Muhammad Bilal

ORCID: 0009-0003-2766-4712
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About
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Research Areas
  • Animal Vocal Communication and Behavior
  • Brain Tumor Detection and Classification
  • Advanced Chemical Sensor Technologies
  • Date Palm Research Studies
  • Spectroscopy and Chemometric Analyses
  • Machine Learning and ELM
  • Lepidoptera: Biology and Taxonomy
  • Face recognition and analysis
  • Advanced Computing and Algorithms
  • Music and Audio Processing
  • Face and Expression Recognition
  • Image Retrieval and Classification Techniques
  • Spectroscopy Techniques in Biomedical and Chemical Research

National University of Sciences and Technology
2023-2024

Despite medical advancements, mosquito-based diseases are still life threating and prevalent, with dengue malaria which contributes for deaths of more than thousands people each year. Detecting mosquitoes' species from their wingbeats acoustic data can be very effective but it is challenging task. A model proposed in this paper classifies different mosquito based on properties sound. Publicly available dataset HumBug used to test the algorithm. Due class imbalances dataset, Receiver...

10.1109/comtech57708.2023.10165480 article EN 2023-03-15

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most approaches place little emphasis on reducing powerful cryptography and instead concentrate increasing rates. In this paper, we have proposed the Hidden Markov Model (HMM) convolutional Neural Network (CNN) models for by using ORL Yale datasets. Facial images from given data sets are divided into 3 portions, 4 5 6 portions corresponding their respective...

10.3390/ai5030079 article EN cc-by AI 2024-09-06

Abstract Despite global health advancements, mosquito borne diseases are still life threating and prevalent, with dengue malaria contributing to thousands of deaths each year. However, the responsibility for transmitting these specific does not lie all species globally. Detecting from their wingbeats acoustic data can be very effective, however, it is a challenging task accurately distinguish based on wingbeat patterns. Our acoustic-based deep learning model classification uses combination...

10.21203/rs.3.rs-3247762/v1 preprint EN cc-by Research Square (Research Square) 2023-08-22
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