Fahad Maqbool
University of Sargodha
26 Papers
44 Citations
Fahad Maqbool is an academic researcher from University of Sargodha. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 5, co-authored 14 publications. Previous affiliations of Fahad Maqbool include Information Technology University.
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Papers
COVID-19 Patient Count Prediction Using LSTM
Muhammad Zahid Iqbal,Feras N. Al-Obeidat,Fahad Maqbool,Saad Razzaq,Sajid Anwar,Abdallah Tubaishat,Muhammad Shahrose Khan,Babar Shah +7 more
TL;DR: Long short-term memory (LSTM) is used to predict the volume of COVID-19 patients in Pakistan and it is revealed that the predicted patients’ count of the proposed model is much closer to the actual patient count.
Dr. Wheat: A Web-based Expert System for Diagnosis of Diseases and Pests in Pakistani Wheat
Fahad Shahbaz Khan,Saad Razzaq,Kashif Irfan,Fahad Maqbool,Ahmad Farid,Inam Illahi +5 more
- 01 Jan 2008
TL;DR: In this paper, a web-based expert system for wheat crop in Pakistan is presented, which is intended to help the farmers, researchers and students and provides an efficient and goal-oriented approach for solving common problems of wheat.
A review on mathematical modelling of Direct Internal Reforming- Solid Oxide Fuel Cells
TL;DR: In this article , the authors reviewed the present status of DIR-SOFC modeling efforts and consolidate their findings in order to highlight the unresolved problems for future research in this field.
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DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms
TL;DR: In this article , the authors proposed DeepClass-Rooms, a digital twin framework for attendance and course contents monitoring for the public sector schools of Punjab, Pakistan, which is cost effective and requires RFID readers and high-edge computing devices at the Fog layer for attendance monitoring and content matching, using convolution neural network for on-campus and online classes.
E-MAP: Efficiently Mining Asynchronous Periodic Patterns
Fahad Maqbool,Shariq Bashir,A. Rauf Baig +2 more
- 01 Jan 2006
TL;DR: The experimental results suggest that mining asynchronous periodic patterns using the proposed E-MAP (Efficient Mining of Asynchronous Periodic Patterns) algorithm is fast and efficient than as compared to previous approach SMCA, which is a three-step based algorithm for mining maximal complex patterns and requires depth-firstenumeration for mining multi events and maximalcomplex patterns.