[BOOK][B] Algorithmic learning in a random world

V Vovk, A Gammerman, G Shafer - 2005 - Springer
Vladimir Vovk Alexander Gammerman Glenn Shafer Second Edition Page 1 Vladimir Vovk
Alexander Gammerman Glenn Shafer Algorithmic Learning in a Random World Second …

Controlling organoid symmetry breaking uncovers an excitable system underlying human axial elongation

GM Anand, HC Megale, SH Murphy, T Weis, Z Lin… - Cell, 2023 - cell.com
The human embryo breaks symmetry to form the anterior-posterior axis of the body. As the
embryo elongates along this axis, progenitors in the tail bud give rise to tissues that …

Structure and evolution of neuronal wiring receptors and ligands

E Cortés, JS Pak, E Özkan - Developmental Dynamics, 2023 - Wiley Online Library
One of the fundamental properties of a neuronal circuit is the map of its connections. The
cellular and developmental processes that allow for the growth of axons and dendrites …

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

MM Bejani, M Ghatee - Transportation Research Part C: Emerging …, 2018 - Elsevier
There are many systems to evaluate driving style based on smartphone sensors without
enough awareness from the context. To cover this gap, we propose a new system namely …

Malicious node detection using machine learning and distributed data storage using blockchain in WSNs

M Nouman, U Qasim, H Nasir, A Almasoud… - IEEE …, 2023 - ieeexplore.ieee.org
In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster
Heads (CHs) to register the nodes using their credentials and also to tackle various security …

Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach

T Desautels, R Das, J Calvert, M Trivedi, C Summers… - BMJ open, 2017 - bmjopen.bmj.com
Objectives Unplanned readmissions to the intensive care unit (ICU) are highly undesirable,
increasing variance in care, making resource planning difficult and potentially increasing …

Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation

A Keddouda, R Ihaddadene, A Boukhari, A Atia, M Arıcı… - Applied Energy, 2024 - Elsevier
This paper presents data-driven models for photovoltaic module temperature prediction and
analyzes the relation and effects of ambient conditions to module temperature. A total of 12 …

Computational design of (100) alloy surfaces for the hydrogen evolution reaction

H Li, S Xu, M Wang, Z Chen, F Ji, K Cheng… - Journal of Materials …, 2020 - pubs.rsc.org
With the rapid development of kinetically-controlled techniques, synthesis of cubic bimetallic
catalysts with tunable components and compositions becomes possible. In recent years …

Engineered porous nanocomposites that deliver remarkably low carbon capture energy costs

MM Sadiq, K Konstas, P Falcaro, AJ Hill… - Cell Reports Physical …, 2020 - cell.com
A key barrier to the use of carbon dioxide capture technologies is the operating energy
requirement, the chief contributor being the energy required to regenerate the capture …

Using transfer learning for improved mortality prediction in a data-scarce hospital setting

T Desautels, J Calvert, J Hoffman… - Biomedical …, 2017 - journals.sagepub.com
Algorithm–based clinical decision support (CDS) systems associate patient-derived health
data with outcomes of interest, such as in-hospital mortality. However, the quality of such …