Presentation1 Associate Professor

Sanjeev Karmakar MCA (Hon.) MTech (CSE), PhD, DSc (Reg.), API: 1100
Associate Professor.
Life and Governing member of: IAENG USA, IAHS USA, ICSR, ISSSLUP India
www. www.bitdurg.org.
Mob: 9340403165; E-mail: dr.karmakars@gmail.com
© 2016,2017; Government of India.
Artificial intelligent chaos forecasting and
interpolation system AI-CFS 1.0 & AI-CIS 1.0
: A Java based system for development of OBPN for CHAOS forecasting.

Natural Neural Network
Vs
Artificial Neural Network
2
Dr. sanjeev karmakar

MY AREA OF RESEARCH using ANN
• Bio-Informatics
• Knowledge Extraction
• Transporters
• Forecasting of chaos.
• Healthcare
• Clinical diagnosis
• Medical image analysis
and interpretation
• ECG signal analysis.
• Drug development.
• Communications
• Wireless circuit
• Non-linear subsystems
• Image processing and compression
• Real time data compression
• Image compression and restoring
• Human face detection.
• Intrusion detection
• Robotics
• Compression
• Business
• Stock market prediction
• Searching best business in www.
• Control System
• Pattern recognition
• Hand written character detection
• Voice detection.
• Satellite cloud classification
• Face recognition
• Traveling salesman problem.
• Interpolation.
3
Dr. Sanjeev karmakar

General Neural Network
Fig. 1: Neural network
4

yk
z1 zp
xix1 xn
1
1
v01
v0j
w0k w1k w2k
v11 v12 vi1 vip vn1
vnp
Dependent variable
Independent variables
zj
v0p
v1j vij
vnj
 

 x
e
xf
1
1
)(
Used BPN Skeleton
Fig. 2: Neural network for forecasting
5

Fig. 2: Non-linear multiple regression (Parametric forecast)Fig. 1: Time Series Analysis and Forecasting
),...,,,,...,,...,,,( 3213211 wwwxxxxfx nn  ),...,,,,...,,...,,,( 321321 wwwppppfT n
BPN in AI-CFS & AI-CISv1.0
6

Obtaining OBPN using AI-CFSv1.0 and AI-
CISv1.0
Optimization of ANN parameters likes :-
i. Number of input vectors (n).
ii. Number of hidden layers (m).
iii. Number of neurons in hidden layers (p).
iv. Number of output neurons (y).
v. Weights and biases . (v, w0i, )
vi. Learning rate (α),
vii. Momentum factors (µ)
viii. Global minima (MG)
ix. Number of epochs (e)
Note: Parameters are data time series specific.
Fig. 3: Optimized ANN
7

Impact of α and µ
Learning rate & momentum factors (α and µ).
Fig. 4.
Performance of the ANN in
α = 0.4 and µ = 0.7; Epochs (e) = 15× 105
Fig. 5.
Performance of the ANN in
α = 0.2 and µ = 0.8; Epochs (e) = 15× 105
wjk (t+1) = wjk(t) + αδkzj + μ {wjk(t) – wjk(t-1)}
vjk(t+1) = vjk(t) + αδkzj + μ {vjk(t) – vjk(t-1)}
vjk = αδjxi
8

Training and Global minima (MG)
Fig. 6. Optimization of model error (MG)
9

AI-CFS v 1.0 can applied for:
Intelligent system configured automatically based on its input variables and target variable.
This system is vary use full in:
1. Meteorological forecasting problems (NMS, IITM Pune, MoES, Government of India.
2. Stock market forecasting problems.
3. Control system and Steel Industry.
4. Other chaos prediction problems.
10

OUTCOME “AI-CFS v 1.0 and AI-
CISv1.0”
Performance:
1. Successfully applied on Spatial Interpolation Rainfall over Mahanadi
river basin.
2. Applied successfully in hot metal prediction and Controlling.
3. Long-range forecast of Monsoon rainfall over smaller region.
4. Successfully applied to make relationship between Astrological
parameters with rainfall over Mahanadi river basin.
Academic :
1. Two PhD and Two M.Tech degree awarded through this system.
Technology:
2. Three copyrights.
3. Publications (Books) and (Journals)
11

