Publications by authors named "Muhammad Farrukh Qureshi"
Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and deep learning architecture. Raw audio waveforms are first transformed into Log Mel Spectrograms, and a fusion of features such as the Discrete Wavelet Transform, the Gabor filter, the Local Binary Pattern, and the Laplacian High Pass Filter, followed by a well-adapted deep model, is proposed to capture crucial spectral and spectral information that can help distinguish between the various forms of fish feeding behavior.
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- - The research presents a new dual-pathway convolutional neural network (DP-CNN) specifically designed for analyzing Log-Mel spectrogram images from multichannel electromyography signals, focusing on performance for both able-bodied and amputee subjects.
- - The DP-CNN achieves high mean accuracies of 94.93% for healthy subjects in NinaPro DB1 and 85.36% for amputee subjects in DB3, showcasing its effectiveness across various datasets.
- - Compared to previous methods, the DP-CNN shows significant performance improvements, with accuracy boosts of up to 39.09% and outperforms transfer learning models, suggesting strong potential for enhancing myoelectric control applications.
Fibroids of the uterus are a common benign tumor affecting women of childbearing age. Uterine fibroids (UF) can be effectively treated with earlier identification and diagnosis. Its automated diagnosis from medical images is an area where deep learning (DL)-based algorithms have demonstrated promising results.
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