Search Results - (( data distribution selection algorithm ) OR ( data distribution practices algorithm ))
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Survey on job scheduling mechanisms in grid environment
Published 2015“…Grid systems provide geographically distributed resources for both computational intensive and data-intensive applications.These applications generate large data sets.However, the high latency imposed by the underlying technologies; upon which the grid system is built (such as the Internet and WWW), induced impediment in the effective access to such huge and widely distributed data.To minimize this impediment, jobs need to be scheduled across grid environments to achieve efficient data access.Scheduling multiple data requests submitted by grid users onto the grid environment is NP-hard.Thus, there is no best scheduling algorithm that cuts across all grids computing environments.Job scheduling is one of the key research area in grid computing.In the recent past many researchers have proposed different mechanisms to help scheduling of user jobs in grid systems.Some characteristic features of the grid components; such as machines types and nature of jobs at hand means that a choice needs to be made for an appropriate scheduling algorithm to march a given grid environment.The aim of scheduling is to achieve maximum possible system throughput and to match the application needs with the available computing resources.This paper is motivated by the need to explore the various job scheduling techniques alongside their area of implementation.The paper will systematically analyze the strengths and weaknesses of some selected approaches in the area of grid jobs scheduling.This helps researchers better understand the concept of scheduling, and can contribute in developing more efficient and practical scheduling algorithms.This will also benefit interested researchers to carry out further work in this dynamic research area.…”
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Development of an effective clustering algorithm for older fallers
Published 2022“…A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. …”
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023“…It provides an increased convergence and globally optimized solutions. The algorithm has been tested using actual customer consumption data from SESB. 10 fold cross validation method is used to confirm the consistency of the detection accuracy. …”
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Mathematical models and optimization algorithms for low-carbon Location-Inventory-Routing Problem with uncertainty
Published 2024“…When compared to the Supply Chain Guru X (SCGX) software, the proposed algorithms offer higher practical applicability.…”
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Hybrid performance measures and mixed evaluation method for data classification problems
Published 2012“…This study investigates two different issues of performance measure in data classification problem. First, this study examines the use of accuracy measure as a discriminator for building an optimized Prototype Selection (PS) algorithm. …”
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Dose verification procedures in radiotherapy
Published 1999“…In our study the dose at selected points for each test condition consisting of beam-phantom configuration irradiated with 6MV photon beam wascomputed with Nucletron PLATO RTS treatment planning computer using pencil beam algorithms. …”
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Sustainable energy management: Artificial intelligence-based electricity consumption prediction in limited dataset environment for industry applications
Published 2024“…In academia, this study proposed an innovative SLR-MLR predictive algorithm and utilized a novel statistical approach to evaluate and select the superior predictive algorithm. …”
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Statistical modelling of time series of counts for a new class of mixture distributions / Khoo Wooi Chen
Published 2016“…Thus coherent forecasting, which is based upon the k-step ahead conditional mean, median, mode and distribution, is considered. For low count series the k-step ahead conditional distribution of the MPT model practically exhibits better performance than the other models. …”
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Comparison of diverse ensemble neural network for large data classification
Published 2015“…DRT is an enhanced algorithm based on distributed random for different neural networks. …”
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Benchmarking Robust Machine Learning Models Under Data Imperfections in Real-World Data Science Scenarios
Published 2026“…Machine learning systems deployed in real-world environments frequently encounter data imperfections such as noise, missing values, class imbalance, and distribution shifts. …”
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An optimal under frequency load shedding scheme for islanded distribution network / Amalina Izzati Md Isa
Published 2018“…Shutting down the Distributed Generation (DG) is no longer appropriate practice when losing main power supply. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani
Published 2018“…In this method, firstly, Weibull density function is utilized to model the wind speed and then several methods are applied to estimate the parameters of the wind speed distribution. To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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Enhancing teaching and learning through data-driven optimization of servicing code demand and lecturer allocation using WEKA analysis
Published 2025“…Complementary K-Means clustering grouped the data into two major clusters, indicating that a clear differentiation between economic-based and entrepreneurship-based courses in terms of student enrolment volume and approval distribution. …”
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Personalized one-shot local adaptation federated learning for mortality prediction in multi-center Intensive Care Unit
Published 2024“…Nevertheless, the EHR datasets preserved by independent institutions differ considerably in both quantity and characteristics, making the overall data non-independently and identically distributed (non-IID). …”
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A new hybrid ensemble feature selection framework for machine learning-based phishing detection system
Published 2019“…In the first phase of HEFS, a novel Cumulative Distribution Function gradient (CDF-g) algorithm is exploited to produce primary feature subsets, which are then fed into a data perturbation ensemble to yield secondary feature subsets. …”
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Prior selection for Gumbel distribution parameters using multiple-try metropolis algorithm for monthly maxima PM10 data
Published 2013“…Focus is on modelling the extreme data based on block maxima approach using Gumbel distribution. …”
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Static and self-scalable filter range selection algorithms for peer-to-peer networks
Published 2011“…Two multiple selection algorithm, which are known as “static filter range selection algorithm” and “self-scalable selection algorithm” are proposed. …”
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