Academic Articles & Research Papers

A Sensor-to-API Data Infrastructure for Building Digital Twins: Harmonizing Multi-Rate IoT Energy and Indoor Data Streams for Proactive Analytics

Digital twins are increasingly used in smart buildings to monitor energy use. This requires reliable sensor data, geospatial information, and semantically rich 3D building geometry. However, building sensor data often has inconsistent sampl...

High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength

Abstract High temperature Nb-Si based alloys face a critical challenge: achieving adequate room-temperature fracture toughness ( > 18 MPa·m1/2) for processing while maintaining high-temperature strength, properties that typically compete wi...

An introduction to quantum machine learning

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT...

Machine-learning reprogrammable metasurface imager

Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we e...

Machine Learning in Virtual Screening

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a partic...

Genetic Algorithms in Search, Optimization and Machine Learning

David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research ar...

A review of machine learning in building load prediction

A Survey on Distributed Machine Learning

The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the qual...

Cryptography and machine learning

Pattern Recognition and Machine Learning, 2006

Proceedings of the 25th international conference on Machine learning

This volume contains the papers accepted to the 25th International Conference on Machine Learning (ICML 2008). ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and...

Machine Learning With Python

In previous chapters, we saw what artificial intelligence is and how machine learning and deep learning techniques are used to train machines to become smart. In these next few chapters, we will learn how machines are trained to take data, ...

Artificial intelligence, machine learning and deep learning

It is increasingly recognized that artificial intelligence has been touted as a new mobile. Because of the high volume of data that being generated by devices, sensors and social media users, the machine can learn to distinguish the pattern...

Machine learning in acoustics: Theory and applications

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep lear...

Machine Learning in Healthcare

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, among...

Machine learning in the Chinese stock market

We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of return prediction factors using various machine learning algorithms. Contrasting previous studies for ...

Integrated Causal Learning Workflow of Brain Iron Deposition in Patients with PD based on QSM.

Corrigendum to "Prediction of Distant Metastasis in Renal Cell Carcinoma Using Machine Learning Algorithms: A Multicenter Cohort Study".

Virtual CT Myelography: A Patch-Based Machine Learning Model to Improve Intraspinal Soft Tissue Visualization on Unenhanced Dual-Energy Lumbar Spine CT

Background: Distinguishing between the spinal cord and cerebrospinal fluid (CSF) non-invasively on CT is challenging due to their similar mass densities. We hypothesize that patch-based machine learning applied to dual-energy CT can accurat...

Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing,...