﻿<?xml version="1.0" encoding="utf-8"?><doi_batch xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd"><head><doi_batch_id>jist-2026060112</doi_batch_id><timestamp>20260601125753</timestamp><depositor><depositor_name>CMV Verlag</depositor_name><email_address>khoffmann@cmv-verlag.com</email_address></depositor><registrant>CMV Verlag</registrant></head><body><journal><journal_metadata language="en"><full_title>Journal of Information Systems and Telecommunication (JIST) </full_title><abbrev_title>jist</abbrev_title><issn media_type="electronic">2322-1437</issn></journal_metadata><journal_issue><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><journal_volume><volume>4</volume></journal_volume><issue>14</issue></journal_issue><journal_article publication_type="full_text"><titles><title>Privacy Preserving Big Data Mining: Association Rule Hiding</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Golnar Assadat	</given_name><surname>Afzali</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>shahriyar</given_name><surname>mohammadi</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.001</doi><resource>http://jist.ir/en/Article/14893</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14893</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14893</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14893</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14893</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14893</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14893</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14893</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	Chen,C.L.P. &amp;amp;Zhang,Ch. (2014). Data Intensive Applications, Challenges, Techniques, and Technologies: A Survey on Big Data. Information Science, Vol.275, pp.314-347.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	Kwon,O. Lee,N&amp;amp;Shin,B. (2014). Data Quality Management, Data Usage Experience and Acquisition Intention of Big Data Analytics, International Journal of Information Management, Vol.34, No.3,pp 387-394.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	Cuzzocrea,A. Leung,C.K.S&amp;amp;Mackinnon,R.K. (2014). Mining Constrained Frequent Item-Sets from Distributed Uncertain Data, Future Generation Computer Systems, Vol.37, pp 117-126.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	Zhang,X. Liu,Ch. Nepal,S. Yang,Ch. Dou,W&amp;amp;Chen,Jinjun. (2014) A Hybrid Approach for Scalable Sub-Tree Anonymization over Big Data using MapReduce on Cloud, Journal of Computer and System Science, Vol.80, No.5, pp 1008-1020.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	Li,Y. Chen,M. Li.Q&amp;amp;Zhen,W. (2012). Enabling Multilevel Trust in Privacy Preserving Data Mining, IEEE Transaction on Knowledge and Data Engineering, Vol.24, No.9, pp 1589-1612.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>#[6]	Wu,Y.H. Chiang,C&amp;amp;Chen,A.L.P. (2007), Hiding Sensitive Association Rules with Limited Side Effects, IEEE Transaction on Knowledge and Data Engineering, Vol.19, No.1, pp 29-42.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>#[7]	Gkoulalas.D,A&amp;amp;Verykios,V.S. (2009). Exact Knowledge Hiding through Database Extension, IEEE Transaction on Knowledge and Data Engineering, Vol.21, No.5, pp 699-713.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>#[8]	Le,H.Q. Arch-int,S. Nguyen,H. Xuan, Arch-int, N. (2013).Association Rule Hiding in Risk Management for Retail Supply Chain Collaboration, Computer in Industry, Vol.64, No.4, pp776-784.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>#[9]	Li,Y.Ch. Yeh,J.Sh&amp;amp;Chang,Ch. (2007), MCIF: An Effective Sanitization Algorithm for Hiding Sensitive Patterns on Data Mining, Advanced Engineering Informatics, Vol.21, No.3, pp 269-280.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>#[10]	Keshavamurthy,B.N. Toshniwal,D&amp;amp;Eshwar,B.K. (2012). Hiding Co-Occurring Prioritized Sensitive Patterns over Distributed Progressive Sequential Data Streams, Journal of Network and Computer Applications, Vol.35, No.3, pp1116-1129.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>#[11]	Wu,X. Zhu,X. Wu,G&amp;amp;Ding,W. (2013). Data Mining with Big Data, IEEE Transaction on Knowledge and Data Engineering, Vol.26, No.1, pp 97-107.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>#[12]	Nergiz, M.E &amp;amp;Gok, M.Z. (2014). Hybrid K-Anonymity, Computers &amp;amp; Security, Vol.44, pp 51-63.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>#[13]	Li, B. Erdin, E. Gunes, M.H. Bebis, G. Shipley,T. (2013). An Overview of Anonymity Technology Usage, Computer Communication, Vol.36, No.12, pp 1269-1283.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>#[14]	Monreale,A. Andrienko,G. Andrienko,N. Giannotti,F. Pedreschi,D. Rinzivillo, S &amp;amp;Wrobel, S. (2010). Movement Data Anonymity through Generalization, Transactions on Data Privacy, Vol.3, No.2.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>#[15]	Kisilevich,S. Rokach,L. Elovici,Y. Shapira, B. (2010). Efficient Multidimensional Suppression for K-Anonymity, IEEE Transaction on Knowledge and Data Engineering, Vol.22, No.3, pp 334-347.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>#[16]	Zhang, G. Yang,Y. Liu, X &amp;amp; Chen, J. (2010). A Time-Series Pattern Based Noise Generation Strategy for Privacy Protection in Cloud Computing, International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp 458-465.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>#[17]	Wang, H. (2013). Quality Measurement for Association Rule Hiding, AASRI Procedia, Vol.5, pp 228-234.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>#[18]	Moustakides,G.V&amp;amp;Verykios, V.S. (2008). A MaxMin Approach for Hiding Frequent Item Sets, Data &amp;amp; Knowledge Engineering, Vol.65, No.1, pp 75-89.	</unstructured_citation></citation><citation key="ref19"><unstructured_citation>#[19]	Wang, Sh. Parikh,B&amp;amp;Jafari, A. (2007). Hiding Informative Association Rule Sets, Expert Systems and Applications, Vol.33, No.2, pp 316-323.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>#[20]	Wang,Ch. Tseng,Sh&amp;amp;Hongm, T. (2006). Flexible Online Association Rule Mining Based on Multidimensional Pattern Relations, Information Science, Vol.167, No.12, pp 1752-1780.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>#[21]	Dasseni, E. Verykios,V.S. Elmagarmid,A.K&amp;amp;Bertino,E. (2001). Hiding Association Rules by Using Confidence and Support, Information Hiding Lecture Notes in Computer Science, Vol.2137, pp 369-383.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>#[22]	Jung, K. Park,S. Cho,S&amp;amp;Park,S. (2014). A Novel Privacy Preserving Association Rule Mining using Hadoop, The Third International Conference on Data Analytics, pp 131-137. </unstructured_citation></citation><citation key="ref23"><unstructured_citation>#[23]	Xu, L. Jiang, C. Wang,J. Yuan,J&amp;amp;Ren, Y. Information Security in Big Data: Privacy and Data Mining. IEEE Access, vol.2, pp. 1149-1176.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>#[24]	Borgelt,Ch&amp;amp; Kruse,R. (2002). Introduction of Association Rules: Apriori Implementation, Physica- Verlog Heidelberg, pp 395-400.