Skip Nav Destination
Article navigation
Volume 2850, Issue 1
17 May 2024
INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS SCIENTIFIC APPLICATIONS: ICMSA2022
3–4 March 2022
Chennai, India
- Previous Article
- Next Article
-
REFERENCES
Research Article| May 17 2024
S. Sudha;
S. Sudha a)
a)Corresponding author: sudha.aarpitha@gmail.com
Search for other works by this author on:
This Site
M. Lathamaheswari;
M. Lathamaheswari b)
1
Department of Mathematics, Hindustan Institute of Technology & Science
, Chennai-603 103,
India
Search for other works by this author on:
This Site
Said Broumi
Said Broumi c)
2
Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II
, Casablanca,
Morocco
Search for other works by this author on:
This Site
Author & Article Information
a)Corresponding author: sudha.aarpitha@gmail.com
b)
lathamax@gmail.com
c)
broumisaid78@gmail.com
AIP Conf. Proc. 2850, 080010 (2024)
-
- Split-Screen
- Views Icon Views
- Article contents
- Figures & tables
- Video
- Audio
- Supplementary Data
- Peer Review
- Open the PDF for in another window
- Tools Icon Tools
Cite Icon Cite
- Search Site
Citation
S. Sudha, M. Lathamaheswari, Said Broumi; Long run behaviour of single valued neutrosophic hidden Markov model. AIP Conf. Proc. 17 May 2024; 2850 (1): 080010. https://doi.org/10.1063/5.0209796
Download citation file:
- Ris (Zotero)
- Reference Manager
- EasyBib
- Bookends
- Mendeley
- Papers
- EndNote
- RefWorks
- BibTex
Crop yield forecasting is the social problem in a current situation of the weather condition. Farmers’ livelihoods are critically dependent on a changing environment that they are failing to fathom without access to sophisticated farming techniques and science-based climate and weather data. To deal with this case and to create the attention and awareness to the farmers, this work attempts to seek out the equilibrium condition for the weather to predict and to detect the crop yield forecasting using hidden markov model. Neutrosophic set is that the universal method to deal with indeterminacy. Markov process could be a stochastic model for estimating the equilibrium of any system, whereas in a hidden markov model the current state emits an observation. This work proposes and applies a proposed concept in agricultural field forecasting utilising a hidden markov model with a single valued neutrosophic number.
Topics
Outreach, Hidden Markov models, Markov processes
REFERENCES
1.
L.R. Rabiner
A tutorial on hidden markov models and selected applications in speech recognition
,
IEEE Proceedings
77
(
2
),
257
–
286
(
1989
).
2.
J. M. Koo C. K. Un
Fuzzy smoothing of HMM parameter in speech recognition
,
Electronic Letters
26
(
11
),
7443
–
7447
(
1990
).
3.
J. Dai J. E. M. Tyler I. G. Mac Kenzie
Fuzzy smoothing of HMM parameters using the Parzen window
,
Electronic Letters
29
(
22
),
1911
–
1913
(
1993
).
4.
D. Tran M. Wagner
Fuzzy hidden Markov models speech and speaker recognition
,
18th Int. Conf. North American Fuzzy Information Processing Society-NAFIPS
(
1999
),
426
–
430
.
5.
M. R. Tarihi A. Taheri H. Babebeyk
A new method for fuzzy hidden markov models in speech recognition
,
Int. Conf. Emerging Technologies, IEEE
,
2005
, p.
36
–
40
.
6.
L. Li J. Hu Q. Huang J. Song
A fuzzy hidden markov model for traffic status classification based on video features, Multi conf
.
Computational Engineering in systems applications
, October
2006
, p.
2050
–
2055
.
7.
Y. Li R. Wang J. Xu
Intrusion detection method based on fuzzy hidden markov model
,
sixth Int. Conf. Fuzzy System and knowledge discovery
, August
2009
, p.
470
–
474
.
8.
