most citedThe Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie

31 citations · 61 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

Multi-Objective Optimization of Water Resource Allocation for Groundwater Recharge and Surface Runoff Management in Watershed Systems

Abbas Sharifi, Hajar Kazemi Naeini, Mohsen Ahmadi +2

Land degradation and air pollution are primarily caused by the salinization of soil and desertification that occurs from the drying of salinity lakes and the release of dust into t…

cs.CY2023★ 31 cited

The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie

Negin Yazdani Motlagh, Matin Khajavi, Abbas Sharifi +1

In the digital era, the integration of artificial intelligence (AI) in education has ushered in transformative changes, redefining teaching methodologies, curriculum planning, and…

eess.IV2023★ 5 cited

Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images

Mohsen Ahmadi, Masoumeh Farhadi Nia, Sara Asgarian +4

In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique…

eess.IV2023★ 1 cited

Supervised Multi-Regional Segmentation Machine Learning Architecture for Digital Twin Applications in Coastal Regions

Mohsen Ahmadi, Ahmad Gholizadeh Lonbar, Mohammadsadegh Nouri +4

This study explores the use of a digital twin model and deep learning method to build a global terrain and altitude map based on USGS information. The goal is to artistically repre…

cs.CV2023★ 24 cited

Application of Segment Anything Model for Civil Infrastructure Defect Assessment

Mohsen Ahmadi, Ahmad Gholizadeh Lonbar, Hajar Kazemi Naeini +4

This research assesses the performance of two deep learning models, SAM and U-Net, for detecting cracks in concrete structures. The results indicate that each model has its own str…