English: Advanced
Arabic: Intermediate
Urdu: Fluent
My Journey as a Developer and Data Scientist
Driven and motivated individual with strong analytical and problem-solving skills, combining coding expertise and teamwork. Proficient in various programming languages and adept in collaborative environments. Poised to contribute innovative solutions and drive positive technological advancements.
Takes on challenging new role harnessing interpersonal skills, collaboration and problem-solving. Driven to deliver high-quality service and consistent results.
Committed manager with exceptional leadership, organisational skills and communication abilities leads high-performing cross-functional teams. Leads projects, company operations and business growth.
06/2025 - Current
02/2024 - 05/2025
08/2023 - 03/2024
03/2021 - 02/2023
Cecos university of IT and Emerging Sciences - Peshawar
09/2015 - 10/2019
University of Malakand - Malakand Pakistan
04/2021 - 12/2024
English: Advanced
Arabic: Intermediate
Urdu: Fluent
Comparative Study of Food Image Classification Performance Using the Xception Architecture
Shah, Mian & Alam, Aftab & Rabbi, Ihsan & Khalid, Shah & Ali, Gohar.
International Journal of Innovations in Science and Technology.. 7. 741-754. (2025).
Food allergies remain a critical issue that needs more research. To identify and manage food allergies, the integration of complex computational approaches is becoming more and more important, opening the door to more individualized and efficient food safety solutions. Which aims to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture. This research investigates the application of image classification techniques for allergen detection in food images. Specifically, we compare two models Model 1 serves as the baseline, trained on 11 classes. Two variations were explored: Model 2 focuseson Pakistani dishes, to investigate the impact of learning rate on the balance between adaptation speed and model precision. The objective is to determine the most effective model for classifying food images therefore Model 2 achieves the highest accuracy of 94%.These findings suggest that Model 2 is a promising candidate for real-world allergen detection applications. Future research will focus on creating a comprehensive new dataset of food images encompassing a wider variety of food items, as well as exploringthe integration of a model similar to model 2 into mobile applications for consumer use
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