Expert Systems for Confirmatory Data Analysis

SummaryConfirmatory Data Analysis (CDA) in randomized comparative (“controlled”) studies with many variables and/or time points of interest finds its limitations in the multiplicity of desired inferential statements which leads to unfeasibly small adjusted significance levels (“Bon-ferronization”) and, thereby, to unduly increased risks of not rejecting false hypotheses. In general, analytical models adequate for such complex data structures and suitable for practical use do not exist as yet. Exploratory Data Analysis (EDA), on the other hand, is usually intended to generate hypotheses and not to lead to final conclusions based on the results of the study.In this paper, it is proposed to fill the conceptual gap between CDA and EDA by “Descriptive Data Analysis” (“DDA”) which concept is mainly based on descriptive inferential statements. The results of a DDA in a controlled study are interpreted simultaneously on the basis of the investigator’s experience with respect to numerically relevant treatment effect differences and on “descriptive significances” as they appear in “near regular” patterns corresponding to the resulting relevant effect differences. A DDA may also contain confirmatory parts and/or tests on global hypotheses at a prechosen maximum risk α of erroneously rejecting true hypotheses. The paper is in parts expository and is addressed to investigators as well as statisticians.

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Modeling of critical systems implementing negative events using dual Petri nets

MATEC Web of Conferences ◽

10.1051/matecconf/201822604001 ◽

2018 ◽

Vol 226 ◽

pp. 04001 ◽

Cited By ~ 2

Author(s):

Marina A. Ganzhur ◽

Aleksei P. Ganzhur ◽

Olga V. Smirnova

Keyword(s):

Fuzzy Logic ◽

Data Analysis ◽

Expert Systems ◽

Petri Nets ◽

Petri Net ◽

Negative Events ◽

Linguistic Variables ◽

Critical Systems ◽

Fuzzy Petri Net ◽

Fuzzy Nets

This work is devoted to solving the problem of modeling critical systems based on the use of modified Petri nets. The dual (binary) Petri net one of the modifications, which allows us to view inversing events at the same time, solving the problem with the possibility of deadlocks. Construction of schemes using fuzzy logic makes it possible to calculate the values of linguistic variables obtained knowledge. Petri dual network allows you to organize the exclusion of negative events by introducing additional links. In accordance with the rules, it is possible to construct a dual fuzzy Petri net, which involves the use of maximum and minimum transitions or appropriate logical calculations of conjunctions and disjunctions. Transition from classical Petri nets to dual fuzzy nets, realizes fuzzy knowledge of logical deriving that gives the chance in construction of expert systems with fuzzy logic solving a problem of data analysis.

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Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation (Preprint)

10.2196/preprints.19740 ◽

2020 ◽

Author(s):

Alfonso J. Rodriguez-Morales ◽

Ram Kumar Singh ◽

S.S. Singh ◽

A. K. Pandey ◽

Vinod Kumar ◽

...

Keyword(s):

Data Analysis ◽

Arima Model ◽

Time Estimation ◽

The United States ◽

World Health ◽

Disease Spread ◽

Analysis Model ◽

Indian States ◽

The World ◽

Confirmatory Data Analysis

BACKGROUND The highly contagious Coronavirus disease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). It is identified as the most severe pandemic disease in human history during the past 100 years. Due to non-availability of specific medication, the World Health Organization (WHO) suggested various measures of precautions and social distance in between the people for the restricting the spread of the COVID-19 disease. Various nation’s administration including the India government called for the regional and local lockdown. OBJECTIVE We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. METHODS The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. RESULTS The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. CONCLUSIONS The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. CLINICALTRIAL wqew ere re

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