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Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

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

“…The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. …”
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Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

“…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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Thematic textual hadith classification: an experiment in rapidminer using support vector machine (SVM) and naïve bayes algorithm by Yusof, Norzihani, Rosidi, Siti Aishah Rosidi, Ibrahim, Nuzulha Khilwani Ibrahim, Ahmed Ali, Ahmed El-Mogtaba Bannga

“…From the results, the different value of accuracy for both SVM and Naïve Bayes Algorithm was 2.4%. The Naïve Bayes Algorithm displayed better result comparing to SVM. …”
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Sentiment Analysis of Airline Reviews Using Naive Bayes Algorithm / Ahmad Firdaus Maliki by Maliki, Ahmad Firdaus

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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 support vector machines (SVM) algorithm obtained the overall best results of 94.5% accuracy, 91.8% precision, 91.7% recall, and 91.1% f-Measure while the naïve bayes (NB) algorithm obtained the best AUC score of 0.944 with the tweet data of Dato Seri Anwar. …”
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Comparison of hidden Markov Model and Naïve Bayes algorithms among events in smart home environment by Babakura, Abba, Sulaiman, Md Nasir, Mustapha, Norwati, Kasmiran, Khairul A.

“…In this paper, we propose Hidden Markov Model (HMM) and Naïve Bayes (NB) to test the accuracy and response time of the home data and to compare between the two algorithms. …”
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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

“…Thus, this project aims to classify and generate a list of potential job applicants by analyzing various attributes of their LinkedIn accounts, such as title, location, skills, education, language, certification, and experience. This project implements the Naive Bayes algorithm as the classification algorithm. …”
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Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak by Ishak, Najihah Salsabila

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Bayesian Network Classifiers for Damage Detection in Engineering Material by Mohamed Addin, Addin Osman

“…The methodology used in the thesis to implement the Bayesian network for the damage detection provides a preliminary analysis used in proposing a novel fea- ture extraction algorithm (f-FFE: the f-folds feature extraction algorithm). …”
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Sleep as a predictor of depression level using Naïve Bayes / Nur Syakinah Md Roduan by Md Roduan, Nur Syakinah

“…Future work on this subject should improve the findings by modifying the variables used and/or by using other methods in term of data collection or the algorithm itself.…”
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Identification model for hearing loss symptoms using machine learning techniques by Nasiru Garba Noma

“…This research presents an efficient way to facilitate the hearing loss symptoms diagnosis process by designing a symptoms identification model that efficiently identify hearing loss symptoms based on air and bone conduction pure-tone audiometry data. The model is implemented using both unsupervised and supervised machine learning techniques in the form of Frequent Pattern Growth (FP-Growth) algorithm as feature transformation method and multivariate Bernoulli naïve Bayes classification model as the classifier. …”
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Analysis of Data Mining Tools for Android Malware Detection by Yusof, Robiah, Abdullah, Raihana Syahirah, Adnan, Nurul Syahirrah, Abd. Jalil, Nurlaily

“…However, the problem arises in deciding the most appropriate machine learning techniques or algorithm on particular tools to be implemented on particular data. …”
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Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

“…An analysis was done to see the effect of implementing different data reduction algorithms in classifying BSR disease. …”
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Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…Since the 1960s, many algorithms for data classification have been proposed. …”
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Emotion recognition and analysis of netizens based on micro-blog during covid-19 epidemic by Jiao, BianBian, Leelavathi, R., Lohgheswary, N., Nopiah, Z. M.

“…The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. …”
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Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…The machine learning algorithm, Support Vector Machine was chosen after comparing it with other algorithms such as Multinomial Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. …”
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Author identification for under-resourced language Kadazandusun by Nursyahirah, Tarmizi, Suhaila, Saee, Dayang Hanani, Abang Ibrahim

“…Besides, this paper also examines the performance of two machine learning algorithms on the KadazanDusun data set by analyzing the stylometric features. …”
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Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary by Mohd Zamary, Nurul Aida

“…The phase of this project is divided into data preprocessing, implementation of the decision tree algorithm, and evaluation of the algorithm and prototype. …”
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Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir by Amir, Nur Hazirah

“…The development model used for this project is Agile model development cycle. It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. …”
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