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>#[25]	Borgelt.Ch. (2005). An Implementation of the FP-Growth Algorith, Proceeding of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pp 1-5.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>COGNISON: A Novel Dynamic Community Detection Algorithm in Social Network</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Hamideh Sadat</given_name><surname>Cheraghchi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Ali</given_name><surname>Zakerolhossieni</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.002</doi><resource>http://jist.ir/en/Article/14894</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14894</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14894</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14894</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14894</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14894</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14894</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14894</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	Newman, M.E., Finding community structure in networks using the eigenvectors of matrices. Physical review E, 2006. 74(3): p. 036104.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	Aynaud, T., et al., Communities in evolving networks: Definitions, detection, and analysis techniques, in Dynamics On and Of Complex Networks, Volume 2. 2013, Springer. p. 159-200.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	Takaffoli, M., et al. Tracking changes in dynamic information networks. in Computational Aspects of Social Networks (CASoN), 2011 International Conference on. 2011. IEEE.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	Greene, D., D. Doyle, and P. Cunningham. Tracking the evolution of communities in dynamic social networks. in Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on. 2010. IEEE.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	Falkowski, T., J. Bartelheimer, and M. Spiliopoulou. Mining and visualizing the evolution of subgroups in social networks. in Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence. 2006. IEEE Computer Society.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>#[6]	Chakrabarti, D., R. Kumar, and A. Tomkins. Evolutionary clustering. in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. 2006. ACM.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>#[7]	G&amp;#246;rke, R., et al., Dynamic graph clustering combining modularity and smoothness. Journal of Experimental Algorithmics (JEA), 2013. 18(1): p. 1.5.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>#[8]	Chi, Y., et al. Evolutionary spectral clustering by incorporating temporal smoothness. in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 2007. ACM.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>#[9]	Lin, Y.-R., et al. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. in Proceedings of the 17th international conference on World Wide Web. 2008. ACM.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>#[10]	Papadopoulos, S., et al., Community detection in social media. Data Mining and Knowledge Discovery, 2012. 24(3): p. 515-554.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>#[11]	Lin, Y.-R., et al., Analyzing communities and their evolutions in dynamic social networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 2009. 3(2): p. 8.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>#[12]	Zhang, J., et al. On-line Evolutionary Exponential Family Mixture. in IJCAI. 2009.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>#[13]	Newman, M.E., Fast algorithm for detecting community structure in networks. Physical review E, 2004. 69(6): p. 066133.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>#[14]	Cafieri, S., P. Hansen, and L. Liberti, Locally optimal heuristic for modularity maximization of networks. Physical Review E, 2011. 83(5): p. 056105.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>#[15]	Eaton, E. and R. Mansbach. A Spin-Glass Model for Semi-Supervised Community Detection. in AAAI. 2012.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>#[16]	Good, B.H., Y.-A. deMontjoye, and A. Clauset, Performance of modularity maximization in practical contexts. Physical Review E, 2010. 81(4): p. 046106.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>#[17]	Ning, H., et al., Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recognition, 2010. 43(1): p. 113-127.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>#[18]	Tang, L., H. Liu, and J. Zhang, Identifying evolving groups in dynamic multimode networks. Knowledge and Data Engineering, IEEE Transactions on, 2012. 24(1): p. 72-85.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>#[19]	Hastings, M.B., Community detection as an inference problem. arXiv preprint cond-mat/0604429, 2006.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>#[20]	Krasnow, M.M., et al., Meeting now suggests we will meet again: Implications for debates on the evolution of cooperation. Scientific reports, 2013. 3.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>#[21]	Pachur, T., L.J. Schooler, and J.R. Stevens, We&amp;#39;ll Meet Again: Revealing Distributional and Temporal Patterns of Social Contact. 2014.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>#[22]	Chi, Y., et al., On evolutionary spectral clustering. ACM Transactions on Knowledge Discovery from Data (TKDD), 2009. 3(4): p. 17.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>#[23]	Xu, K.S., M. Kliger, and A.O. Hero Iii, Adaptive evolutionary clustering. Data Mining and Knowledge Discovery, 2014. 28(2): p. 304-336.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>#[24]	Wu, M. and B. Sch&amp;#246;lkopf. A local learning approach for clustering. in Advances in neural information processing systems. 2006.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>#[25]	Strehl, A. and J. Ghosh, Cluster ensembles---a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research, 2003. 3: p. 583-617.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>#[26]	Yao, Y., Information-theoretic measures for knowledge discovery and data mining, in Entropy Measures, Maximum Entropy Principle and Emerging Applications. 2003, Springer. p. 115-136.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>#[27]	Eagle, N., A.S. Pentland, and D. Lazer, Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 2009. 106(36): p. 15274-15278.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>#[28]	More, J. and C. Lingam, Current trends in reality mining. 2013, IRJES.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>#[29]	Zhang, H., R. Dantu, and J.W. Cangussu, Socioscope: Human relationship and behavior analysis in social networks. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 2011. 41(6): p. 1122-1143.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>#[30]	Kaufman, L. and P.J. Rousseeuw, Finding groups in data: an introduction to cluster analysis. Vol. 344. 2009: John Wiley &amp;amp; Sons.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>#[31]	Rousseeuw, P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 1987. 20: p. 53-65.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>#[32]	Newman, M.E., Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 2006. 