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Extended spatial decision tree algorithm for classifying hotspot occurrence
Published 2013“…This work proposes a new spatial decision tree algorithm namely the extended spatial ID3 decision tree algorithm to classify hotspots occurrence from a forest fires dataset that contains point, line and polygon features. …”
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Cancer Prediction Based On Data Mining Using Decision Tree Algorithm
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Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu
Published 2014“…Multilayer Perceptron algorithm has been the best algorithm with the highest success percentage in both of the programs; Decision Trees has been the algorithm which has the lowest success percentage again in both of the programs. …”
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Waste management using machine learning and deep learning algorithms
Published 2020“…For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. …”
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An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease
Published 2019“…Coronary artery disease (CAD) is one of the deadly diseases in the world, especially in developed countries. This disease is not epidemic but it re-mains the single most common cause of death. …”
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Conference or Workshop Item -
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Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia
Published 2025“…It is crucial for developing countries such as Malaysia to be able to accurately predict future municipal solid waste generations in order to achieve high-quality waste management. …”
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Hybridization of SLIC and extra tree for object based image analysis in extracting shoreline from medium resolution satellite images
Published 2018“…Several techniques have been developed to monitor the conditions of land covers across the world, such as aerial photography, ground survey, and remote sensing. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
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Car dealership web application
Published 2022“…The transfer learning algorithm pre-trained the River adaptive random forest regressor and classifier by transferring the tree structures and weights from the Scikit-learn fitted random forest regressor and classifier, respectively. …”
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A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera
Published 2015“…The performance evaluation of a real time developed test-bed scenario proves the feasibilities of integrating the method for transmission line maintenance.…”
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Prediction of international rice production using long short- term memory and machine learning models
Published 2025“…A total of nine ML and one DL models are trained and tested on the international dataset, which contains the rice production details of 192 countries over the last 62 years. Notably, linear regression and the LSTM algorithm predict rice production with the highest percentage of R-squared (R 2), 98.40% and 98.19%, respectively. …”
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Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar
Published 2021“…These algorithms were developed by using prewar shophouses dataset from 2004 until 2018 based on factors of heritage properties. …”
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Classification of water quality using artificial neural network
Published 2020“…Then, the model performance was compared with the k-NN and Decision Tree models. The results obtained after training and testing the network showed that ANN performed better than to k-NN and Decision Tree model with the values of accuracy, sensitivity, precision and RMSE is 95.24%, 93.51%, 94.43% and 0.207 respectively. …”
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Predictive modelling of student academic performance using machine learning approaches : a case study in universiti islam pahang sultan ahmad shah
Published 2024“…Furthermore, it may cause a significant loss of human capital for the country. The main goal of the study was to develop the most accurate predictive model for predicting students’ performance levels using machine learning techniques such as multinomial logistic regression, decision trees, Random Forest, k-nearest neighbor, Naïve Bayes, and support vector machine. …”
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An improved diabetes risk prediction framework : An Indonesian case study
Published 2018“…Lack of diagnosis for diabetes often transpire in some ASEAN countries with relatively diminutive doctor to patient ratio.Essentially,it is believed that a systematic framework to predict diabetes risk factors is crucial for refining diagnostics and improving accuracy. …”
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