|
|
|
|
|
Muhammad Waseem, Ali Hasan Jaffry, Muhammad Azam, Ijaz Ahmad, Adnan Abbas and Jae-Eun Lee
Food security for the growing global population is closely associated with the variations in agricultural yield at the regional scale. Based on this perspective, the current study was designed to determine the impacts of drought on wheat production in th...
ver más
|
|
|
|
|
|
|
|
|
Joseph M. Ackerson, Rushit Dave and Naeem Seliya
Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to...
ver más
|
|
|
|
|
|
|
|
|
Yen-Cheng Chu, Yun-Jie Jhang, Tsung-Ming Tai and Wen-Jyi Hwang
The objective of this study is to present novel neural network (NN) algorithms and systems for sensor-based hand gesture recognition. The algorithms are able to classify accurately a sequence of hand gestures from the sensory data produced by acceleromet...
ver más
|
|
|
|
|
|
|
|
|
Tessfu Geteye Fantaye, Junqing Yu and Tulu Tilahun Hailu
Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. Howev...
ver más
|
|
|
|
|
|
|
|
|
Boxuan Yue, Junwei Fu and Jun Liang
Recurrent neural networks (RNN) are efficient in modeling sequences for generation and classification, but their training is obstructed by the vanishing and exploding gradient issues. In this paper, we reformulate the RNN unit to learn the residual funct...
ver más
|
|
|
|