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Academic Articles & Research Papers
Showing 20 of 654 academic articles
Mercedes Gimeno-Segovia, Hugo Cable, Gabriel J Mendoza, Pete Shadbolt et al. • New Journal of Physics
(2017)
Many existing schemes for linear-optical quantum computing (LOQC) depend on multiplexing (MUX), which uses dynamic routing to enable near-deterministic gates and sources to be constructed using heralded, probabilistic primitives. MUXing acc...
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Open Access (DOAJ)
DOI: 10.1088/1367-2630/aa7095
Nanting Huang, Xiaoyu Li, Xin Yang, Li Xie et al. • Sensors
(2026)
The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by adva...
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Open Access (DOAJ)
DOI: 10.3390/s26133997
K. C. Chen, W. Dai, C. Errando-Herranz, S. Lloyd et al. • PRX Quantum
(2021)
A quantum random access memory (qRAM) is considered an essential computing unit to enable polynomial speedups in quantum information processing. Proposed implementations include the use of neutral atoms and superconducting circuits to const...
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Open Access (DOAJ)
DOI: 10.1103/PRXQuantum.2.030319
Recep Evrim Ozgen, Ahmet Yazar, Mustafa Serdar Osmanca • IEEE Access
(2026)
Future sixth-generation (6G) wireless networks are expected to evolve beyond traditional communication infrastructures and incorporate native sensing and environmental awareness capabilities. While previous generations primarily focused on ...
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Open Access (DOAJ)
DOI: 10.1109/ACCESS.2026.3679953
Isabella Aboderin • Journal of Long-Term Care
(2019)
Context: Perspectives from sub-Saharan Africa (SSA) have featured little in the expanding global debate on long-term care (LTC) policy thus far – despite SSA countries’ embrace of global commitments on the development of equitable and susta...
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Open Access (DOAJ)
DOI: 10.31389/jltc.5
Georgiana Țucudean, Marian Bucos, Bogdan Drăgulescu, Cătălin Daniel Căleanu • PeerJ Computer Science
(2024)
Natural language processing (NLP) tasks can be addressed with several deep learning architectures, and many different approaches have proven to be efficient. This study aims to briefly summarize the use cases for NLP tasks along with the ma...
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DOI: https://doi.org/10.7717/peerj-cs.2222
PMC: 11322986
Rui Zhao, Haofeng Hu, Yun Li, Yuze Fan et al. • Sensors
(2024)
Urban traffic congestion poses significant economic and environmental challenges worldwide. To mitigate these issues, Adaptive Traffic Signal Control (ATSC) has emerged as a promising solution. Recent advancements in deep reinforcement lear...
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Open Access (DOAJ)
DOI: 10.3390/s24196202
Jalaj Thanaki (2017)
Leverage the power of machine learning and deep learning to extract information from text dataAbout This BookImplement Machine Learning and Deep Learning techniques for efficient natural language processingGet started with NLTK and implemen...
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Francesco Maria Cianciaruso • Discover Artificial Intelligence
(2026)
Abstract The increasingly rapid development of deep learning algorithms is radically transforming society. The potential of neural models is paving the way for the automation of areas previously unrelated to AI, from medical diagnostics to ...
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Open Access (DOAJ)
DOI: 10.1007/s44163-026-01111-8
Aditya Malte, Pratik Ratadiya • arXiv (Cornell University)
(2019)
In this paper, we present a study of the recent advancements which have helped bring Transfer Learning to NLP through the use of semi-supervised training. We discuss cutting-edge methods and architectures such as BERT, GPT, ELMo, ULMFit amo...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1910.07370
Chenglong Dao, Jun Qi • Buildings
(2024)
Urban blue–green spaces (UBGSs) are a significant avenue for addressing the worldwide mental health crisis. To effectively optimise landscape design and management for the promotion of health benefits from UBGS, it is crucial to objectively...
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Open Access (DOAJ)
DOI: 10.3390/buildings14051426
Cristian Cosentino, Simone Gatto, Pietro Liò, Fabrizio Marozzo • Future Internet
(2026)
Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. ...
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Open Access (DOAJ)
DOI: 10.3390/fi18030174
Guoyu Zuo, Minghui Zhang, Jingwei Ren, Qifei Wu et al. • CAAI Transactions on Intelligence Technology
(2026)
ABSTRACT Knee joint moment estimation is a critical component in biomechanical analysis, with profound implications for rehabilitation assessment and the development of assistive devices like exoskeletons. However, existing data‐driven appr...
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Open Access (DOAJ)
DOI: 10.1049/cit2.70165
Zhen Yang, Minjie Xu, Zhangfeng Liu, Da Qin et al. • Dianxin kexue
(2013)
As the major part of big data research, the audio frequency's value has not been discovered. The features of big data, and the automatic speech recognition（ASR）technology were studied, and the value was mined through the method that combine...
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Open Access (DOAJ)
Shijie Chen, Yu Zhang, Qiang Yang • ACM Computing Surveys
(2024)
Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. ...
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Open Access
DOI: https://doi.org/10.1145/3663363
Mosima A. Masethe, Sunday O. Ojo, Hlaudi D. Masethe • Computers
(2025)
Meaning conflation deficiency (MCD) presents a continual obstacle in natural language processing (NLP), especially for low-resourced and morphologically complex languages, where polysemy and contextual ambiguity diminish model precision in ...
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Open Access (DOAJ)
DOI: 10.3390/computers14090402
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton et al. • Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo)
(2020)
We introduce Sta n z a , an open-source Python natural language processing toolkit supporting 66 human languages.Compared to existing widely used toolkits, Sta n z a features a language-agnostic fully neural pipeline for text analysis, incl...
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DOI: https://doi.org/10.18653/v1/2020.acl-demos.14
Susilowati, Aprizal, Dyah Indriana Kusumastuti • Discover Artificial Intelligence
(2026)
Abstract This study introduces an ensemble machine learning (EML) framework to enhance short-duration rainfall-intensity prediction under limited-data conditions. By integrating multiple base learners within a stacking architecture, the pro...
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Open Access (DOAJ)
DOI: 10.1007/s44163-026-01772-5
Wenpeng Yin, Katharina Kann, Mo Yu, Hinrich Schütze • arXiv (Cornell University)
(2017)
Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle var...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1702.01923
Vinit Mehta, Charu Sharma, Karthick Thiyagarajan • Sensors
(2025)
With the rapid advancement of artificial intelligence and robotics, the integration of Large Language Models (LLMs) with 3D vision is emerging as a transformative approach to enhancing robotic sensing technologies. This convergence enables ...
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Open Access (DOAJ)
DOI: 10.3390/s25206394