Search Results - (( developing solution means algorithm ) OR ( java implication based algorithm ))

Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…In the first and second phases, a threshold fuzzy c-means clustering algorithm as a clusterer and a pattern ensemble learning method based on the incremental genetic-based algorithms are proposed respectively. …”
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Data clustering using the bees algorithm by Pham, D.T, Otri, S., Afify, A., Mahmuddin, Massudi, Al-Jabbouli, H.

“…The authors’ team have developed a new population based search algorithm called the Bees Algorithm that is capable of locating near optimal solutions efficiently. …”
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Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems by Umar, Umar Ali

“…The objectives of this research are to develop an algorithm for integrated scheduling and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm, ensure the algorithm validity and improvement on the performance of the developed algorithm. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

“…In the first sub-algorithm, the state mean propagation removes the Gaussian white noise to obtain the expected solution. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Clustering Approach In Wireless Sensor Networks Based On K-Means: Limitations And Recommendations by Hassan, Ali Abdul-hussian, Md Shah, Wahidah, Husien, Ali Mohamed, Talib, Mohammed Saad, Mohammed, Ali Abdul Jabbar, Othman, Mohd Fairuz Iskandar

“…One of most popular cluster algorithms that utilizing into organize sensor nodes is K-means algorithm. …”
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering by Rashed, Alwatben Batoul

“…Results showed that MOPSO-CD had better performance and a strong superiority in the IGD with the lowest mean of 9.50E-4, while the HV showed the lowest mean of 9.40E-1 compared to other algorithms. …”
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Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy by Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., El-Shafie A.

A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…The research starts with developing the hybrid deep learning model consisting of DNN and a K-Means Clustering Algorithm. …”
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Dynamic Economic Dispatch For Power System by Hussein, Saif Tahseen

“…The solution algorithm based on a constraint relaxation and period-by-period is developed and tested. …”
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Seed disperser ant algorithm for optimization / Chang Wen Liang by Chang , Wen Liang

“…The Seed Disperser Ant Algorithm (SDAA) is developed based on the evolution or expansion process of Seed Disperser Ant (Aphaenogaster senilis) colony. …”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat by Sakamat, Norzehan

“…The combined method is called Hybrid K-MeansCGA. Modifications of K-Means structures were done by inserting genetic algorithm operators and tuning the population. …”
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A COMPUTATIONAL ALGORITHM for the NUMERICAL SOLUTION of NONLINEAR FRACTIONAL INTEGRAL EQUATIONS by Amin, R., Senu, N., Hafeez, M.B., Arshad, N.I., Ahmadian, A.L.I., Salahshour, S., Sumelka, W.

“…In this paper, we develop a numerical method for the solution of nonlinear fractional integral equations (NFIEs) based on Haar wavelet collocation technique (HWCT). …”
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Approximate maximum clique algorithm (AMCA): A clever technique for solving the maximum clique problem through near optimal algorithm for minimum vertex cover problem by Fayaz, Muhammad, Arshad,, Shakeel, Shah,, Abdul Salam, Shah, Asadullah

“…Vertex support Algorithm (VSA). Mean of Neighbors of Minimum Degree Algorithm (MNMA), Modified Vertex Support Algorithm (MVSA), Maximum Adjacent Minimum Degree Algorithm (MAMA), and Clever Steady Strategy Algorithms (CSSA). …”
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Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD) by Rashed, Alwatben Batoul, Hamdan, Hazlina, Mohd Sharef, Nurfadhlina, Sulaiman, Md Nasir, Yaakob, Razali, Abubakar, Mansir

“…Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

“…The statistical error estimation exhibits a mean absolute error of 11.5, and root mean squared error of 0.87. …”
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Hybrid particle swarm optimization algorithm with fine tuning operators by Murthy, G.R., Arumugam, M.S., Loo, C.K.

“…This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). In order to accelerate the PSO algorithms to obtain the global optimal solution, three fine tuning operators, namely mutation, cross-over and root mean square variants are introduced. …”
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Effect of adopting different dispatching rules on the mean flow time in a two machine batch-shop problem by Abdelraheem Elhaj, Hazir Farouk

“…Ali Allahverdi, 1998 obtained the optimal solutions for minimizing mean flow time in a two-machine flow shop with Sequence-independent set up times by using three heuristic algorithms. …”
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Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

“…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. This thesis aims to develop the simple and effective imbalanced classification algorithms by previously improving the algorithms performance of general classifiers i.e. …”
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