X. Zhang F. Naghdy
Human motion recognition through fuzzy hidden markov model
,
Int. Conf. Computational Intelligence for Modelling, Control and Automation, and Int. Con on Intelligent Agents, Web Technologies and Internet commerce
, November
2005
, p.
450
–
456
.
9.
H. Uguz A. Ozturk R. Saracoglu A. Arslan
A biomedical system based on fuzzy discrete hidden markov model for the diagnosis of the brain diseases
,
Expert systems with Applications
35
,
1104
–
1114
(
2008
).
10.
H. Ugus A. Arslam R. Saracoglu I. Turkoglu
Detection of heart valve disease by using fuzzy discrete hidden markov model
,
Expert systems with Applications
34
,
2799
–
2811
(
2008
).
11.
F. Salzenstein W. Pieczynski
Parameter estimation in hidden fuzzy markov random fields and image segmentation
,
Graphical models and Image processing
59
(
4
),
205
–
220
(
1997
).
12.
R. Sujatha T.M. Rajalaxmi B. Praba
Fuzzy hidden markov chain for web applications
,
International journal of Information Technology & Decision Making
,
12
(
4
),
813
–
835
.
13.
C. Carincotte S. Derrode S. Bourennane
Unsupervised change detection on SAR images using fuzzy hidden markov chains.
44
(
2
),
432
–
441
(
2006
).
14.
S. Kiefer S. Sistla A. Prasad
2016
).
Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in computer sciens-LICS’16-Distinguishing hidden markov chains
, p.
66
–
75
.
15.
Ali A. Ghorbani Xiaowen Xu
A fuzzy markov model approach for predicting user navigation.
16.
Erik I.
Verriest School of ECE Georgia Institute of Technology Atlanta, GA 30332-0250.
17.
Da Costa G. Claudilene B. Benjamin D. Neto D Adriao
Attanassove’s intutionistic fuzzy probability and markov chain
.
Knowledge-based systems.
43
,
52
–
62
(
2013
).
18.
D. Nagarajan M. Lathamaheswari
2020
)
Long-run behaviour of interval Neutrosophic markov chain in Optimization theory based on Neutrosophic and plithogenic sets
,
151
–
168
. DOI:
.
19.
R. Sujatha T.M. Rajalaxmi
Hierarchical Fuzzy Hidden Markov Chain for Web Applications
,
International Journal of Information Technology & Decision Making
, Vol
15
, (
2016
).
20.
G. Kuppswami R. Sujatha
Markov chain based on Neutrosophic numbers in decision making
,
Kuwali Journal of Science
, Vol
48
(
4
),
1
–
16
(
2021
).
21.
L.R Rabiner
A tutorial on hidden markov models and selected applications in speech recognition
,
Proceedings of the IEEE
, vol
77
, pp.
257
–
286
(
1989
).
22.
H. Wang F. Smarandache Y. Zhang R. Sundarraman
Single valued Neutrosophic sets
,
Multispace Multistruct.
4
,
410
–
413
(
2010
).
23.
S. Ridvan K. Turkey
Multi-criteria Neutrosophic decision making methods based on score and accuracy functions under Neutrosophic environment
. Doi :
(
2015
).
24.
F. Smarandache
Introduction to Neutrosophic measure, Neutrosophic integral and Neutrosophic probability
(
2013
). Doi:
.
This content is only available via PDF.
© 2024 Author(s). Published under an exclusive license by AIP Publishing.
2024
Author(s)
0 Views
View Metrics
Citing articles via
Google Scholar
Sign up for alerts
- Most Read
- Most Cited
Phytochemical analysis of bioactive compounds in ethanolic extract of Sterculia quadrifida R.Br.
Siswadi Siswadi, Grace Serepina Saragih
Radiation measurements at the WMO/CIMO testbed site Lindenberg
Stefan Wacker, Ralf Becker, et al.
The implementation of reflective assessment using Gibbs’ reflective cycle in assessing students’ writing skill
Lala Nurlatifah, Pupung Purnawarman, et al.
