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Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context
Published 0024“…These platforms enhance academic performance by fostering collaborative learning environments and generating extensive data from every user interaction. …”
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202
A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models
Published 2024“…The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. …”
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203
RHEEMix in the data jungle: a cost-based optimizer for cross-platform systems.
Published 2020“…Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. …”
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Defining quantitative rules for identifying influential researchers: Insights from mathematics domain
Published 2024“…To perform the experiment, we focused on the field of mathematics and used a dataset containing 525 individuals who received awards and 525 individuals who did not receive awards. The rules were developed for each parameter category using the Decision Tree Algorithm, which achieved an average accuracy of 70 to 75 percent for identifying awardees in mathematics domains. …”
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A new family of multi-step quasi-Newton algorithms for unconstrained optimization
Published 1999“…It concentrates on deriving a variable-metric family of minimum curvature algorithms for unconstrained optimization. The derivation is based on considering a rational model, with a certain tuning parameter, where the aim is to develop a general framework that encompasses all possible two-step minimum curvature algorithms generated by appropriate parameter choices. …”
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Recursive least-squares backpropagation algorithm for stop-and-go decision-directed blind equalization
Published 2002“…To overcome this problem, in this work, a fast converging recursive least squares (RLS)-based complex-valued backpropagation learning algorithm is derived for S&G-DD blind equalization. …”
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211
Development of multivariable PID controller gains in presence of measurement noise
Published 2017“…The development of the proposed optimal algorithm is based on minimising a stochastic performance index in presence of erroneous initial conditions, white measurement noise, and white process noise. …”
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Environmental/economic power dispatch using multiobjective evolutionary algorithms: a comparative study
Published 2003“…A comparative study of newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) applied to a nonlinear power system multiobjective optimization problem is presented in this paper. …”
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213
Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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214
Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm
Published 2020“…In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. …”
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215
Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands
Published 2020“…A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. …”
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Using machine learning for disease detection. (c2013)
Published 2016“…Classification consists of predicting group membership for new data instances by learning from pre-classified data instances. …”
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218
Benchmarking Concept Drift Detectors for Online Machine Learning
Published 2022“…Concept drift detection is an essential step to maintain the accuracy of online machine learning. The main task is to detect changes in data distribution that might cause changes in the decision bound aries for a classification algorithm. …”
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Deep Reinforcement Learning for Resource Constrained HLS Scheduling
Published 2022“…The two main steps in HLS are: operations scheduling and data-path allocation. In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. …”
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masterThesis -
220
Positive Unlabelled Learning to Recognize Dishes as Named Entity
Published 2019“…I work with Yelp dataset, going through each text review, using each noun as a candidate, label the positive samples using the aforementioned lookup table, then using Positive Unlabelled learning techniques to recognise more entities within the unlabelled data, by predicting the probability for each candidate. …”
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