application of Artificial neural networking in genetic diversity
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Transcript of application of Artificial neural networking in genetic diversity
Welcome…..
ARTIFICIAL NEURAL NETWORK APPLICATION IN DIVERSITY ANALYSIS
Gajendra C.V2016846101
Forest College and Research Institute, Mettupalayam
FOR - 809 BIOMETRIC ANALYSIS (1+1)
Introduction - ANN
• The bioinformatics refers to the application of computational and mathematical
techniques in biological analysis
• To evaluate, as a strategy for genetic diversity analysis, the bioinformatics approach
(multivariate) called artificial neural network (ANN)
• Information that flows through the network affects the structure of the ANN
because a neural network changes or learns, in a sense based on that input and
output
• ANNs have three layers that are interconnected. The first layer consists of input
neurons. Those neurons send data on to the second layer, which in turn sends the
output neurons to the third layer
• Used in various fields – horticulture, agriculture, forestry, medicine , defence etc.,
Why ANN
• ANN’s can capture more complex features of the data, which is not
always possible with traditional statistical techniques
• The greatest advantage of ANN’s over the conventional methods is that
they do not require detailed information about the physical processes
of the system to be modelled
• Used to characterize the genetic structure plants as criteria and
indicators for the selection of promising genotypes for breeding
programs, aside from the conservation of germplasm
• Used in Perennial species and annuals – wide range of applicability
A distinguishing the biological neuron versus artificial neuron
Comparative schemes of biological and artificial neural system. X= input variable; W= weight of in input; θ= internal threshold value; f=transfer function
Working of ANN
In the development of neural network, two databases were tested to feed the input layer of the network; original means and standardized means, by the technique of
principal components
Best or OptimumHigh or low
Types of artificial neural networksThe are many artificial neural networks……
1. Feed-forward and neural network
2. Radial basis function (RBF) network
3. Kohonen self organising network
4. learning vector quantization
5. Recurrent neural network
6. Modular neural networks
7. Physical neural network
8. Other types of networks
(holographic associative memory)
Using neural networks in practice• Classification
– In marketing : consumer spending classification– In defence: radar and sonar image classification– In medicine: ultrasound and ECG image classification, medical diagnosis– In agriculture : soil map classification (nutrient)
• Recognition and identification– in general Computing and telecommunications: speech, vision and handwriting
recognition– In finance: signature verification and bank note verification
• Assessment– In engineering : Product inspection monitoring and control– In agriculture/ forestry : diversity analysis, identification
• Forecasting and prediction– In finance: foreign exchange rate and stock market forecasting– In agril. : crop yield forecasting– In meteorology : weather prediction
Artificial neural network analysis of genetic diversity in Carica papaya L. (Cibelle et al., 2011)
• The study of genetic diversity is fundamental in the preliminary selection of accessions with superior characteristics – fruit agronomic traits.
• n= 37, m=8, k=4
Fruit weight, fruit length, fruit diameter, flesh thickness, firmness, external and internal fruit, soluble solids and incidence of skin freckles
Kohonen Neural Networks - KNN Artificial Neural Networks ANN
1. To find out the best matching neuron in terms of similarity –
criterion of minimum distance between the accessions.
2. The synaptic weight vector is the criterion for acceptance or
rejection of a group of accessions or plants
3. The similarity between the input and the neuron was measured
as the average Euclidean distance between vectors
Output ANN
The groups generated by the ANN facilitate the selection of divergent genotypes for improvement by the generation of hybrids, since they allow the selection of genotypes indicated for crosses from different heterotic groups. Thus, the probability of obtaining
superior genotypes is greater
Energy input output analysis and application of artificial neural networks for predicting greenhouse basil production
(Pahlavan et al., 2012)
The ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions
Artificial neural networks as a tool for plant identification: A case study on Vietnamese tea accessions
(Camilla Pandolfi et al., 2009)
Output graphs obtained by the BPNN. Each frame is dedicated to a specific accession and shows the BPNN output for the input represented by the phyllometric parameters of 40 leaves. Reported lines show the averaged
output data
Back-Propagation Neural Networks
The dendrogram obtained from the UPGMA clusteranalysis of the 17 tea accessions.
Artificial Intelligence: A novel approach to model, understand and optimize cereals genetic transformation
• To understand the cereal genetic transformations the ANNs, genetic algorithms and neuro-fuzzy logic, have been employed in plant science
Objective : To find the combination of inputs that will provide the “optimum/best/highest”
Application of ANN in forestry
Artificial Neural Networks in Forest Resource Management includes….– Forest land mapping and classification – Forest growth and dynamics modelling– Spatial data analysis and modelling– Plant disease dynamics modelling– Climate change and ecology– Predicting Tree Height and Forest Stock Volume– Hydrology – predicting the surface runoff
Changhui Peng and Xuezhi Wen, 1999
Conclusion
• Neural networks are regularly used to model parts of living organisms and to investigate the internal mechanisms of the brain
• It was observed that the neural network was not influenced by scale of input data. The classification by original data was the same as when using standardized data
• The neural network tends to perform better when the data are more heterogeneous, characterizing the plants with regard to their groups
Thank you……