Summary
Muhammad Bilal is a Lecturer in the Department of Computer Science at Namal University, Mianwali. He holds an M.S. in Computer Science with specialization in Machine Learning in Bioinformatics. His teaching and research focus on artificial intelligence, data analysis, and machine learning applications.
He has extensive academic experience across multiple institutions and has published research in areas such as sentiment analysis, fake review detection, and genomic data classification. His research interests include AI in healthcare, cybersecurity, and natural language processing. He has supervised over 50 final year projects and actively contributes to academic research, innovation, and student development.
Academic Background
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 - Present
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
Namal University