Evaluation of Artificial Neural Network in Classifying Human Gender Based on Odour

Sabri, Ahmed; Alfred, Rayner

doi:10.1007/978-981-10-8276-4_31

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When emitted gases cannot be extracted, this will result in missing values for some of the VOCs gases, in which in most cases it may happen. Hence, the 15 gases dataset will consist of missing values, even when we have decided to choose the short listed 15 gases [7]. Replacing the missing gases with other values such as 1 or 0 or random number between 0 and 1 will be another option.…”

Section: B Classification Results Using Incomplete Datasetmentioning

confidence: 99%

“…The input dataset for all different classifier methods contains 15 Gases obtained from 15 persons [7]. Table 1 shows all stable VOCs emitted by human body.…”

Section: Classifiers Input Data Setmentioning

confidence: 99%

“…Twenty samples of male and female humanodour were then collected with 15 out of 198 (VOCs) selected from each person using both feature selection entropy method and feature selection Chi-square method for detecting and classifying gender via artificial neural networks (ANN). Several topologies of ANN are tested through variation number of neurons and hidden layers as well [7]. Gender detection system reach accuracy percentage of 94.7%, 94.6%, 93.4% with four layers/thirty neurons, three layers/thirty neurons and two layers/twenty neurons of network structures tested respectively [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of Handling Missing Values of VOCS Gases Emitted From Human for Human Detection

Sabri¹,

Alfred²

2019

IJRTE

View full text Add to dashboard Cite

The main objective of this paper is to investigate the effects of replacing missing values of Volatile Organic Compounds (VOC) that represent gases emitted from human on the accuracy of classifying individual person. These missing values will be replaced with three possibilities, which include 0, 1, and Random number between 0 and 1. The effects of using these predefined values on the classification accuracy are investigated by conducting experiments that involve applying a list of classification methods to classify 15 humans using human odour. Each person is characterized by their own pre-selected 15 gases emitted from their sweats. In this paper, we also study and determine the minimum number of gases that is required to produce acceptable results to correctly classify an individual person based on the gases emitted from their bodies. Based on the results obtained from the conducted experiments, the maximum and minimum allowable numbers of missing gases in human odour samples in reference to human emitted gases are 4 and 3. The best accuracy result when missing values are introduced in the odour dataset is the ensemble Bagged Trees.

show abstract

Section: B Classification Results Using Incomplete Datasetmentioning

confidence: 99%

“…The input dataset for all different classifier methods contains 15 Gases obtained from 15 persons [7]. Table 1 shows all stable VOCs emitted by human body.…”

Section: Classifiers Input Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Handling Missing Values of VOCS Gases Emitted From Human for Human Detection

Sabri¹,

Alfred²

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…The Body odour of humans consists of numerous forms of components. Research conducted earlier has focused to identify the relevant Volatile Organic Compounds (VOCs) which can be utilized for detecting gender [5]. Twenty samples of male and female human odour were then collected with 15 out of 198 (VOCs) selected from each person using both entropy and Chi-square test for detecting and classifying gender via artificial neural networks.…”

mentioning

confidence: 99%

Odour Based Human Identification and Classification using Neural Networks

2019

IJRTE

View full text Add to dashboard Cite

Biometrics permits an individual to be authenticated and identified by computer systems following on a set of verifiable and identifiable data that are precise and unique in nature. This mechanism constitutes a cutting-edge method of identifying an individual since it precisely establishes more explicit and direct connection with humans than mere passwords since biometrics tend to use measurable behavioral and physiological characteristics of human. In this paper, a framework for human identification is proposed distinctively based on specific human odour features. 15 samples of female and male human odour are collected from different age groups, only 15 effective Volatile Organic Compounds (VOCs) are chosen. In this paper, several diverse functions of neural network activation are tested such as Levenberg-Marquardt backpropagation, Gradient descent backpropagation, and Resilient backpropagation. Besides, numerous neural network topologies are tested by means of variety hidden layers and different number of neurons and. Different energy functions were tested TAN- Sigmoid transfer, Linear transfer, and LOG- Sigmoid transfer. Considering the obtained results, employing two hidden layers with more neurons in the hidden layers- to be specific: 15 neurons in every layer- has yielded better accuracy in performance with an accuracy rate of 100%. The unsurpassed framework for algorithm learning to be used for human identification can be backpropagation learning algorithm named the Levenberg-Marquardt. The best function for activation established in this paper is the function of TANSigmoid transfer. The performance accuracy consistency in recognizing human can be enhanced using a big number of study samples.

show abstract

A Robust Gender Identification System for Speaker Recognition Using Linear Discriminant Analysis Stepwise Dimension Reduction

Sharma

Singla

2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Evaluation of Artificial Neural Network in Classifying Human Gender Based on Odour

Cited by 4 publications

References 15 publications

Effects of Handling Missing Values of VOCS Gases Emitted From Human for Human Detection

Effects of Handling Missing Values of VOCS Gases Emitted From Human for Human Detection

Odour Based Human Identification and Classification using Neural Networks

A Robust Gender Identification System for Speaker Recognition Using Linear Discriminant Analysis Stepwise Dimension Reduction

Contact Info

Product

Resources

About