Ph.D Computer Science, International Islamic University Islamabad, Pakistan. (Pursuing (in foreign evaluation process)).
MS Software Engineering, Bahria University Islamabad Campus, Pakistan. April 2013.
BSE Bachelor’s in Software Engineering (PEC Registered), Fatima Jinnah Women University Rawalpindi, Pakistan. 2009
- Lecturer at National University of Modern Languages, Islamabad, Pakistan .
From September 2023 to Date.
- Lecturer at Iqra University Islamabad, Pakistan .
From September 2021 to June 2023.
- Visiting Lecturer at Bahria University Islamabad, Pakistan .
From February 2020 to August 2021.
- Visiting Lecturer at International Islamic University Islamabad, Pakistan .
From September 2018 to January 2021.
Journal Publications:
- S. Arshad, S. Khalid, "Combining diverse classifiers by learning weights robust to the presence of class label noise," in Bahria University Journal of Information and Communication Technology., vol. 07, no. 01, pp. 1743-1751, December 2014,
- S. Khalid, S. Arshad, S. Jabbar, “Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level” in The Scientific World Journal, 2014.
- S. Arshad, T. Amjad, A. Hussain, “Dermo-Seg: ResNet-UNet Architecture and Hybrid-Loss Function for Detection of Differential Structures to Diagnosis Pigmented Skin Lesions”, in MDPI Diagnostics, vol. 13, no. 2924, September 2023.
Conference Publications
-
- S. Khalid, S. Arshad, “Framework for constructing hybrid classifier using weight learning to combine heterogeneous classifiers”, in IEEE 5th International Conference on Computational Intelligence, Modeling and Simulation. Seoul, South Korea, September 2013.
- S. Khalid, S. Arshad, “A Robust Ensemble based Approach to Combine Heterogeneous Classifiers in the Presence of Class Label Noise”, in IEEE 5th International Conference on Computational Intelligence, Modeling and Simulation. Seoul, South Korea, September 2013.
Machine Learning
Artificial Intelligence
Deep Learning
Digital Image Processing
Data Structures an Algorithms
Education
Ph.D Computer Science, International Islamic University Islamabad, Pakistan. (Pursuing (in foreign evaluation process)).
MS Software Engineering, Bahria University Islamabad Campus, Pakistan. April 2013.
BSE Bachelor’s in Software Engineering (PEC Registered), Fatima Jinnah Women University Rawalpindi, Pakistan. 2009
Experience
- Lecturer at National University of Modern Languages, Islamabad, Pakistan .
From September 2023 to Date.
- Lecturer at Iqra University Islamabad, Pakistan .
From September 2021 to June 2023.
- Visiting Lecturer at Bahria University Islamabad, Pakistan .
From February 2020 to August 2021.
- Visiting Lecturer at International Islamic University Islamabad, Pakistan .
From September 2018 to January 2021.
Publications
Journal Publications:
- S. Arshad, S. Khalid, "Combining diverse classifiers by learning weights robust to the presence of class label noise," in Bahria University Journal of Information and Communication Technology., vol. 07, no. 01, pp. 1743-1751, December 2014,
- S. Khalid, S. Arshad, S. Jabbar, “Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level” in The Scientific World Journal, 2014.
- S. Arshad, T. Amjad, A. Hussain, “Dermo-Seg: ResNet-UNet Architecture and Hybrid-Loss Function for Detection of Differential Structures to Diagnosis Pigmented Skin Lesions”, in MDPI Diagnostics, vol. 13, no. 2924, September 2023.
Conference Publications
-
- S. Khalid, S. Arshad, “Framework for constructing hybrid classifier using weight learning to combine heterogeneous classifiers”, in IEEE 5th International Conference on Computational Intelligence, Modeling and Simulation. Seoul, South Korea, September 2013.
- S. Khalid, S. Arshad, “A Robust Ensemble based Approach to Combine Heterogeneous Classifiers in the Presence of Class Label Noise”, in IEEE 5th International Conference on Computational Intelligence, Modeling and Simulation. Seoul, South Korea, September 2013.
Interests
Machine Learning
Artificial Intelligence
Deep Learning
Digital Image Processing
Data Structures an Algorithms