Search Results - (( model evaluation bayes algorithm ) OR ( text classification modeling algorithm ))

Evaluating machine learning algorithms for sentiment analysis: a comparative study to support data-driven decision making by Mohamad Daud, Nur Hafiza, Shafii, Nor Hayati, Md Nasir, Diana Sirmayunie, Fauzi, Nur Fatihah

“…Upon evaluating the models, the findings reveal notable differences in accuracy. …”
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Machine learning approach of predicting Airline flight delay using Naïve Bayes Algorithm / Ahmad Adib Baihaqi Shukri ... [et al.] by Shukri, Ahmad Adib Baihaqi, Mohamed Yusoff, Syarifah Adilah, Warris, Saiful Nizam, Abu Bakar, Mohd Saifulnizam, Kadar, Rozita

“…This study aims to study the Naïve Bayes algorithm for flight delay prediction. The objective is to develop a reliable flight delay prediction model using the Naïve Bayes algorithm and evaluate its performance. …”
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Sentiment analysis regarding marital issues using Naive Bayes algorithm / Farah Nabila Mohd Razali by Mohd Razali, Farah Nabila

“…By analyzing data from social media, primarily Twitter, the research identifies key challenges in marriages, including communication breakdowns, financial stress, and infidelity. The Naive Bayes algorithm was chosen for its efficiency in text classification and ability to handle large volumes of unstructured data. …”
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Airline flight delay prediction using Naïve Bayes algorithm / Ahmad Adib Baihaqi Shukri by Shukri, Ahmad Adib Baihaqi

“…This study aims to study the Naïve Bayes algorithm for flight delay prediction. The objective is to develop a reliable flight delay prediction model using the Naïve Bayes algorithm and evaluate its performance. …”
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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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Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad by Ahmad, Nurul Atirah

“…This project implements the Naive Bayes algorithm as the classification algorithm. …”
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Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary by Mohd Zamary, Nurul Aida

“…To evaluate the model, the model accuracy, precision, recall, F1- score, and confusion matrix were used. …”
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A New Model For Network-Based Intrusion Prevention System Inspired By Apoptosis by Mohammed Nadir bin Ali

Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad, Khyrina Airin Fariza Abu Samah and Nuwairah Aimi Ahmad... by Ahmad, Nurul Atirah, Abu Samah, Khyrina Airin Fariza, Ahmad Kushairi, Nuwairah Aimi

“…This project implements the Naive Bayes algorithm as the classification algorithm. …”
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Sentiment analysis for online fashion clothing review using Support Vector Machine (SVM) algorithm / Muhammad Hakam Musawi Mohd Hamdi by Mohd Hamdi, Muhammad Hakam Musawi

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Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout by Dzakiyullah, Nur Rachman

“…The second experiment introduced a novel dropout regularization technique called multi-channel weighted dropout, designed to enhance model generalization. Comparative evaluations with existing dropout methods demonstrated the superior performance of the proposed technique, particularly when applied within the Algorithm Adaptation framework using DNNs. …”
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Optimizing sentiment analysis of Indonesian texts: Enhancing deep learning models with genetic algorithm-based feature selection by Siti, Mujilahwati, Noor Zuraidin, Mohd Safar, Ku Muhammad Naim, Ku Khalif, Nasyitah, Ghazalli

“…To address this challenge, feature selection (FS) is conducted during the data pre-processing phase with the objective of enhancing the learning accuracy and efficiency of the model. This study examines the optimization of Indonesian text sentiment analysis through the integration of feature selection using a genetic algorithm (GA) with deep learning models. …”
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An enhanced gated recurrent unit with auto-encoder for solving text classification problems by Zulqarnain, Muhammad

“…Finally, a Batch Normalization method is incorporated in the GRU and AE for improving the performance of the proposed ES-GRU model. The proposed model has been evaluated on seven benchmark text datasets and compared with six baselines well-known multiclass text classification approaches included standard GRU, AE, Long Short Term Memory, Convolutional Neural Network, Support Vector Machine, and Naïve Bayes. …”
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Classification of metamorphic virus using n-grams signatures by A Hamid, Isredza Rahmi, Md Sani, Nur Sakinah, Abdullah, Zubaile, Mohd Foozy, Cik Feresa, Kipli, Kuryati

“…Then, the virus cluster is evaluated using Naïve Bayes algorithm in terms of accuracy using performance metric. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Bian, Hui

“…Traditional machine learning algorithms, such as Decision Trees, Naive Bayes, Random Forest, Random Trees, Multi-Layer Perceptron, and Support Vector Machines, have been extensively applied to address these threats. …”
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Flood prediction model for Kuala Terengganu area using predictive analytics by Mohd Zamri, Najwa An-Nisa

“…Three classification algorithms were tested: Decision Tree, Naive Bayes, and Random Forest. …”
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Multitasking deep neural network models for Arabic dialect sentiment analysis by Alali, Muath Mohammad Oqlah

“…The proposed NCNN achieved a higher macro average recall (R) and outperforms Naive Bayes (NB) on task A (three polarities) and Voting model on task B (two polarities) on the SemEval-2017 Arabic dialect Twitter dataset. …”
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A COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTION OF AUTISM SPECTRUM DISORDER USING SCREENING DATA by Yeap, Ming Yue

“…The performance of the classification models for ASD will be compared. Finally, the best classification model for ASD prediction was a model trained using the Support Vector Machine (SVM) algorithm…”
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Benchmarking performance of document level classification and topic modeling by Bhatti, Muhammad Shahid, Ullah, Azmat, Latip, Rohaya, Sohail, Abid, Riaz, Anum, Hassan, Rohail

“…Text classification of low resource language is always a trivial and challenging problem. …”
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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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