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Machine Learning for Cultural Heritage: A Survey

Marco Fiorucci, Marina Khoroshiltseva, Massimiliano Pontil, Arianna Traviglia et al. • Pattern Recognition Letters (2020)

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the unde...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.patrec.2020.02.017

Machine learning and its applications: A review

Sheena Angra, Sachin Ahuja • International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) (2017)

Nowadays, large amount of data is available everywhere. Therefore, it is very important to analyze this data in order to extract some useful information and to develop an algorithm based on this analysis. This can be achieved through data m...

OPENALEX DOI: https://doi.org/10.1109/icbdaci.2017.8070809

How to Read Articles That Use Machine Learning

Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause, Lily H. Peng • JAMA (2019)

In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and e...

OPENALEX DOI: https://doi.org/10.1001/jama.2019.16489

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

Machine learning in cardiovascular medicine: are we there yet?

Khader Shameer, Kipp W. Johnson, Benjamin Scott Glicksberg, Joel T. Dudley et al. • Heart (2018)

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encomp...

OPENALEX DOI: https://doi.org/10.1136/heartjnl-2017-311198

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

Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques

Anwer Shees, Mohd Tariq, Arif I. Sarwat • Energies (2024)

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement...

DOAJ Open Access (DOAJ) DOI: 10.3390/en17235870

Practical Machine Learning with Python

Dipanjan Sarkar, Raghav Bali, Tushar Sharma • Apress eBooks (2017)

This book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, desi...

OPENALEX DOI: https://doi.org/10.1007/978-1-4842-3207-1

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

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

Machine Learning Algorithms - A Review

Batta Mahesh • International Journal of Science and Research (IJSR) (2020)

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. Learning algorithms in many applications that's we make use of dail...

OPENALEX Open Access DOI: https://doi.org/10.21275/art20203995

Machine Learning

Yagang Zhang • InTech eBooks (2010)

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large...

OPENALEX Open Access DOI: https://doi.org/10.5772/217