Search Results - (( java implementation phase algorithm ) OR ( parameter reduction factor algorithm ))

Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

“…All the algorithm for the engine has been developed by using Java script language. …”
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The PID controller parameter tuning based on a modified differential evolutionary optimization algorithm for the intelligent active vibration control of a combined single link robo... by Moloody, Abbas, As’arry, Azizan, Hong, Tang Sai, Raja Kamil, ., Zolfagharian, Ali

“…The Crossover Probability Factor (CPF) as the Certain Ratio (CR) and the Mutation Factor (MF) of the algorithm are gradually altered during algorithm iteration to enhance the method's performance during optimization. …”
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Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

Combined modal parameters-based index for damage identification in a beamlike structure: theoretical development and verification by Fayyadh, M.M., Razak, H.A., Ismail, Z.

“…The new index called Combined Parameter Index (CPI) compares the factor of reduction in stiffness according to reduction in natural frequencies and also the factor of reduction in stiffness according to change in mode shape. …”
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Enhancing Harmony Search Metaheuristic Algorithm for Coverage Efficiency, Test Suite Reduction, and Running Time in Combinatorial Interaction Testing by Muazu, Aminu Aminu, Hashim, Ahmad Sobri

“…Despite HS’s tendency to get stuck in local optima, we dynamically adjust its parameter values in our proposed eHS. The experimental results demonstrate that eHS outperforms the other algorithms for CIT in terms of coverage efficiency and test suite reduction. …”
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Development of a phantom and metal artifact correction (MAC) algorithm for post-operative spine computed tomography (CT) imaging / Noor Diyana Osman by Osman, Noor Diyana

“…The last part is the development of a metal artifact correction (MAC) algorithm and evaluation of the proposed algorithm in artifacts reduction in CT images. …”
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Efficient hybrid reduction for binary based information system in soft set theory by Mohd Rose, Ahmad Nazari

“…HRSS consists of two(2) types of parameter reduction and a newly proposed object reduction. …”
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Optimal route checking using genetic algorithm for UiTM's bus services / Tengku Salman Fathi Tengku Jaafar by Tengku Jaafar, Tengku Salman Fathi

“…Although from human logical thinking, the route can be generated easily but the calculation of checking the route whether it is optimal route or not is difficult and will take long time to be implemented. This research study with the development of the Optimal Route Checking Using Genetic Algorithm system should solve this scenario. …”
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PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah by Abdullah, Siti Muniroh

“…Results suggest that the PSO algorithm is viable alternative to other established algorithms for LLS parameter estimation. …”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

“…An analysis was done to see the effect of implementing different data reduction algorithms in classifying BSR disease. The results showed that the impedance parameter was the best in classifying BSR severity levels compared to the other dielectric properties. …”
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Improved dung beetle optimization algorithm and finite element analysis for spindle optimization by Haohao, Ma, As’arry, Azizan, Xuping, Wu, Ismail, Mohd Idris Shah, Ramli, Hafiz Rashidi, Saad, Mohd Sazli, Delgoshaei, Aidin

“…The obtained optimal parameters guide the construction of a finite element model, considering additional factors like stiffness and maximum stress. …”
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Increment of insecure RSA private exponent bound through perfect square RSA diophantine parameters cryptanalysis by Wan Mohd Ruzai, Wan Nur Aqlili, Nitaj, Abderrahmane, Kamel Ariffin, Muhammad Rezal, Mahad, Zahari, Asbullah, Muhammad Asyraf

“…The public parameters of the RSA cryptosystem are represented by the pair of integers N and e. …”
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Optimization of super twisting sliding mode control gains using Taguchi method by Jamaludin, Zamberi, Chiew, Tsung Heng, Bani Hashim, Ahmad Yusairi, Rafan, Nur Aidawaty, Abdullah, Lokman

“…Optimized algorithm achieved 9.3% of reduction in root mean square of tracking error and 38.4% of reduction in chattering experimentally.…”
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Optimization of fast fourier transform based on twiddle factor using genetic algorithm on raspberry pi by Ghazi, Firas Faisal

“…Over the years the Genetic Algorithms (GA) proved to be one of the best methods for optimization. …”
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Design of intelligent Qira’at identification algorithm by Kamarudin, Noraziahtulhidayu

“…The process of the SPAP Algorithm is to extend parameters of the Affine Projection Block with two different selections of windowing length that affect the final accuracy on pattern classification. …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

“…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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Study on sparseness effects over NMF applied for automatic text summarization by Batcha, Nowshath Kadhar, Murugesan, Raja Kumar, A. Aziz, Normaziah

“…While, storage capability relates to the extent of data reduction process achieved by NMF. The parametric values that serve as input to the NMF process include initialization method, rank of factorization, sparseness measure and maximum iteration. …”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters by Abdul Syukor, Mohamad Jaya, Mu*ath Ibrahim, Mohammad Jarrah, Mohd Asyadi Azam, Mohd Abid, Mohd Razali, Muhamad

“…Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Transformer T-joint optimization using particle swarm optimization and hemisphere-shape design of the core by Yehya, Omar Sharaf Al-Deen

“…The losses in transformers can be significantly reduced, especially in the core by improving the performance of the joint design. Several factors and parameters contribute to core losses such as shape of joint, gaps in between the joint parts, thickness of laminations, overlapping, orientation and number of laminations per stack. …”
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