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Ali Asghar Alesheikh
Researcher at K.N.Toosi University of Technology
Publications - 155
Citations - 2860
Ali Asghar Alesheikh is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 21, co-authored 135 publications receiving 2315 citations. Previous affiliations of Ali Asghar Alesheikh include Islamic Azad University.
Papers
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Coastline change detection using remote sensing
TL;DR: In this article, the authors examined the current methods of coastline change detection using satellite images and proposed a new procedure based on a combination of histogram thresholding and band ratio techniques.
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Hospital site selection using fuzzy AHP and its derivatives.
TL;DR: A Multi-Criteria Decision Analysis process that combines Geographical Information System analysis with the Fuzzy Analytical Hierarchy Process is developed, and this process is used to determine the optimum site for a new hospital in the Tehran urban area.
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A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping
TL;DR: This study proposes an indirect assessment strategy that shares in the advantages of quantitative and qualitative assessment methods and employs a fuzzy inference system (FIS) to model expert knowledge, and an artificial neural network (ANN) to identify non-linear behavior and generalize historical data to the entire region.
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Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
Wei Chen,Haoyuan Hong,Mahdi Panahi,Himan Shahabi,Yi Wang,Ataollah Shirzadi,Saied Pirasteh,Ali Asghar Alesheikh,Khabat Khosravi,Somayeh Panahi,Fatemeh Rezaie,Shaojun Li,Abolfazl Jaafari,Dieu Tien Bui,Baharin Bin Ahmad +14 more
TL;DR: Wang et al. as mentioned in this paper presented an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China.
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Weighting Spatial Information in GIS for Copper Mining Exploration
TL;DR: It is shown that knowledge-driven methods are very much affected by the degree of knowledge and the specialization of experts and AHP is the most successful method among knowledge- driven class and could predict the characteristics of 82% of boreholes correctly.