103(23): p. 8577-8582.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Analysis and Evaluation of Techniques for Myocardial Infarction Based on Genetic Algorithm and Weight by SVM</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>hojatallah</given_name><surname>hamidi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Atefeh</given_name><surname>Daraei</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.003</doi><resource>http://jist.ir/en/Article/14895</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14895</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14895</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14895</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14895</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14895</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14895</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14895</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	A.S. Go, D. Mozaffarian, V.L. Roger, E.J. Benjamin, J.D. Berry, W.B. Borden, D.M. Bravata, S. Dai, E.S. Ford, C.S. Fox, and S. Franco.&amp;quot;Heart Disease and Stroke Statistics--2013 Update: A Report From the American Heart Association&amp;quot;. Circulation, Vol. 127, pp. e6-e245, 2012.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	A. Ahmadi, H. Soori, Y. Mehrabi, K. Etemad, T. Samavat, and A. Khaledifar.&amp;quot;Incidence Of Acute Myocardial Infarction In Islamic Republic Of Iran: A Study Using National Registry Data In 2012&amp;quot;.  Eastern Mediterranean health journal, Vol. 21, pp. 5-12, 2015.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	C. Wiener, C. Brown, A. Hemnes and T. Harrison. Harrison&amp;#39;s principles of internal medicine. New York: McGraw-Hill Medical, 2012, pp. 455-456.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	F. Mohammadi, A. Taherian, M. Hosseini and M. Rahgozar. &amp;quot;Effect of Home-Based Cardiac Rehabilitation on Quality of Life in the Patient with Myocardial Infarction&amp;quot;. Journal of Rehabilitation, Vol. 7, pp. 11-19, 2006. [In Persian]</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	J. E. Hall. Guyton and Hall Textbook of Medical Physiology. New York: Saunders, 2015, pp. 264-266.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>#[6]	R. Dhingra, J. Shaw and L. A. Kirshenbaum. &amp;quot;molecular regulation of apoptosis signaling pathway in heart&amp;quot; in Apoptosis: Modern Insights into Disease from Molecules to Man, 1st ed., V. R. Preedy, Ed. Florida: CRC Press, 2010, pp. 382-385.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>#[7]	N. Esfandiari, M. Babavalian, A. Moghadam and V. Tabar. &amp;quot;Knowledge discovery in medicine: Current issue and future trend&amp;quot;. Expert Systems with Applications, Vol. 41, pp. 4434-4463, 2014. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>#[8]	M. Jabbar, B. Deekshatulu and P. Chandra. &amp;quot;Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm&amp;quot;. Procedia Technology, Vol. 10, pp. 85-94, 2013.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>#[9]	S. Kumar and G. Sahoo. &amp;quot;Classification of Heart Disease Using Na&amp;#239;ve Bayes and Genetic Algorithm&amp;quot;. Computational Intelligence in Data Mining, Vol. 2, pp. 269-282, 2014.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>#[10]	U. Fayyad and R. Uthurusamy. &amp;quot;Data mining and knowledge discovery in databases&amp;quot;. Communications of the ACM, Vol. 39, pp. 24-26, 1996.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>#[11]	P. Tan, M. Steinbach and V. Kumar. Introduction to data mining. Boston: Pearson Addison Wesley, 2005. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>#[12]	I. Benjamin, R. C. Griggs, E. J Wing and J. Fitz. Andreoli and Carpenter&amp;#39;s Cecil Essentials of Medicine.New York: Saunders, 2015, pp. 93-102.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>#[13]	W. Baxt, F. Shofer, F. Sites and J. Hollander. &amp;quot;A neural computational aid to the diagnosis of acute Myocardial Infarction&amp;quot;. Annals of Emergency Medicine, Vol. 39, pp. 366-373, 2002.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>#[14]	M. Qazi, G. Fung, S. Krishnan, J. Bi, R. Bharat Rao and A.S. Katz. &amp;quot;Automated heart abnormality detection using sparse linear classifiers&amp;quot;. Engineering in Medicine and Biology Magazine, Vol. 26, pp. 56-63, 2007.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>#[15]	D. Conforti, D. Constanzo and R. Guido. &amp;quot;Medical decision making: A case study within the cardiology domain&amp;quot;. Journal on Information Technology in Healthcare, Vol. 5, pp. 343–356, 2007.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>#[16]	S. Patil and Y. Kumaraswamy. &amp;quot;Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network&amp;quot;. European Journal of Scientific Research, Vol. 31, pp. 642-656, 2009.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>#[17]	M. Karaolis, J. Moutiris, L. Papaconstantinou and C. Pattichis. &amp;quot;Association rule analysis for the assessment of the risk of coronary heart events.&amp;quot; in Engineering in Medicine and Biology Society. EMBC 2009. Annual International Conference of the IEEE, 2009, pp. 6238 - 6241.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>#[18]	M. Arif, I. Malagore and F. Afsar. &amp;quot;Automatic Detection and Localization of Myocardial Infarction Using Back Propagation Neural Networks&amp;quot;, in 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), 2010, pp. 2151-7614.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>#[19]	H. Masethe and M. Masethe, &amp;quot;Prediction of Heart Disease using Classification Algorithm,&amp;quot; in Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, 2014.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>#[20]	N. Krishnaraj and R. Vinothkumar. &amp;quot;Heart Disease Prediction using GA and MLBPN&amp;quot;. International Journal of Applied Management &amp;amp; Business Utility, Vol. 2, pp. 17- 24, 2014.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>#[21]	N. Safdarian, N. Dabanloo and G. Attarodi. &amp;quot;A new pattern recognition method for detection and localization of Myocardial Infarction using t-wave integral and total integral as extracted features from one cycle of ECG signal&amp;quot;. Journal of Biomedical Science and Engineering, Vol. 07, pp. 818-824, 2014.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>#[22]	N. Bhaskar. &amp;quot;Performance analysis of Support Vector Machine and Neural Networks in detection of Myocardial Infarction&amp;quot;. Procedia Computer Science, Vol. 46, pp. 20-30, 2015.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>#[23]	L. Sharma, R. Tripathy and S. Dandapat. &amp;quot;Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction&amp;quot;. IEEE Transactions on Biomedical Engineering, Vol. 62, pp. 1827-1837, 2015.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>#[24]	P. Kora and S. Kalva. &amp;quot;Improved Bat algorithm for the detection of Myocardial Infarction&amp;quot;. SpringerPlus, Vol. 4, pp. 1-18, 2015.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>#[25]	U. Fayyad and R. Uthurusamy. &amp;quot;Data mining and knowledge discovery in databases&amp;quot;. Communications of the ACM, Vol. 39, pp. 24-26, 1996.