1. Carvalho, B. M., et al. (2015). "Ecological niche modelling predicts southward expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), vector of Leishmania (Leishmania) amazonensis in South America, under climate change." PLoS One 10(11): e0143282. [ DOI:10.1371/journal.pone.0143282] 2. Ghatee MA, Haghdoost AA, Kooreshnia F, Kanannejad Z, Parisaie Z, Karamian M, Moshfe A. Role of environmental, climatic risk factors and livestock animals on the occurrence of cutaneous leishmaniasis in newly emerging focus in Iran. J Infect Public Health. 2018 May-Jun;11(3):425-433. doi: 10.1016/j.jiph.2017.12.004. Epub 2017 Dec 26. PMID: 29287805. [ DOI:10.1016/j.jiph.2017.12.004] 3. Eslami, S. and Hasanloom, S. (2019). "Modeling the condition of coral reefs using support vector machine regression and applying spectral indices." Iranian Journal of Marine Technology 6(1): 31-44. 4. Rajabi, M., et al. (2016). "A spatially explicit agent-based modeling approach for the spread of cutaneous leishmaniasis disease in central Iran, Isfahan." Environmental modelling & software 82: 330-346. [ DOI:10.1016/j.envsoft.2016.04.006] 5. Mollalo, A., et al. (2014). "Spatial and spatio-temporal analysis of human brucellosis in Iran." Transactions of the Royal Society of Tropical Medicine and Hygiene 108(11): 721-728. [ DOI:10.1093/trstmh/tru133] 6. Mollalo, A., et al. (2014). "Spatial and statistical analyses of the relations between vegetation cover and incidence of cutaneous leishmaniasis in an endemic province, northeast of Iran." Asian Pacific journal of tropical disease 4(3): 176-180. [ DOI:10.1016/S2222-1808(14)60500-4] 7. Yu, W., Liu, T., Valdez, R. et al. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inform Decis Mak 10, 16 (2010). [ DOI:10.1186/1472-6947-10-16] 8. Murad, A. and B. F. Khashoggi (2020). "Using GIS for disease mapping and clustering in Jeddah, Saudi Arabia." ISPRS International Journal of Geo-Information 9(5): 328. [ DOI:10.3390/ijgi9050328] 9. Nia, A. M. and A. Alimohammadi (2015). "Geographic Information System-Based Analysis of the Spatial Distribution of Zoonotic Visceral Leishmaniasis and Crimean Congo Haemorrhagic Fever in Provinces of Iran." 10. AhangarCani, M., et al. (2016). "Predictive Map of Spatio-Temporal Distribution of Leptospirosis Using Geographical Weighted Regression and Multilayer Perceptron Neural Network Methods." Journal of Geomatics Science and Technology 6(2): 79-98. 11. Akhavan, P., et al. (2014). "Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System." International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences [ DOI:10.5194/isprsarchives-XL-2-W3-19-2014] 12. Shirzadi, M. (2012). "Leishmaniasis skin care guide (leishmaniasis) in Iran." Iran, Ministry of Health, Treatment and Medical Education, Disease Management Center Office of Joint Diseases Control between humans and animals. 13. Moore, D. A. and T. E. Carpenter (1999). "Spatial analytical methods and geographic information systems: use in health research and epidemiology." Epidemiologic reviews 21(2): 143-161. [ DOI:10.1093/oxfordjournals.epirev.a017993] 14. Franke, C. R., et al. (2002). "Trends in the temporal and spatial distribution of visceral and cutaneous leishmaniasis in the state of Bahia, Brazil, from 1985 to 1999." Transactions of the Royal Society of Tropical Medicine and Hygiene 96(3): 236-241. [ DOI:10.1016/S0035-9203(02)90087-8] 15. Ramos, W. R., et al. (2014). "Anthropic effects on sand fly (Diptera: Psychodidae) abundance and diversity in an Amazonian rural settlement, Brazil." Acta tropica 139: 44-52. [ DOI:10.1016/j.actatropica.2014.06.017] 16. Mollalo, A., et al. (2015). "Geographic information system‐based analysis of the spatial and spatio‐temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north‐east of Iran." Zoonoses and public health 62(1): 18-28. [ DOI:10.1111/zph.12109] 17. Dehghani, A., et al. (2019). "Epidemiological Study and Spatial Modeling of Cutaneous Leishmaniasis in Bushehr Province Using the Geographic Information System (GIS) from 2011 to 2015." Journal of Community Health Research. [ DOI:10.18502/jchr.v8i3.1566] 18. Pasini, A., et al. (2020). "Neural network modelling for estimating linear and nonlinear influences of meteo-climatic variables on Sergentomyia minuta abundance using small datasets." Ecological Informatics 65 : 101055. [ DOI:10.1016/j.ecoinf.2020.101055] 19. Tabasi, M. and A. A. Alesheikh (2017). "A Review of the Applications of Agent Based Simulation in Epidemic Diseases (Case study: Cutaneous Leishmaniasis)." Geospatial Engineering Journal 8(2): 11-23. 