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Found 19 papers, 8 papers with code
Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing
We evaluate FedMobile using a real-world mobile sensing dataset for influenza-like symptom recognition.
Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline
To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety.
Graph Neural Networks in IoT: A Survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on.
Improving mathematical questioning in teacher training
High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies.
Evaluation of mathematical questioning strategies using data collected through weak supervision
Using recent advances in uncertainty quantification, we evaluated our conversational agent for usability and analyzed the practicality of incorporating a human-in-the-loop approach for data collection and system evaluation for a mathematical questioning scenario.
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares.
LonelyText: A Short Messaging Based Classification of Loneliness
Loneliness does not only have emotional implications on a person but also on his/her well-being.
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation
Our experimentation shows that the proposed doubly robust offline learning algorithms performed significantly better than baseline approaches, suggesting that this type of recommender algorithm could improve emotion regulation.
Gender Detection on Social Networks using Ensemble Deep Learning
Analyzing the ever-increasing volume of posts on social media sites such as Facebook and Twitter requires improved information processing methods for profiling authorship.
Reward Shaping for Human Learning via Inverse Reinforcement Learning
The goal of this aid is to increase the speed with which humans can learn good policies for specific tasks.
Enabling Smartphone-based Estimation of Heart Rate
The promising results provide preliminary evidence of the feasibility of combining smartphone sensor data with wearable sensor data for continuous heart rate monitoring.
Women in ISIS Propaganda: A Natural Language Processing Analysis of Topics and Emotions in a Comparison with Mainstream Religious Group
Online propaganda is central to the recruitment strategies of extremist groups and in recent years these efforts have increasingly extended to women.
Text Classification Algorithms: A Survey
In this paper, a brief overview of text classification algorithms is discussed.
Analysis of Railway Accidents' Narratives Using Deep Learning
These reports contain a variety of fixed field entries including primary cause of the accidents (a coded variable with 389 values) as well as a narrative field which is a short text description of the accident.
Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings.
An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition.
RMDL: Random Multimodel Deep Learning for Classification
This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification.
Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data
Negative affect is a proxy for mental health in adults.
HDLTex: Hierarchical Deep Learning for Text Classification
This is because along with this growth in the number of documents has come an increase in the number of categories.
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