Recent advances in stochastic gradient descent in deep learning
In the age of artificial intelligence, the best approach to handling huge amounts of data is a
tremendously motivating and hard problem. Among machine learning models, stochastic …
tremendously motivating and hard problem. Among machine learning models, stochastic …
Deep learning in diverse intelligent sensor based systems
Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …
virtually all fields of science and engineering. The increasing complexity and the large …
Social media big data analytics: A survey
Big data analytics has recently emerged as an important research area due to the popularity
of the Internet and the advent of the Web 2.0 technologies. Moreover, the proliferation and …
of the Internet and the advent of the Web 2.0 technologies. Moreover, the proliferation and …
Beyond the flow decomposition barrier
We introduce a new approach to the maximum flow problem. This approach is based on
assigning arc lengths based on the residual flow value and the residual arc capacities. Our …
assigning arc lengths based on the residual flow value and the residual arc capacities. Our …
EmergencyNet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion
Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing
technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their …
technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their …
A deep multi-modal neural network for informative Twitter content classification during emergencies
People start posting tweets containing texts, images, and videos as soon as a disaster hits
an area. The analysis of these disaster-related tweet texts, images, and videos can help …
an area. The analysis of these disaster-related tweet texts, images, and videos can help …
Robust classification of crisis-related data on social networks using convolutional neural networks
The role of social media, in particular microblogging platforms such as Twitter, as a conduit
for actionable and tactical information during disasters is increasingly acknowledged …
for actionable and tactical information during disasters is increasingly acknowledged …
[PDF][PDF] Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles.
Abstract UnmannedAerial Vehicles (UAVs), equipped with camera sensors can facilitate
enhanced situational awareness for many emergency response and disaster management …
enhanced situational awareness for many emergency response and disaster management …
[PDF][PDF] Damage Identification in Social Media Posts using Multimodal Deep Learning.
Social media has recently become a digital lifeline used to relay information and locate
survivors in disaster situations. Currently, officials and volunteers scour social media for any …
survivors in disaster situations. Currently, officials and volunteers scour social media for any …
Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case …
We introduce an analytical framework for analyzing tweets to (1) identify and categorize fine-
grained details about a disaster such as affected individuals, damaged infrastructure and …
grained details about a disaster such as affected individuals, damaged infrastructure and …