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1
Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning
Published 2021“…SVM model with cubic kernel is preferable as the training time taken is relatively lower than Ensemble Bagged Tree due to the ensemble algorithms are more complex. …”
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Photogrammetric unmanned aerial vehicle for digital terrain model estimation under oil palm tree canopy area / Suzanah Abdullah
Published 2021“…Following the application of a new methodology on the real site, the result indicated the consistency of DTM values of all the algorithms at different flying heights but there were relatively small differences between all the algorithms used. …”
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3
Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data
Published 2020“…Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). …”
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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…However, the developed algorithms only consider the selected features from a peak model based on the understanding of the EEG signals characteristics. …”
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5
Predictive Modelling of Stroke Occurrence among Patients using Machine Learning
Published 2023“…Advanced machine learning algorithms, including logistic regression, decision trees, random forests, and support vector machines, were utilized to analyses the dataset and develop a predictive model. …”
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Optimizing tree planting areas through integer programming and improved genetic algorithm
Published 2012“…Therefore, a hybrid algorithm through an incorporation of Integer Programming and Improved Genetic Algorithm was proposed for planting lining design. …”
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7
Reverse migration prediction model based on machine learning / Azreen Anuar
Published 2024“…And the third objective is to evaluate reverse migration prediction model based on machine learning analysis. For this purpose, three (3) algorithms have been assessed, namely, the Random Forest, Decision Tree, and Gradient Boosted Tree. …”
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8
Vibration-based structural damage detection and system identification using wavelet multiresolution analysis / Seyed Alireza Ravanfar
Published 2017“…Meanwhile, there has also been a pressing need for real-time monitoring to avoid sudden catastrophic disasters. Therefore, a new reference-free damage detection algorithm is proposed. …”
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Fraud detection in shipping industry based on location using machine learning comparison techniques
Published 2023“…A number of popular existing algorithms were used to execute the model developed in Rapid tool such as Naïve Bayes , Neural Net , Deep Learning, Decision Tree, Logistic Regression, SVM and k-NN. …”
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Recommendation System Model For Decision Making in the E-Commerce Application
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Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data
Published 2017“…Tree-centric modelling is appealing because it is based on summing the biomass of individual trees, but until algorithms can detect understory trees reliably and estimate biomass from crown dimensions precisely, areas-based modelling will remain the method of choice.…”
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An Illustration of Generalised ARMA (GARMA) Time Series Modelling of Forest Area in Malaysia.
Published 2012“…The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation. …”
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A hybrid deep CNN model for fast class-incremental food classification / Aymen Taher Ahmed al-Ashwal
Published 2019“…Moreover, adding new classes or new food images features has no significant consequence on the model knowledge. …”
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Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar
Published 2021“…To overcome these limitations, this research has proposed five machine learning algorithms namely Linear Regression, Lasso, Ridge, Random Forest and Decision Tree. …”
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15
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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16
Advanced data mining techniques for landslide susceptibility mapping
Published 2021“…The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. …”
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Power line corridor vegetation encroachment detection from satellite images using retinanet and support vector machine
Published 2023“…In this dissertation, a new vegetation encroachment detection method was proposed by studying the feasibility of using the visible-light band of highresolution satellite images using the RetinaNet deep learning model and Support Vector Machine algorithm (SVM). …”
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A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection
Published 2022“…The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab–knowledge discovery and data mining) dataset. …”
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Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques
Published 2021“…Finally, the Buildings Footprint developed based on the algorithm was compared with the Buildings Footprint developed manually to assess the accuracy of the results. …”
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A study of graduate on time (GOT) for Ph.D students using decision tree model
Published 2019“…Among all models, decision tree model with Entropy algorithm perform the best by scoring the highest accuracy rate (72%) and sensitivity rate (95%). …”
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