Search alternatives:
modeling algorithm » scheduling algorithm (Expand Search)
testing algorithm » cosine algorithm (Expand Search)
spatial modeling » statistical modeling (Expand Search)
data testing » data using (Expand Search)
modeling algorithm » scheduling algorithm (Expand Search)
testing algorithm » cosine algorithm (Expand Search)
spatial modeling » statistical modeling (Expand Search)
data testing » data using (Expand Search)
-
1
-
2
Web Applications Security Testing: Genetic Algorithms Based Test Data Generator
Published 2020Get full text
masterThesis -
3
Security testing and evaluation of cryptography algorithms. (c2003)
Published 2003Get full text
Get full text
masterThesis -
4
A genetic algorithm for corrective retesting. (c1995)
Published 1995Subjects: Get full text
Get full text
masterThesis -
5
Automated Mutation-Based Test Data Generation: Genetic Algorithm Game-Like Approach
Published 2020Get full text
masterThesis -
6
-
7
Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams
Published 2021“…Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. …”
-
8
A genetic algorithm for testable data path synthesis
Published 2017Get full text
Get full text
Get full text
Get full text
conferenceObject -
9
CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments
Published 2024“…<p dir="ltr">In our research, we address the problem of coordination and planning in heterogeneous multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this problem has been framed as a task allocation problem that maps tasks to robots. …”
-
10
Test Vector Decomposition Based Static Compaction Algorithms for Combinational Circuits
Published 2003“…Testing system-on-chips involves applying huge amounts of test data, which is stored in the tester memory and then transferred to the chip under test during test application. …”
Get full text
article -
11
Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
Get full text
article -
12
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
Get full text
Get full text
Get full text
masterThesis -
13
Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
Published 2016Get full text
doctoralThesis -
14
Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
Get full text
article -
15
Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations
Published 1992“…Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. …”
Get full text
Get full text
Get full text
article -
16
-
17
-
18
A multi-class discriminative motif finding algorithm for autosomal genomic data. (c2015)
Published 2016“…Then, it searches for population discriminative motifs or differentiable sequence of SNPs, by implementing Probabilistic Suffix Trees data structures. We initially tested the efficiency and performance of our method on several simulated datasets and then applied it on a real genomic data that has different populations from the Middle East and North Africa (MENA) region. …”
Get full text
Get full text
masterThesis -
19
Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification
Published 2025“…<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. …”
-
20