Search Results - (( variable generation based algorithm ) OR ( using function means algorithm ))

Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…Adopting the medoid instead of the mean can enhance the efficiency. However, the complexity of the kmedoid based algorithms in general is more than the complexity of the k-means based algorithms. …”
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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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Quaternion-based dynamic algorithm for random generation of solid 4D cylindrical curves in RVE modeling by Hamat, Sanusi, Ishak, Mohamad Ridzwan, Kelly, Piaras, Salit, Mohd Sapuan, Yidris, Noorfaizal, Ali, Syamir Alihan Showkat, Hussin, Mohd Sabri, Mohd Dawi, Mohd Syedi Imran

“…Micro-scale RVEs were successfully generated across fiber volume fractions up to 50 %, validated through second‐order orientation tensor analysis and statistical evaluations using Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs). …”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…While the firefly algorithm solution is superior, it has a higher time complexity compared to other algorithms used when there are more hidden layers and neurons. …”
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Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH by Norhafidzah, Mohd Saad

“…Using meteorological data for one reference year, the Monte-Carlo simulation is performed in the Beta probability density function (PDF) to model continuous random variables of solar irradiances. …”
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems by Lim, Siew Mooi

“…The rationale behind developing algorithms using real encoding of chromosome representations is the limitations of binary encoding. …”
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Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm by Ehteram M., Panahi F., Ahmed A.N., Huang Y.F., Kumar P., Elshafie A.

A new hybrid multiaxial fatigue life model based on critical plane continuum damage mechanics and genetic algorithm by Masood, Kamal

“…The model is simple in application with the use of genetic algorithm for model calibration making use of only the material fatigue limit. …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

“…Nevertheless, AC is not required for LCM if the original multi-spectral image is used. The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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Zero distortion-based steganography for handwritten signature by Iranmanesh, Vahab

“…Thus, the generated stego signature (s) is used to make stego key (k) based on the zero-distortion approach to represent the secret message (m) in a binary format. …”
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Neural network-based prediction models for physical properties of oil palm medium density fiberboard / Faridah Sh. Ismail by Sh. Ismail, Faridah

“…Through rank selection technique, the chromosomes are sorted based on the fitness function to learn about the population of current generation. …”
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Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis by Mohamed Elfaki, Faiz Ahmed

“…Residual-based approaches are used to assess the validity of the two models (MW, CC) assumptions. …”
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A headway and order scheme based mixed integer goal programming model for railway rescheduling / Zuraida Alwadood by Alwadood, Zuraida

“…The model is solved by means of preemptive goal programming technique, using MATLAB r2014a, which automatically generates the optimal solution to the problem. …”
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Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation by Bahomaid, Ameen A., Alsewari, Abdulrahman A., Zamli, Kamal Z., Alsariera, Yazan A.

“…This study presented the most recent variable interaction strength (VS) CT strategy using an enhanced variant in the greedy algorithm. …”
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Identification of debris flow initiation zones using topographic model and airborne laser scanning data by Lay, Usman Salihu, Pradhan, Biswajeet

“…Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). …”
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Message based random variable length key encryption algorithm. by Mirvaziri, Hamid, Jumari, Kasmiran, Ismail, Mahamod, Mohd Hanapi, Zurina

“…In order to overcome analysis mentioned above a new algorithm is designed that is based on random numbers and also can defeat time and memory constraints. …”
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Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing by S. Ahmed, Bestoun

“…This thesis presents the design and implementation of a new interaction test generation strategy, known as the Particle Swarm-based Test Generator (PSTG), for generating t-way and variable-strength test suites. …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

“…It is attained successfully by combining the mean in K-Means algorithm, minimum and maximum in K-Midranges algorithm and compute their average as mean cluster of Hybrid mean. …”
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Determining the preprocessing clustering algorithm in radial basis function neural network by S.L. Ang, H.C. Ong, H.C. Law

“…Three types of method used in this study to find the centres include random selections, K-means clustering algorithm and also K-median clustering algorithm. …”
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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

“…With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). …”
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