4 papers
FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
Jun Bai, Rajib Rana, Di Wu +5
Federated Learning (FL) offers a privacy-preserving framework for training audio classification (AC) models across decentralized clients without sharing raw data. However, Federate…
AI-assisted summary of suicide risk Formulation
Rajib Rana, Niall Higgins, Kazi N. Haque +5
Background: Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's…
Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)
Kazi Nazmul Haque, Rajib Rana, Tasnim Jarin +1
This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised fea…
Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning
Rajib Rana, Niall Higgins, Kazi Nazmul Haque +4
Ensuring accurate call prioritisation is essential for optimising the efficiency and responsiveness of mental health helplines. Currently, call operators rely entirely on the calle…