Namal University

MR. MUHAMMAD BILAL

Lecturer
  • Department of Computer Science
  • 189
  • muhammad.bilal@namal.edu.pk
Summary

Academic Background
PhD(CS) (Machine Learning in Cybersecurity) National University of Sciences & Technology (NUST) in progress
MSCS (Classification of Retroviruses Based on Genomic Data Using RVGC) University of Sargodha 2019
BSCS ( Human Computer Interaction Using Camera ) University of Sargodha 2016
Experience
Lecturer Namal University Mianwali 12-Feb-2024 - continue
Lecturer Capital University of Science and Technology Islamabad 23-Sep-2022 - 12-Feb-2024
Visiting Lecturer University of Mianwali 16-Aug-2019 - 22-Sep-2022
Visiting Lecturer University of Sargodha 01-Sep-2016 - 16-Aug-2019
Journal Publications
Bridging the Language Gap: Evaluating Text Data Pre-processing and Classification Techniques in Urdu Sentiment Analysis 20-Jun-2024 The Asian Bulletin of Big Data Management
Classification of Retroviruses Based on Genomic Data Using RVGC 24-Aug-2021 CMC-Computers, Materials & Continua
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR FAKE REVIEW DETECTION 01-Jun-2021 International Journal of Computational Intelligence in Control
Courses
  • Data structures
  • Artificial Intelligence
  • Graph Algorithms
  • Soft Computing
  • Theory of Automata and Formal Languages
  • Data Analysis and Visualization
Solar Powered and IOT Based Apparatus for Cold Storage Fostering Transformative Collaborations: CFLI and NUST Unite for an Ambitious Venture A milestone moment unfolded as Amanda DeSadeleer, 1st Secretary at the High Commission of Canada in Pakistan, visited NUST. The spotlight was on an innovative cold chain solution, a brainchild of SMME, nurtured by CFLI's support. Standing shoulder to shoulder with this initiative, the local partner, PODA - Pakistan, played a pivotal role. Dr Syed Hussain Imran Jaffery of SMME provided a comprehensive briefing, unveiling the project's intricacies. Three schools of NUST- SMME, SEECS and NBS have joined forces, exemplifying a dynamic partnership for the success of this groundbreaking technology. The stakeholders are confident about the project's potential to enhance farm productivity and reduce post-harvest wastage.
Classification of Retroviruses Based on Genomic Data Using RVGC Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms. AIDS, cancer, neurologic disorders, and other clinical conditions can all be fatal due to retrovirus infections. Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods. The National Center for Biotechnol-ogy Information (NCBI) promotes science and health by making biomedical and genomic data available to the public. This research aims to classify the different types of rotavirus genome sequences available at the NCBI. First, nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage. Based on some significant results, the number of features used for classification is reduced to five. The classification shall be carried out in two phases. The first phase of classification shall select only two features. Unclassified data in the first phase is transferred to the next phase, where the final decision is taken with the remaining three features. Three data sets of animals and human retroviruses are selected; the training data set is used to minimize the classifier's number and training; the validation data set is used to validate the models. The performance of the classifier is analyzed using the test data set. Also, we use decision tree, naive Bayes, k-nearest neighbors, and vector support machines to compare results. The results show that the proposed approach performs better than the existing methods for the retrovirus's imbalanced genome-sequence dataset. Details