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>#[26]	N. G. B. Amma. &amp;quot;Cardiovascular disease prediction system using genetic algorithm and Neural Network,&amp;quot; in International Conference on Computing, Communication and Applications (ICCCA), 2012, pp. 1-5.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>#[27]	Han J, Kamber M, Pei J. Data mining: concepts and techniques. Morgan Kaufmann, 2011. </unstructured_citation></citation><citation key="ref28"><unstructured_citation>#[28]	M. Pacharne and V. Nayak. &amp;quot;Feature Selection Using Various Hybrid Algorithms for Speech Recognition,&amp;quot; in Computational Intelligence and Information Technology, 1st ed., V. Das and N. Thankachan, Ed. Berlin: Springer Berlin Heidelberg, 2011, pp. 652-656.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>#[29]	S. Kalmegh. &amp;quot;Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News&amp;quot;. IJISET - International Journal of Innovative Science, Engineering &amp;amp; Technology, Vol. 2, pp. 438-446, 2015.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>#[30]	M. Pal. &amp;quot;Random forest classifier for remote sensing classification&amp;quot;. International Journal of Remote Sensing, Vol. 26, pp. 217-222, 2005.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>#[31]	Y. Wang and J. Vassileva, &amp;quot;Bayesian Network-based trust model.&amp;quot; in International Conference on  Web Intelligence, IEEE/WIC, 2003, pp.372-378.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>#[32]	 R. Ganesh Kumar and Y. Kumaraswamy. &amp;quot;Performance Analysis Of Soft Computing Techniques For Classifying Cardiac Arrhythmia&amp;quot;. Indian Journal of Computer Science and Engineering (IJCSE), Vol. 4, pp. 459-465, 2014.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>#[33]	M. Gardner and S. Dorling. &amp;quot;Artificial Neural Networks (the multilayer perceptron)—a review of applications in the atmospheric sciences&amp;quot;. Atmospheric Environment, Vol. 32, pp. 2627-2636, 1998.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>#[34]	K. Polat, S. G&amp;#252;neş and A. Arslan. &amp;quot;A cascade learning system for classification of diabetes disease: Generalized Discriminant Analysis and Least Square Support Vector Machine&amp;quot;. Expert Systems with Applications, Vol. 34, pp. 482-487, 2008.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>#[35]	S. Gunn. &amp;quot;Support Vector Machines for Classification and Regression&amp;quot;. University of Southampton, Technical Report, 1998.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>#[36]	J. Platt. &amp;quot;Sequential minimal optimization:A fast algorithm for training Support Vector Machines&amp;quot;. Microsoft Research, Technical report MSR-TR-98-141998. </unstructured_citation></citation><citation key="ref37"><unstructured_citation>#[37]	D. Hand and K. Yu. &amp;quot;Idiot&amp;#39;s Bayes: Not So Stupid after All?&amp;quot;, International Statistical Review/Revue Internationale de Statistique, Vol. 69, p. 385, 2001.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>#[38]	R. Alizadehsani, J. Habibi, B. Bahadorian, H. Mashayekhi, A. Ghandeharioun, R. Boghrati and Z. Alizadeh Sani. &amp;quot;Diagnosis of Coronary Arteries Stenosis Using Data Mining&amp;quot;, Journal of Medical Signals and Sensors, Vol. 2, pp. 153-159, 2012.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>#[39]	A. Onan. &amp;quot;A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer&amp;quot;. Expert Systems with Applications, Vol. 42, pp. 6844-6852, 2015.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>#[40]	M. Heydari, M. Teimouri, Z. Heshmati and S. Alavinia, &amp;quot;Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran&amp;quot;, International Journal of Diabetes in Developing Countries, 2015, pp.1-7.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Optimization of Random Phase Updating Technique for Effective Reduction in PAPR, Using Discrete Cosine Transform</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Babak</given_name><surname>Haji Bagher Naeeni</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.004</doi><resource>http://jist.ir/en/Article/14896</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14896</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14896</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14896</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14896</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14896</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14896</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14896</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	V. K. Singh, A. Goel, A. Sharma. “Reducing Peak to Average Power Ratio of OFDM by Using Selective Mapping”. International Journal of Research in Information Technology, vol. 2.No.4, p-400-407, April 2014.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	T. Jiang and G. Zhu, “Complement block coding for reduction in peak to average power ratio of OFDM signals.” IEEE Radio Communications, vol. 43, no. 9, pp. s17-s22, Sept. 2005.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	Md. Ibhrahim Abdullah et.al, “Comparative Study of PAPR Reduction Techniques in OFDM”. ARPN journal of system and software, vol. 1, no. 8, pp. 263-269, nov 2011.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	P. Phoomchusak, C. Pirak, “Adaptive tone-reservation PAPR technique with optimal subcarriers allocation awareness for multi-user OFDMA systems.” 2011 the 13th International Conference on Advanced Communication Technology (ICACT), 2011.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	H. Chen, H. Liang. “Combined Selective Mapping and Binary Cyclic Codes for PAPR Reduction in OFDM Systems.” IEEE Transactions on Wireless Communications, vol.6, no.10, Oct. 2007.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	Nikookar, H., and Lidsheim, k.S.,“Random phase updating  algorithm for OFDM transmission with low PAPR.” IEEE Transaction on Broadcasting, Vol.48, Jun 2002.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	H. Ochiai and H. Imai, “Performance of the deliberate clipping with symbol selection for strictly band-limited OFDM systems.” IEEE J. Sel. Areas Commun., vol. 18, no. 11, Nov. 2000.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	Jayalath, A., Tellambura, C., “Adaptive PTS approach for reduction of peak-to-average power ratio of OFDM signal.” Electronics Letters, Vol.36, No.14, pp. 1226-1228, 2000.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	S. Gazor, R. AliHemmati, “Tone reservation for OFDM systems by maximizing signalto-distortion ratio.” IEEE Transactions on Wireless Communications, 11(2), 2012.