Machine learning facilitated business intelligence (Part I) Neural networks learning algorithms and applications
Purpose The purpose of this paper is to conduct a comprehensive review of the noteworthy
contributions made in the area of the Feedforward neural network (FNN) to improve its …
contributions made in the area of the Feedforward neural network (FNN) to improve its …
Prediction of groundwater quality using efficient machine learning technique
To ensure safe drinking water sources in the future, it is imperative to understand the quality
and pollution level of existing groundwater. The prediction of water quality with high …
and pollution level of existing groundwater. The prediction of water quality with high …
Learning to optimize: Training deep neural networks for interference management
Numerical optimization has played a central role in addressing key signal processing (SP)
problems. Highly effective methods have been developed for a large variety of SP …
problems. Highly effective methods have been developed for a large variety of SP …
Pointgrid: A deep network for 3d shape understanding
T Le, Y Duan - Proceedings of the IEEE conference on …, 2018 - openaccess.thecvf.com
This paper presents a new deep learning architecture called PointGrid that is designed for
3D model recognition from unorganized point clouds. The new architecture embeds the …
3D model recognition from unorganized point clouds. The new architecture embeds the …
Predicting the price of bitcoin using machine learning
The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD
can be predicted. The price data is sourced from the Bitcoin Price Index. The task is …
can be predicted. The price data is sourced from the Bitcoin Price Index. The task is …
Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks
H Yu, Z Wu, S Wang, Y Wang, X Ma - Sensors, 2017 - mdpi.com
Predicting large-scale transportation network traffic has become an important and
challenging topic in recent decades. Inspired by the domain knowledge of motion prediction …
challenging topic in recent decades. Inspired by the domain knowledge of motion prediction …
Deep reinforcement learning for building HVAC control
Buildings account for nearly 40% of the total energy consumption in the United States, about
half of which is used by the HVAC (heating, ventilation, and air conditioning) system …
half of which is used by the HVAC (heating, ventilation, and air conditioning) system …
A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area
This research proposes and evaluates a new approach for flash flood susceptibility mapping
based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high …
based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high …
Learning to optimize: Training deep neural networks for wireless resource management
For decades, optimization has played a central role in addressing wireless resource
management problems such as power control and beamformer design. However, these …
management problems such as power control and beamformer design. However, these …
Entangled conditional adversarial autoencoder for de novo drug discovery
Modern computational approaches and machine learning techniques accelerate the
invention of new drugs. Generative models can discover novel molecular structures within …
invention of new drugs. Generative models can discover novel molecular structures within …