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An Efficient Distributed Reinforcement Learning Architecture for Long-Haul Communication Between Actors and Learner

Shin Morishima, Hiroki Matsutani • IEEE Access (2024)

A computing cluster that interconnects multiple compute nodes is used to accelerate distributed reinforcement learning that uses DQN (Deep Q-Network). In distributed reinforcement learning, actor nodes acquire experiences by interacting wit...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3402835

Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning

Daichi Kando, Satoshi Tomioka, Naoki Miyamoto, Ryosuke Ueda • Applied Sciences (2019)

In an optical measurement system using an interferometer, a phase extracting technique from interferogram is the key issue. When the object is varying in time, the Fourier-transform method is commonly used since this method can extract a ph...

DOAJ Open Access (DOAJ) DOI: 10.3390/app9173529

Development of an automated two pronuclei detection system on time‐lapse embryo images using deep learning techniques

Noritaka Fukunaga, Sho Sanami, Hiroya Kitasaka, Yuji Tsuzuki et al. • Reproductive Medicine and Biology (2020)

Abstract Purpose To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. Methods An algorithm was developed by explicitly...

DOAJ Open Access (DOAJ) DOI: 10.1002/rmb2.12331

Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks

Santu Mondal, Sneha Ray, Aritra Acharyya, Rudra Sankar Dhar et al. • IEEE Access (2025)

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regim...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3567410

Interpretable Intersection Control by Reinforcement Learning Agent With Linear Function Approximator

Somporn Sahachaiseree, Takashi Oguchi • IET Intelligent Transport Systems (2025)

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximat...

DOAJ Open Access (DOAJ) DOI: 10.1049/itr2.70034

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Sun-Feel Yang, So-Won Choi, Eul-Bum Lee • Energies (2023)

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affe...

DOAJ Open Access (DOAJ) DOI: 10.3390/en16114271

Discovering subtypes with imaging signatures in the Motoric Cognitive Risk Syndrome Consortium using weakly supervised clustering

Bhargav Teja Nallapu, Ali Ezzati, Helena M. Blumen, Kellen K. Petersen et al. • Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring (2025)

ABSTRACT INTRODUCTION Understanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia. METHODS We used data from six cohorts from the MCR ...

DOAJ Open Access (DOAJ) DOI: 10.1002/dad2.70197

Predicting Urban Quality of Life Using Machine Learning on Street Images and Personal Information with Statistical Feature Selection: A Comparative Study of Bangkok and London

Ployrada Suvarnakuta, Intouch Prakaisak, Pittipol Kantavat, Boonserm Kijsirikul et al. • Engineered Science (2025)

This study proposes a machine learning framework for predicting perceived urban quality of life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavi...

DOAJ Open Access (DOAJ) DOI: 10.30919/es1972

Effect of Word Sense Disambiguation on Neural Machine Translation: A Case Study in Korean

Quang-Phuoc Nguyen, Anh-Dung Vo, Joon-Choul Shin, Cheol-Young Ock • IEEE Access (2018)

With the advent of robust deep learning, neural machine translation (NMT) has achieved great progress and recently become the dominant paradigm in machine translation (MT). However, it is still confronted with the challenge of word ambiguit...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2018.2851281

Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning

Tomoyuki Ito, Thuy Duong Nguyen, Yutaka Saito, Yoichi Kurumida et al. • mAbs (2023)

Despite the advances in surface-display systems for directed evolution, variants with high affinity are not always enriched due to undesirable biases that increase target-unrelated variants during biopanning. Here, our goal was to design a ...

DOAJ Open Access (DOAJ) DOI: 10.1080/19420862.2023.2168470

IEEE Access Special Section Editorial: 5G and Beyond Mobile Wireless Communications Enabling Intelligent Mobility

Ke Guan, Markus Rupp, Thomas Kurner, Cesar Briso et al. • IEEE Access (2020)

Increasing urbanization is one major trend that shapes tomorrow’s society; by 2050, more than 85% of the developed world’s population will live in a comparatively small number of ever-growing cities. Within such cities ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2020.3037635

Emergentist View on Generative Narrative Cognition: Considering Principles of the Self-Organization of Mental Stories

Taisuke Akimoto • Advances in Human-Computer Interaction (2018)

We consider the essence of human intelligence to be the ability to mentally (internally) construct a world in the form of stories through interactions with external environments. Understanding the principles of this mechanism is vital for r...

DOAJ Open Access (DOAJ) DOI: 10.1155/2018/6780564

Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospective cohort study design

Kohji Nishida, Ryo Kawasaki, Yiming Qian, Liangzhi Li et al. • BMJ Open (2024)

Objective Despite extensive exploration of potential biomarkers of cardiovascular diseases (CVDs) derived from retinal images, it remains unclear how retinal images contribute to CVD risk profiling and how the results can inform lifestyle m...

DOAJ Open Access (DOAJ) DOI: 10.1136/bmjopen-2023-078609