Search Results - (( model validation bayes algorithm ) OR ( text classification using algorithm ))*

Intent-IQ: customer’s reviews intent recognition using random forest algorithm by Mazlan, Nur Farahnisrin, Ibrahim Teo, Noor Hasimah

“…Data annotation using semi-supervised learning which is self-training technique need to be accomplished as the dataset is unlabeled. Two machine learning model is chosen to build the classification models which are Random Forest (RF) algorithm and Multinomial Naïve Bayes (MNB) algorithm. …”
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Analyzing customer reviews for ARBA Travel using sentiment analysis by Abdullah, Nurulain

“…Three machine learning algorithms which are Naive Bayes, Logistic Regression, and Support Vector Machine, were implemented and evaluated using cross-validation and performance metrics such as accuracy, precision, recall, and F1- score. …”
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Sentiment analysis of customer review for Tina Arena Beauty by Amri, Nur Najwa Shahirah

“…Three machine learning algorithms Naive Bayes, Random Forest, and Support Vector Machine (SVM) were evaluated, and SVM achieved the highest accuracy and was selected as the final classifier. …”
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Predicting students’ STEM academic performance in Malaysian secondary schools using educational data mining by Termedi @ Termiji, Mohammad Izzuan

“…It proceeds through three phases of Need Analysis, Development of the Model and Evaluation of the Model. Four different data mining classification algorithms which are Random Forest, PART, J48 and Naive Bayes will be used on the dataset. …”
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Predicting hearing loss symptoms from Audiometry data using FP-Growth Algorithm and Bayesian Classifier by G. Noma, Nasir, Mohd Khanapi, Abd Ghani, Mohamad Khir , Abdullah, Noorizan , Yahya

“…Both multivariate Bernoulli and multinomial naïve Bayes models were used with and without the feature extraction. …”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

“…Meanwhile, the MLP was found to be the ideal model with a BCR value of 0.964, AUC and PRC having the same value of 1.000, model accuracy of 96.43%, and a Kappa coefficient of 0.95. …”
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Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour by Pebrianti, Dwi, Ariawan, Angga, Bayuaji, Luhur, Mahdiana, Deni, ,, Rusdah

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Advanced data mining techniques for landslide susceptibility mapping by Ibrahim, M.B., Mustaffa, Z., Balogun, A.-L., Hamonangan Harahap, I.S., Ali Khan, M.

“…The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. …”
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MODELLING ANALYSIS FOR ACCURATE TROPICAL WEATHER FORECASTING by Calvin, Wong Qin Jie

“…The Random Forest, K-Nearest Neighbors, Support Vector Machines, XGBoost and Naïve Bayes algorithm is proposed to validate the model for rainfall prediction, which is proven to operate well with excellent accuracy in previous researches. …”
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Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat by Fatima, Jannat

“…Hyper-parameter tuning has been used in all the algorithms using k-fold cross validation to have the best accuracy and to avoid the over-fitting issue. …”
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Sentiment Analysis of Sexual Harassment in Malaysia on Twitter Using Machine Learning Algorithms by Nurellezia, Suleiman

“…The transformed data is then modelled using machine learning algorithms such as Naïve Bayes classifier and Support Vector Machine to predict the overall sentiment of tweets, in which the finding depicted an overall positive sentiment surrounding the issue. …”
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A comparative analysis of machine learning algorithms for diabetes prediction by Alansari, Waseem Abdulmahdi, Masnizah Mohd

“…The methodology involves data collection, pre-processing, and training the algorithms using k-fold cross-validation. The results indicate that pre-processing steps and dataset characteristics significantly impact algorithm performance. …”
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Detection of DDoS attacks in IoT networks using machine learning algorithms by Abdulrahman Alwan, Alwan Ahmed, Shah, Asadullah, Abdulrahman Alwan, Alwan Abdullah, Laghari, Shams Ul Arfeen

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Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir by Amir, Nur Hazirah

“…It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. …”
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GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms by Tella, A., Balogun, A.-L.

“…Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70 of the dataset, while 30 was used for cross-validation. …”
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Prediction of novel doping agent through the integration of chemical and biological data using in silico method by Mohd Rosman, Nurul Ain

“…Two validations were performed on the models which are internal and external validation. …”
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In silico prediction of novel doping agent through the integration of chemical and phenotypic data by Raduan, Muhammad Artif

“…The internal validation showed that the combination of MACCS and Naive Bayes model performed best with a sensitivity value of 0.5950 when a cut off of rank 5 was applied. …”
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Enhanced ontology-based text classification algorithm for structurally organized documents by Oleiwi, Suha Sahib

“…This research combines the ontology and text representation for classification by developing five algorithms. …”
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Analyzing Algorithms to Detect Disaster Events using Social Media by Azlan F.A., Ahmad A., Yussof S., Ghapar A.A.

Text Extraction Algorithm for Web Text Classification by Theab, Mustafa Muwafak

“…This study provides a text extraction algorithm for web text classification. …”
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