Patents/Copyrights (IPR)
Patent/Copyrights/Technology transfer /Product/ Process at national level
(Science): No. 09
No Number Country Title Year
1. 54497/2014-CO/SW India Artificial Intelligent Chaos Forecasting System Version 1.0
(AI-CFS v 1.0).
Filed : 2014
Registered: Feb 2016
2. 54496/2014-CO/SW India Artificial Intelligent Climate Interpolation System Version 1.0
(AI-CFS V 1.0).
Filed : 2014
Registered: Feb 2016
3. 3127/2017-CO/SW India Chhattisgarh Soil Meteorological Information System
(www.cgmetis.in).
Registered: Sep 2017
4. SW-10355/2018 India CR-DCE-SSPv1.0 (Class quantum resistance delayed capability
exchange secure simple pairing.
Filed: Dec. 2017
Registered: May 2018
5. SW-11582/2018 India SSP-APK-DECEv 1.0. 11/08/2018.
6. SW-12848/2019 India Innovative training algorithm of BP neural network up to global
minima for n-parametric forecast.
31/07/2019
Registered 19/9/2019
7. SW-12864/2019 India Innovative algorithm to obtain optimum BP neural network
(OBPN) for chaos prediction…
31/07/2019
8. SW-12857/2019 India Pixel-peak based fractal image compression for grayscale images 31/07/2019
9. 12067/2019-CO/SW India Innovative Training algorithm of Back-Propagation Neural
Network up to global minima for Time Series Forecast and Its
Software Implementation using Java
31/07/2019
Re-Scrutiny
12

13
(AI-CFS v 1.0) ; NMM IITM Pune

Case :#1
Modeling and Simulation of OBPN for forecasting of Long-
Range monsoon AWR over a smaller homogeneous region
through Astrological Parameters and its verification for 2019.
 Sanjeev Karmakar1 & Shreerup Goswami2
 1Bhilai Institute of Technology, Bhilai House, Durg-491001, Chhattisgarh, India
 2Department of Earth Sciences, Sambalpur University, Jyoti Vihar, Burla-768019, Odisha, India.
 e-mail: dr.karmakars@gmail.com; goswamishreerup@gmail.com
Accepted : Iran Journal of Computer Science (Springer Nature), Accepted to be published
14

Fig. Mahanadi river basin, Chhattisgarh, India. Geographically located at 80° 28' to 86° 43' E 19° 8' to 23° 32' N, Total Catchment area
Chhattisgarh + Odissa is 141589 Km2. Average Water Resource Potential (MCM) is 66880. Source: India-WRIS (http://india-
wris.nrsc.gov.in) and Google Map.
15

