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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
Published 2016Get full text
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Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…The primary tool used in the data supply chain is data batch processing which requires efficient scheduling. …”
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Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog- Cloud Computing Environment
Published 2019Subjects: Get full text
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Predicting Dropouts among a Homogeneous Population using a Data Mining Approach
Published 2019Subjects: Get full text
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Optimizing Energy Consumption of Cloud Computing IaaS
Published 2017Subjects: Get full text
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GENETIC SCHEDULING OF TASK GRAPHS
Published 2020“…A genetic algorithm for scheduling computational task graphs is presented. …”
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GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing
Published 2024“…In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), a novel optimization approach tailored for task scheduling in cloud computing environments. …”
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Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence
Published 2020“…One of the best algorithms in terms of the result is the Long Short Term Memory (LSTM) since it is based on recurrent neural networks which uses loop as a method to learn from heuristics data. …”
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Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment
Published 2022“…Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. …”
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Proactive Fault Tolerance and Minimizing Task Execution Failure in A Cloud Data Center
Published 2024Get full text
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Predict Student Success and Performance factors by analyzing educational data using data mining techniques
Published 2022“…The research study is performed as experimental analysis and develop models from nine machine learning algorithms including KNN, Naïve Bayes, SVM, Logistic regression, Decision Tree, Random forest, Adaboost, Bagging Classifier, and voting Classifier. …”
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A simulated evolution approach to task-matching and scheduling in heterogeneous computing environments
Published 2020“…Abstract This paper applies a simulated evolution (SE) approach to the problem of matching and scheduling dependent tasks in a heterogeneous suite of computers interconnected via a high-speed network. …”
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Using machine learning to support students’ academic decisions
Published 2019“…This research tests and compares the performance of Decision Trees, Random Forests, Gradient-Boosted trees, and Deep Learning machine learning regression algorithms to predict student GPA. …”
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An Uncertainty Based Genetic Algorithm Approach for Project Resource Scheduling
Published 2016“…Several nonlinear optimization models were developed for this purpose assuming uniform resource availability and sequence based project tasks. The work presented in thesis add to the existing literature in a proposing the use of a genetic algorithm uncertain approach to resource- scheduling in projects. …”
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Allocation and re-allocation of data in a grid using an adaptive genetic algorithm
Published 2006“…Allocation and re-allocation of data in a grid using an adaptive genetic algorithm. …”
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Optimizing Document Classification: Unleashing the Power of Genetic Algorithms
Published 2023“…Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. …”
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Efficient Approximate Conformance Checking Using Trie Data Structures
Published 2021“…By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. …”
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