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Nonlinear State Estimation Using Hybrid Robust Cubature Kalman Filter</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Behrooz</given_name><surname>Safarinejadian</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohsen</given_name><surname>Taher</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.005</doi><resource>http://jist.ir/en/Article/14897</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14897</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14897</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14897</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14897</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14897</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14897</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14897</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>R. Grover and P. Y. C. Hwang, Introduction to random signals and applied Kalman filtering. Willey N. Y., 1992.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	M. S. Grewal and A. P. Andrews, Kalman filtering: theory and practice using MATLAB. John Wiley &amp;amp; Sons, 2011.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	I. Arasaratnam and S. Haykin, “Cubature Kalman filters,” IEEE Trans. On Autom. Control, vol. 54, no. 6, pp. 1254–1269, Jun. 2009.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	I. Arasaratnam, “Cubature Kalman filtering: theory &amp;amp; applications,” Ph. D. Thesis, 2009.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	B. Safarinejadian, M. A. Tajeddini, and A. Ramezani, “Predict time series using extended, unscented, and cubature Kalman filters based on feed-forward neural network algorithm,” 3rd International Conference on Control Instrumentation and Automation, (ICCIA), 2013, pp. 159–164. </unstructured_citation></citation><citation key="ref6"><unstructured_citation>#[6]	M. Havlicek, K. J. Friston, J. Jan, M. Brazdil, and V. D. Calhoun, “Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering,” Neuroimage, vol. 56, no. 4, pp. 2109–2128, 2011.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>#[7]	D. Macagnano and G. T. F. de Abreu, “Multitarget tracking with the cubature Kalman probability hypothesis density filter,” Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2010, pp. 1455–1459. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>#[8]	K. P. B. Chandra, D.-W. Gu, and I. Postlethwaite, “Cubature Kalman filter based localization and mapping,” World Congress, 2011, pp. 2121–2125.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>#[9]	F. Yang, Z. Wang, and Y. Hung, “Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises,” IEEE Trans. On Autom. Control, vol. 47, no. 7, pp. 1179-1183, 2002.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>#[10]	Z. Dong and Z. You, “Finite-horizon robust Kalman filtering for uncertain discrete time-varying systems with uncertain-covariance white noises,” IEEE Signal Process. Lett., vol. 13, no. 8, pp. 493–496, 2006.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>#[11]	U. Shaked and C. E. de Souza, “Robust minimum variance filtering,” IEEE Trans. On Signal Process., vol. 43, no. 11, pp. 2474–2483, 1995.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>#[12]	Y. Theodor and U. Shaked, “Robust discrete-time minimum-variance filtering,” IEEE Trans. On Signal Process., vol. 44, no. 2, pp. 181–189, 1996.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>#[13]	S. Habibi, “The smooth variable structure filter,” Proc. IEEE, vol. 95, no. 5, pp. 1026–1059, 2007.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>#[14]	S. Dey and J. B. Moore, “Risk-sensitive filtering and smoothing via reference probability methods,” IEEE Trans. On Autom. Control, vol. 42, no. 11, pp. 1587–1591, 1997.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>#[15]	H. Li and M. Fu, “A linear matrix inequality approach to robust H∞ filtering,” IEEE Trans. On Signal Process., vol. 45, no. 9, pp. 2338–2350, 1997.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>#[16]	R. S. Mangoubi, Robust estimation and failure detection: A concise treatment. Springer Science &amp;amp; Business Media, 2012.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>#[17]	L. Xie, Y. C. Soh, and C. E. de Souza, “Robust Kalman filtering for uncertain discrete-time systems,” IEEE Trans. On Autom. Control, vol. 39, no. 6, pp. 1310–1314, 1994.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>#[18]	S. J. Kwon, “Robust Kalman filtering with perturbation estimation process for uncertain systems,” IEE Proc.-Control Theory Appl., vol. 153, no. 5, pp. 600–606, 2006.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>#[19]	M. G. S. Bruno and A. Pavlov, “Improved sequential Monte Carlo filtering for ballistic target tracking,” IEEE Trans. On Aerosp. Electron. Syst., vol. 41, no. 3, pp. 1103–1108, 2005.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>#[20]	B. Teixeira, J. Chandrasekar, H. J. Palanthandalam-Madapusi, L. Torres, L. A. Aguirre, and D. S. Bernstein, “Gain-constrained Kalman filtering for linear and nonlinear systems,” IEEE Trans. On Signal Process., vol. 56, no. 9, pp. 4113–4123, 2008.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Quality Assessment Based Coded Apertures for Defocus Deblurring</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mina</given_name><surname>Masoudifar</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Hamid Reza</given_name><surname>Pourreza</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.006</doi><resource>http://jist.ir/en/Article/14898</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14898</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14898</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14898</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14898</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14898</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14898</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14898</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	K. Mitra, O. Cossairt, and A. Veeraraghavan, &amp;quot;To denoise or deblur: parameter optimization for imaging systems,&amp;quot; in IS&amp;amp;T/SPIE Electronic Imaging, 2014, pp. 90230G-90230G-6.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	C. Zhou and S. Nayar, &amp;quot;What are good apertures for defocus deblurring?,&amp;quot; in Computational Photography (ICCP), 2009 IEEE International Conference on, 2009, pp. 1-8.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	A. Veeraraghavan, R. Raskar, A. Agrawal, A. Mohan, and J. Tumblin, &amp;quot;Dappled photography: Mask enhanced cameras for heterodyned light fields and coded aperture refocusing,&amp;quot; ACM Transaction on Graphics, vol. 26, no.3, p. 69, 2007.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	B. Masia, L. Presa, A. Corrales, and D. Gutierrez, &amp;quot;Perceptually optimized coded apertures for defocus deblurring,&amp;quot; Computer Graphics Forum, vol. 31, no.6, pp. 1867-1879, 2012.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	A. Levin, R. Fergus, F. Durand, and W. T. Freeman, &amp;quot;Image and depth from a conventional camera with a coded aperture,&amp;quot; ACM Transactions on Graphics, vol. 26, no. 3, p. 70, 2007.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>#[6]	S. Hiura and T. Matsuyama, &amp;quot;Depth measurement by the multi-focus camera,&amp;quot; in Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, 1998, pp. 953-959.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>#[7]	M. Martinello, &amp;quot;Coded aperture imaging,&amp;quot; Heriot-Watt University, 2012.