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Addressing practical challenge of using autopilot drone for asphalt surface monitoring: Road detection, segmentation, and following

Hormazd Ranjbar, Perry Forsythe, Alireza Ahmadian Fard Fini, Mojtaba Maghrebi et al. • Results in Engineering (2023)

According to the recent world bank report, around 80% of a life-cycle cost of a road is devoted to maintenance which includes monitoring and repair processes. To more effectively keep road serviceability, knowing the current status of road ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.rineng.2023.101130

Neural Networks for Pattern Recognition

Albert Nigrin • The MIT Press eBooks (1993)

In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural network...

OPENALEX DOI: https://doi.org/10.7551/mitpress/4923.001.0001

An Introduction to Neural Networks

Kevin Robert Gurney • CRC Press eBooks (2018)

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counte...

OPENALEX DOI: https://doi.org/10.1201/9781315273570

Overview of Artificial Neural Networks

Jinming Zou, Yi Han, Sung‐Sau So • Methods in molecular biology (2008)

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to ...

OPENALEX DOI: https://doi.org/10.1007/978-1-60327-101-1_2

Neural Networks in Materials Science.

H. K. D. H. Bhadeshia • ISIJ International (1999)

There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach o...

OPENALEX Open Access DOI: https://doi.org/10.2355/isijinternational.39.966

Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks

Yaoda Xu, Maryam Vaziri-Pashkam • NeuroImage (2022)

Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance in the human brain remain unknown. Likewise, despite the ability of convolutional neural ne...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.neuroimage.2022.119635

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

An Introduction to Neural Networks

James A. Anderson • The MIT Press eBooks (1995)

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain funct...

OPENALEX DOI: https://doi.org/10.7551/mitpress/3905.001.0001

Face recognition with Bayesian convolutional networks for robust surveillance systems

Umara Zafar, Mubeen Ghafoor, Tehseen Zia, Ghufran Ahmed et al. • EURASIP Journal on Image and Video Processing (2019)

Abstract Recognition of facial images is one of the most challenging research issues in surveillance systems due to different problems including varying pose, expression, illumination, and resolution. The robustness of recognition method st...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13640-019-0406-y

Fundamentals of Deep Learning

P. V. Chandrika, Sandeep Kelkar • Advances in medical diagnosis, treatment, and care (AMDTC) book series (2024)

This chapter deals with the basic understanding of the deep learning model. It starts with a view of artificial intelligence, machine learning, and deep learning. It also represents how these three concepts are related to each other. The en...

OPENALEX DOI: https://doi.org/10.4018/979-8-3693-1281-0.ch001

Automated Website Fingerprinting through Deep Learning

Vera Rimmer, Davy Preuveneers, Marc Juarez, Tom Van Goethem et al. • Network and Distributed System Security Symposium (2018)

Several studies have shown that the network traffic that is generated by a visit to a website over Tor reveals information specific to the website through the timing and sizes of network packets.By capturing traffic traces between users and...

OPENALEX Open Access DOI: https://doi.org/10.14722/ndss.2018.23105

Condensing AI-Based Attitude Control Using Kolmogorov–Arnold Networks for Memory Efficiency

Kirill Djebko, Patrick Schurk, Tom Baumann, Frank Puppe et al. • Aerospace (2025)

Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the Inn...

DOAJ Open Access (DOAJ) DOI: 10.3390/aerospace12121039

On the Origin of Deep Learning

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 ...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1702.07800

Scalable and accurate deep learning with electronic health records

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai et al. • npj Digital Medicine (2018)

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variabl...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41746-018-0029-1 PMC: 6550175