scholarly journals Decomposition of Data Mining Algorithms into Unified Functional Blocks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ivan Kholod ◽  
Mikhail Kupriyanov ◽  
Andrey Shorov
Keyword(s):  
Data Mining ◽  
Data Mining Algorithms ◽  
Functional Blocks ◽  
Mining Algorithms

The present paper describes the method of creating data mining algorithms from unified functional blocks. This method splits algorithms into independently functioning blocks. These blocks must have unified interfaces and implement pure functions. The method allows us to create new data mining algorithms from existing blocks and improves the existing algorithms by optimizing single blocks or the whole structure of the algorithms. This becomes possible due to a number of important properties inherent in pure functions and hence functional blocks.

scholarly journals Cloud for Distributed Data Analysis Based on the Actor Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ivan Kholod ◽  
Ilya Petukhov ◽  
Andrey Shorov
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Sets ◽  
Distributed Data ◽  
Actor Model ◽  
Confidential Information ◽  
Data Mining Algorithms ◽  
Functional Blocks ◽  
Mining Algorithms ◽  
Distributed Data Analysis

This paper describes the construction of a Cloud for Distributed Data Analysis (CDDA) based on the actor model. The design uses an approach to map the data mining algorithms on decomposed functional blocks, which are assigned to actors. Using actors allows users to move the computation closely towards the stored data. The process does not require loading data sets into the cloud and allows users to analyze confidential information locally. The results of experiments show that the efficiency of the proposed approach outperforms established solutions.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4891-4904
Author(s):  
Selahattin Bardak ◽  
Timucin Bardak ◽  
Hüseyin Peker ◽  
Eser Sözen ◽  
Yildiz Çabuk
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Bending Strength ◽  
Wood Preservation ◽  
Ambient Conditions ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
The Cost ◽  
Mining Algorithms ◽  
The Relationship

Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

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