Results 31 to 40 of about 85,238 (262)

GENERATIONS IN BAYESIAN NETWORKS

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019
This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents ...
Alexander Litvinenko, Natalya Litvinenko, Orken Mamyrbayev, Assem Shayakhmetova   +3 more
doaj   +1 more source

Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer

Journal of Innovation Information Technology and Application, 2023
The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density.
Anaclet Tshikutu Bikengela, Remy Mutapay Tshimona, Pierre Kafunda Katalay, Simon Ntumba Badibanga, Eugène Mbuyi Mukendi   +4 more
doaj   +1 more source

Solving inference problems of Bayesian networks by probabilistic computing

AIP Advances, 2023
Recently, probabilistic computing approach has shown its broad application in problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit called p-bit constitutes a basic building block ...
Seokmin Hong
doaj   +1 more source

Bayesian networks approach on intelligent system design for the diagnosis of heat exchanger

Jurnal Ilmiah SINERGI, 2022
The heat exchanger highly influences the series of cooling processes. Therefore, it is required to have maximum performance. Some of the factors causing a decrease in its performance are increased pressure drop in the Plate Heat Exchanger (PHE ...
Dedik Romahadi, Fajar Anggara, Rikko Putra Youlia, Hifdzul Luthfan Habibullah, Hui Xiong   +4 more
doaj   +1 more source

Bayesian Neural Networks

CoRR, 2018
This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling.
Vikram Mullachery, Aniruddh Khera, Amir Husain   +2 more
openaire   +3 more sources

Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks

Machines, 2023
In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in
Madeleine Martinsen, Amare Desalegn Fentaye, Erik Dahlquist, Yuanye Zhou   +3 more
doaj   +1 more source

Reconstructing enzyme evolution by protein engineering

FEBS Letters, EarlyView.
Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler, Markus R. Busch, Reinhard Sterner   +2 more
wiley   +1 more source

Proposition of a modeling and an analysis methodology of integrated reverse logistics chain in the direct chain

Journal of Industrial Engineering and Management, 2016
Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain.
Faycal Mimouni, Abdellah Abouabdellah
doaj   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

Bayesian optimization on networks

Journal of Computational Physics
This paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points.
W. Li, D. Sanz-Alonso, R. Yang
openaire   +4 more sources