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>#[8]	A. Sellent and P. Favaro, &amp;quot;Which side of the focal plane are you on?,&amp;quot; in Computational Photography (ICCP), 2014 IEEE International Conference on, 2014, pp. 1-8.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>#[9]	A. Sellent and P. Favaro, &amp;quot;Optimized aperture shapes for depth estimation,&amp;quot; Pattern Recognition Letters, vol. 40, pp. 96-103, 2014.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>#[10]	Y. Bando, B.-Y. Chen, and T. Nishita, &amp;quot;Extracting depth and matte using a color-filtered aperture,&amp;quot; ACM Transactions on Graphics, vol. 27, no.5, p. 134., 2008.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>#[11]	C. Zhou, S. Lin, and S. K. Nayar, &amp;quot;Coded aperture pairs for depth from defocus and defocus deblurring,&amp;quot; International Journal of Computer Vision, vol. 93, no. 1, pp. 53-72, 2011.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>#[12]	Y. Takeda, S. Hiura, and K. Sato, &amp;quot;Fusing depth from defocus and stereo with coded apertures,&amp;quot; in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 2013, pp. 209-216.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>#[13]	A. Chakrabarti and T. Zickler, &amp;quot;Depth and deblurring from a spectrally-varying depth-of-field,&amp;quot; in Computer Vision–ECCV 2012, ed: Springer, 2012, pp. 648-661.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>#[14]	A. Ashok and M. A. Neifeld, &amp;quot;Pseudorandom phase masks for superresolution imaging from subpixel shifting,&amp;quot; Applied optics, vol. 46, no. 12,  pp. 2256-2268, 2007.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>#[15]	S. K. Nayar, &amp;quot;Computational cameras: Approaches, benefits and limits,&amp;quot; Technical Rep. 2011.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>#[16]	C. Zhou and S. K. Nayar, &amp;quot;Computational cameras: Convergence of optics and processing,&amp;quot; Image Processing, IEEE Transactions on, vol. 20, no. 12, pp. 3322-3340, 2011.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>#[17]	E. Caroli, J. Stephen, G. Di Cocco, L. Natalucci, and A. Spizzichino, &amp;quot;Coded aperture imaging in X-and gamma-ray astronomy,&amp;quot; Space Science Reviews, vol. 45, no.3, pp. 349-403, 1987.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>#[18]	S. R. Gottesman and E. Fenimore, &amp;quot;New family of binary arrays for coded aperture imaging,&amp;quot; Applied optics, vol. 28, no. 20, pp. 4344-4352, 1989.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>#[19]	W. T. Welford, &amp;quot;Use of annular apertures to increase focal depth,&amp;quot; Journal of the Optical Society of America, vol. 50, no. 8, pp. 749-752, 1960.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>#[20]	M. Mino and Y. Okano, &amp;quot;Improvement in the OTF of a defocused optical system through the use of shaded apertures,&amp;quot; Applied Optics, vol. 10, no. 10, pp. 2219-2225, 1971.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>#[21]	P. C. Hansen, J. G. Nagy, and D. P. O&amp;#39;leary, Deblurring images: matrices, spectra, and filtering: Siam, 2006.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>#[22]	P. Campisi and K. Egiazarian, Blind image deconvolution: theory and applications: CRC press, 2007.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>#[23]	H. R. Sheikh and A. C. Bovik, &amp;quot;Image information and visual quality,&amp;quot; Image Processing, IEEE Transactions on, vol. 15, no. 2, pp. 430-444, 2006.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>#[24]	H. R. Sheikh, M. F. Sabir, and A. C. Bovik, &amp;quot;A statistical evaluation of recent full reference image quality assessment algorithms,&amp;quot; Image Processing, IEEE Transactions on, vol. 15, no. 11, pp. 3440-3451, 2006.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>#[25]	A. Lahoulou, A. Bouridane, E. Viennet, and M. Haddadi, &amp;quot;Full-reference image quality metrics performance evaluation over image quality databases,&amp;quot; Arabian Journal for Science and Engineering, vol. 38, no. 9, pp. 2327-2356, 2013.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	Y. Liu, J. Wang, S. Cho, A. Finkelstein, and S. Rusinkiewicz, &amp;quot;A no-reference metric for evaluating the quality of motion deblurring,&amp;quot; ACM Transaction on Graphics, vol. 32, no. 6, p. 175, 2013.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	O. Cossairt, M. Gupta, and S. K. Nayar, &amp;quot;When does computational imaging improve performance?,&amp;quot; Image Processing, IEEE Transactions on, vol. 22, no. 2, pp. 447-458, 2013.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	K. Mitra, O. S. Cossairt, and A. Veeraraghavan, &amp;quot;A Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing,&amp;quot; Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 36, no. 10, pp. 1909-1921, 2014.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	Y. Weiss and W. T. Freeman, &amp;quot;What makes a good model of natural images?,&amp;quot; in Computer Vision and Pattern Recognition, 2007. CVPR&amp;#39;07. IEEE Conference on, 2007, pp. 1-8.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	Y. Gao, &amp;quot;Population size and sampling complexity in genetic algorithms,&amp;quot; in Proc. of the Bird of a Feather Workshops, 2003, pp. 178-181.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	http://www.cs.washington.edu/research/imagedatabase/groundtruth/</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Design, Implementation and Evaluation of Multi-terminal Binary Decision Diagram based Binary Fuzzy Relations</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Hamid</given_name><surname>Alavi Toussi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Bahram</given_name><surname>Sadeghi Bigham</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.007</doi><resource>http://jist.ir/en/Article/14899</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14899</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14899</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14899</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14899</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14899</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14899</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14899</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	E. Clarke, O. Grumberg, and D. Long. Symbolic Model Checking for Sequential Circuit Verification. In IEEE Transactions on Computer Aided Design, 1994.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	Marc Berndl, OndˇrejLhot&amp;#180;ak, FengQian, Laurie Hendren, and NavindraUmanee. Points-to analysis using BDDs. In Proceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Inplementation, pages 103–114. ACM Press, 2003.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	John Whaley and Monica Lam. Clonning-based context-sensitive Pointer Alias analysis using Binary Decision Diagrams. In Proceeding of PLDI, 2004.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	OndˇrejLhot&amp;#180;ak, Stephen Curial, and Jos&amp;#180;e Nelson Amaral. Using XBDDs and ZBDDs in points-to analysis. Software, Practice and Experience, 39(2):163–188, 2009.