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yield scheduling » self scheduling (Expand Search), ahead scheduling (Expand Search)
using algorithm » cosine algorithm (Expand Search)
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181
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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182
Correlation Clustering via s-Club Cluster Edge Deletion
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masterThesis -
183
A method for the minimum coloring problem using genetic algorithms
Published 2006“…The algorithm was implemented and tested on various set of instances including large DIMACS challenge benchmark graphs, all yielding favorable results.…”
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conferenceObject -
184
Parallel scatter search algorithms for exam timetabling. (c2009)
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masterThesis -
185
A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…A parallel simulator, based on PVM, was implemented for the proposed algorithm on a Linux Cluster. Many graphs were tested, all yielding optimum solutions…”
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186
Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
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doctoralThesis -
187
Time-varying volatility model equipped with regime switching factor: valuation of option price written on energy futures
Published 2025“…To determine the parameters of the regime switching model and identify when economic states change, we employ the EM algorithm, utilizing real gas futures price data. We validate our closed-form solution for the option pricing through simulations employing the generalized antithetic variates Monte-Carlo technique. …”
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188
A fast exact sequential algorithm for the partial digest problem
Published 2016“…Two types of simulated data, random and Zhang, are used to measure the efficiency of the algorithm. …”
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189
Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach
Published 2006“…The algorithm is tested using 200 randomly generated 2-variable 4-valued functions. …”
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190
Simulated annealing and genetic algorithms for exam scheduling. (c1997)
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masterThesis -
191
Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…Among these, K-means is widely used for efficiently solving clustering problems. …”
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masterThesis -
192
Second-order conic programming for data envelopment analysis models
Published 2022“…Data envelopment analysis (DEA) is a widely used benchmarking technique. …”
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193
A Scatter search algorithm for exam scheduling. (c2006)
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masterThesis -
194
Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition
Published 2021“…Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. …”
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195
Data mining approach to predict student's selection of program majors
Published 2019“…The approach includes a methodology to manage data mining projects, sampling techniques to handle imbalanced data and multiclass data, a set of classification algorithms to predict and measures to evaluate performance of models. …”
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196
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197
Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques
Published 2024“…The DinoLite Microscope’s high-resolution images are used to measure the length and width of wheat anthers. …”
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198
A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008)
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masterThesis -
199
A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs†
Published 2010“…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and k colours. …”
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200
An image processing and genetic algorithm-based approach for the detection of melanoma in patients
Published 2018“…The second phase classifies lesions using a Genetic Algorithm. Our technique shows a significant improvement over other well-known algorithms and proves to be more stable on both training and testing data.…”
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