home - mohammed alshahrani
Mohammed ALi Alshahrani, PhD
Applied college
Assistant proffessor
Applied College
Computer department
Computer science - artificial intelligence
Qualifications
Doctor of Philosophy degree 2020
I have graduated with a Doctor of Philosophy degree in computer science from the University of Essex – United Kingdom
Masters degree 2013
I have graduated with a masters degree in computer science from the University of Waikato - New Zealand
Post graduate diploma 2011
I have graduate with a post graduate diploma in Software and Information Technology from Lincoln University – New Zealand
Graduate diploma 2010
I have graduate with a graduate diploma in Software and Information Technology from Lincoln University – New Zealand
English language 2008-2009
achieved an advanced level equivalent to IELTS 6.5. From 28/4/2008 to 24/7/2009.
Bachelor degree 2007
Bachelor degree in education: major computer science
Experiences
Currently I am working at Najran University as Assistant Professor Since Dec, 2020
I was assigned as the vice-dean of the Supporting Services at the Applied College Jan, 2022- Oct 2022
I was assigned as assiatent of the CEO at the Applied College for the Supporting Services Jan, 2022- Oct 2022
I am assigned as the head of computer department at the Applied College Since Jan, 2021 until june, 2022
I worked at Najran University as a lecturer May, 2014-Dec 2020
I worked with MKCL Arabia as IT trainer at (King Saud University) Sep, 2013-May, 2014
Specialties and Skills
Assistant Professor at compurer department
Training Courses
الدورات التدربية
الأبحاث العلمية
Alshahrani, M. (2020). Exploring embedding vectors for emotion detection (Doctoral dissertation, University of Essex).
Alshahrani, M., Samothrakis, S., Fasli, M. (2019, July). Identifying idealised vectors for emotion detection using CMA-ES. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 157-158).
Alshahrani, M., Samothrakis, S., Fasli, M. (2017, October). Word mover’s distance for affect detection. In 2017 International Conference on the Frontiers and Advances in Data Science (FADS) (pp. 18-23). IEEE.
Alshahrani, M. A. A. (2013). Real Time Vehicle License Plate Recognition on Mobile Devices (Doctoral dissertation, University of Waikato).
Ahmed, I. A., Senan, E. M., Rassem, T. H., Ali, M. A., Shatnawi, H. S. A., Alwazer, S. M., & Alshahrani, M. (2022). Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques. Electronics, 11(4), 530.
Al-Jabbar, M., Alshahrani, M., Senan, E. M., & Ahmed, I. A. (2023). Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted. Diagnostics, 13(10), 1753.
Al-Jabbar, M., Alshahrani, M., Senan, E. M., & Ahmed, I. A. (2023). Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features. Bioengineering, 10(3), 383.
Al-Jabbar, M., Alshahrani, M., Senan, E. M., & Ahmed, I. A. (2023). Multi-Method Diagnosis of Histopathological Images for Early Detection of Breast Cancer Based on Hybrid and Deep Learning. Mathematics, 11(6), 1429.
Alshahrani, M., Al-Jabbar, M., Senan, E. M., Ahmed, I. A., & Saif, J. A. M. (2023). Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features. Diagnostics, 13(17), 2783.
courses:
field traning
data structure
Introduction to databases
fundamental Programming
applied project
الساعات المكتبية
الوصف
| 8-9 | 9-10 | 10-11 | 11-12 | 12-1 | 1-2 | |
|---|---|---|---|---|---|---|
| الأحد Sunday |
c-35 | c-35 | c-35 | c-35 | ||
| الاثنين Monday |
c-35 | c-35 | ||||
| الثلاثاء Tuesday |
c-35 | c-35 | c-35 | c-35 | ||
| الأربعاء Wednesday |
c-35 | c-35 | c-35 | |||
| الخميس Thursday |
c-35 |