scholarly journals Studying the capabilities of the analytical system based on the machine learning method

2020 ◽  
Vol 30 (3) ◽  
pp. 112-126
Author(s):  
S. V. Palmov
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Predictive Models ◽  
Arithmetic Mean ◽  
Machine Learning Method ◽  
Learning Method ◽  
Learning Tools ◽  
Analytical System ◽  
Reliability And Robustness ◽  
Almost All

Data analysis carried out by machine learning tools has covered almost all areas of human activity. This is due to a large amount of data that needs to be processed in order, for example, to predict the occurrence of specific events (an emergency, a customer contacting the organization’s technical support, a natural disaster, etc.) or to formulate recommendations regarding interaction with a certain group of people (personalized offers for the customer, a person’s reaction to advertising, etc.). The paper deals with the possibilities of the Multitool analytical system, created based on the machine learning method «decision tree», in terms of building predictive models that are suitable for solving data analysis problems in practical use. For this purpose, a series of ten experiments was conducted, in which the results generated by the system were evaluated in terms of their reliability and robustness using five criteria: arithmetic mean, standard deviation, variance, probability, and F-measure. As a result, it was found that Multitool, despite its limited functionality, allows creating predictive models of sufficient quality and suitable for practical use.

scholarly journals Machine Learning Method for Detecting and Analysis of Fraud Phone Calls Datasets

2020 ◽  
Vol 8 (6) ◽  
pp. 3806-3810
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Machine Learning Method ◽  
File Format ◽  
Learning Method ◽  
Phone Calls ◽  
Specific Device ◽  
Suggested Strategy ◽  
Almost All ◽  
Discovery Method

While using non-stop advancement of correspondences industry, almost all clients steadily appreciate various interchanges companies. To accomplish persuasive and moderate identification with regard to telecom deceit clients, all of us propose an effective and suitable extortion customer discovery method dependent on customer's Call detail Record (CDR). The suggested strategy contains two segments, specific device learning component and file format discovery element. In the equipment wisdom component, a support Vector machine (SVM) computation dependent on aimed knowledge is actually utilized to team clients making use of outline characteristics. Detail evaluation is similarly completed regarding separating the actual detail associated with networks. Outcomes show that these strategies will help rapidly character the ad calls. The actual investigations display that the technique can achieve high reputation precision regarding 97.56%, which exhibit that the proposed technique has progressively brilliant execution in examination with the best in class draws near

scholarly journals Explainable t-SNE for single-cell RNA-seq data analysis

2022 ◽  
Author(s):  
Henry Han ◽  
Tianyu Zhang ◽  
Mary Lauren Benton ◽  
Chun Li ◽  
Juan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Data Analysis ◽  
Dimension Reduction ◽  
Single Cell ◽  
Method Development ◽  
Robustness Analysis ◽  
High Dimensional ◽  
Machine Learning Method ◽  
Learning Method

Single-cell RNA (scRNA-seq) sequencing technologies trigger the study of individual cell gene expression and reveal the diversity within cell populations. To measure cell-to-cell similarity based on their transcription and gene expression, many dimension reduction methods are employed to retrieve the corresponding low-dimensional embeddings of input scRNA-seq data to conduct clustering. However, the methods lack explainability and may not perform well with scRNA-seq data because they are often migrated from other fields and not customized for high-dimensional sparse scRNA-seq data. In this study, we propose an explainable t-SNE: cell-driven t-SNE (c-TSNE) that fuses the cell differences reflected from biologically meaningful distance metrics for input scRNA-seq data. Our study shows that the proposed method not only enhances the interpretation of the original t-SNE visualization for scRNA-seq data but also demonstrates favorable single cell segregation performance on benchmark datasets compared to the state-of-the-art peers. The robustness analysis shows that the proposed cell-driven t-SNE demonstrates robustness to dropout and noise in dimension reduction and clustering. It provides a novel and practical way to investigate the interpretability of t-SNE in scRNA-seq data analysis. Unlike the general assumption that the explainanbility of a machine learning method needs to compromise with the learning efficiency, the proposed explainable t-SNE improves both clustering efficiency and explainanbility in scRNA-seq analysis. More importantly, our work suggests that widely used t-SNE can be easily misused in the existing scRNA-seq analysis, because its default Euclidean distance can bring biases or meaningless results in cell difference evaluation for high-dimensional sparse scRNA-seq data. To the best of our knowledge, it is the first explainable t-SNE proposed in scRNA-seq analysis and will inspire other explainable machine learning method development in the field.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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