13,228 research outputs found
Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: A Systematic Review
Background: The use of machine learning (ML) in mental health (MH) research
is increasing, especially as new, more complex data types become available to
analyze. By systematically examining the published literature, this review aims
to uncover potential gaps in the current use of ML to study MH in vulnerable
populations of immigrants, refugees, migrants, and racial and ethnic
minorities.
Methods: In this systematic review, we queried Google Scholar for ML-related
terms, MH-related terms, and a population of a focus search term strung
together with Boolean operators. Backward reference searching was also
conducted. Included peer-reviewed studies reported using a method or
application of ML in an MH context and focused on the populations of interest.
We did not have date cutoffs. Publications were excluded if they were narrative
or did not exclusively focus on a minority population from the respective
country. Data including study context, the focus of mental healthcare, sample,
data type, type of ML algorithm used, and algorithm performance was extracted
from each.
Results: Our search strategies resulted in 67,410 listed articles from Google
Scholar. Ultimately, 12 were included. All the articles were published within
the last 6 years, and half of them studied populations within the US. Most
reviewed studies used supervised learning to explain or predict MH outcomes.
Some publications used up to 16 models to determine the best predictive power.
Almost half of the included publications did not discuss their cross-validation
method.
Conclusions: The included studies provide proof-of-concept for the potential
use of ML algorithms to address MH concerns in these special populations, few
as they may be. Our systematic review finds that the clinical application of
these models for classifying and predicting MH disorders is still under
development
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition
Lazy learning: a biologically-inspired plasticity rule for fast and energy efficient synaptic plasticity
When training neural networks for classification tasks with backpropagation,
parameters are updated on every trial, even if the sample is classified
correctly. In contrast, humans concentrate their learning effort on errors.
Inspired by human learning, we introduce lazy learning, which only learns on
incorrect samples. Lazy learning can be implemented in a few lines of code and
requires no hyperparameter tuning. Lazy learning achieves state-of-the-art
performance and is particularly suited when datasets are large. For instance,
it reaches 99.2% test accuracy on Extended MNIST using a single-layer MLP, and
does so 7.6x faster than a matched backprop networkComment: 13 pages, 6 figure
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
We consider dynamic pricing strategies in a streamed longitudinal data set-up
where the objective is to maximize, over time, the cumulative profit across a
large number of customer segments. We consider a dynamic probit model with the
consumers' preferences as well as price sensitivity varying over time. Building
on the well-known finding that consumers sharing similar characteristics act in
similar ways, we consider a global shrinkage structure, which assumes that the
consumers' preferences across the different segments can be well approximated
by a spatial autoregressive (SAR) model. In such a streamed longitudinal
set-up, we measure the performance of a dynamic pricing policy via regret,
which is the expected revenue loss compared to a clairvoyant that knows the
sequence of model parameters in advance. We propose a pricing policy based on
penalized stochastic gradient descent (PSGD) and explicitly characterize its
regret as functions of time, the temporal variability in the model parameters
as well as the strength of the auto-correlation network structure spanning the
varied customer segments. Our regret analysis results not only demonstrate
asymptotic optimality of the proposed policy but also show that for policy
planning it is essential to incorporate available structural information as
policies based on unshrunken models are highly sub-optimal in the
aforementioned set-up.Comment: 34 pages, 5 figure
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
We propose Compressed Vertical Federated Learning (C-VFL) for
communication-efficient training on vertically partitioned data. In C-VFL, a
server and multiple parties collaboratively train a model on their respective
features utilizing several local iterations and sharing compressed intermediate
results periodically. Our work provides the first theoretical analysis of the
effect message compression has on distributed training over vertically
partitioned data. We prove convergence of non-convex objectives at a rate of
when the compression error is bounded over the course
of training. We provide specific requirements for convergence with common
compression techniques, such as quantization and top- sparsification.
Finally, we experimentally show compression can reduce communication by over
without a significant decrease in accuracy over VFL without compression
MATURE-HEALTH: HEALTH Recommender System for MAndatory FeaTURE choices
Balancing electrolytes is utmost important and essential for appropriate
functioning of organs in human body as electrolytes imbalance can be an
indication of the development of underlying pathophysiology. Efficient
monitoring of electrolytes imbalance not only can increase the chances of early
detection of disease, but also prevents the further deterioration of the health
by strictly following nutrient controlled diet for balancing the electrolytes
post disease detection. In this research, a recommender system MATURE Health is
proposed and implemented, which predicts the imbalance of mandatory
electrolytes and other substances presented in blood and recommends the food
items with the balanced nutrients to avoid occurrence of the electrolytes
imbalance. The proposed model takes user most recent laboratory results and
daily food intake into account to predict the electrolytes imbalance. MATURE
Health relies on MATURE Food algorithm to recommend food items as latter
recommends only those food items that satisfy all mandatory nutrient
requirements while also considering user past food preferences. To validate the
proposed method, particularly sodium, potassium, and BUN levels have been
predicted with prediction algorithm, Random Forest, for dialysis patients using
their laboratory reports history and daily food intake. And, the proposed model
demonstrates 99.53 percent, 96.94 percent and 95.35 percent accuracy for
Sodium, Potassium, and BUN respectively. MATURE Health is a novel health
recommender system that implements machine learning models to predict the
imbalance of mandatory electrolytes and other substances in the blood and
recommends the food items which contain the required amount of the nutrients
that prevent or at least reduce the risk of the electrolytes imbalance.Comment: Author version of the pape
Augmented classification for electrical coil winding defects
A green revolution has accelerated over the recent decades with a look to replace existing transportation power solutions through the adoption of greener electrical alternatives. In parallel the digitisation of manufacturing has enabled progress in the tracking and traceability of processes and improvements in fault detection and classification. This paper explores electrical machine manufacture and the challenges faced in identifying failures modes during this life cycle through the demonstration of state-of-the-art machine vision methods for the classification of electrical coil winding defects. We demonstrate how recent generative adversarial networks can be used to augment training of these models to further improve their accuracy for this challenging task. Our approach utilises pre-processing and dimensionality reduction to boost performance of the model from a standard convolutional neural network (CNN) leading to a significant increase in accuracy
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
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