Search Results - (( adoption two different estimation algorithm ) OR ( adoption _ differences evolution algorithm ))

Channel quality indicator for long term evolution system based on adaptive threshold feedback compression scheme by Abdulhasan, Muntadher Qasim

“…The percentage difference is 2.1%,compared with the full feedback method, with only 0.5% degradation. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…Two parameters (population size and generation numbers) are adaptively adopted from number of remaining ranking features. …”
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Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review by Diandra Chika Fransisca, Sukono, Diah Chaerani, Nurfadhlina Abdul Halim

Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Md. Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

“…The Savitzky-Golay (SG) filter was adopted for smoothing and denoising the EEG dataset. …”
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Estimating Face Emotion using Genetic Algorithm by Karthigayan, M., Mohd Rizon, Muhamed Juhari, Sazali, Yaacob, Nagarayan, R.

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End-to-end DVB-S2X system design with deep learning-based channel estimation over satellite fading channels by Mfarej, Sumaya Dhari Awad

“…The Normalized Mean Square Error (NMSE) and the BER perfor�mances for different DVB-S2X system MODCODs are investigated and the results for these algorithms are compared with the conventional Minimum Mean Square Er�ror (MMSE) and Least Square (LS) channel estimation techniques. …”
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Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem by Anniza, Hamdan, San Nah, Sze, Say Leng, Goh, Kang Leng, Chiew, Wei King, Tiong

“…Research works published from the year 2018 to 2022 are identified in terms of problem representation and evolutionary strategies adopted. The mechanisms and strategies used in evolutionary algorithms to address different types of combinatorial optimization problems are discovered. …”
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A comparative study for parameter selection in online auctions by Gan, Kim Soon

“…In this work, three different models of genetic algorithms are considered. …”
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Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

“…Finally, the predictive capability of valid NLHRS models has also been tested and adjusted for different strata of the population. Firstly, the study concludes that the adoptions of the flexible approach in estimation can model the binary feature of CVDs and non-linear paths in the complex path models. …”
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Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means by Azam, M.H., Hasan, M.H., Hassan, S., Abdulkadir, S.J.

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A bit-serial sum of absolute difference accelerator for variable block size motion estimation of H.264 by Fatemi, M.R.H., Ates, H.F., Salleh, R.

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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems by Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir

“…MRFO is a recently new introduced algorithm that consists of strategy of foraging adopted by Manta Ray while Gradient-based Mutation (GbM) is a feasibility-and solution repair strategy adopted from ϵ-Matrix-Adaptation Evolution Strategy (ϵ-MAES). …”
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Assist-as-needed control strategy for upper-limb rehabilitation based on subject’s functional ability by Ahmed Mounis, Shawgi Younis, Zainul Azlan, Norsinnira, Sado, Fatai

“…Moreover, there is also the need for consistency and repeatability of the functional ability estimation in line with the clinical procedure to facilitate a wider clinical adoption. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Environmental factors and spatial heterogeneity affect occupancy estimates of waterbirds in Peninsular Malaysia by Chukwuemeka, Onwuka Martins, Oluwatobi, Emmanuel Olaniyi, Hussin, Mohamed Zakaria

“…An automatic linear modelling algorithm and geographic information systems were employed to compute the importance ratios of 17 eco-climatic factors (hydrology 9; climate 5; waterscape 1 and landscape 2). …”
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Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

“…A data transformation approach is introduced, based on the different data characteristics using the maximum coefficient of determination (R2) and maximum p-value approaches. …”
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Monitoring and assessment of weld penetration condition during pulse mode laser welding using air-borne acoustic signal by Mohd Fadhlan, Mohd Yusof

“…To develop the signal processing algorithm, multi-lag phase space (MLPS) method was adopted in which some modifications on its original algorithm were made by introducing the localized crest factor (CF) thresholding method to reduce the influence of noise. …”
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DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS by AYOUB MOHAMMED, MOHAMMED ABDALLA

“…It was found that (by the Group Method of Data Handling algorithm), length of the pipe, wellhead pressure, and angle of inclination have a pronounced effect on the pressure drop estimation under these conditions. …”
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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network by Zanial W.N.C.W., Malek M.B.A., Reba M.N.M., Zaini N., Ahmed A.N., Sherif M., Elshafie A.

“…Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. …”