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Found 8 papers, 5 papers with code
A Machine Learning Approach for Optimizing Hybrid Quantum Noise Clusters for Gaussian Quantum Channel Capacity
This work contributes to the advancement of quantum communication by visualizing hybrid quantum noise in higher dimensions and optimizing the capacity of the quantum channel by using machine learning (ML).
Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis
Unsupervised learning methods have become increasingly important in deep learning due to their demonstrated large utilization of datasets and higher accuracy in computer vision and natural language processing tasks.
Frequency-centroid features for word recognition of non-native English speakers
The objective of this work is to investigate complementary features which can aid the quintessential Mel frequency cepstral coefficients (MFCCs) in the task of closed, limited set word recognition for non-native English speakers of different mother-tongues.
Analyzing the impact of feature selection on the accuracy of heart disease prediction
In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an essential factor.
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs
This provides a good learning experience for the students.
Identifying Stroke Indicators Using Rough Sets
We propose a novel rough-set based technique for ranking the importance of the various EHR records in detecting stroke.
Analysis of French Phonetic Idiosyncrasies for Accent Recognition
However, there is a scope of improvement in speech recognition systems in identifying the nuances and accents of a speaker.
An Overview on Application of Machine Learning Techniques in Optical Networks
Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data.
