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spatial scheduling » optimal scheduling (Expand Search)
groups algorithm » mould algorithm (Expand Search)
event based » agent based (Expand Search)
spatial scheduling » optimal scheduling (Expand Search)
groups algorithm » mould algorithm (Expand Search)
event based » agent based (Expand Search)
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Extreme Early Image Recognition Using Event-Based Vision
Published 2023“…Unlike frame-based imagers, event-based imagers output asynchronous pixel events without the need for global exposure time, therefore lowering both power consumption and latency. …”
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A novel encryption algorithm using multiple semifield S-boxes based on permutation of symmetric group
Published 2023“…The presented algorithm is mainly based on the Shannon idea of substitution–permutation network where the process of substitution is performed by the proposed S<sub>8</sub> semifield substitution boxes and permutation operation is performed by the binary cyclic shift of substitution box transformed data. …”
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Hardening the ElGamal cryptosystem in the setting of the second group of units. (c2011)
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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). …”
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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. …”
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Generic metadata representation framework for social-based event detection, description, and linkage
Published 2020“…The latter are required as the building blocks for event-based Collective Knowledge (CK) organization, where CK underlines the combination of all known data, information, and metadata concerning a given concept or event. …”
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CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments
Published 2024“…We propose a cooperative, load-balancing task allocation and scheduling algorithm based on sequential single-item auctions (CoLoSSI) that explicitly considers the non-atomicity of tasks, promotes synergies between agents, and enables cooperation while maintaining computational tractability. …”
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Metaheuristic Algorithm for State-Based Software Testing
Published 2018“…SA evolves a solution by minimizing an energy function that is based on testing objectives such as coverage, diversity, and continuity of events. …”
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MoveSchedule
Published 1995“…The timing of these needs is inferred from the activity schedule. The layout construction algorithm that underlies MoveSchedule uses Constraint Satisfaction to find the set of all positions that meet the constraints on resources' positions and Linear Programming to find the optimal positions that minimize resource transportation and relocation costs. …”
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Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm
Published 2022“…Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. …”
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A Survey of Data Clustering Techniques
Published 2023“…Clustering, an unsupervised learning technique, aims to identify a specific number of clusters to effectively categorize the data through data grouping. Hence, clustering is related to many fields and is used in various applications that deal with large datasets. …”
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Process Mining over Unordered Event Streams
Published 2020“…This requires online algorithms that, instead of keeping the whole history of event data, work incrementally and update analysis results upon the arrival of new events. …”
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Predicting Dropouts among a Homogeneous Population using a Data Mining Approach
Published 2019“…Our research relies solely on pre-college and college performance data available in the institutional database. Our research reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. …”
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Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem
Published 2024“…We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. …”
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Predicting the Heats of Fusion of Ionic Liquids via Group Contribution Modeling and Machine Learning
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