Fig.: Astrological System (Effective region)
16

Year Nakshatra
and Charan
Rashi
and
Lagna
Position of Graha and Rashi AWR
in mm.
1 2 3 4 5 6 7 8 9 10 11 12
2001 7.2 4.2 1.62 2.35 3.8 4.7 5 6 7 8 9.1 10 11 12.4 7555.3
2002 17.3 9.2 1.6 2.34158 3.3 4 5 6 7 8.9 9 10 11 12 6361.3
2003 27.4 1 1.63 2 3.5 4.3 5 6 7 8.9 9 10 11 12.41 7852.3
2004 9.4 5 1.68 2.615 3 4 5.37 6 7.9 8 9 10 11 12.2 6269.0
2005 2.1 10 1.64 2 3.5 4 5 6.39 7 8 9 10 11.1 12.28 6466.6
2006 3.2 2.2 1.5 2 3.1 4.5 5 6.9 7.3 8 9 10 11 12.248 6927.5
2007 12.1 2 1.62 2.4 3 4.5 5.9 6 7 8.3 9 10 11.18 12 6710.8
2008 23.4 2.11 0 2.2 3 4.1 5.59 6 7 8 9.3 10 11.78 12 3983.8
2009 6.1 2.2 1.6 2 3 4.7 5.5 6 7 8 9 10 11 12.41 5440.0
2010 16.1 8.2 1.62 2.4 3.9 4.1 5 6.5 7 8 9.8 10 11.3 12 6903.2
2011 26.2 12.2 1.6 2 3.9 4 5 6 7 8 9.8 10 11 12.241 6577.2
2012 8.3 5.2 1.36 2.6 3 4 5.71 6 7.5 8.8 9 10 11 12.2 6678.5
2013 19.1 9.2 1.62 2.3 3 4 5 6 7.58 8 9 10 11 12 7969.1
2014 1.4 2.2 1.63 2 3.3 4 5 6.1 7.58 8 9 10 11 12.4 7462.5
2015 11.1 6.2 1.61 2.24 3 4.3 5 6.87 7 8.5 9 10 11 12.9 5388.2
2016 22.2 10.2 1.62 2 3 4 5.38 6 7 8.15 9 10 11.9 12 6273.1
2017 5.1 3.2 1.26 2.1 3 4 5.8 6.3 7 8 9.5 10 11 12.46 6238.4
2018 14.3 7.2 1 2.6 3 4.8 5 6 7.37 8 9.15 10.9 11 12.26 ?
2019 24.4 12.2 1.6 2.1 3.8 4 5 6 7 8.3 9.95 10 11 12.274 ? 17

Fig. 5: MSEs after 10 epochs of training
with different values of
p=2,3,4,…,20 in 5 experiments
Fig. 6: MSE in different values of
p = 5, 2, 5, 4 and 8
Fig. 7: Models and corresponding MSEs
with e=10
Experiments Modeling MSE
1. n=4, m=1, p=5, α=0.3, µ= 0.74, e=10 3.45443E-04
2. n=4, m=1, p=2, α=0.61, µ=0.89, e=10 3.24608E-04
3. n=4, m=1, p=5, α=0.45, µ=0.9, e=10 3.49492E-04
4. n=4,m=1, p=4, α=0.3, µ=0.95, e=10 3.35083E-04
5. n=4, m=1, p=8, α=0.17, µ=0.77, e=10 3.38727E-04
18

Fig. 8. MSE after 500000 epochs of training of 03
different significant architecture
Fig. 9. MAD % of LPA between actual and predicted after
500000 epochs of training and in testing independently
of 03 different significant architecture
19

Fig. 10. MSEs with different values of epochs Fig. 11. MAD % of LPA with different number of epochs
20

Fig. 12. Performance of training and testing with different number of epochs
21

22
Yr Actual
(% of
LPA
Deviation from Actual (in %)
4864 e=100000 e=200000 e=300000 e=400000 e=500000 e=600000 e=7000000 e=1000000
2001 115.7 11.2 10.7 10.5 9.1 8 8.6 7.7 7.4 7.4
2002 97.4 7.1 7.2 7.1 7.3 7.4 7.3 7.6 7.6 7.6
2003 120.2 16.1 15.8 15.7 15.1 14.7 14.9 14.5 14.3 14.3
2004 96 10.1 9.8 9.5 8.8 8.2 8.4 7.9 7.6 7.5
2005 99 5.4 5.6 5.7 6.4 6.8 6.6 7.1 7.2 7.3
2006 106 3.6 3.7 3.9 4.3 4.6 4.5 4.6 4.6 4.6
2007 102.7 1.9 1.5 1.1 0.1 1.1 0.7 1.7 2.1 2.4
2008 61 0.6 0.2 0.2 0.3 0.4 0.4 0.4 0.5 0.5
2009 83.3 20.8 20.5 20.3 19.3 18.6 18.9 18.2 17.9 17.8
2010 105.7 2.9 2.3 1.8 0.6 0.5 0.1 0.9 1.3 1.4
2011 100.7 2.6 3.1 3.6 4.8 5.9 5.5 6.2 6.6 6.7
2012 102.2 2.6 2.9 3.3 4.6 5.7 5.2 6.2 6.6 6.7
2013 122 18 18.3 18.1 18.9 19.2 18.8 19.2 19.2 19
Yr Actual
(% of
LPA
Deviation from actual (in %)
e=4864 e=100000 e=200000 e=300000 e=400000 e=500000 e=600000 e=7000000 e=1000000
2014 114.2 9.4 9.4 9.3 9 8.8 8.9 8.6 8.6 8.5
2015 82.5 22.1 21.9 21.9 21.4 21 21.2 21.1 21 21.1
2016 96 8.8 8.5 8.3 7.5 6.9 7.1 6.7 6.5 6.4
2017 95.5 3.4 3.2 2.4 1.5 0.6 0.6 0 0.4 0.7
Table.18: Testing Performance- deviation from actual (in %)
Table.16: Training Performance - deviation from actual (in %)

