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Stealing Hyperparameters in Machine Learning

Binghui Wang, Neil Zhenqiang Gong • IEEE Symposium on Security and Privacy (S&P) (2018)

Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentia...

OPENALEX Open Access DOI: https://doi.org/10.1109/sp.2018.00038

DaDianNao: A Machine-Learning Supercomputer

Yunji Chen, Tao Luo, Shaoli Liu, Shijin Zhang et al. • IEEE/ACM International Symposium on Microarchitecture (MICRO) (2014)

Many companies are deploying services, either for consumers or industry, which are largely based on machine-learning algorithms for sophisticated processing of large amounts of data. The state-of-the-art and most popular such machine-learni...

OPENALEX DOI: https://doi.org/10.1109/micro.2014.58

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques

Simran Saggu, Hirad Daneshvar, Reza Samavi, Paulo Pires et al. • BMC Medical Informatics and Decision Making (2024)

Abstract Background The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet menta...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-024-02450-1

Analysis of applying machine learning methods in rock classification problems on core samples

Alexander I. Kochegurov, Vladislav I. Denisov, Elizaveta A. Zadorozhnykh • Известия Томского политехнического университета: Инжиниринг георесурсов (2024)

Relevance. Core studies in the oil and gas industry allow us to obtain some filtration and capacity characteristics of rocks, as well as to give an idea of the composition and structure of the subsoil. This information is extremely importan...

DOAJ Open Access (DOAJ) DOI: 10.18799/24131830/2024/9/4792

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Masoud Mahdianpari, Bahram Salehi, Mohammad Rezaee, Fariba Mohammadimanesh et al. • Remote Sensing (2018)

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applicatio...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs10071119

Empirical Asset Pricing via Machine Learning

Shihao Gu, Bryan Kelly, Dacheng Xiu • National Bureau of Economic Research (2018)

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia.We demonstrate large economic gains to investors using machine learning forecasts, in some cases...

OPENALEX Open Access DOI: https://doi.org/10.3386/w25398

Machine Learning with TensorFlow

Nishant Shukla • CERN Document Server (European Organization for Nuclear Research) (2018)

Summary Machine Learning with Tensor Flow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding Tensor Flow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub forma...

OPENALEX

Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

Xiuhua April Si, Jinxiang Xi • Journal of Nanotheranostics (2021)

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challengin...

DOAJ Open Access (DOAJ) DOI: 10.3390/jnt2030007

Generative-Adversarial-Network-Based Image Reconstruction for the Capacitively Coupled Electrical Impedance Tomography of Stroke

Mikhail Ivanenko, Damian Wanta, Waldemar T. Smolik, Przemysław Wróblewski et al. • Life (2024)

This study investigated the potential of machine-learning-based stroke image reconstruction in capacitively coupled electrical impedance tomography. The quality of brain images reconstructed using the adversarial neural network (cGAN) was e...

DOAJ Open Access (DOAJ) DOI: 10.3390/life14030419

DATA MINING AND MACHINE LEARNING IN ASTRONOMY

Nicholas M. Ball, Robert J. Brunner • International Journal of Modern Physics D (2010)

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding ...

OPENALEX Open Access DOI: https://doi.org/10.1142/s0218271810017160

Decentralized Distributed Sequential Neural Networks Inference on Low-Power Microcontrollers in Wireless Sensor Networks: A Predictive Maintenance Case Study

Yernazar Bolat, Iain Murray, Yifei Ren, Nasim Ferdosian • Sensors (2025)

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challeng...

DOAJ Open Access (DOAJ) DOI: 10.3390/s25154595

Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study

Ziru Fu, Yu Cheng Hsu, Christian S Chan, Chaak Ming Lau et al. • Journal of Medical Internet Research (2024)

BackgroundSentiment analysis is a significant yet difficult task in natural language processing. The linguistic peculiarities of Cantonese, including its high similarity with Standard Chinese, its grammatical and lexical uniqueness, and its...

DOAJ Open Access (DOAJ) DOI: 10.2196/51069

Hybrid-Timescale Physics-Informed Neural Network for Electrical Equivalent Impedance Identification in Induction Heating Systems

Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan, Denis Navarro et al. • IEEE Open Journal of the Industrial Electronics Society (2026)

This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction...

DOAJ Open Access (DOAJ) DOI: 10.1109/OJIES.2026.3663897

Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase

Sagar Chokshi, Piyush Gohil, Vijay Parmar, Vijaykumar Chaudhary • Mechanics of Advanced Composite Structures (2025)

Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. In the present study, Artificial Neural Network (ANN), a computationa...

DOAJ Open Access (DOAJ) DOI: 10.22075/macs.2024.33874.1664

Machine learning in pain research

Jörn Lötsch, Alfred Ultsch • Pain (2017)

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1097/j.pain.0000000000001118 PMC: 5895117