Abstract: This paper introduces the CAT model, achieving 98.9% accuracy on the ShipsEar dataset by combining CNN and ViT with a fusion and separation mechanism for underwater acoustic signal ...
Abstract: This research paper focuses on the development, improvement and evaluation of a machine learning model for recognizing Indian Sign Language (ISL) hand gestures in emergency situations. By ...
Abstract: This manuscript delineates a sophisticated framework for the recognition of handwritten Hindi characters, employing diverse deep learning methodologies. Within this study, we proffer a ...
Abstract: Predictive maintenance, utilising anomalous sound classification, demonstrates a strong potential to identify mechanical faults in industrial machinery. This research proposes a machine ...
Abstract: Recent advances in automatic speech recognition (ASR) have largely focused on real-valued neural network models (e.g., Whisper) that use only the magnitude of the speech signal’s spectrogram ...
Abstract: The need for advanced Speech Emotion Recognition (SER) systems has grown with the development of human-machine interaction technologies. This paper introduces a CNN model specifically ...
Abstract: Audio feature selection and neural network architecture play crucial roles in speech recognition performance. This paper presents a comparative analysis of Artificial Neural Networks (ANNs) ...
Abstract: This study introduces a sophisticated floral identification system based on Deep Learning and Machine Learning to improve species classification accuracy. The system combines VGG16 CNN for ...
Abstract: The development of autonomous driving technology presents challenges for vision systems, especially in complex environments such as changing weather, lighting, and tunnels. Traditional ...
Abstract: The recognition of handwritten regional scripts remains a significant challenge in the domain of Optical Character Recognition (OCR), particularly for complex languages such as Telugu. This ...
Abstract: In this study, we studied unsupervised multiview learning techniques focused on maximizing correlation, particularly Deep Canonically Correlated Autoencoders (DCCAE). The goal of this study ...
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