2025 : 4 : 13
فارسی
Mohsen Ramezani
Academic rank:
Assistant Professor
ORCID:
Education:
PhD.
ScopusId:
2135
HIndex:
Faculty:
Faculty of Engineering
Address:
Phone:
E-mail:
m.ramezani [at] uok.ac.ir
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Education
Courses
Research activities
Research interests
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Education
PhD. in Computer engineering-Artificial Intelligence , Semnan university , Iran
(2013 - 2018)
Thesis title:
MSc. in Computer engineering-Artificial Intelligence , University of Kurdistan , Iran
(2011 - 2013)
Thesis title:
BSc. in Information Technology engineering , University of Kurdistan , Iran
(2007 - 2011)
Thesis title:
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Courses
Bachelor Of Science
Master Of Science
Concepts of computer systems
Data Mining
Artificial Intelligence
Software design
Fundamentals of programming in c++
Computer graphics
Data structures and algorithms
Concepts of Operating Systems
Advanced Programming
Machine Vision
Research activities
Journal Papers
Conference Papers
Theses
A New Deep Vision-Based Identifier As An Intelligent Herbicide Spraying Agent For Potato Farm Application
(1403)
Robust deep image-watermarking method by a modified Siamese network
Ako Bartani, Fardin Akhlaghian Tab, Alireza Abdollahpouri, Mohsen Ramezani (2024)
An adaptive optic-physic based dust removal method using optimized air-light and transfer function
Ako Bartani, Alireza Abdollahpouri, Mohsen Ramezani, Fardin Akhlaghian Tab (2022)
A Deep Human Action Representation For Retrieval Application
Mohsen Ramezani, Fardin Akhlaghian Tab, Farzin Yaghmaee (2022)
A novel extreme learning machine based kNN classification method for dealing with big data
Amin Shokrzade, Mohsen Ramezani, Fardin Akhlaghian Tab, Mahmud Abdulla Mohammad (2021)
A new generalized collaborative filtering approach on sparse data by extracting high confidence relations between users
Mohsen Ramezani, Fardin Akhlaghian Tab, Alireza Abdollahpouri, Mahmud Abdulla Mohammad (2021)
ELM-NET, a closer to practice approach for classifying the big data using multiple independent ELMs
Amin Shokrzade, Fardin Akhlaghian Tab, Mohsen Ramezani (2020)
Motion pattern based representation for improving human action retrieval
Mohsen Ramezani, Farzin Yaghmaee (2018)
Retrieving Human Action by Fusing the Motion Information of Interest Points
Mohsen Ramezani, Farzin Yaghmaee (2018)
A review on human action analysis in videos for retrieval applications
Mohsen Ramezani, Farzin Yaghmaee (2016)
A novel video recommendation system based on efficient retrieval of human actions
Mohsen Ramezani, Farzin Yaghmaee (2016)
A pattern mining approach to enhancing the accuracy of collaborative filtering in sparse data domains
Mohsen Ramezani, Parham Moradi, Fardin Akhlaghian Tab (2014)
Eliminating the Repetitive Motions as a Preprocessing step for Fast Human Action Retrieval
Mohsen Ramezani, Farzin Yaghmaee (2019)
Content-based human actions retrieval by a novel low complex action representation
Mohsen Ramezani, Farzin Yaghmaee (2014)
Using the Fuzzy Clustering Algorithm to Improve the Content-Based Action Retrieval
Mohsen Ramezani, Farzin Yaghmaee (2014)
Content-based retrieval of human actions by analysing the statistical information of features
Mohsen Ramezani, Farzin Yaghmaee (2014)
Improve performance of collaborative filtering systems using backward feature selection
Mohsen Ramezani, Parham Moradi, Fardin Akhlaghian Tab (2013)
A New Hybrid Clustering Algorithm for Improving Results of Recommender Systems
Mohsen Ramezani, Parham Moradi (2013)
Design and implementation of a high-speed online system based on deep learning for grading of oleaster (Elaeagnus angustifolia) fruit
(1402)
A Filter-Wrapper Many-Objective Multi-label Feature Selection
(1402)
The effect of content marketing on audience attraction in Instagram social network
(1401)
presenting a two-stream method based on traditional and deep complementary features to detect human activity in video
(1401)
GAN-Based Guided Image Inpainting By User-defined Side Information
Mohsen Ramezani, Fardin Akhlaghian Tab (2022)
Research interests
Information retrieval in videos
Big data
Analyzing the human actions in videos
Collaborative filtering systems
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Links
Academic Website
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