Search Results - (( model validation bayes algorithm ) OR ( _ classification model algorithm ))

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

“…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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Data Mining Analysis Of Chronic Kidney Disease (CKD) Level by Mohd Harizi, Muhammad Hafizam Afiq

“…Adding the uncertain class the best accuracy obtained was 98.5% using the SMO algorithm. A predictive classification model that determines the accuracy for three classification classes was developed accordingly using the SMO algorithm.…”
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An Automated System For Classifying Conference Papers by Ngan, Seon Choon Han

“…This project is aimed to develop an automated web-based conference paper system for the manual process of assigning papers to reviewers by using classification models. The project is also aimed to select the best classification model for the system, based on an empirical study. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…In order to further validate the position of the tagging in the pallet box of the Random Forest model developed, a different predefined location was used to validate the model. …”
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Analyzing Algorithms to Detect Disaster Events using Social Media by Azlan F.A., Ahmad A., Yussof S., Ghapar A.A.

Enhanced mechanism to handle missing data of Hadith classifier by Aldhlan, Kawther A., Zeki, Ahmed M., Zeki, Akram M.

“…Meanwhile, with naïve bayes algorithm, the accurate rate has been improved by 0.6%. …”
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Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning by Idris, S., Badruddin, N.

“…The binary classification for Robust and Frail with MCI achieved the highest accuracy, with Gaussian Naïve Bayes showing the highest holdout method accuracy of 70.5, as well as the highest cross validation accuracy of 74. …”
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Mining The Basic Reproduction Number (R0) Forecast For The Covid Outbreak by Rajogoval, Illayakantthan

“…The COVID-19 Basic Reproduction Number, R0 a predictive model is developed using a linear regression classification algorithm to predict the COVID-19 Basic Reproduction Number, Robased on the actual COVID-19 Basic Reproduction Number, R0. …”
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Analyzing customer reviews for ARBA Travel using sentiment analysis by Abdullah, Nurulain

“…Among these, Naive Bayes achieved the highest performance with an accuracy of 93.67% and an F1-score of 93.54%, making it the most effective model for sentiment classification in this context. …”
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Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques by Mas Ira Syafila, Mohd Hilmi Tan, Mohd Faizal, Jamlos, Ahmad Fairuz, Omar, Kamarulzaman, Kamarudin, Mohd Aminudin, Jamlos

“…High accuracy shows the capability of the classification model to correctly predict the G. boninense detection while high F1-score indicates that the classification is able to validate the detection of G. boninense correctly with low misclassification rate. …”
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Sentiment analysis of customer review for Tina Arena Beauty by Amri, Nur Najwa Shahirah

“…Future work may involve expanding the dataset, integrating real-time feedback systems, and evaluating advanced algorithms to improve classification performance further. …”
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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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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

“…The main benefit of this study is the development of an appropriate model for early identification and severity classification of BSR disease in oil palms via remote sensing and data mining approaches rapidly and cost-effectively.…”
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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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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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Development of machine learning sentiment analyzer and quality classifier (MLSAQC) and its application in analysing hospital patient satisfaction from Facebook reviews in Malaysia by A Rahim, Afiq Izzudin

“…Results: The average F1-score for topic classification was between 0.687 and 0.757 for all models. …”
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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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Automated diagnosis of diabetes using entropies and diabetic index by Acharya, U.R., Fujita, H., Bhat, S., Koh, J.E.W., Adam, M., Ghista, D.N., Sudarshan, V.K., Chua, K.P., Chua, K.C., Molinari, F., Ng, E.Y.K., Tan, R.S.

“…This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. …”
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