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1
Electricity load profile determination by using fuzzy C-means and probability neural network / Norhasnelly Anuar
Published 2015“…This method will give the best result when clustering the overlapped data in load profile. PNN is a fast training process to do the classification activities. …”
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2
Intuitive content management system via manipulation and duplication with if-else rules classification
Published 2018“…As a result, ICMS can transform dynamic websites into static websites with faster load speed using manipulation method mixed with data mining classification prediction and Boyer-Moore Horspool algorithm which can be classified, edited, adjustable and searched more precisely. …”
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3
Detection and classification of conflict flows in SDN using machine learning algorithms
Published 2021“…Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. …”
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4
Assessment of cognitive load using multimedia learning and resting states with deep learning perspective
Published 2019“…The results shows that alpha brain wave produced consistent behavior using for all cognition tasks based on pre-trained CNN models for classification and cognitive load assessment. © 2018 IEEE…”
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5
SUDOKU HELPER
Published 2015“…In this paper research, author presents an algorithm to provide a tutorial for any Sudoku player who got stuck during the solving process. …”
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6
Load balancing and server consolidation in cloud computing environments: a meta-study
Published 2019“…This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. …”
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7
Review of Plug-Based Load Energy Management Systems (PLEMS) for energy and comfort management of buildings
Published 2016“…The reviews indicate that it is essential to improve plug-based load equipments efficiency standards. Classification of these electrical devices according to their consumption patterns would facilitate energy wastage analysis and predictions of future consumption patterns. …”
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An optimal under frequency load shedding scheme for islanded distribution network / Amalina Izzati Md Isa
Published 2018“…Two new algorithms i.e., Load Classification based Fuzzy Logic (LCFL) and Binary Evolutionary Programming (BEP) are introduced in the module. …”
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9
Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System
Published 2019“…Overall, robustness of the proposed algorithm is tested under different faults scenarios, taking into account several factors such as fault inception angles, line fault resistance, ground fault resistance, and the size of loads. …”
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10
Dynamic load balancing policy in grid computing with multi-agent system integration
Published 2011“…The policy in dynamic load balancing, classification and function are variety based on the focus study for each research. …”
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11
Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification
Published 2019“…After that, the simulation results were validated in the Load Test, and it shows that structure made from PLA material was able to withstand load with 1.7kg while the structure made from ABS material is only able to withstand load with 1.6kg. …”
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12
Dynamic load balancing policy with communication and computation elements in grid computing with multi-agent system integration
Published 2011“…The policy in dynamic load balancing, classification and function are variety based on the focus study for each research. …”
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Power system security assessment using artificial neural network: article / Mohd Fathi Zakaria
Published 2010“…This paper presented an application of Artificial Neural Network (ANN) in steady state stability classifications. A multi layer feed forward ANN with Back Propagation Network algorithm is proposed in determining the steady state stability classifications. …”
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14
Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy
Published 2023Subjects:Article -
15
Fault classification in smart distribution network using support vector machine
Published 2023“…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”
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Service based load balance mechanism using software-defined networks / Ahmed Abdelaziz Abdelltif Osman
Published 2017“…In the heterogeneous environment, SBLB mechanism demonstrates 51% decrease in average response time and 47% decrease in reply time as compared to theHAproxy load balancer. Besides, our proposed mechanism also outperforms round robin algorithm in both environments. …”
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17
An optimized variant of machine learning algorithm for datadriven electrical energy efficiency management (D2EEM)
Published 2024“…The deliverable of this phase is the selection of the best candidate of machine learning algorithm for university campus energy load prediction. …”
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18
An application of a novel technique for assessing the operating performance of existing cooling systems on a university campus
Published 2018“…Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. …”
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High impedance fault detection and identification based on pattern recognition of phase displacement computation
Published 2018“…Subsequently, an automatic HIF classification algorithm based on predefined indices is proposed to perform event identification and HIF detection. …”
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Gender Classification: A Convolutional Neural Network Approach
Published 2016“…Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. …”
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