Search Results - (( data normalization based algorithm ) OR ( parameter validation study algorithm ))

Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

“…Thus, this study proposes the development of algorithms to detect and classify jamming attacks using a set of parameters on the physical layer and MAC in MANET. …”
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Confidence intervals (CI) for concentration parameter in von Mises distribution and analysis of missing values for circular data / Siti Fatimah binti Hassan by Hassan, Siti Fatimah

“…In this study, three imputation methods are considered namely expectation-maximization (EM) algorithm and data augmentation (DA) algorithm. …”
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Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia by Jumin E., Basaruddin F.B., Yusoff Y.B.M., Latif S.D., Ahmed A.N.

Statistical performance of agglomerative hierarchical clustering technique via pairing of correlation-based distances and linkage methods by Nurshaziana, Mohamad Shamsuri

“…To validate the clustering model on real data, the Spearman-average algorithm was applied to cluster Juru river basin data based on five water quality parameters. …”
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A new hybrid multiaxial fatigue life model based on critical plane continuum damage mechanics and genetic algorithm by Masood, Kamal

“…Material parameter including stress sensitivity factor for normal or shear stress can be incorporated to improve the calibration process. …”
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Spectral discrimination and index development of roofing materials and conditions using field spectroscopy and worldview-3 satellite image by Samsudin, Sarah Hanim

“…Spectral index of NDCCI and NDMCI found to be effective in providing roof degradation status map in effective time-manner and parameter-free algorithm compared to normal supervised classification scheme. …”
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Estimating Crack Effects on Electrical Characteristics of PV Modules Based on Monitoring Data and I-V Curves by Feng L., Zhang J., Kiong T.S., Ding K., Amin N., Hamelmann F.U.

“…Meanwhile, an innovative parameter optimization algorithm based on particle swarm optimization is developed to extract the parameters. …”

Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

“…Non-normal and nonlinear data variables are addressed, hence data characterization is presented. …”
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Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques by Googhari, Shahram Karimi

“…Cross validation of training and validation data sets was also considered to obtain the best data set. …”
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Optimization of solenoid driver and controller for gaseous fuel high-pressure direct injector using model-based approach by Mohamad Hafidzul Rahman, Alias

“…An optimization study was conducted using Normal Boundary Intersection (NBI) algorithm in MATLAB Model-Based Calibration (MBC) Toolbox to produce an optimal injector setup. …”
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Rockfall hazard assessment based on airborne laser scanning data and GIS in tropical region by Fanos, Ali Mutar

“…Multi rockfall scenarios were produced based on a range of mechanical parameters values. …”
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Comparison between specifications of linear regression and spatial-temporal autoregressive models in mass appraisal valuation for single storey residential property by Jahanshiri, Ebrahim

“…A complete data analysis was carried out on the datasets. Furthermore, various spatial, temporal and spatio-temporal neighbourhood and weighting schemes, optimization algorithms and lag and error modelling scenarios were created and tested with the data. …”
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Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes by Hossen, Md. Arif, Hasan, Md. Munirul, Ahmed, Yunus, Azrina, Abd Aziz, Nurashikin, Yaacof, Leong, Kah Hon

“…The optimization of the input parameters for CO2 photoconversion was comprehensively validated using the predicted and experimental data. …”
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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

“…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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Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…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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Development Of Distributed Grid-Based Hydrological Model And Floodplain Inundation Management System by Al_Fugara, A’kif Mohammed Salem

“…The simulation algorithms of the rainfall-runoff model have operated on grid bases compatible with the MATLAB programming language, which has been used to write instructions to many grid-based operations. …”
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Classification of Cardiac Disorders Based on Electrocardiogram Data with Fuzzy Cognitive Map (FCM) Algorithm Approach by Sumiati, ., Hoga, Saragih, T.K.A, Rahman, Viktor Vekky, Ronald Repi, Agung, Triayudi

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The effect of dose calculation algorithms on the normal tissue complication probability values of thoracic cancer by Ahmad, Noor Ashikin

“…Purpose: To identify the effect of dose calculation algorithms on the Normal Tissue Complication Probability values of thoracic cancer. …”
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A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…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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Data normalization techniques in swarm-based forecasting models for energy commodity spot price by Yusof, Yuhanis, Mustaffa, Zuriani, Kamaruddin, Siti Sakira

“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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