The Effect of Channel Size on Performance of 1D CNN Architecture for Automatic Detection of Self-Reported COVID-19 Symptoms on Twitter
Social media has a crucial role as the most generally accessed source of information by the
public for obtaining information on various topics, such as COVID-19 and natural disasters …
public for obtaining information on various topics, such as COVID-19 and natural disasters …
LSTM and Bi-LSTM models for identifying natural disasters reports from social media
Natural disaster events are occurrences that cause significant losses, primarily resulting in
environmental and property damage and in the worst cases, even loss of life. In some cases …
environmental and property damage and in the worst cases, even loss of life. In some cases …
Harvesting Natural Disaster Reports from Social Media with 1D Convolutional Neural Network and Long Short-Term Memory
The employment of social media platforms is progressively assuming pivotal roles in natural
disaster management as early warning and monitoring systems. In emergencies, social …
disaster management as early warning and monitoring systems. In emergencies, social …
A Social Community Sensor for Natural Disaster Monitoring in Indonesia Using Hybrid 2D CNN LSTM
One of the most notable advantages of utilizing social media is its ability to aid
communication during natural disasters, specifically in regards to disseminating information …
communication during natural disasters, specifically in regards to disseminating information …
3D word embedding vector feature extraction and hybrid CNN-LSTM for natural disaster reports identification
Social media contain various information, such as natural disaster reports. Artificial
intelligence is used to identify reports from eyewitnesses early for disaster warning systems …
intelligence is used to identify reports from eyewitnesses early for disaster warning systems …
Bridging the Gap: A Case Study of Utilizing Social Media to Accelerate Recovery and Structuring Disaster Management
S Mohammad, A Al Jobair, F Shaiara… - … Conference on Human …, 2024 - Springer
The widespread availability of the internet and smartphones and active participation in
social media platforms reflect a substantial advancement in information and communication …
social media platforms reflect a substantial advancement in information and communication …
[PDF][PDF] Identification of Social Media Posts Containing Self-reported COVID-19 Symptoms using Triple Word Embeddings and Long Short-Term Memory
The COVID-19 pandemic has permeated the global sphere and influenced nearly all nations
and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of …
and regions. Common symptoms of this pandemic include fever, cough, fatigue, and loss of …
Accurate Deep Learning-Based Sleep Apnea Detection from Cardiac Physiological Signals
MK Delimayanti, AT Muharram… - 2023 IEEE Asia …, 2023 - ieeexplore.ieee.org
Sleep apnea is a severe sleep disorder characterized by interrupted breathing during sleep.
Early detection and accurate diagnosis of sleep apnea are essential to avoiding potentially …
Early detection and accurate diagnosis of sleep apnea are essential to avoiding potentially …
Tackling an Unbalanced Dataset for Classifying Indonesian E-Commerce Reviews Using Multi Word Embedding Model
Product reviews are an integral part of ecommerce. By Q2 2020 alone, the average of e-
commerce visitors in Indonesia almost reached 400 million. This vast amount of traffic …
commerce visitors in Indonesia almost reached 400 million. This vast amount of traffic …