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	WatisLeelapatra, KanchanaKanchanasut, and ChidchanokLursinsap. Displacement BDD and geometric transformations of binary decision diagram encoded images. Pattern Recognition Letters, 29:438–456, March 2008.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>#[6]	Mike Starkey, Randy Bryant, and Y Bryant. Using ordered binary-decision diagrams for compressing images and image sequences. Technical report, CMU-CS, 1995.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>#[7]	Edmund M.and Fujita Clarke, M., McGeer, P C., McMillan, K., Yang, J C-Y, and X Zhao. Multi-Terminal Binary Decision Diagrams: An Eﬃcient Data-Structure for Matrix Representation. Formal Methods in System Design, 1997.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>#[8]	R. I. Bahar, E. A. Frohm, C. M. Gaona, E. Macii, A. Pardo, and F. Somenzi. Algebraic Decision  Diagrams and Their Applications. Formal Methods in System Design, 10, 1997.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>#[9]	Jorn Lind-Nielsen. BuDDy library. http://sourceforge.net/projects/buddy/, 2002.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>#[10]	D. Bugaychenko. On application of multi-rooted binary decision diagrams to probabilistic model checking. In Veriﬁcation, Model Checking, and Abstract Interpretation, pages 104–118. Springer, 2012.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>#[11]	Ben Hardekopf  and Calvin Lin. Flow-sensitive pointer analysis for millions of lines of code. In Code Generation and Optimization (CGO), 2011 9th Annual IEEE/ACM International Symposium on, pp. 289-298. IEEE, 2011.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>#[12]	D. Yu. Bugaychenko and I. P. Soloviev. Application of multiroot decision diagrams for integer functions. MATHEMATICS, 43(2):92–97, 2010.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>#[13]	Randal E. Bryant. Graph Based Algorithm for Boolean function manipulation. In IEEE Transactions on Computers, 1985.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>#[14]	L. A. Zadeh. Fuzzy Sets. Information and Control, pages 338–353, 1965.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>#[15]	ShivaniAgarwal. UIUC Image Database for Car Detection. http://cogcomp.cs.illinois.edu/Data/Car/, April 2002. Accessed: 2012-08-20.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>#[16]	D. Martin, C. Fowlkes, D. Tal, and J. Malik. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In Proceeding of 8th International Conference on Computer Vision, volume 2, pages 416–423, July 2001.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>#[17]	Jayaram K. Udupa and Punam K. Saha. Fuzzy Connectedness and Image Segmentation. In Proceeding of the IEEE, volume 91, 2003.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>#[18]	Pedro F. Felzenszwalb. Eﬃcient Graph-Based Image Segmentation. Journal of Computer Vision, 59(2), 2004.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>#[19]	M. Delgado, N. Mann, M. Martn-Bautista, D. Snchez, and M. Vila. Mining fuzzy association rules: An overview. Soft Computing for Information Processing and Analysis, 164:351–373, 2005.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>#[20]	Ulrich Bodenhofer, EykeHllermeier, Frank Klawonn, and Rudolf Kruse. Special issue on soft computing for information mining. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 11:397–399, 2007.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>#[21]	AmelBorgi and Herman Akdag. Knowledge based supervised fuzzy-classiﬁcation: An application to image processing. Annals of Mathematics and Artiﬁcial Intelligence, 32(1-4):67–86, 2001.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>#[22]	Ariel Gmez, Carlos Len, Jorge Ropero, Alejandro Carrasco, and JoaqunLuque. Sabio: Soft agent for extended information retrieval. Applied Artiﬁcial Intelligence, 27(4):249–277, 2013.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>#[23]	Hamid A. Toussi and BahramSadeghiBigham, Design, Implementation and Evaluation of MTBDD based Fuzzy Sets and Binary Fuzzy Relations,  preprint arXiv:1403.1279 [cs.DS], [Online]. Available: http://arxiv.org/abs/1403.1279  </unstructured_citation></citation><citation key="ref24"><unstructured_citation>#[24]	Hamid A. Toussi and Abbas Rasoolzadegan, Flow-sensitive points-to analysis for Java programs using BDDs, In Proceeding of 4th International Conference on Computer and Knowledge Engineering (ICCKE), pp.380,386, 29-30 Oct. 2014</unstructured_citation></citation><citation key="ref25"><unstructured_citation>#[25]	Ben Hardekopf and Calvin Lin. Semi-sparse flow-sensitive pointer analysis,  In ACM SIGPLAN Notices, vol. 44, no. 1, pp. 226-238. ACM, 2009.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Unsupervised Segmentation of Retinal Blood Vessels Using the Human Visual System Line Detection Model</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mohsen</given_name><surname>Zardadi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Nasser</given_name><surname>Mehrshad</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Seyyed Mohammad</given_name><surname>Razavi</surname></person_name></contributors><publication_date media_type="online"><month>6</month><day>24</day><year>2016</year></publication_date><pages><first_page>1</first_page><last_page>10</last_page></pages><doi_data><doi>10.7508/jist.2016.02.008</doi><resource>http://jist.ir/en/Article/14900</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jist.ir/en/Article/Download/14900</resource></item><item crawler="google"><resource>http://jist.ir/en/Article/Download/14900</resource></item><item crawler="msn"><resource>http://jist.ir/en/Article/Download/14900</resource></item><item crawler="altavista"><resource>http://jist.ir/en/Article/Download/14900</resource></item><item crawler="yahoo"><resource>http://jist.ir/en/Article/Download/14900</resource></item><item crawler="scirus"><resource>http://jist.ir/en/Article/Download/14900</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jist.ir/en/Article/Download/14900</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	B. Bowling, Kanski&amp;#39;s Clinical Ophthalmology: A Systematic Approach, Eighth ed. Sydney, Australia: Elsevier Health Science, 2015.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>#[2]	M. Esmaeili, H. Rabbani, A. Dehnavi, and A. Dehghani, &amp;quot;Automatic detection of exudates and optic disk in retinal images using curvelet transform,&amp;quot; IET image processing, vol. 6, pp. 1005-1013, 2012.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>#[3]	S. W. Franklin and S. E. Rajan, &amp;quot;Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images,&amp;quot; IET Image Processing, vol. 8, pp. 1-9, 2014.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>#[4]	M. J. Fowler, &amp;quot;Microvascular and macrovascular complications of diabetes,&amp;quot; Clinical Diabetes, vol. 26, pp. 77-82, 2008.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>#[5]	J. Anitha, C. K. S. Vijila, and D. J. Hemanth, &amp;quot;An Overview of Computational Intelligence Techniques for Retinal Disease Identification Applications,&amp;quot; International Journal of Reviews in Computing, vol. 5, pp. 29-46, 2009.