Lect. Shahen Mohammed Saleh

Full Name : Shaheen Mohammed Saleh Ahmed Ali

Email : This email address is being protected from spambots. You need JavaScript enabled to view it.

Qualifications : Master of Geophysics

University, Faculty, Department: Gokurova University, Graduate School of Natural and Applied Sciences.

Experience:

A data science expert specializing in applying artificial intelligence and deep machine learning to geophysical and environmental data. He holds a degree in Earth Sciences from the University of Baghdad and advanced studies in geophysics from Gukurova University in Turkey. He has considerable academic and practical experience in the field of geology. Demonstrated experience in using GIS to address challenges in geophysics, especially in monitoring natural disasters. His research focuses on modeling the Earth's layers using artificial intelligence techniques. He received training in remote sensing applications from prestigious and sober international centers and published many papers in prestigious journals. Familiar with drawing programs, geographical analysis, various operating systems and Microsoft office programs.

University Information: -

College College of Science

Branch or Department : Applied Geology

Scientific Title : Assistant Lecturer

Degree : Master

General Specialization : Earth Sciences

Specialization : Geophysics

Scientific Activities:

SPIE Europe/Technical Programmes 2015 in Toulouse, France, 21-24 September.
World Geology Conference in Ankara, Turkey on 14/4/2017 at the ODTÜ Center for Culture and Congresses.
Conference on Geological Studies at the Department of Archeology, University of Baghdad / Department of Geology. In 2015
IEEE International Conference on Computing Science and Information Technology and its Applications - 2019 in Kirkuk, Iraq, March 3-5, 2019.
Seismic symposium at the Iraqi Geologists Syndicate in Baghdad on March 12, 2023
Seismic conference at the University of Basrah on March 16, 2023
Contact information with the professor:

Published Researches:

• Automatic post-tsunami loss modeling using deep learning CNN case study: Miyagi and Fukushima Japan tsunami
• Detecting abnormal seismic activity areas of Anatolian plate and deformation directions using Python Geospatial libraries
• Improved deep learning (inception) CNN for detecting small tectonic plates of broken Anatolian, Turkey
• Multivariate Time Series Forecasting with LSTM for Madrid, Spain pollution
• Water quality monitoring of Al-Habbaniyah Lake using remote sensing and in situ measurements
• WATER QUALITY MONITORING OF AL-GHARRAF RIVER SOUTHERN OF IRAQ BY USING GIS MAPPING BASED WATER QUALITY PARAMETERS
• The Most Dangerous Subsidence Detecting of Mosul Dam by Multi-Pass Differential SAR Interferometry using Sentinel 1A Satellite Images from 2014 to 2016
• Automatic Mapping of Lineaments Using Shaded Relief Images Derived from Digital Elevation Model (DEM) in Kirkuk Northeast Iraq
• Detecting the most Archaeological looting in Iraq using High Resolution Imagery and digital image processing of LANDSAT8 Imagery
• A Comparative Study between Landsat-8 OLI and Landsat-7 ETM+, for sensor signal-to-noise performance, Spectral Distortion, and spectral signature matching: A Study in the IRAQ Landscap