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Efficient GPU Implementation of the McMurchie–Davidson Method for Shell-Based ERI Computations

Haruto Fujii, Yasuaki Ito, Nobuya Yokogawa, Kanta Suzuki et al. • Applied Sciences (2025)

Quantum chemistry offers the formal machinery to derive molecular and physical properties arising from (sub)atomic interactions. However, as molecules of practical interest are largely polyatomic, contemporary approximation schemes such as ...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15052572

Edge AI for Industrial Visual Inspection: YOLOv8-Based Visual Conformity Detection Using Raspberry Pi

Marcelo T. Okano, William Aparecido Celestino Lopes, Sergio Miele Ruggero, Oduvaldo Vendrametto et al. • Algorithms (2025)

This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieve...

DOAJ Open Access (DOAJ) DOI: 10.3390/a18080510

Using different computing systems to solve the automatic cloud classification problem according to MODIS satellite data by probabilistic neural network

Aleksey Viktorovich Skorokhodov, Sergey Vladimirovich Aksenov, Andrey Vladimirovich Aksenov, Dmitriy Nikolaevich Laykom • Известия Томского политехнического университета: Инжиниринг георесурсов (2017)

The relevance of the research is caused by the necessity to develop algorithms and software to classify the cloud types based on single-layer cloud on the satellite images received from MODIS spectral radiometer used in Terra and Aqua remot...

DOAJ Open Access (DOAJ)

Deep Learning in Medical Imaging: General Overview

June‐Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee et al. • Korean Journal of Radiology (2017)

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishin...

OPENALEX cc-by-nc DOI: https://doi.org/10.3348/kjr.2017.18.4.570 PMC: 5447633

Deep Learning in Medical Imaging

Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin et al. • Neurospine (2019)

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learn...

OPENALEX cc-by-nc DOI: https://doi.org/10.14245/ns.1938396.198 PMC: 6945006

Deep Learning with Microfluidics for Biotechnology

Jason Riordon, Dušan Sovilj, Scott Sanner, David A. Sinton et al. • Trends in biotechnology (2018)

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.tibtech.2018.08.005

Deep learning for denoising

Siwei Yu, Jianwei Ma, Wenlong Wang • Geophysics (2019)

ABSTRACT Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the ...

OPENALEX Open Access DOI: https://doi.org/10.1190/geo2018-0668.1

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Sebastian Raschka, Joshua M. Patterson, Corey Nolet • Information (2020)

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the...

OPENALEX cc-by DOI: https://doi.org/10.3390/info11040193