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>#[6]	M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, et al., &amp;quot;An ensemble classification-based approach applied to retinal blood vessel segmentation,&amp;quot; Biomedical Engineering, IEEE Transactions on, vol. 59, pp. 2538-2548, 2012.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>#[7]	A. Hoover, V. Kouznetsova, and M. Goldbaum, &amp;quot;Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,&amp;quot; Medical Imaging, IEEE Transactions on, vol. 19, pp. 203-210, 2000.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>#[8]	L. Gang, O. Chutatape, and S. M. Krishnan, &amp;quot;Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter,&amp;quot; Biomedical Engineering, IEEE Transactions on, vol. 49, pp. 168-172, 2002.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>#[9]	M. G. Cinsdikici and D. Aydın, &amp;quot;Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm,&amp;quot; Computer methods and programs in biomedicine, vol. 96, pp. 85-95, 2009.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>#[10]	M. A. Amin and H. Yan, &amp;quot;High speed detection of retinal blood vessels in fundus image using phase congruency,&amp;quot; Soft Computing, vol. 15, pp. 1217-1230, 2011.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>#[11]	F. Zana and J.-C. Klein, &amp;quot;Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,&amp;quot; Image Processing, IEEE Transactions on, vol. 10, pp. 1010-1019, 2001.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>#[12]	M. Fraz, S. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, et al., &amp;quot;An approach to localize the retinal blood vessels using bit planes and centerline detection,&amp;quot; Computer methods and programs in biomedicine, 2011.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>#[13]	A. M. Mendonca and A. Campilho, &amp;quot;Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,&amp;quot;Medical Imaging, IEEE Transactions on, vol. 25, pp. 1200-1213, 2006.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>#[14]	B. S. Lam and H. Yan, &amp;quot;A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields,&amp;quot; Medical Imaging, IEEE Transactions on, vol. 27, pp. 237-246, 2008.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>#[15]	B. S. Lam, Y. Gao, and A.-C. Liew, &amp;quot;General retinal vessel segmentation using regularization-based multiconcavity modeling,&amp;quot; Medical Imaging, IEEE Transactions on, vol. 29, pp. 1369-1381, 2010.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>#[16]	B. Al-Diri, A. Hunter, and D. Steel, &amp;quot;An active contour model for segmenting and measuring retinal vessels,&amp;quot; Medical Imaging, IEEE Transactions on, vol. 28, pp. 1488-1497, 2009.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>#[17]	G. Gardner, D. Keating, T. Williamson, and A. Elliott, &amp;quot;Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,&amp;quot; British journal of Ophthalmology, vol. 80, pp. 940-944, 1996.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>#[18]	C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, &amp;quot;Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,&amp;quot; British Journal of Ophthalmology, vol. 83, pp. 902-910, 1999.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>#[19]	M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abramoff, &amp;quot;Comparative study of retinal vessel segmentation methods on a new publicly available database,&amp;quot; in Medical Imaging 2004, pp. 648-656.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>#[20]	J. Staal, M. D. Abr&amp;#224;moff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, &amp;quot;Ridge-based vessel segmentation in color images of the retina,&amp;quot; Medical Imaging, IEEE Transactions on, vol. 23, pp. 501-509, 2004.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>#[21]	J. V. Soares, J. J. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, &amp;quot;Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,&amp;quot; Medical Imaging, IEEE Transactions on, vol. 25, pp. 1214-1222, 2006.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>#[22]	D. Mar&amp;#237;n, A. Aquino, M. E. Geg&amp;#250;ndez-Arias, and J. M. Bravo, &amp;quot;A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,&amp;quot; Medical Imaging, IEEE Transactions on, vol. 30, pp. 146-158, 2011.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>#[23]	J. P. Jones and L. A. Palmer, &amp;quot;An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex,&amp;quot; Journal of Neurophysiology, vol. 58, pp. 1233-1258, 1987.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>#[24]	D. G. Albrecht and W. S. Geisler, &amp;quot;Motion selectivity and the contrast-response function of simple cells in the visual cortex,&amp;quot; Visual neuroscience, vol. 7, pp. 531-546, 1991.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>#[25]	D. C. Somers, S. B. Nelson, and M. Sur, &amp;quot;An emergent model of orientation selectivity in cat visual cortical simple cells,&amp;quot; The Journal of neuroscience, vol. 15, pp. 5448-5465, 1995.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>#[26]	D. Ferster, S. Chung, and H. Wheat, &amp;quot;Orientation selectivity of thalamic input to simple cells of cat visual cortex,&amp;quot; Nature, vol. 380, pp. 249-252, 1996.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>#[27]	C. Grigorescu, N. Petkov, and M. A. Westenberg, &amp;quot;Contour detection based on nonclassical receptive field inhibition,&amp;quot; Image Processing, IEEE Transactions on, vol. 12, pp. 729-739, 2003.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>#[28]	D. H. Hubel and T. N. Wiesel, &amp;quot;Receptive fields, binocular interaction and functional architecture in the cat&amp;#39;s visual cortex,&amp;quot; The Journal of physiology, vol. 160, p. 106, 1962.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>#[29]	J. G. Daugman, &amp;quot;Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,&amp;quot; Optical Society of America, Journal, A: Optics and Image Science, vol. 2, pp. 1160-1169, 1985.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>#[30]	Research Section, Digital Retinal Image for Vessel Extraction(DRIVE) Database. Utrecht, The Netherlands, Univ. Med. Center Utrecht, Image Sci. Inst. [Online]. Available: http://www.isi.uu.nl/Research/Databases/DRIVE/[Feb. 1, 2016]</unstructured_citation></citation><citation key="ref31"><unstructured_citation>#[31]	STARE database, STARE ProjectWebsite. Clemson, SC, Clemson Univ. [Online]. Available: http://www.ces.clemson.edu/~ahoover/stare/[Feb. 1, 2016]</unstructured_citation></citation><citation key="ref32"><unstructured_citation>#[32]	X. Jiang and D. Mojon, &amp;quot;Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,&amp;quot; Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, pp. 131-137, 2003.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>#[33]	X. You, Q. Peng, Y. Yuan, Y.-m. Cheung, and J. Lei, &amp;quot;Segmentation of retinal blood vessels using the radial projection and semi-supervised approach,&amp;quot; Pattern Recognition, vol. 44, pp. 2314-2324, 2011.</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>