Fig.13. Deviation between actual and predicted in % with different number of epochs
23

Year Actual AWR
(in mm.)
Predicted AWR
(in mm.)
Deviation Actual
(% of LPA)
Predicted
(% of LPA)
Deviation
(% of LPA)
2018 6532.8 5902.2 630.6 100 90.4 9.6
2019 6472.6 6940.4 467.8 99.1 106.3 7.2
Table 20. Forecasted AWR over Maghanadi river basin Chhattisgarh India for the year of 2018 and 2019.
24

Conclusions
In this presentation effort for modeling of ANN for chaotic motion like Climate data series in
parametric and time series forecasting. Before training the optimum architecture of ANN is pre-
requisite . And also training up to the global minima is must for optimum performance .
The systems AI-CFS v1.0 is ready for meteorologist, scientist, as well as planners those are
involved in meteorological problems. This system may also useful in downscaling of CFS v 2.0 USA
model (WMO problem) to LRF of monsoon rainfall over smaller region. Also this system is useful
for scholars those are involved in research specially “chaos prediction” .
www.cgmetis .in (recently uploaded) is available online is direct useful for the agriculture
planners, researchers of the state and scholars.
26

Relevant Publications
Journals.
[1]. BPN Model for Long-Range Forecast of Monsoon Rainfall over a Very Smaller Geographical
Region and Its Verification for 2012, J. GEOFIZIKA, 30, pp. 49-66, 2013. (Ex. SCI Journal)
[2]. Impact of Learning Rate and Momentum Factor in the Performance of Back-Propagation
Neural Network to Identify Internal Dynamics of Chaotic Motion, Kuwait J. Sci. 41 (2), pp.151-
174, 2014. (Ex. SCI Journal)
Books.
[1]. Applications of ANN in long-range weather forecasting, Lambert Academic publishing,
ISBN: 978-3-659-26670-6, Akademikerverlag GmbH & Co. KG, Germany,2013.
[2]. Development of back-propagation neural network for prediction of chaotic data time
series. ISBN: 978-3-8325-3304-5, Logos Verlag Verlin GmbH, Germany, 2012.
27

Dr. Sanjeev Karmakar
Associate Professor (Computer Science & Applications)
Bhilai Institute of Technology, Durg
Bhilai House, Durg, 491001, Chhattisgarh
Phone: 0788- 2321163/ 2323997 / 2334424, Fax: 0788-2210163,
Mob: 9340403165.
www.bitdurg.org
Secretary, IRNet Research Group, Chhattisgarh Chapter, INDIA.
Advisory Board Member, Int. J. of ANN, BioInfo Publications, INDIA.
IEEE trans Reviewer.
Expert Board of Study, Computer Science, Kalyan PG College, Bhilai, Chhattisgarh, India.
Co-Editor, CSVTU Research Journal, INDIA.
PhD supervisor of Government Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, INDIA.
Membership:
International Association of Engg. USA.
International Association of Hydrological Science (IAHS) USA.
Indian Society of Soil Survay and Land Use Planning (ISSSLUP) India.
Governing member : International Consortium of Scientists and Researchers, USA.
THANKS
28

Presentation1 Associate Professor

Recommended

Recommended

More Related Content

Similar to Presentation1 Associate Professor

Similar to Presentation1 Associate Professor (20)

Recently uploaded

Recently uploaded (20)

Presentation1 Associate Professor