Search Results - (( using optimization means algorithm ) OR ( data distribution function algorithm ))
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Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…One of the main issues in genetic k-means based algorithms is their sensitivity to outliers and unevenly distributed clusters due to the mean compromised computations. …”
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Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
Published 2015“…The developed stochastic algorithm has the capability to measure the parametric distribution function and validate the predict the reliability rate, mean time to failure and hazard rate. …”
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Improvement on optimal coordination of directional overcurrent relays in mesh distribution network system using artificial intelligence technique
Published 2015“…However,the Levenberg–Marquardt nonlinear function fitting algorithm application on solving the novel hybrid GA-ANN technique predicted the nonlinear time –current function fitting of each relay effectively with minimum mean square error (mse) between the target output and the actual output for effective generalization during supervised training of the network. …”
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Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH
Published 2021“…A hybrid population – based stochastic optimization method named MVMO-SH algorithm is proposed to optimize PVDG locations and sizes in the grid system network. …”
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Heuristic resource allocation algorithm for controller placement in multi-control 5G based on SDN/NFV architecture
Published 2021“…The integration of Software Defined Networking (SDN) and Network Function Virtualization (NFV) is considered to be an efficient solution that enables the forecasting of highly scalable, optimal performance of 5G networks by providing an effective means of network functionality. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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Logistic regression methods for classification of imbalanced data sets
Published 2012“…Classification of imbalanced data sets is one of the important researches in Data Mining community, since the data sets in many real-world problems mostly are imbalanced class distribution. …”
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Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli
Published 2014“…The direct idea of making the conventional neural network learning algorithm more powerful towards outlying data is by replacing the mean square error (MSE) with a different symmetric and continuous cost function. …”
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Book Section -
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Tracking moving targets in wireless sensor networks using extended diffusion strategies of distributed Kalman filter
Published 2013“…As a simulation study, we applied the algorithms in a network to track the position and speed of a projectile and compared the results with real world circumstances, using the concept of transient mean square deviations of network as a cost function. …”
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Conference or Workshop Item -
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Enhancing high-dimensional streaming data analysis: optimizing Online Feature Selection for handling drift using optimization technique and ensemble learning
Published 2024“…The PSO-OSFS method is underpinned by the adaptive threshold particle representation of particle swarm optimization and enhanced fitness function using minimization of mean absolute deviation of dependency among fea-ture subsets. …”
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Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…It is conflicting with the standard K-means that return different clustering structure in each run, while K-means + + replicates several times to achieve minimum objective function. …”
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Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]
Published 2017“…In doing this, two ANN algorithms (MLP and RBF) and SMO algorithm were employed and compared. …”
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Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios
Published 2015“…In order to increase the system efficiency and maximize the power generation, constructed operation models were optimized. To determine the optimum solution in each policy, real coded genetic algorithm is used as an optimization technique. …”
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Energy balancing mechanisms for decentralized routing protocols in wireless sensor networks
Published 2012“…Finally, we propose Self-Decision Route Selection scheme which is an improvement of the Hop-based Spanning Tree (HST) algorithm that is used in some routing protocols such as AODV and DSR. …”
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The new enhancement of OLSR energy-saving system in ADHOC network / Suhazlan Suhaimi
Published 2018“…As such, this study was undertaken with the main aim to enhance the energy-saving system of MANET, with which nodes can function without failure by using a newly developed algorithm of the OLSR protocol. …”
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Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques
Published 2007“…Daily reservoir inflow was computed using a water balance equation. The reservoir inflow and rainfall data sets were examined for normal distribution and the best data transformation was used. …”
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Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam
Published 2023“…The monthly storage capacity fails in 2077, substantially earlier than the other three algorithms in Scenario 2. The LFWOA was used in this study to improve the efficacy of the algorithms by delivering a more accurate monthly storage capacity and reservoir risk assessment for Scenario 2 of RCP 2.6. …”
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Final Year Project / Dissertation / Thesis -
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Optimized clustering with modified K-means algorithm
Published 2021“…Clustering technique is able to find hidden patterns and to extract useful information from huge data. Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of clusters (k). …”
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