Search Results - (( java data classification algorithm ) OR ( using learner detection algorithm ))

Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

“…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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Classification System for Heart Disease Using Bayesian Classifier by Magendram, Anusha

“…This system was developing base on to three main part which is data processing, testing and implementation of the algorithm. …”
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A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection by Basheer G.S., Ahmad M.S., Tang A.Y.C.

“…We discuss both agent and multi-agent systems and focus on the implications of the theory of detecting learning styles that constitutes behaviors of learners when using online learning systems, learner's profile, and the structure of multi-agent learning systems. …”

An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…Major problems in classification task are large amount of training data, large number of features and different behavior of data streams that reduce accuracy and increase computational cost in classifier training phase. …”
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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

“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites by Alsariera, Y.A., Adeyemo, V.E., Balogun, A.O., Alazzawi, A.K.

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A web-based implementation of k-means algorithms by Lee, Quan

“…Firstly, k-luster could incorporate additional clustering algorithms, or even classification algorithms in the future. …”
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An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection by Shing, Chiang Tan, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Lim, Pey, Yun Goh, Chee, Peng Lim

“…In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. …”
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Face classification for three major ethnic of Orang Asli using Back Propagation Neural Network / Nor Intan Shafini Nasaruddin by Nasaruddin, Nor Intan Shafini

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Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…The results of the experiments show that the proposed combined technique has remarkable benefits over the detection using only supervised or unsupervised learners.…”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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Development of an automated tool for detecting errors in tenses by Tengku Nor Rizan Tengku Mohd Maasum, Siti Hamin Stapa, Nazlia Omar, Mohd Juzaiddin Ab Aziz, Saadiyah Darus

“…This proves that ATDEiT™ has the potential to be used as an automated tool for detecting errors in tenses for ESL learners.…”
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

“…In this method, permission-based features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. Using a number of candidate detectors from an improved Apriori Algorithm with Particle Swarm Optimization, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. …”
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool by Nurshafiqa Saffah, Mohd Sharif

“…From the data analysis using WEKA software, the production rules classifier (PART) is found to be the most accurate classification algorithm in classifying the emotion which yields the highest precision percentage of 99.6% compared to J48 (99.5%) and Naïve Bayes (96.2%). …”
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Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

“…Machine Learning is one of the ways as a preventive method by applying the data collected from the clogging experiment in the vibration lab to build up a machine learning model for classification of flow blockage levels in the centrifugal pump. …”
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Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…Future work should be focusing on data collection of the E-Nose sensors and the improvement of the learning algorithm robustness towards environmental noise during data acquisition, such as evaporation and contamination of odor samples.…”
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A parallel ensemble learning model for fault detection and diagnosis of industrial machinery by Shing, Chiang Ta, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Sim, Seera, Manjeevan, Chee, Peng Lim

“…The base learners adopt a hybrid Back-Propagation (BP) and Particle Swarm Optimization (PSO) algorithms to exploit the corresponding local and global optimization capabilities for identifying optimal features and improving FDD performance. …”
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A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines by Tahan, M., Muhammad, M., Abdul Karim, Z.A.

“…Two back-propagation training algorithms, namely the Levenberg–Marquardt and Bayesian regularization algorithms, and the k-fold cross-validation technique, were employed to train the optimal networks using a training data set. …”
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EMOTION RECOGNITION USING GALVANIC SKIN RESPONSE (GSR) SIGNAL by RAMOS UKAR, YAKOBUS

“…The classified affective GSR signals with labels were obtained from the arousal seven-point emotional scale approach using machine learning algorithms. Features and class labels can import into the Classification Learner application in MATLAB software to train and test various classifiers. …”
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