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Academic Articles & Research Papers
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Bernd Jähne, Horst W. Haussecker, Peter Geißler (1999)
Contents of Volume One: Preface. Contributors. B. Jahne, Introduction. Illumination and Image Formation. H. Hausecker, Radiation. H. Hausecker, Interaction of Radiation with Matter. P. Geisler, Geometric and Wave Optics. H. Hausecker, Radio...
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Xin Shi, Zhengchao Xu, Sunan Ge, Xueqing Zhao et al. • Complex & Intelligent Systems
(2026)
Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data...
DOAJ
Open Access (DOAJ)
DOI: 10.1007/s40747-025-02205-z
Rafael Arthur Rocha Miranda, Francisco Assis da Silva, Mário Augusto Pazoti, Almir Olivette Artero et al. • Colloquium Exactarum
(2013)
The handwritten character recognition in digital images is an important and challenging area of
study in Computer Vision, with several possibilities for applications to facilitate the daily work of
the people. This paper presents an alg...
DOAJ
Open Access (DOAJ)
DOI: 10.5747/ce.2013.v05.n2.e062
Xiaosai Duan, Suihuai Yu, Jianjie Chu, Dengkai Chen et al. • Buildings
(2022)
The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefo...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/buildings12111880
Engin Derya Gezer, Abdullah Uğur Birinci, Aydın Demir, Hasan Öztürk et al. • BioResources
(2024)
The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN)...
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Open Access (DOAJ)
Kevin M. Swingler (1996)
Techniques for building neural networks: introduction what are neural networks? how does neural computing differ from traditional programming? how are neural networks built? how do neural networks learn? what do I need to build an MLP? the ...
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Filippo Amato, Alberto Botana López, Eladia María Peña‐Méndez, Petr Vaňhara et al. • Journal of Applied Biomedicine
(2013)
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that mu...
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Open Access
DOI: https://doi.org/10.2478/v10136-012-0031-x
James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness et al. • Proceedings of the National Academy of Sciences
(2017)
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitabl...
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Open Access
DOI: https://doi.org/10.1073/pnas.1611835114
PMC: 5380101
Bo Li, Ningjun Jiang, Xiaole Han • Sensors
(2023)
The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profile...
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Open Access (DOAJ)
DOI: 10.3390/s23041764
Mikhail Fedotov, Viktor Korotitsky, Sergei Koveshnikov • Nanomaterials
(2024)
Resistive random-access memory (RRAM) is a crucial element for next-generation large-scale memory arrays, analogue neuromorphic computing and energy-efficient System-on-Chip applications. For these applications, RRAM elements are arranged i...
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Open Access (DOAJ)
DOI: 10.3390/nano14080668
Subbulakshmi M, Mohammad Malik Mubeen S, Mohamed Ali E A • AIP Advances
(2026)
Digital images acquired through imaging systems often lose their original fidelity due to various types of noise introduced during acquisition, storage, or transmission, consequently reducing clarity and adversely affecting subsequent image...
DOAJ
Open Access (DOAJ)
DOI: 10.1063/5.0332993
Volker Pernice, Benjamin Staude, Stefano Cardanobile, Stefan Rotter • PLoS Computational Biology
(2011)
Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, ...
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Open Access (DOAJ)
DOI: 10.1371/journal.pcbi.1002059
Sudheesh Parathakkatt, Vaisakh Kizhuveetil, Gokul G. K., Rajashree Ajay Markandewar • eXPRESS Polymer Letters
(2026)
Worm-like micelles (WLMs) are dynamic, self-assembling supramolecular structures that exhibit complex viscoelastic behaviour due to their ability to undergo reversible scission, fusion, branching, and sequence rearrangement. This review pro...
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Open Access (DOAJ)
DOI: 10.3144/expresspolymlett.2026.36
Kishan G. Mehrotra, Chilukuri Krishna Mohan, Sanjay Ranka • The MIT Press eBooks
(1996)
Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have ...
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DOI: https://doi.org/10.7551/mitpress/2687.001.0001
Zhiquan He, Meshari Alazmi, Jingfen Zhang, Dong Xu • PLoS ONE
(2013)
Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) b...
DOAJ
Open Access (DOAJ)
DOI: 10.1371/journal.pone.0074006
Yuchao Feng, Jianwei Zheng, Mengjie Qin, Cong Bai et al. • Remote Sensing
(2021)
Owing to the outstanding feature extraction capability, convolutional neural networks (CNNs) have been widely applied in hyperspectral image (HSI) classification problems and have achieved an impressive performance. However, it is well know...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/rs13214407
Fathaddin Muhammad Taufiq, Sari Alvita Kumala, Sutansyah Daddy, Ulfah Baiq Maulinda et al. • E3S Web of Conferences
(2024)
Relative permeability is a substantial parameter for estimating multi-phase fluid flow in porous rocks. It is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Relative permea...
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Open Access (DOAJ)
DOI: 10.1051/e3sconf/202450003019
Jeffrey M. Ede • Machine Learning Science and Technology
(2020)
Abstract Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applic...
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DOI: https://doi.org/10.1088/2632-2153/abd614
Anindya Gupta, Philip John Harrison, Håkan Wieslander, Nicolas Pielawski et al. • Cytometry Part A
(2018)
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and ho...
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cc-by-nc
DOI: https://doi.org/10.1002/cyto.a.23701
PMC: 6590257
Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow et al. • International Conference on Machine Learning
(2011)
The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to dev...
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