Engr. Sannia Arshad

Contact me

  •   Department of Software Engineering
  •   051 9265100
  •   sania.arshad@numl.edu.pk

Engr. Sannia Arshad

Lecturer

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:

 

  1. 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,
  2. 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.
  3. 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

    1. 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. 
    2. 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:

 

  1. 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,
  2. 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.
  3. 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

    1. 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. 
    2. 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