's onset age of hypertension from current health indicators and provides a novel idea for early warning of hypertension.

关键词： Hypertension Support vector machines Big data Predictive models Prediction algorithms Diseases Testing

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