Search Results - (( based evaluation based algorithm ) OR ( variable estimation using algorithm ))

Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique by Hardev Singh, Jitvinder Dev Singh

“…In the video and frame selection, pre-defined video which have different type of motion and size is used for the algorithm evaluation purpose. The threshold calculation is based on the video selected. …”
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Electricity demand forecasting in Turkey and Indonesia using linear and nonlinear models based on real-value genetic algorithm and extended Nelder-Mead local search by Wahab, Musa

“…These problems resulted in estimation errors and high computational cost. Hybrid genetic algorithm (GA) and Nelder-Mead local search mode 1 has been used to minimize demand estimation errors. …”
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An adaptive routing algorithm: enhanced confidence-based Q routing algorithm in network traffic by Yap, Soon Teck, Othman, Mohamed

“…Confidence-based Q (CQ) Routing Algorithm is an adaptive network routing algorithm. …”
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Neighbour-based on-demand routing algorithms for mobile ad hoc networks by Ejmaa, Ali Mohamed E.

“…All the three proposed algorithms are evaluated using discrete event simulation, in particular Network Simulator tool (NS2), and compared with the latest routing algorithm (NCPR ) and fundamental algorithm (AODV) using five performance metrics. …”
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Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic by Yap, Soon Teck

“…These two adaptive routing algorithms enhance the existing Confidence-based Q (CQ) and Confidence-based Dual Reinforcement Q (CDRQ) Routing Algorithms. …”
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Model selection approaches of water quality index data by Kamarudin, Nur Azulia, Ismail, Suzilah

“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm by Nur Azulia, Kamarudin

“…However, SURE-Autometrics has not been estimated using maximum likelihood estimation (MLE). …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Moreover, the third test used ten natural domain datasets obtained from UCI Repository, in about fifteen different experiments, using three to four different Machine Learning Algorithms for performance evaluation. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

“…The hybrid model is a novel approach for estimating sediment load based on various input variables. …”

Bayesian logistic regression model on risk factors of type 2 diabetes mellitus by Chiaka, Emenyonu Sandra

“…The significant variables determined by maximum likelihood method were then estimated using the BLR method. …”
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Hybrid histogram and neural based call admission control for VBR video traffic. by Khalil, Ibrahim, Mohd Ali, Borhanuddin

“…In this paper, we have proposed a hybrid Neural Network (NN) approach to estimate cell loss rate of Variable Bit Rate (VBR) Video traffic for Call Admission Control (CAC) purpose in ATM environment Existing CAC algorithms, which are mostly based on on-off model, do not appear to apply well to VBR video traffic. …”
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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei by Alireza , Pourdaryaei

“…The proposed feature selection technique comprises of Multi-objective Binary-valued Backtracking Search Algorithm (MOBBSA). It is used to search within a number of input variables combinations and to select the feature subsets, which minimizes simultaneously vice-versa the estimation error and the feature numbers. …”
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A review on particle swarm optimization algorithm and its variants to human motion tracking by Saini, S., Rambli, D.R.B.A., Zakaria, M.N.B., Sulaiman, S.B.

“…Particle swarm optimization (PSO) is a population-based globalized search algorithm which has been successfully applied to address human motion tracking problem and produced better results in high-dimensional search space.This paper presents a systematic literature survey on the PSO algorithm and its variants to human motion tracking. …”
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Power System State Estimation In Large-Scale Networks by NURSYARIZAL MOHD NOR, NURSYARIZAL

“…The gain and the Jacobian matrices associated with the basic algorithm require large storage and have to be evaluated at every iteration, resulting in more computation time. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

“…This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. …”

Slight-Delay Shaped Variable Bit Rate (SD-SVBR) Technique for Video Transmission by Ahmad Suki, Che Mohamed Arif

“…It is designed based on the Shaped VBR (SVBR) algorithm and was implemented in the Network Simulator 2 (ns-2). …”
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Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application by Hossain Lipu M.S., Hannan M.A., Hussain A.

Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms by Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.

Battery remaining useful life estimation based on particle swarm optimization-neural network by Zuriani, Mustaffa, Mohd Herwan, Sulaiman

“…The dataset employed for this investigation comprises eight input parameters and one output variable, representing the battery RUL. In conducting an analysis, the performance of the PSO NN model is compared with hybrid NN with Cultural Algorithm (CA-NN) and Harmony Search Algorithm (HSA-NN), as well as the standalone Autoregressive Integrated Moving Average (ARIMA). …”
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Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin K., Ul-Saufie A.Z., Senin S.F., Zainudin Z., Rashid A.M., Abu Bakar N.F., Anas Abd Wahid M.Z., Azha S.F., Abd-Wahab F., Wang L., Sahar F.N., Osman M.S.

“…The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). …”