Search Results - (( model evaluation case algorithm ) OR ( _ identification learning algorithm ))*

Opposition- based simulated kalman filters and their application in system identification by Kamil Zakwan, Mohd Azmi

“…The overall performance is evaluated based on six case studies. According to the experimental results, COOBSKF provides average of maximum model validation up to 90%. …”
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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan by Meisam , Gordan

“…After evaluating the results of these algorithms, a hybrid Artificial Neural Network-based Imperial Competitive Algorithm (ANN-ICA) was presented in the deployment step of the proposed methodology to identify the structural damage of illustrative structures. …”
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A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh by Kuldeep Kaur , Ragbir Singh

“…To evaluate the efficacy of the models, publicly available financial and credit card data sets are evaluated. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models. by Kwad, Ayad Mahmood

“…Another method is system identification which is also called experimental modeling. …”
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Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus by Darus, Zamzuhairi

“…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. …”
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Zero distortion-based steganography for handwritten signature by Iranmanesh, Vahab

“…Thus, developing a steganographic algorithm to use cover media (c) without raising attention is the most challenging task in data hiding. …”
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Real time electrocardiogram identification with multi-modal machine learning algorithms by Waili, Tuerxun, Mohd Nor, Rizal, Sidek, Khairul Azami, Abdul Rahman, Abdul Wahab, Guven, Ghokan

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Adaptive Linear System Identification over Simulated Wireless Environment by Elamin, Musab Jabralla Omer Elamin

“…The work looks thoroughly on three forms of instantaneous learning algorithms which are: first order algorithms (e.g. least mean square (LMS)), second order algorithms (e.g. …”
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Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics by Mohammad Jarrah, Mu'ath Ibrahim

“…Chemical Vapor Deposition (CVD) is the most efficient method for CNTs production.However,using CVD method encounters crucial issues such as customization,time and cost.Therefore,Response Surface Methodology (RSM) is proposed for modeling and the ABC-βHC is proposed for optimization purpose to address such issues.The selected CNTs characteristics are CNTs yield and quality represented by the ratio of the relative intensity of the D and G-bands (ID/IG).Six case studies are generated from collected dataset including four cases of CNTs yield and one case of ID/IG as single objective optimization problems,while the sixth case represents multi-objective problem.The input parameters of each case are a subset from the set of input parameters including reaction temperature,duration,carbon dioxide flow rate,methane partial pressure,catalyst loading,polymer weight and catalyst weight.The models for the first three case studies were mentioned in the original work.RSM is proposed to develop polynomial models for the output responses in the other three cases and to identi significant process parameters and interactions that could affect the CNTs output responses.The developed models are validated using t-test,correlation and pattern matching.The predictive results have a good agreement with the actual experimental data.The models are used as objective functions in optimization techniques.For multi-objective optimization,this study proposes Desirability Function Approach (DFA) to be integrated with other proposed algorithms to form hybrid techniques namely RSM-DFA,ABC-DFA and ABC-βHC-DFA.The proposed algorithms and other selected well-known algorithms are evaluated and compared on their CNTs yield and quality.The optimization results reveal that ABC-βHC and ABC-βHC-DFA obtained significant results in terms of success rate,required time,iterations,and function evaluations number compared to other well-known algorithms.Significantly,the optimization results from this study are better than the results from the original work of the collected dataset.…”
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A Comparative of Two-Dimensional Statistical Moment Invariants Features in Formulating an Automated Probabilistic Machine Learning Identification Algorithm for Forensic Application by Zun Liang, Chuan, David, Chong Teak Wei, Connie, Lee Wai Yan, Muhammad Fuad Ahmad, Nasser, Nor Azura Md, Ghani, Abdul Aziz, Jemain, Choong-Yeun, Liong

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RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION by CATUR ANDRYANI, NUR AFNY

“…This thesis proposes derivative free learning, using finite difference, methods for fixed size RBF network in comparison to gradient based learning for the application of system identification. …”
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Date store management in sliding window based on-line identification algorithms by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George W

“…Sliding window identification algorithms use information from a store of data to optimise on-line learning. …”
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Wavelet network based online sequential extreme learning machine for dynamic system modeling by Mohammed Salih, Dhiadeen, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Raja Ahmad, Raja Mohd Kamil

“…The proposed model used as system identification for nonlinear dynamic systems. The main advantage of OSELM over conventional algorithms is the ability of updating network weights sequentially through data sample-by-sample in a single learning step. …”
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Identification model for hearing loss symptoms using machine learning techniques by Nasiru Garba Noma

“…The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
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Privacy Preserving Features Selection for Data Mining using Machine Learning Algorithms by Anuar N.K., Bakar A.A., Ahmad A.R., Yussof S., Rahim F.A., Ramli R., Ismail R.

Identification of non-linear dynamic systems using fuzzy system with constrained membership functions by Yaakob, Mohd. Shafiek

“…In this study, the identification performance of the SFS trained by the back-propagation (BP) algorithm forms the basis of comparison when evaluations were made on the performance of the newly proposed CFS models. …”
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A multi-objective genetic type-2 fuzzy extreme learning system for the identification of nonlinear dynamic systems by Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.

“…However, other than RMSE, the maximum absolute error (MAE) for each of identification samples is very important. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. …”
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Unifying the evaluation criteria of many objectives optimization using fuzzy Delphi method by Mohammed, Rawia Tahrir, Yaakob, Razali, Mohd Sharef, Nurfadhlina, Abdullah, Rusli

“…Lastly, the most suitable criteria outcomes are formulated in the unifying model and evaluate by experts to verify the appropriateness and suitability of the model in assessing the MaOO algorithms fairly and effectively.…”
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Deep learning in public health: Comparative predictive models for COVID-19 case forecasting by Muhammad Usman Tariq, Muhammad Usman Tariq, Ismail, Shuhaida

“…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. …”
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