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encoding algorithm » cosine algorithm (Expand Search)
data scheduling » task scheduling (Expand Search), ahead scheduling (Expand Search)
mean algorithm » deer algorithm (Expand Search), search algorithm (Expand Search)
encoding algorithm » cosine algorithm (Expand Search)
data scheduling » task scheduling (Expand Search), ahead scheduling (Expand Search)
mean algorithm » deer algorithm (Expand Search), search algorithm (Expand Search)
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Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
Published 2021“…The paper proposes a concurrent job scheduling algorithm in a multi-energy data source environment using Apache Spark. …”
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A Framework for Predictive Modeling in Sustainable Projects
Published 2012Get full text
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43
Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study
Published 2021“…This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). …”
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Online Recruitment Fraud (ORF) Detection Using Deep Learning Approaches
Published 2024“…In recent studies, traditional machine learning and deep learning algorithms have been implemented to detect fake job postings; this research aims to use two transformer-based deep learning models, i.e., Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT-Pretraining Approach (RoBERTa) to detect fake job postings precisely. …”
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Competitive learning/reflected residual vector quantization for coding angiogram images
Published 2003“…Medical images need to be compressed for the purpose of storage/transmission of a large volume of medical data. Reflected residual vector quantization (RRVQ) has emerged recently as one of the computationally cheap compression algorithms. …”
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Multidimensional Gains for Stochastic Approximation
Published 2019“…The proposed algorithms here aim for per-iteration minimization of the mean square estimate error. …”
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Evacuation of a highly congested urban city
Published 2017“…The algorithm uses Dijkstras algorithm to find the shortest path(s) and a modified greedy algorithm to assign maximum flows to selected paths given a specific schedule per time interval. …”
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conferenceObject -
49
Design and analysis of entropy-constrained reflected residual vector quantization
Published 2002“…Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. …”
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Analysis of Using Machine Learning to Enhance the Efficiency of Facilities Management in the UAE
Published 2022“…This study addresses these issues by Implementing Machine Learning (ML) algorithms using data from Building Management Systems (BMS) and FM maintenance reports, focussing on predictive maintenance for Fresh Air Handling Units. …”
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51
ISSP
Published 2017“…Many decision-support tools were developed to assist planners in space scheduling but these were limited to providing the user with a platform that ties spatial and temporal data in the project and left it up to the user to decide on positions of resources and schedule adjustments to solve spatial conflicts that may arise in the process of constructing site layouts over time.The ISSP system, presented here, provides a graphical user-interactive interface with underlying layout and scheduling algorithms that construct feasible layout and schedule solutions under 2-dimensional spatial constraints between resources. …”
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52
Assigning proctors to exams using scatter search. (c2006)
Published 2006Get full text
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53
A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem
Published 2021“…Moreover, the results achieved by modified CHIOare compared against the results of other 13 well-regarded algorithms. For the first data set, the modifiedCHIO is able to gain the same results as the other comparative methods in two out of ten instances andacceptable results in the rest. …”
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FoGMatch
Published 2019“…Simulation results reveal that our solution outperforms the two common scheduling algorithms (i.e., Min-Min and Max-Min) in terms of IoT services execution makespan, fog nodes resource utilization efficiency and execution time.…”
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Decomposition-based wind power forecasting models and their boundary issue: An in-depth review and comprehensive discussion on potential solutions
Published 2022“…These methods generally disaggregate the original time series data into sub-time-series with better stationarity, and then the target data is predicted based on the sub-series. …”
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Machine Learning-Based Approach for EV Charging Behavior
Published 2021Get full text
doctoralThesis -
59
An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System
Published 2019“…Despite having different performance results on predicting failures, most of the models produced close outcomes. Meaning no “perfect” machine learning algorithm that will produce good results at particular problem, in fact for each type of problem a specific algorithm is suited and might achieves good outcome, while another algorithm fails heavily. …”
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Efficient Seismic Volume Compression using the Lifting Scheme
Published 2000“…In addition, the lifting scheme offers: 1) a dramatic reduction of the required auxiliary memory, 2) an efficient combination with parallel rendering algorithms to perform arbitrary surface and volume rendering for interactive visualization, and 3) an easy integration in the parallel I/O seismic data loading routines. …”
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