Conference Papers

[1] Taheri, S.M., Haghighi-Nezhad, I. (2003), Bayesian estimator with fuzzy
data under fuzzy loss function, Proc. of International Conference on Advances
in Statistical Inferential Methods, KIMEP, Almaty (Kazakhstan), pp. 454-459.
[2] Taheri, S.M. (2004), Statistics with non-precise data: a main approach in
the future, Proc. of the International Conference: Statistics- Investment in the
Future, Prague (Czech Republic), In: Metadata Section, pp. 52-62.
[3] Taheri, S.M., Mohammadi, J. (2004), Application of fuzzy regression in soil
science, Proc. of the 8th World Multi-Conference on Systemics, Cybernetics
and Informatics, Florida (U.S.A.), 6: 311-316.
[4] Mohammadi, J., Taheri, S.M. (2004), Imprecise modeling of some physical
and chemical soil characteristics, Proc. of the 5th Iran Conference on Fuzzy
Systems, Tehran (Iran), pp. 215-222.
8
[5] Taheri, S.M. (2004), C-fuzzy numbers, Proc. of the Third World Conference
on Intelligent Systems for Industrial Automation, Tashkent (Uzbakistan), pp.
75-79.
[6] Taheri, S.M., Azizi, R. (2005), Information energy of the image fuzzy sets,
Proc. of the 11th IFSA Congress, Beijing (China), pp. 61-65.
[7] Nasiri, M., Taheri, S.M., Tarkesh, H. (2007), Applying genetic-fuzzy
approach to model polyester dyeing, Proc. of the 12th IFSA Congress, Cancon
(Mexicu), PS20.
[8] Taheri, S.M., Zarei, R. (2007), Bayesian reliability in vague environment,
ISI Bulletin, Vol. LXII, Proc. 56th ISI Conf., Lisboa (Portugal), pp. 2080-2083.
[9] Arefi, M., Taheri, S.M. (2007), Testing statistical hypothesis using fuzzy
critical region in model with nuisance parameter, ISI Bulletin, Vol. LXII, Proc.
56th Conf., Lisboa (Portugal), pp. 4537-4540.
[10] Azizi, M.A., Malaek, B., Ashrafizadeh, M., Taheri, S.M. (2008), Application
of fuzzy system theory to conceptual airplane design cycle, Proc. of the 16th
International Conference on Mechanical Engineering – ISME2008, Kerman
(Iran), pp. 131-138.
[11] Taheri, S.M., Kelkinnama, M. (2008), Fuzzy least absolutes regression,
Proc. of the 4th International IEEE Conference on Intelligent Systems, Varna
(Bulgaria), 11, pp. 55-58.
[12] Taheri, S.M., Mirzaei Yeganeh, S. (2009), Logistic regression with nonprecise
response, Proc. of the 57th ISI Conf., Durban (South Africa), pp. 98-
101.
[13] Mirzaei Yeganeh, S., Taheri, S.M. (2009), Possibilistic logistic regression
by linear programming approach, Proc. of the 7th Seminar on Probability and
Stochastic Processes, Isfahan (Iran), pp. 139-143.
[14] Chachi, J., Taheri, S.M. (2010), Two general classes of similarity
measures on intuitionistic fuzzy sets, Proc. of the 11th International
Conference on Intelligent Technologies, Bangkok (Thailand), pp. 88-92.
9
[15] Taheri, S.M., Hesamian, G., Viertl, R. (2010), Contingency table with
fuzzy categories, Proc. of the 11th International Conference on Intelligent
Technologies, Bangkok (Thailand), pp. 100-104.
[16] Chachi, J., Taheri, S.M. (2011), Fuzzy statistical tests based on fuzzy
confidence intervals, Proc. of the 14th IFSA Congress, Surabaya-Bali
(Indonesia), pp. FS:1-6.
[17] Pourahmad, S., Taheri, S.M., Ayatollahi, S.M.T. (2011), Least squares
fuzzy logistic regression, a new model in clinical diagnosis, Proc. of the 14th
IFSA Congress, Surabaya-Bali (Indonesia), pp. RW-203:1-4.
[18] Chachi, J., Taheri, S.M., Rezaei Pazhand, H. (2011), An interval-based
approach to fuzzy regression for fuzzy input-output data, Proc. of 2011 IEEE
International Conference on Fuzzy Systems, Taipei (Taiwan), pp. 2859-2863.
[19] Chachi, J., Taheri, S.M. (2011), A least-absolutes approach to multiple
fuzzy regression, Proc. of the 58th World Statistics Congress of the ISI
(International Statistical Institute), Dublin (Ireland), CPS077-01.
[20] Namdari, M., Abadi, A., Taheri, S.M., Esmaillzadeh, A., Sarbakhsh, P.
(2012), Application of fuzzy logistic regression in modeling food patterns,
Proc. of the 11th Iranian Statistical Conference, Iran Univ. of Science and
Technology, Tehran (Iran), pp. 413-420.
[21] Poostchi, H., Akbarzadeh-T, M.R., Taheri, S.M. (2012), Distributed
adaptive spanning tree for data gathering in wireless sensor networks, Proc.
of the 2nd IEEE International Conference on Parallel, Distributed and Grid
Computing, India, pp. 147-152.
[22] Hesamian, G., and Taheri, S.M. (2013), Credibility theory oriented sign
test for imprecise observations and imprecise hypotheses, In: R. Kruse et al.
(Eds.), Advances in Intelligent Systems and Computing (No. 190: Synergies of
Soft Computing and Statistics for Intelligent Data Analysis), Springer, pp. 153-
164, (Selected papers of the 6th International Conference on Soft Methods in
Probability and Statistics (SMPS)), Konstanz (Germany.)