User profiles for N. Flann

Nicholas S. Flann

Associate Professor of Computer Science, Utah State University
Verified email at usu.edu
Cited by 2984

Parallel genetic simulated annealing: a massively parallel SIMD algorithm

H Chen, NS Flann, DW Watson - IEEE transactions on parallel …, 1998 - ieeexplore.ieee.org
… A block error correction code can be represented as a threetuple, (n, M, d), where n is the
number of bits in each word in a code; M is the number of words in the code; and d is the …

Biocellion : accelerating computer simulation of multicellular biological system models

S Kang, S Kahan, J McDermott, N Flann… - …, 2014 - academic.oup.com
Motivation: Biological system behaviors are often the outcome of complex interactions among
a large number of cells and their biotic and abiotic environment. Computational biologists …

A six-wheeled omnidirectional autonomous mobile robot

KL Moore, NS Flann - IEEE Control Systems Magazine, 2000 - ieeexplore.ieee.org
… Flat, connecting the graph through a node N. The Hill-Toe object will start the command
sequence for the edge leaving node N and entering the hill with a spin-in-place command to cor- …

Explanation-based learning and reinforcement learning: A unified view

TG Dietterich, NS Flann - Machine Learning, 1997 - Springer
… In our case, the number of rectangles will be n/ρ, where n is the total number of states in the
state space. The number of visible line segments cut cannot exceed 2ρ. So the time to insert …

[HTML][HTML] Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images

A Brim, NS Flann - Plos one, 2022 - journals.plos.org
Billions of dollars are traded automatically in the stock market every day, including algorithms
that use neural networks, but there are still questions regarding how neural networks trade. …

[PS][PS] Genetic simulated annealing for scheduling data-dependent tasks in heterogeneous environments

P Shroff, DW Watson, NS Flann… - … Workshop (HCW'96), 1996 - cseweb.ucsd.edu
… Given: • a set of n tasks which are interdependent and may require transfer of data between
them, denoted as … For the scheduling problem defined in Section 1, the goal is to find an …

deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host–pathogen protein–protein interactions

…, CD Loaiza, N Duhan, N Flann - Briefings in …, 2022 - academic.oup.com
… *N*1 representation of an amino acid sequence is the input of the network, where M is the
length of the amino acid sequence and N … a dynamic value, which depends on the value of N. …

A study of explanation-based methods for inductive learning

NS Flann, TG Dietterich - Machine Learning, 1989 - Springer
… Elsewhere, [Flann, 1989], we describe the logical language it employs and the … n that
required less-than-or-equal-to m training examples to obtain correct performance. The quantity n/…

Parallel simulated annealing and genetic algorithms: a space of hybrid methods

H Chen, NS Flann - International Conference on Parallel Problem Solving …, 1994 - Springer
… Temperature was then lowered every n function evaluations, where n was set to equal the
population size. The cooling schedule was geometric, where T/+t = aT/ and a set such that the …

Fast design optimization method utilizing a combination of artificial neural networks and genetic algorithms for dynamic inductive power transfer systems

…, S Saha, R Nimri, A Kamineni, NS Flann… - IEEE Open Journal …, 2022 - ieeexplore.ieee.org
… where n is the number of nodes on the output layer (n = 90), Y is the normalized output
parameters of FEM, and ˆY is the normalized predicted output parameters. Nodes on each hidden …