Search Results - (( course evaluation study algorithm ) OR ( bayes classification clustering algorithm ))

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  1. 1
    Anomaly-based intrusion detection through K-means clustering and naives Bayes classification

    Anomaly-based intrusion detection through K-means clustering and naives Bayes classification by Mohamed Yassin, Warusia, Udzir, Nur Izura, Muda, Zaiton, Sulaiman, Md. Nasir

    Published 2013
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  2. 2
    Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification

    Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification by Yassin, Warusia, Udzir, Nur Izura, Muda, Zaiton, Sulaiman, Md Nasir

    Published 2013
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  3. 3
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). Phase 2 is to study the performance of proposed integration system which using the data clustered to be used as train data for Naïve Bayes classifier. …”
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  4. 4
    Comparison of Naïve bayes classifier with back propagation neural network classifier based on f - folds feature extraction algorithm for ball bearing fault diagnostic system

    Comparison of Naïve bayes classifier with back propagation neural network classifier based on f - folds feature extraction algorithm for ball bearing fault diagnostic system by Osman Mohamed Addin, Addin, Salit, Mohd Sapuan, Othman, Mohamed, Ahmed Ali, Basheer Ahmed

    Published 2011
    “…The f-folds feature extraction algorithm has been used with different number of folders and clusters. …”
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  5. 5
    A Naïve-Bayes classifier for damage detection in engineering materials

    A Naïve-Bayes classifier for damage detection in engineering materials by Addin, O., Salit, Mohd Sapuan, Mahdi Ahmad Saad, Elsadig, Othman, Mohamed

    Published 2007
    “…The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naïve-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. …”
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  6. 6
    Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu

    Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu by Pala, Tuba, Camurcu, Ali Yilmaz

    Published 2014
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  7. 7
    Classification of metamorphic virus using n-grams signatures

    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

    Published 2020
    “…Then, the virus cluster is evaluated using Naïve Bayes algorithm in terms of accuracy using performance metric. …”
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  8. 8
    Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic

    Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic by Renita Astri, Lai Po Hung, Suaini Binti Sura, Ahmad Kamal Ariffin Mohd Rus, Rina Yuliet

    Published 2023
    “…By using reviews on Google Maps on the attractions of Air Manis Beach, Padang Beach, Pasir Jambak Beach, Nirwana Beach, and Pasir Putih Beach, clustering is carried out with the Naive Bayes classification algorithm. …”
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  9. 9
    Machine learning approach for stress detection based on alpha-beta and theta-beta ratios of EEG signals

    Machine learning approach for stress detection based on alpha-beta and theta-beta ratios of EEG signals by Altaf, Hunain, Ibrahim, Siti Noorjannah, Mohd Azmin, Nor Fadhillah, Asnawi, Ani Liza, Walid, Balqis Hanisah, Harun, Noor Hasmiza

    Published 2021
    “…This work explores the impact of bandpower of alpha/beta and theta/beta ratios when combined with other features to classify two-levels of human stress based on EEG signals using five commonly used machine learning algorithms. A classification model is developed from the clustering model gained and Naïve Bayes shows the highest accuracy which is 95% in compared to the other four common machine learning algorithms (i.e., SVM, Logistic, IBk, and SGD) by using WEKA. …”
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  10. 10
    Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin

    Depression prediction system from Twitter’s tweet by using sentiment analysis / Nur Amalina Kamaruddin by Kamaruddin, Nur Amalina

    Published 2020
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  11. 11
    Exploring the impact of social media on political discourse: a case study of the Makassar mayoral election

    Exploring the impact of social media on political discourse: a case study of the Makassar mayoral election by Jufri, Aedah, Abd Rahman, Suarga, .

    Published 2024
    “…Election dynamics are examined using the naïve Bayes approach. To increase the accuracy and efficiency of text mining operations, especially in result validation, text clustering, and classification, the k-means algorithm and support vector machines (SVM) were used. …”
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  12. 12
    An improved hybrid learning approach for better anomaly detection

    An improved hybrid learning approach for better anomaly detection by Mohamed Yassin, Warusia

    Published 2011
    “…The proposed hybrid approach will be clustering all data into the corresponding group before applying a classifier for classification purposes. …”
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  13. 13
    Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms

    Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

    Published 2020
    “…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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  14. 14
    Artificial intelligence system for pineapple variety classification and its quality evaluation during storage using infrared thermal imaging

    Artificial intelligence system for pineapple variety classification and its quality evaluation during storage using infrared thermal imaging by Mohd Ali, Maimunah

    Published 2022
    “…Several machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, support vector machine, decision tree, and Naïve Bayes were applied for the classification of pineapple varieties. …”
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  15. 15
    An application of predicting student performance using kernel k-means and smooth support vector machine

    An application of predicting student performance using kernel k-means and smooth support vector machine by Sajadin, Sembiring

    Published 2012
    “…In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. …”
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  16. 16
    Using algorithmic taxonomy to evaluate lecturer workload

    Using algorithmic taxonomy to evaluate lecturer workload by Hashim, Ruhil Hayati, Abdul Hamid, Jamaliah, Selamat, Mohd Hasan, Ibrahim, Hamidah, Abdullah, Rusli, Mohayidin, Mohd Ghazali

    Published 2006
    “…The taxonomy is a dynamic hierarchy that extracts validated parameters from the dataset. Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.…”
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  17. 17
    Using algorithmic taxonomy to evaluate lecture workload: a case study of services application prototype in the UPM KM Portal

    Using algorithmic taxonomy to evaluate lecture workload: a case study of services application prototype in the UPM KM Portal by Abdul Hamid, Jamaliah, Mohayidin, Mohd Ghazali, Selamat, Mohd Hasan, Ibrahim, Hamidah, Abdullah, Rusli, Hashim, Ruhil Hayati

    Published 2006
    “…The taxonomy is a dynamic hierarchy that extracts validated parameters from the dataset. Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.…”
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  18. 18
    Automated study plan generator using rule-based and knapsack problem

    Automated study plan generator using rule-based and knapsack problem by Salahuddin, Lizawati, Hashim, Ummi Rabaah, Salahuddin, Lizawati

    Published 2022
    “…Hence, this study aims to propose an algorithm to generate an automated and accurate study plan throughout the study duration. …”
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  19. 19
    Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm

    Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm by Foo, Yao Heng

    Published 2025
    “…University course timetabling problem (UCTP) is a scheduling problem that requires courses to be assigned to the limited time slots, classrooms, and lecturers, while adhering to a set of predefined constraints. …”
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  20. 20
    Using algorithmic taxonomy to evaluate lecture workload: A case study of services application prototype in the UPM KM portal

    Using algorithmic taxonomy to evaluate lecture workload: A case study of services application prototype in the UPM KM portal by Abdul Hamid, Jamaliah, Mohayidin, Mohd Ghazali, Selamat, Mohd Hassan, Ibrahim, Hamidah, Abdullah, Rusli, Hashim, Ruhil Hayati

    Published 2006
    “…Lecturer workload at universities includes three major categories: teaching, research and services.Teaching workload is influence by various factors such as level taught courses, number of student, credit and contact hour and off campus or on campus course design.The UPM has a KM Portal that contains sets of metadata on lecturer profile and knowledge assets.The Lecturer profile contains information lecturer teaching, research, publication and many more.We constructed an algorithmic taxonomy based at the lecturer profile data to measure lecturer teaching workload.This method measures the lecturer teaching workload.The taxonomy is a dynamic hierarchy that extracts validated parameters from the dataset.Results of the study highlight the contributions of this algorithmic method in better evaluation of teaching workload for lecture.…”
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