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Working with neural networks

Dan W. Hammerstrom • IEEE Spectrum (1993)

As explanation of how neural networks operate is given. Programming of neural networks and regular computers is compared. Matching a neural network to an application is discussed. Designing and training a neural network is examined.>...

OPENALEX DOI: https://doi.org/10.1109/6.222230

Brain tumor grading based on Neural Networks and Convolutional Neural Networks

Yuehao Pan, Weimin Huang, Zhiping Lin, Wanzheng Zhu et al. • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2015)

This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, wit...

OPENALEX DOI: https://doi.org/10.1109/embc.2015.7318458

Neural networks for signal processing

Bart Kosko • CERN Document Server (European Organization for Nuclear Research) (1992)

This collection of essays explores neural networks applications in signal and image processing, function and estimation, robotics and control, associative memories, and electrical and optical networks. Intended as a companion to Neural Netw...

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A Complex Network Epidemiological Approach for Infectious Disease Spread Control with Time-Varying Connections

Alma Y. Alanis, Gustavo Munoz-Gomez, Nancy F. Ramirez, Oscar D. Sanchez et al. • Algorithms (2024)

This work introduces an impulsive neural control algorithm designed to mitigate the spread of epidemic diseases. The objective of this paper is the development of a vaccination strategy based on a PIN-type impulsive controller based on an o...

DOAJ Open Access (DOAJ) DOI: 10.3390/a17120577

Kalman Filtering and Neural Networks

• Wiley eBooks (2001)

From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear appr...

OPENALEX DOI: https://doi.org/10.1002/0471221546

Fundamentals of Artificial Neural Networks

Mohamad H. Hassoun • Proceedings of the IEEE (1996)

From the Publisher: As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Funda...

OPENALEX DOI: https://doi.org/10.1109/jproc.1996.503146

3D Octave and 2D Vanilla Mixed Convolutional Neural Network for Hyperspectral Image Classification with Limited Samples

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

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

Enrique González-Plaza, David García, Jesús-Ignacio Prieto • Inventions (2023)

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models ...

DOAJ Open Access (DOAJ) DOI: 10.3390/inventions8040088

Deep learning in nano-photonics: inverse design and beyond

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...

OPENALEX Open Access DOI: https://doi.org/10.1364/prj.415960

Deep Learning

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....

OPENALEX public-domain DOI: https://doi.org/10.1201/9781003390824

A Preliminary Study on the Auto-Segmentation of Targets and Organs at Risk in Pediatric Total Marrow and Lymphoid Irradiation

Zhihua Xie BS, Jinfeng Liu PhD, Lixia Hou MS, Haoran Feng MS et al. • Technology in Cancer Research & Treatment (2026)

Introduction Leukemia is one of the most prevalent cancers in children. The use of total marrow and lymphoid irradiation (TMLI) via helical tomotherapy (TOMO) as a conditioning regimen prior to bone marrow transplant (BMT) has been widely a...

DOAJ Open Access (DOAJ) DOI: 10.1177/15330338261426261

Design of a TSR-based project learning strategy for biochemistry undergraduate teaching and research labs: a case study

Camille R. Reaux, Shelby A. Meche, Jordan M. Grider, Soundharya Dhanabal et al. • Frontiers in Education (2025)

Given the exponential growth of biochemical data and deep effect of computational methods on life sciences, there is a need to rethink undergraduate curricula. A project-oriented learning approach based on the Triangular Spatial Relationshi...

DOAJ Open Access (DOAJ) DOI: 10.3389/feduc.2024.1455173

Providing a framework for designing electronic and virtual learning environments based on the principles and criteria of cognitive flexibility

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, ...

DOAJ Open Access (DOAJ) DOI: 10.22061/tej.2023.9686.2885

Deep Learning

Byron Havard, Jianxia Du, Anthony A. Olinzock • Quarterly review of distance education (2005)

A structure for online discussion within a framework for deep learning provides three strategies for dynamic online discussion; flexible peer, structured topic, and collaborative task discussion. This article examines the discussion structu...

OPENALEX Open Access DOI: https://doi.org/10.1108/qrde-06-2005-0006