Search Results - (( data distribution ((some algorithm) OR (svm algorithm)) ) OR ( _ evaluation model algorithm ))
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1
Classification of imbalanced travel mode choice to work data using adjustable svm model
Published 2021“…This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling. …”
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2
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…However, by changing the distribution of both classes, the original classes distribution that are followed by that particular data will be violated. …”
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Thesis -
3
Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis
Published 2023Conference Paper -
4
Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
Published 2024“…The sensitivity analyses found the most effective models for predicting HPG for three scenarios using graphical distribution data (Taylor diagram). …”
Article -
5
A comparative analysis of LSTM, SVM, and GSTANN models for enhancing solar power prediction
Published 2024“…The main objective is identifying the most effective algorithm for precise solar power forecasting. The methodology involves training both models on historical solar power data and evaluating their performance against the Graph Spatial-Temporal Attention Neural Network (GSTANN) benchmark. …”
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Proceeding Paper -
6
Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration
Published 2022“…This was done through the Bayesian weight assignments to combine the favourable traits of the individual models. However, the BMA algorithm was found to be rigid as it was results-oriented and might opt to omit some base models if their performance were significantly poorer than the others. …”
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Final Year Project / Dissertation / Thesis -
7
The Performance of Chlorophyll-a Distribution Estimation by Using Ratio Algorithm on Landsat-8 in Sungai Merbok Estuary / Jesse Vince Rabing ... [et al.]
Published 2022“…This study explores the applicability of ratio algorithms for estimation of the chl- a concentration at Sungai Merbok by assessing chl-a distribution pattern built by the algorithms and evaluating each algorithm for their errors compared to in-situ data. …”
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8
A stylometry approach for blind linguistic steganalysis model against translation-based steganography
Published 2023“…However, accuracy of blind steganalysis algorithms highly depend on the features selected from the input data especially when attacking embedding techniques in TBS. …”
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9
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The overall accuracy of the Support Vector Machine SVM and Random Forest RF classifiers revealed that three of the six algorithms exhibited higher ranks in the landslide detection. …”
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10
Optimal network reconfiguration and intelligent service restoration prediction technique based on Cuckoo search spring algorithm / Mohamad Izwan Zainal
Published 2022“…The performance of the distribution network is very important, and it is characterized by some measurable item such as voltage profile and losses, to evaluate the actual value comply with the system needs. …”
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11
Parallel execution of distributed SVM using MPI (CoDLib)
Published 2023Subjects: “…Distributed SVM…”
Conference paper -
12
Random sampling method of large-scale graph data classification
Published 2024“…Finally, we classified the graphs of data blocks using the SVM algorithm. In experimental evaluation, our proposed method outperformed state-of-the-art graph kernels on graph classification datasets in terms of accuracy.…”
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13
Non-Parametric and Parametric Estimations of Cure Fraction Using Right-and Interval-Censored Data
Published 2011“…In this thesis, we considered two methods via the expectation maximization (EM) algorithm for cure rate estimation based on the BCH model using the two censoring types common to cancer clinical trials; namely, right and interval censoring. …”
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14
Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023“…This paper presents an intelligent system to reduce Non Technical Loss (NTL) using hybrid Support Vector Machine (SVM) and Genetic Algorithm (GA). The main motivation for this research is to assist Sabah Electricity Sdn. …”
Conference Paper -
15
Dynamic robust bootstrap method based on LTS estimators
Published 2009“…We call this method Dynamic Robust Bootstrap-LTS based (DRBLTS) because here we have employed the LTS estimator in the modified bootstrap algorithm. The performance of the DRBLTS is evaluated by real data sets and simulation study. …”
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16
Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique
Published 2013“…The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optim ization of the gam ma and large margin parameters are done using the PSO algorithm. …”
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Conference or Workshop Item -
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Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms
Published 2020“…However, RF extracted oil palm information better than the SVM. The algorithms were compared and the McNemar's test showed significant values for comparisons between SVM and CART and RF and CART. …”
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Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System
Published 2010“…Ontologies are used as a data model for service description, whereas the services are to accomplish the description process. …”
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19
Semantic-Based, Scalable, Decentralized and Dynamic Resource Discovery for Internet-Based Distributed System
Published 2010“…Ontologies are used as a data model for service description, whereas the services are to accomplish the description process. …”
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20
Logistic regression methods for classification of imbalanced data sets
Published 2012“…Classification of imbalanced data sets is one of the important researches in Data Mining community, since the data sets in many real-world problems mostly are imbalanced class distribution. …”
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