[1] Chou YC, Yao L. Automatic diagnosis system of electrical equipment using infrared thermography. In: Proceedings of the international conference of soft computing and pattern recognition; 2009. p. 155–160.
[2] NFPA, NFPA 70B: Recommended practice for electrical equipment maintenance, Quincy, Massachusetts. National fire protection association; 2006.
[3] Standard for Infrared Inspection of Electrical Systems & Rotating Equipment, Infraspection Institute, 2008 (2011) 〈http://www.armco-inspections.com/files/ ir/Electrical%20Rotating%20Std.pdf〉.
[4] ASTM, ASTM E 1934: Standard Guide for Examining Electrical and Mechanical Equipment with Infrared Thermography. West Conshohocken, Pennsylvania. ASTM International; 2005.
[5] A.S.N. Huda, S. Taib , K.H. Ghazali , M.S. Jadin. A new thermographic NDT for condition monitoring of electrical components using ANN with confidence level analysis. ISA Transaction (2014).
[6] M.E. Tavakol, C. Lucas, S. Sadri, E.Y.K. Ng, Analysis of breast thermography using fractal dimension to establish possible difference between malignant and benign patterns, Journal of Health Care Engineering 1 (1) (2010) 27–43.
[7] Xinsheng Zhang, Xinbo Gao.Minghu Wang, MCs Detection Approach Using Bagging and Boosting based Twin Support Vector.IEEE International Conference on systems, Man and Cybernetics San Antonio,TX,USA-October 2009.
[8] Lizak F, Kolcun M. Improving reliability and decreasing losses of electrical system with infrared thermography. Acta Electrotechn Informa 2008;8,(1):60-3
[9] Mohd Shawal Jadin, Soib Taib, Suriadi. Evaluating the Thermal Condition of Electrical Equipment via IRT Image Analysis. 8th IMT-GT Uninet Biosciences Conference Banda Aceh. (2012) 22-24
[10] Rajesh Kumar Tripathy, Sailendra Mahanta and Subhankar Paul. Artificial intelligence-based classification of breast cancer using cellular images. An international journal to further the chemical sciences.Issue 18, 2014.
[11] Bartosz Krawczyk, Gerald Schaefer. A hybrid classifier committee for analysing asymmetry features in breast thermograms. Applied Soft Computing.Elseiver. 20 (2014) 112–118.
[12] Cruz-Ramirez Nicandro, Mezura-Montes Efren,Ameca-Alducin María Yaneli, Martin-Del-Campo-Mena Enrique, Acosta-Mesa Hector Gabriel, Pérez-Castro Nancy, Guerra-Hernández Alejandro, Hoyos-Rivera Guillermo de Jesus, and Barrientos-Martínez Rocio Erandi. Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using
Bayesian Network Classifiers. Hindawi Publishing CorporationComputational and Mathematical Methods in Medicine Volume 2013, Article ID 264246, 10 pages.
[13] Marina Milosevic, Dragan Jankovic, Aleksandar Peulic. Thermography Based Breast Cancer Detection Using Texture Features and Minimum Varianc Equantizat. ISSN 1611-2156.EXCLI Journal (2014)1204-1215.
[14] Gang-Min Lim,Dong-Myung Bae and Joo-Hyung Kim.Fault diagnosis of rotating machine by thermography method on support vector machine.Journal of Mechanical Science and Technology 28(8) (2014) 2947-2952.
[15] S.Julian Savari Antony, Dr.S.Ravi. Breast Cancer Detection on Thermogram at Preliminary Stage by Using Fuzzy Inferences System. Journal of Theoretical and Applied Information Technology.Elseiver. 31st October 2014. Vol. 68 No.3.
[16] http://www.heico.ir/standards.aspx(2015.12.20).
[17] J. Udupa and S. Samarasekera, “Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation,” Graph. Model. Image Process. 1996.
[18] Rafael C. Gonzalez Richard E. Woods.digital image processing, Prentice Hall; 3 edition (August 31, 2007). ISBN-10: 013168728X-ISBN-13: 978-0131687288.
[19] A. Gebejes , R. Huertas. Texture Characterization based on Grey-Level Co-occurrence Matrix. Conference of Informatics and Management Sciences. March, 25. - 29. 2013.
[20] Tsair-Fwu Lee, Ming-Yuan Cho and Fu-Min Fang2. Features Selection of SVM and ANN Using Particle Swarm Optimization for Power Transformers Incipient Fault Symptom Diagnosis. International Journal of Computational Intelligence Research. ISSN 0973-1873 Vol.3, No. 1 (2007), pp. 60-65.
[21] Divya Tomar,Sonali Agarwal.Twin Support Vector Machine: A review from 2007 to 2014.Egyptian Informatics Journal (2015)16,55-69.
[22] Mohd Shawal Jadin, Soib Taib,Kamarul Hawari Ghazali. Feature extraction and classification for detecting the thermal faults in electrical installations.Elsevier.57 (2014)15–24
[23] Tsair-Fwu Lee, Ming-Yuan Cho and Fu-Min Fang2. Features Selection of SVM and ANN Using Particle Swarm Optimization for Power Transformers Incipient Fault Symptom Diagnosis. International Journal of Computational Intelligence Research. ISSN 0973-1873 Vol.3, No. 1 (2007), pp. 60-65
[24] A. S. Nazmul Huda · Soib Taib. A Comparative Study of MLP Networks Using Backpropagation Algorithms in Electrical Equipment Thermography. Arab J Sci Eng (2014) 39:3873–3885.
[25] Van Tung Tran,Tan Tung Phan,and Bo-SukYang. Intelligent Fault Diagnosis System using BEMD based Thermal Image Enhancement and Support Vector Machines. October 27-28, 2011.
[26] U Rajendra Acharya, Tan Jen Hong, Joel E W Koh, Vidya K Sudarshan, Sharon Yeo Wan Jie, Too Cheah Loon, Chua Kuang Chua, E Y K Ng, Louistong. Automated Diagnosis of Dry Eye Using Infrared Thermography Images. 08 October 2015.
[27] Jayadeva R. and Chandra S., "Twin Support Vector Machines for Pattern Classification," IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. 29, pp. 905-910,2007.
[28] Webb G. I. and Zheng Z., "Multistrategy ensemble learning reducing error by combining ensemble learning techniques Knowledge and Data Engineering, IEEE Transactions onvol. 16, pp. 980-991, 2004 .
[29] Zhi-Hua Z. and Yang Y., "Ensembling local learners Through Multimodal perturbation," Systems, Man, and Cybernetics ,Part B, IEEE Transactions on, vol. 35, pp. 725-735, 2005.
[30] Zhang X. Boosting twin support vector machine approach for MCs detection. In: Asia-pacific conference on information processing (APCIP 2009), vol. 1; 2009. p. 149–52.
[31] A. del Amo, J. Montero and V. Cutello, (1999) On the principles of fuzzy classification, Proceedings of 18th North American FuzzyInformation Processing Society Annual Conference, 675–679.
[32] G. Schaefer, T. Nakashima, M. Zavisek, Analysis of breast thermograms based on statistical image features and hybrid fuzzy classification, LNCS 5538, Springer, 2008, pp. 753–762.
Thank you for copying data from http://www.arastirmax.com