Results 281 to 290 of about 977,116 (306)

Development of Pesticide-Likeness Scores and Models for Predicting Pesticide Activity of Molecular Scaffolds with Machine Learning. [PDF]

ACS Omega
Sakai Y, Kaneko H.
europepmc   +1 more source

Artificial Neural Network-Based Conveying Object Measurement Automation System Using Distance Sensor. [PDF]

Sensors (Basel)
Heo HB, Park SH.
europepmc   +1 more source

Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies. [PDF]

ACS Omega
Baluta S, Suresh V, Chmielowska M, Smeesters L, Cabaj J.   +4 more
europepmc   +1 more source

National Institutes of Health-Funded Artificial Intelligence and Machine Learning Research, 2019-2023: Cross-Sectional Study.

J Med Internet Res
Le JP, Morrison J, Malhotra A, Nemati S, Wardi G, Ford JS.   +5 more
europepmc   +1 more source

Development of QSAR Models and Web Applications for Predicting hDHFR Inhibitor Bioactivity Using Machine Learning. [PDF]

Molecules
Maattallaoui I, Sakho M, Maatallaoui A, Catalán EB, Aouad NE.   +4 more
europepmc   +1 more source

Immunological testing and machine learning in detecting latent tuberculosis among high-risk groups (nature review). [PDF]

Front Med (Lausanne)
Starshinova A, Sabirova A, Kudryavtsev I, Rubinstein A, Churilov LP, Belyaeva E, Anastasia K, Sharipov RA, Tukfatullin RK, Nikolenko N, Dovgalyuk I, Kudlay D.   +11 more
europepmc   +1 more source

Active learning with kernel machines

, 2011
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Active Learning of Introductory Machine Learning

Proceedings. Frontiers in Education. 36th Annual Conference, 2006
This paper describes a computer-based training program for active learning of Agent Technology, Expert Systems, Neural Networks and Case-Based Reasoning by undergraduate students using a simple agent framework. While many Machine Learning (ML) and Artificial Intelligence (AI) courses teach ML and AI concepts by means of programming assignments, these ...
Maja Pantic, Reinier Zwitserloot
openaire   +1 more source

Activation-Kernel Extraction through Machine Learning

2017 New Generation of CAS (NGCAS), 2017
Machine Learning flooded many research fields, including Electronic Design Automation (EDA). The availability of algorithms that can solve complex problems through generic rule formulations represent a fresh opportunity to improve existing design paradigms. In this work we investigate the use of machine learning to manipulate logic circuits.
Tenace, Valerio, Calimera, Andrea
openaire   +1 more source