Results 11 to 20 of about 8,744,617 (299)

ML-CB: Machine Learning Canvas Block [PDF]

Proceedings on Privacy Enhancing Technologies, 2021
Abstract With the aim of increasing online privacy, we present a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting. Because the act of canvas fingerprinting uses, at its core, a JavaScript program, and because many of these programs are reused across ...
Nathan Reitinger, Michelle L. Mazurek
openaire   +2 more sources

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review [PDF]

, 2023
In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s)
Eusébio Costa, Costa, Eusébio, Pinto, Agostinho Sousa, Abreu, António, Agostinho Sousa Pinto, Paiva, Jerónimo, António Abreu, Jerónimo Paiva   +7 more
core   +1 more source

Machine Learning (ML)

, 2023
Machine learning (ML) is important in many industries like healthcare, finance, retail, marketing, and autonomous vehicles. It helps with things like diagnosing diseases, personalizing medicine, detecting fraud, and making recommendations. However, ML also has some challenges like making sure the data is decent quality, avoiding biases, and being able ...
openaire   +2 more sources

A Review of Causality for Learning Algorithms in Medical Image Analysis

, 2022
Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area.
Vlontzos, Athanasios, Kainz, Bernhard, Rueckert, Daniel   +2 more
core   +1 more source

Machine Learning at Microsoft with ML.NET [PDF]

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author.
Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupré, Vadim Eksarevskiy, Eric Erhardt, Costin Eseanu, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Shon Katzenberger, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Sarthak Shah, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, Yiwen Zhu   +33 more
openaire   +4 more sources

Contrasting Explain-ML with Interpretability Machine Learning Tools in Light of Interactive Machine Learning Principles

Journal on Interactive Systems, 2022
The way Complex Machine Learning (ML) models generate their results is not fully understood, including by very knowledgeable users. If users cannot interpret or trust the predictions generated by the model, they will not use them.
Bárbara Gabrielle C. O. Lopes, Liziane Santos Soares, Raquel Oliveira Prates, Marcos André Gonçalves   +3 more
doaj   +1 more source

Stock Market Prediction Using Machine Learning(ML)Algorithms

Advances in Distributed Computing and Artificial Intelligence Journal, 2020
Stocks are possibly the most popular financial instrument invented for building wealth and are the centerpiece of any investment portfolio. The advances in trading technology has opened up the markets so that nowadays nearly anybody can own stocks.
Muhammad UMER, Muhammad AWAIS, Muhammad MUZAMMUL   +2 more
doaj   +1 more source

Explainable Mental Health Chat Support using MKO-DEBiGRU with Knowledge Graph and SHAP [PDF]

ITM Web of Conferences
Recently, mental health chat support has become increasingly important for providing timely assistance and guidance to users seeking psychological assistance.
Nagarathna P., Thalla Srujana, Patil Sujata, Reddy K Phanindra   +3 more
doaj   +1 more source

ML Confidential: Machine Learning on Encrypted Data [PDF]

, 2013
We demonstrate that by using a recently proposed somewhat homomorphic encryption (SHE) scheme it is possible to delegate the execution of a machine learning (ML) algorithm to a compute service while retaining confidentiality of the training and test data.
Graepel, T., Lauter, K., Naehrig, M.
openaire   +3 more sources

Optimizing Distributed Energy Storage Deployment in Smart Grids for Enhanced Grid Performance and Energy Management [PDF]

E3S Web of Conferences
Large-scale hydroelectric is the most mature kind of energy storage, but medium- and small-scale plants are used widely with renewable energy sources that are likely to be integrated in the next generation electrical distribution system or smart grid ...
Reddy K. Jyothsna, G Veeresh, Kansal Lavish, Sharma Saurabh, Al-Rubaye Taqi, Arun Vanya   +5 more
doaj   +1 more source