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Faisal Mehmood, Shabir Ahmad, Taeg Keun Whangbo • Mathematics
(2023)
Deep learning is a sub-branch of artificial intelligence that acquires knowledge by training a neural network. It has many applications in the field of banking, automobile industry, agriculture, and healthcare industry. Deep learning has pl...
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Open Access (DOAJ)
DOI: 10.3390/math11061360
Ahatesham Bhuiyan, Eftekhar Hossain, Mohammed Moshiul Hoque, M. Ali Akber Dewan • Heliyon
(2024)
Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research ha...
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Open Access (DOAJ)
DOI: 10.1016/j.heliyon.2024.e36272
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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Minqi Li, Xiangjian He, Kin‐Man Lam, Kaibing Zhang et al. • IET Image Processing
(2021)
Abstract Face hallucination is a super‐resolution technique specially designed to reconstruct high‐resolution faces from low‐resolution faces. Most state‐of‐the‐art algorithms leverage position‐patch prior knowledge of human faces to better...
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Open Access (DOAJ)
DOI: 10.1049/ipr2.12269
Mirko van der Baan, Christian Jutten B • Geophysics
(2000)
Abstract Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to fin...
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DOI: https://doi.org/10.1190/1.1444797
Qi‐Jun Zhang, K.C. Gupta • Artech House, Inc. eBooks
(2000)
From the Publisher:
Discover the new, unconventional alternatives for conquering RF and microwave design and modeling problems using neural networks information processing systems that can learn, generalize, and even allow model developm...
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John Hancock, Taghi M. Khoshgoftaar • Journal Of Big Data
(2020)
Abstract This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover n...
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DOI: https://doi.org/10.1186/s40537-020-00305-w
Qingyang Xiao, Kaiyuan Liu, Yuhui Hong, Haixu Tang • ACS in focus
(2024)
In recent years, breakthroughs in artificial intelligence (AI) have dramatically transformed many fields. Developments in neural networks primarily drive the advances. These powerful models have demonstrated unprecedented capabilities in le...
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DOI: https://doi.org/10.1021/acsinfocus.7e8012
LiMin Fu (1994)
From the Publisher:
Neural Networks in Computer Intelligence provides basic concepts,algorithms,and analysis of important neural network models developed to date,with emphasis on the importance of knowledge in intelligent system design. Th...
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Chris M. Bishop • Review of Scientific Instruments
(1994)
Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to learn the solution to...
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DOI: https://doi.org/10.1063/1.1144830
Yao He, Jing Yang, Shaobo Li, Jianjun Hu et al. • Complex & Intelligent Systems
(2024)
Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the pa...
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Open Access (DOAJ)
DOI: 10.1007/s40747-024-01350-1
Peter R. Wiecha, Arnaud Arbouet, Christian Girard, Otto L. Muskens • Photonics Research
(2021)
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nano-structures. Many of the recent works on machine-learning inverse design are highly specific, and the...
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Open Access
DOI: https://doi.org/10.1364/prj.415960
Leon Herrmann, Stefan Kollmannsberger • Computational Mechanics
(2024)
Abstract The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies w...
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DOI: https://doi.org/10.1007/s00466-023-02434-4
Dulani Meedeniya • Chapman & Hall/CRC eBooks
(2023)
This book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising....
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public-domain
DOI: https://doi.org/10.1201/9781003390824
Zhenkang Wang, Nan Xia, Jiale Liang, Jiechen Wang • Geo-spatial Information Science
(2026)
China’s rapid urbanization has led to the emergence of urban villages (UVs), whose governance is crucial for urban quality and built environment management. However, conventional identification methods rely on high-resolution remote sensing...
DOAJ
Open Access (DOAJ)
DOI: 10.1080/10095020.2026.2711549
Z. Karami • Fanāvarī-i āmūzish
(2023)
Background and Objectives: In order to solve the problem of education in complex and unstructured domains, Spiro and his colleagues proposed the theory of cognitive flexibility, in which they advocated multiple forms of pedagogical models, ...
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Open Access (DOAJ)
DOI: 10.22061/tej.2023.9686.2885
Xiaoxiao Ma, Jia Xin Wu, Shan Xue, Jian Yu Yang et al. • IEEE Transactions on Knowledge and Data Engineering
(2021)
Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of thes...
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Open Access
DOI: https://doi.org/10.1109/tkde.2021.3118815
Yayin Xu, Ying Zhou, Przemysław Sekuła, Lieyun Ding • Developments in the Built Environment
(2021)
The development of artificial intelligence technology is currently bringing about new opportunities in construction. Machine learning is a major area of interest within the field of artificial intelligence, playing a pivotal role in the pro...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.dibe.2021.100045
Haohan Wang, Bhiksha Raj • arXiv (Cornell University)
(2017)
This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1702.07800
Liurong Tao, Haoran Ren, Zhiwei Gu • Remote Sensing
(2023)
Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismi...
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Open Access (DOAJ)
DOI: 10.3390/rs15040891