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Parallel Algorithms for Distinguishing Nondeterministic Finite State Machines
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doctoralThesis -
164
Graph Contraction for Mapping Data on Parallel Computers
Published 1994“…We then present experimental results on using contracted graphs as inputs to two physical optimization methods; namely, genetic algorithm and simulated annealing. …”
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Mapping realistic data sets on parallel computers
Published 1993“…The GC algorithm allows large-scale mapping to become efficient, especially when slow but high-quality mappers are used.…”
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Parallel genetic algorithm for disease-gene association
Published 2011“…In this work, we combine few successful strategies from the literature and present a parallel genetic algorithm for the Tag SNP Selection problem. Our results compared favorably with those of a recognized tag SNP selection algorithm using three different data sets from the HapMap project.…”
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Particle swarm optimization algorithm: review and applications
Published 2024“…The main procedure of the PSO algorithm is presented. Future researchers can use the collected data in this survey as baseline information on the PSO and PSO's applications.…”
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Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO
Published 2022“…In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. …”
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New enumeration algorithm for regular boolean functions
Published 2018“…This algorithm exploits the equivalence between regular Boolean functions and positive threshold functions that can be used to represent instances of the knapsack problem. …”
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Parallel algorithm for hardware implementation of inverse halftoning
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The Effects of Data Mining on Small Businesses in Dubai
Published 2011“…While there are numerous studies on the best data mining models and their uses, even on certain industries, this study focuses on the applications more than the algorithms and models and their usefulness for small businesses specifically. …”
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Spider monkey optimizations: application review and results
Published 2024“…Optimization algorithms are applied to find efficient solutions in different problems in several fields such as the routing in wireless networks, cloud computing, big data, image processing and scheduling, and so forth. …”
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A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation
Published 2021“…The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. …”
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An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting
Published 2021“…First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. …”
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Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey
Published 2024“…As new <u>malware</u> gets introduced frequently by <u>malware developers</u>, it is very challenging to come up with comprehensive algorithms to detect this malware. …”
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The effects of data balancing approaches: A case study
Published 2023“…<p dir="ltr">Imbalanced datasets affect the performance of machine learning algorithms adversely. To cope with this problem, several resampling methods have been developed recently. …”
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Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems
Published 2023“…First, a feature selection tool using PSO Algorithm is developed. Then, in order to maximize the diversity between data samples and improve the effectiveness of using PSO algorithm for feature selection, the Euclidean distance metric is used in order to reduce the data and maximize the diversity between data samples. …”
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Intelligent Bilateral Client Selection in Federated Learning Using Game Theory
Published 2022“…To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. …”
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masterThesis