Search Results - (( data normalization based algorithm ) OR ( parameter evaluation study algorithm ))
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1
An alternative approach to normal parameter reduction algorithms for decision making using a soft set theory / Sani Danjuma
Published 2017“…This study contributes significantly in reducing the computational complexity and running time as compared with Normal Parameter Reduction algorithm (NPR) and New Efficient Normal Parameter Reduction algorithm (NENPR).…”
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2
Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. …”
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3
Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan
Published 2015“…In this study, three imputation methods are considered namely expectation-maximization (EM) algorithm and data augmentation (DA) algorithm. …”
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4
Dynamic robust bootstrap method based on LTS estimators
Published 2009“…The performance of the DRBLTS is evaluated by real data sets and simulation study. The numerical examples indicate that the DRBLTS is more efficient than the other methods.…”
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5
Performance evaluation and enhancement of EDCA protocol to improve the voice capacity in wireless network
Published 2017“…Through the proposed algorithm, the Minimum Contention Window (CWmin) and Arbitration Inter Frame Space (AIFS) parameters were adapted based on the percentage of the collision in the network. …”
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6
Handgrip strength evaluation using neuro fuzzy approach
Published 2010“…The expert rules define the membership function for the fuzzy system. The fuzzy model based on the membership function, fed in by the neural network will intelligently classify the data. …”
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7
Peak pressure analysis of foot plantar distribution based on image processing algorithm
Published 2018“…The proposed image processing that based on the related parameters. Thus, the image information of the pressure sensor can solve the balancing problem for those who have a problem during standing and walking.…”
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8
Investigating the reliability of machine learning algorithms as an advanced tool for ozone concentration prediction
Published 2023“…This Ozone (O3) forecast study was carried out by using diversity air pollutants data, namely Sulfur dioxide (SO2), carbon monoxide (CO), nitrogen oxides (NOx), nitrogen dioxide (NO2), and meteorological parameters namely humidity (Hum), wind speed (WS). …”
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Interpolation and Extrapolation Techniques Based Neural Network in Estimating the Missing Ionospheric TEC Data
Published 2024Proceedings Paper -
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Modelling of intelligent intrusion detection system: making a case for snort
Published 2018“…This MLFNN with BP algorithm was simulated using MATLAB software. The performance of this classifier was evaluated based on three parameters: accuracy, sensitivity, and False Positive Rate (FPR). …”
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11
Interpolation and extrapolation techniques based Neural Network in estimating the missing ionospheric TEC data
Published 2024“…Normalized RMSE, RMSE and relative correction are computed for both methods to evaluate the capability of NN to interpolate and extrapolate the missing data. …”
Conference Paper -
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Designing of prediction model for parameter optimization in cnc machining based on artificial neural network / Armansyah ... [et al.]
Published 2025“…These outputs were then utilized to train an ANN prediction model based on a feed-forward backpropagation (FFBP) algorithm. …”
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13
Artificial Neural Network: The Alternative Method to Obtain the Dimension of Ankle Bone Parameters
Published 2017“…In the present study, we propose an alternative method of ankle morphometric measurement using neural network computational model based solely on existing data measurements and demographic information. …”
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14
IoT-based Machine Learning Comparative Models of Stream Water Parameters Forecasting for Freshwater Lobster
Published 2024“…Previously, ARIMA, Neural Network Autoregressive (NNETAR), and Naïve Bayes, were run and evaluated in R Studio to identify the best algorithm for stream analytics. …”
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15
Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.
Published 2022“…The proposed method aimed to obtain a maximally informative mathematical model that can describe the actual dynamic behaviors of a system, using the DC motor as a case study. The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. …”
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16
Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines
Published 2020“…The model limitation is in retraining for new knowledge with changes in the power system network topology and lacks robustness. This current study proposes an intelligent data mining approach for the Machine Learning- Adaptive Distance Relay (ML-ADR) fault classification model using novel extracted 1-cycle transient voltage and current signals hidden knowledge from both healthy and faulty lines parameters. …”
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17
Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes
Published 2025“…Six popular ML algorithms of regression, kernel and neural network-based models were applied to predict the gas-phase CO2 photoconversion rate. …”
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An expert integrative approach for sediment load simulation in a tropical watershed.
Published 2013“…Prediction of highly non-linear behaviour of suspended sediment flow in rivers is of prime importance in environmental studies and watershed management. In this study, the predictive performance of artificial neural network (ANN) integrated with genetic algorithm (GA) was assessed. …”
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19
Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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20
Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour
Published 2010“…In the case of 3D study of pole - dipole data, the gradient descent with momentum and an adaptive learning rate algorithm is found to be the most efficient paradigm. …”
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