Results 41 to 50 of about 34,789 (293)

Reinforcement Learning via Recurrent Convolutional Neural Networks [PDF]

2016 23rd International Conference on Pattern Recognition (ICPR), 2016
Deep Reinforcement Learning has enabled the learning of policies for complex tasks in partially observable environments, without explicitly learning the underlying model of the tasks. While such model-free methods achieve considerable performance, they often ignore the structure of task. We present a natural representation of to Reinforcement Learning (
Shankar, Tanmay, Dwivedy, Santosha K., Guha, Prithwijit   +2 more
openaire   +2 more sources

Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase

CT Lilun yu yingyong yanjiu
Identifying primary phases of seismic waveforms is a routine task in seismic data processing. Owing to the low efficiency of manual identification and the influence of human subjective factors, many methods for the automatic identification of the primary
Tianzhe WANG, Wanji ZHANG, Shanbo QI, Guoming JIANG   +3 more
doaj   +1 more source

Deep Learning–Assisted Differentiation of Four Peripheral Neuropathies Using Corneal Confocal Microscopy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah, Ioannis N. Petropoulos, Mark Stettner, Maryam Ferdousi, Uazman Alam, Nathan Efron, Ahmed Serag, Rayaz A. Malik   +7 more
wiley   +1 more source

Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Applied Computer Systems, 2020
Deep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision.
Obaid Falah, Babadi Amin, Yoosofan Ahmad
doaj   +1 more source

Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett, Abigail Dubois, Melanie Leguizamon, Alexander Song, Paula Trujillo, Colin D. McKnight, Ciaran M. Considine, Manus J. Donahue, Daniel O. Claassen   +8 more
wiley   +1 more source

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

Advanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha, Yingshan Ma, Yong Nam An, Sung‐Un An, Hyeon Hak Jung, Suvi‐Tuuli Varjamo, Jiwon Yoo, Junho Min, Hanvit Kim, Faisal Ahmed, Sang Hoon Chae, Young Min Song, Weiwei Cai, Tawfique Hasan, Zhipei Sun, Dong‐Ho Kang, Hyeon‐Jin Shin, Yunyun Dai, Hoon Hahn Yoon   +18 more
wiley   +1 more source

A novel framework for cryptocurrency price forecasting: integrating dual attention mechanisms, genetic algorithm feature selection, and Hybrid Adam-PSO optimization

Cogent Engineering
This paper proposes a robust framework for cryptocurrency price forecasting by integrating technical indicators, genetic algorithm based feature selection, and hybrid deep learning models. Technical indicators capture historical patterns, while a genetic
Susrita Mahapatro, Prabhat Kumar Sahu, Asit Kumar Subudhi   +2 more
doaj   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Direct load control of thermostatically controlled loads based on sparse observations using deep reinforcement learning

CSEE Journal of Power and Energy Systems, 2019
This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment.
Frederik Ruelens, Bert J. Claessens, Peter Vrancx, Fred Spiessens, Geert Deconinck   +4 more
doaj   +1 more source

Energy Consumption Prediction of Office Buildings Based on CNN-RNN Combined Model

Shanghai Jiaotong Daxue xuebao, 2022
In order to accurately reflect the operation characteristics of office buildings, a convolutional neural network(CNN)-recurrent neural network(RNN)combined model for energy consumption prediction of office buildings is proposed by using the good feature ...
ZENG Guozhi, WEI Ziqing, YUE Bao, DING Yunxiao, ZHENG Chunyuan, ZHAI Xiaoqiang
doaj   +1 more source