Search Results - (( developing nonlinear tree algorithm ) OR ( java implementation learning algorithm ))

Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

“…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate by Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow by Khan N., Kamaruddin M.A., Ullah Sheikh U., Zawawi M.H., Yusup Y., Bakht M.P., Mohamed Noor N.

“…Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. …”

Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

“…Illustrations and algorithms are incorporated into the procedures. Non-normal and nonlinear data variables are addressed, hence data characterization is presented. …”
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Development of prediction model for phosphate in reservoir water system based machine learning algorithms by Latif S.D., Birima A.H., Ahmed A.N., Hatem D.M., Al-Ansari N., Fai C.M., El-Shafie A.

Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination by Nallakukkala, Sirisha, Tackie-Otoo, Bennet Nii, Aliyu, Ruwaida, Lal, Bhajan, Nallakukkala, Jagadish Ram Deepak, Devi, Gayathri

“…In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. …”
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Talkout : Protecting mental health application with a lightweight message encryption by Gavin Teo Juen

“…The investigation of lightweight message encryption algorithms is conducted with systematic quantitative literature and experiment implementation in Java and Android running environment. …”
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Do CEO and chairman characteristics affect green innovation? evidence from a comparative analysis of machine learning models by Xue, Ruixiang, Ong, Tze San, Demir, Ezgi

“…Using the extreme gradient boosting (XGBoost) algorithm, which is at the forefront of machine learning algorithms, this study comprehensively examines the impact of CEO and chairman characteristics on corporate green innovation. …”
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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

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Artificial Neural Network‑Based Prediction of Nipa Sugar Production in Sarawak, Malaysia by Muzamil, Ayoub, Ana Sakura, Zainal Abidin, Kasumawati, Lias, Imtiyaz Akbar, Najar, Rasli, Muslimen

“…The FFANN model incorporates key environmental variables, including temperature, humidity, wind speed, atmospheric pressure and sap yield, and is trained using the resilient backpropagation (RPROP+) algorithm. The model’s performance was compared to classical time series models (ARIMA and seasonal naïve) and a decision tree regression model. …”
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Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods by Al-Sabaeei, Abdulnaser M., Alhussian, Hitham, Abdulkadir, Said Jadid, Giustozzi, Filippo, Mohd Jakarni, Fauzan, Md Yusoff, Nur Izzi

“…Evaluation of the XGB regression and DTR performance shows that the developed ML models outperform the RSM in predicting the MSCR rutting parameters.…”
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A malware analysis and detection system for mobile devices / Ali Feizollah by Ali, Feizollah

“…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…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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An application of predicting student performance using kernel k-means and smooth support vector machine by Sajadin, Sembiring

“…In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. …”
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Optimising acoustic features for source mobile device identification using spectral analysis techniques / Mehdi Jahanirad by Mehdi , Jahanirad

“…The proposed feature sets along with selected feature extraction methods from the literature are analyzed and compared by using supervised learning techniques (i.e. support vector machines, nearest-neighbor, naïve Bayesian, neural network, logistic regression, and ensemble trees classifier), as well as unsupervised learning techniques (i.e. probabilistic-based and nearest-neighbor-based algorithms). …”
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