Search Results - (( leaf classification tree algorithm ) OR ( java adaptation optimization algorithm ))
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Performance analysis of machine learning algorithms for classification of infection severity levels on rubber leaves
Published 2023“…Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). …”
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Effective k-Means Clustering in Greedy Prepruned Tree-based Classification for Obstructive Sleep Apnea
Published 2022“…Incorporation of prepruned decision trees to kmeans clustering through one to three types of tree-depth controllers and cluster partitioning was done to develop a combined algorithm named as Greedy Pre-pruned Treebased Clustering (GPrTC) algorithm. …”
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Classification model for hotspot occurrences using a decision tree method
Published 2011“…This work demonstrates the application of a decision tree algorithm, namely the C4.5 algorithm, to develop a classification model from forest fire data in the Rokan Hilir district, Indonesia. …”
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Delineating mangrove forest zone using spectral reflectance
Published 2020“…The use of SID and SAM may provide the most promising classification algorithm for improving mangrove species mapping. …”
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Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
Published 2014“…The algorithms were tested to classify the leaf samples into four levels of disease severity. …”
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Parallel distributed genetic algorithm development based on microcontrollers framework
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Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques
Published 2022“…This study aims to predict the ratings of Google Play Store apps using decision trees for classification in machine learning algorithms. …”
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Assessment of near-infrared and mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm plantation
Published 2013“…The results indicated that LDA-based model resulted in high average overall classification accuracies of 92% (leaf samples) and 94% (trunk samples) when mid-infrared absorbance spectra were analyzed. …”
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FT-IR absorbance data for early detection of oil palm fungal disease infestation
Published 2012“…At present study, we propose to apply a mid-infrared spectroscopy technique for detection of infected oil palm trees at three stages of infection. Leaf samples of healthy, mild, moderate and sever-infected trees were measured using Fourier transform infrared (FTIR) spectrometers system to obtain absorbance data from the range of 2.55-25.05 μm. …”
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Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with Orange Data Mining
Published 2025“…The ReliefF feature selection technique was used to determine the most influential wavelengths for the classification of FLS disease severity in soybean. Next, machine learning (ML) methods (i.e. decision tree, gradient boosting, random forest, stacking, and neural network) were used to classify FLS of soybean. …”
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Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy
Published 2018“…Thus, this paper investigated the feasibility of utilizing electrical properties such as impedance, capacitance, dielectric constant, and dissipation factor in early detection of BSR disease in oil palm tree. Leaf samples from different oil palm trees (healthy, mild, moderate, and severely-infected) were collected and measured using a solid test fixture (16451B, Keysight Technologies, Japan) connected to an impedance analyzer (4294A, Agilent Technologies, Japan) at a frequency range of 100 kHz–30 MHz with 300 spectral interval. …”
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Hyperspectral spectroscopy and imbalance data approaches for classification of oil palm's macronutrients observed from frond 9 and 17
Published 2020“…This paper highlights the application of hyperspectral sensing in conjunction with imbalance approaches and machine learning (ML) algorithms to monitor the nutrients status of mature oil palm. …”
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Feature Selection and Ensemble Meta Classifier for Multiclass Imbalance Data Learning
Published 2018“…The aim of this paper is to investigate the effects of combining feature selection and ensemble classifiers on the prediction performance in addressing the multiclass imbalance data learning .This research uses data obtained from the Malaysian medicinal leaf images shape data and three other large benchmark data sets in which six ensemble methods from Weka machine learning tool were selected to perform the classification task.These ensemble methods include the AdaboostM1, Bagging, Decorate, END, MultiboostAB, and Rotation Forest.In addition, five base classifiers were used; Naïve Bayes, SMO, J48, Random Forest, and Random Tree in order to examine the performance of the ensemble methods. …”
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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions
Published 2017“…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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