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element processing » melt processing (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
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element processing » melt processing (Expand Search)
data algorithm » jaya algorithm (Expand Search), deer algorithm (Expand Search)
cc3d algorithm » rd algorithm (Expand Search)
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Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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Brain Source Localization in the Presence of Leadfield Perturbations
Published 2015Get full text
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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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Socially Motivated Approach to Simulate Negotiation Process
Published 2014Get full text
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Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Published 2024“…Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. …”
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Optimization of Commercially Off the Shelf (COTS) Electric Propulsion System for Low Speed Fuel Cell UAV
Published 2013Get full text
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An improved kernelization algorithm for r-Set Packing
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A kernelization algorithm for d-Hitting Set
Published 2010“…For a given parameterized problem, π, a kernelization algorithm is a polynomial-time pre-processing procedure that transforms an arbitrary instance of π into an equivalent one whose size depends only on the input parameter(s). …”
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Intelligent Rapidly-Exploring Random Tree Star Algorithm
Published 2024Get full text
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A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…Our model uses a large number of simple processing elements or neurons, based on the McCulloch-Pitts binary neuron. …”
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XBeGene: Scalable XML Documents Generator by Example Based on Real Data
Published 2012“…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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A Data-Driven Decision-Making Framework for Fleet Management in the Government Sector of Dubai
Published 2024“…The proposed framework comprises key elements: Important Decisions derived from interviews with transportation leaders, Knowledge Management enhanced by AI algorithms, Data Mining/Analysis utilizing historical data, the Fleet Management System employing Oracle ERP, and a Data-Driven Decision Support Framework that leans towards the extended framework approach. …”
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The Use of Microwave Tomography in Bone Healing Monitoring
Published 2019Get full text
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Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey
Published 2024“…<p dir="ltr">Mobile devices have become an essential element in our day-to-day lives. The chances of mobile attacks are rapidly increasing with the growing use of mobile devices. …”
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Correlation Clustering with Overlaps
Published 2020“…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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