20. Mansour, S. (2016). "Spatial analysis of public health facilities in Riyadh Governorate, Saudi Arabia: a GIS-based study to assess geographic variations of service provision and accessibility." Geo-spatial Information Science 19(1): 26-38. [ DOI:10.1080/10095020.2016.1151205] 21. Nia, A. M. and A. Alimohammadi (2015). "Geographic Information System-Based Analysis of the Spatial Distribution of Zoonotic Visceral Leishmaniasis and Crimean Congo Haemorrhagic Fever in Provinces of Iran." 22. Liu, G., et al. (2013). "The use of spatial autocorrelation analysis to identify PAHs pollution hotspots at an industrially contaminated site." Environmental monitoring and assessment 185(11): 9549-9558. [ DOI:10.1007/s10661-013-3272-6] 23. Mathur, M. (2015). "Spatial autocorrelation analysis in plant population: An overview." Journal of Applied and Natural Science 7(1): 501-513. [ DOI:10.31018/jans.v7i1.639] 24. Rogers, D. J. and S. E. Randolph (2003). "Studying the global distribution of infectious diseases using GIS and RS." Nature Reviews Microbiology 1(3): 231-237. [ DOI:10.1038/nrmicro776] 25. Darand, M., et al. (2017). "Spatial autocorrelation analysis of extreme precipitation in Iran." Russian Meteorology and Hydrology 42(6): 415-424. [ DOI:10.3103/S1068373917060073] 26. Shafabakhsh, G. A., et al. (2017). "GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran." Journal of traffic and transportation engineering (English edition) 4(3): 290-299. [ DOI:10.1016/j.jtte.2017.05.005] 27. Węglarczyk, S. (2018). " Kernel density estimation and its application [ DOI:10.1051/itmconf/20182300037] 28. " Volume 23, 2018. 29. Ameri, M. and Molayem. M. (2006). " Application of artificial neural networks for analysis of flexible pavements." International Journal of Industrial Engineering and Production Management, International Journal of Engineering 17(5). 30. Dehghani, A., et al. (2010). "Estimation of Daily Pan Evaporation by Using MLP,RBF and Recuurent Neural Networks." Journal of water and soil Conservation 17(2): 49-67. 31. Rezaee, Z., et al. (2020). "Application of Time Series Models in Business Research: Correlation, Association, Causation." Sustainability 12(12): 4833. [ DOI:10.3390/su12124833] 32. Neenwi, S., et al. (2013). "Predicting the Nigerian stock market using artificial neural network." European Journal of Computer Science and Information 1(1): 30-39. 33. Astray, G., et al. (2010). "The use of artificial neural networks to forecast biological atmospheric allergens or pathogens only as Alternaria spores." Journal of Environmental Monitoring 12(11): 2145-2152. [ DOI:10.1039/c0em00248h] 34. Bahrami, B. and A. Ghorbani (2015). "Evaluation of Application of Neural Network and Regression Models to Predict Species Diversity Using Some Soil and Physiographic Factors (Case Study: Urmia Ruin Watershed)". Journal of Natural Ecosystems of Iran. 5 (2): 65 -80. 35. Huang, K.-Y. and K.-J. Chen (2011). "Multilayer perceptron for prediction of 2006 world cup football game." Advances in Artificial Neural Systems 2011 [ DOI:10.1155/2011/374816] 36. Mollalo, A., et al. (2018). "Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran." Acta tropica 188: 187-194. [ DOI:10.1016/j.actatropica.2018.09.004] 37. Abd Latif, Z. and M. H. Mohamad (2015). Mapping of dengue outbreak distribution using spatial statistics and geographical information system. 2015 2nd International Conference on Information Science and Security (ICISS), IEEE. [ DOI:10.1109/ICISSEC.2015.7371016] 38. Yu, W., Liu, T., Valdez, R. et al. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inform Decis Mak 10, 16 (2010). [ DOI:10.1186/1472-6947-10-16]
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