Results 101 to 110 of about 405,044 (354)

A Dislocation Perspective on Strength and Toughness in Ceramics

Advanced Engineering Materials, EarlyView.
Dislocations in ceramics enjoy a long but yet under‐appreciated history. The three research waves for dislocations in ceramics highlight the topic evolution over the last 90 years. This review focuses on the impact of dislocation on strength and toughness in ceramics.
Xufei Fang
wiley   +1 more source

Flipped Learning and Artificial Intelligence

The recent emergence of Artificial Intelligence (AI) has the potential to influence the teaching-learning process. Some of the most used pedagogical approaches of the last decade have been Flipped Classroom and Flipped Learning. This article explores the
Ramon Palau, David López-Villanueva, Raúl Santiago   +2 more
core   +1 more source

Introductory Approaches for Applying Artificial Intelligence in Clinical Medicine

, 2022
The urge of computerized, automatized medical decision making as well as having more efficient and organized health data records for financial and medical purposes have brought the necessity to introduce artificial intelligence algorithms to healthcare ...
Niklas Lidströmer, Lidströmer, N., Ashrafian, H., Aresu, Federica,, Hutan Ashrafian, Federica Aresu   +5 more
core   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Deep learning approaches for energy consumption forecasting: analyzing stress factors and optimizing models for future demand

Discover Applied Sciences
Energy demand forecasting is crucial to the creation of reliable and sustainable energy systems, given the rising global consumption and the increasing integration of renewable energy sources.
Soham Navale, Nivedita Mishra, Sheetal Borhade   +2 more
doaj   +1 more source

Artificial Intelligence and Machine Learning in Anesthesiology [PDF]

Anesthesiology, 2019
Abstract Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning.
openaire   +2 more sources

DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation

, 2021
The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from the unlabeled data.
Li, Gaolei, TIan, Yuan, Wu, Jun, Chen, Mingzhe, Assoc Advancement Artificial Intelligence, Yan, Zhicong, Li, Shenghong, Poor, H. Vincent   +7 more
core   +1 more source

Ethical and Bias Considerations in Artificial Intelligence (AI)/Machine Learning.

Modern Pathology
As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI models will require careful scrutiny.
Matthew G. Hanna, Liron Pantanowitz, Brian R. Jackson, Octavia Palmer, Shyam Visweswaran, J. Pantanowitz, Mustafa Deebajah, H. Rashidi   +7 more
semanticscholar   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Optimising ITS behaviour with Bayesian networks and decision theory

, 2001
We propose and demonstrate a methodology for building tractable normative intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term student modelling and decision theory to select the next tutorial action.
Mitrovic, Antonija, Mayo, Michael
core