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241
Three-phase simulated annealing algorithms for exam scheduling
Published 2003“…We empirically compare 3PSA with a 4-phase clustering-based heuristic algorithm using realistic data. Our experimental results show that 3PSA produces good exam schedules, which are better than those of the clustering heuristic procedure.…”
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conferenceObject -
242
Recent Advances of Chimp Optimization Algorithm: Variants and Applications
Published 2023“…Chimp Optimization Algorithm (ChOA) is one of the recent metaheuristics swarm intelligence methods. …”
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243
Mining airline data for CRM strategies. (c2006)
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masterThesis -
244
A FAMILY OF NORMALIZED LEAST MEAN FOURTH ALGORITHMS
Published 2020“…In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algorithm, the convergence behavior of these algorithms is independent of the input data correlation statistics. …”
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245
A hybrid graph representation for recursive backtracking algorithms
Published 2017“…The use of efficient data structures is necessary for fast graph modification modules as well as fast take-back procedures. …”
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conferenceObject -
246
(k, l)-Clustering for Transactional Data Streams Anonymization
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conferenceObject -
247
A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities
Published 2021“…To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented. …”
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248
Parallel Algorithms for Distinguishing Nondeterministic Finite State Machines
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doctoralThesis -
249
Metaheuristic Optimization Algorithms for Training Artificial Neural Networks
Published 2012“…The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. …”
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250
Intelligent Rapidly-Exploring Random Tree Star Algorithm
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doctoralThesis -
251
Formal synthesis of VLSI layouts from algorithmic specifications
Published 2020“…In this paper we present a formal approach for high level synthesis. This formal high level syntesis system uses recursive algorithms to model the behaviour to be synthesized. …”
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252
Computational Experience On Four Algorithms For The Hard Clustering Problem
Published 2020“…Several algorithms have been developed to solve this problem which include the k-means algorithm, the simulated annealing algorithm, the tabu search algorithm, and the genetic algorithm. …”
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253
Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks
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doctoralThesis -
254
Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts
Published 2022“…So far none addresses the use of Threat Intelligence (IT) data in Ensemble Learning algorithms to improve the detection process, nor does it work as a function of time, that is, taking into account what happens on the network in a limited time interval. …”
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255
Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent Magnet Synchronous Motor Drives
Published 2004“…At each operating condition a genetic algorithm is used to optimize the PI controller parameters in a closed-loop vector control scheme. …”
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256
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258
Developing an online hate classifier for multiple social media platforms
Published 2020“…Although researchers have found that hate is a problem across multiple platforms, there is a lack of models for online hate detection using multi-platform data. …”
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259
A comparison of optimization heuristics for the data mapping problem
Published 1997“…In this paper we compare the performance of six heuristics with suboptimal solutions for the data mapping problem of two dimensional meshes that are used for the numerical solution of Partial Differential Equations(PDEs) on multicomputers. …”
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260
Data redundancy management for leaf-edges in connected environments
Published 2022“…Although the sensed data could be useful for various applications (e.g., event detection in cities, energy management in commercial buildings), it first requires pre-processing to clean various inconsistencies (e.g., anomalies, redundancies, missing values). …”
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