Data
alcohol-qcm-sensor

alcohol-qcm-sensor

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Author: M. Fatih Adak, Peter Lieberzeit, Purim Jarujamrus, Nejat Yumusak Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Alcohol+QCM+Sensor+Dataset) - 2019 Please cite: [M. Fatih Adak, Peter Lieberzeit, Purim Jarujamrus, Nejat Yumusak, Classification of alcohols obtained by QCM sensors with different characteristics using ABC based neural network, Engineering Science and Technology, an International Journal, 2019, ISSN 2215-0986](https://www.sciencedirect.com/science/article/pii/S2215098619303337?via%3Dihub) In the dataset there are 5 types of dataset.QCM3, QCM6, QCM7, QCM10, QCM12In each of dataset, there is alcohol classification of five types,1-octanol, 1-propanol, 2-butanol, 2-propanol, 1-isobutanolIn this file the 5 datasets are merged into 1 dataset, with each25 entries representing 1 original dataset, respectively.The gas sample is passed through the sensor in five differentconcentrations. These concentrations are:Concentration Air ratio (ml) Gas ratio (ml)1 0.799 0.2012 0.700 0.3003 0.600 0.4004 0.501 0.4995 0.400 0.600There are two channels in the sensor. One of these circles forms channel 1,and the other forms channel 2. MIP and MP ratios used in the QCM sensors are,Sensor name MIP ratio NP ratioQCM3 1 1QCM6 1 0QCM7 1 0.5QCM10 1 2QCM12 0 1 In this file, the 5 datasets are merged into 1, with each 25 rows representing 1 original dataset.

15 features

0.799_0.201numeric124 unique values
0 missing
0.799_0.201.1numeric123 unique values
0 missing
0.700_0.300numeric124 unique values
0 missing
0.700_0.300.1numeric122 unique values
0 missing
0.600_0.400numeric125 unique values
0 missing
0.600_0.400.1numeric124 unique values
0 missing
0.501_0.499numeric125 unique values
0 missing
0.501_0.499.1numeric125 unique values
0 missing
0.400_0.600numeric123 unique values
0 missing
0.400_0.600.1numeric123 unique values
0 missing
1-Octanolnumeric2 unique values
0 missing
1-Propanolnumeric2 unique values
0 missing
2-Butanolnumeric2 unique values
0 missing
2-propanolnumeric2 unique values
0 missing
1-isobutanolnumeric2 unique values
0 missing

19 properties

125
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
15
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0.12
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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