Search Results - (( course evaluation study algorithm ) OR ( user classification swarm algorithm ))*

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  1. 1
    Malicious URL classification using artificial fish swarm optimization and deep learning

    Malicious URL classification using artificial fish swarm optimization and deep learning by Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, K. Nour, Mohamed, M. Asiri, Mashael, M. Al-Sharafi, Ali, Othman, Mahmoud, Motwakel, Abdelwahed

    Published 2023
    “…With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. …”
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  2. 2
    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam

    Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

    Published 2017
    “…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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  3. 3
    Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy

    Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy by Al-Dabbagh, Rawaa Dawoud, Neri, Ferrante, Idris, Norisma, Baba, Mohd Sapiyan

    Published 2018
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  4. 4
    An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection

    An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection by Shing, Chiang Tan, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Lim, Pey, Yun Goh, Chee, Peng Lim

    Published 2023
    “…During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. …”
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  5. 5
    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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  6. 6
    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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  7. 7
    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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  8. 8
    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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  9. 9
    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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  10. 10
    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients

    Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients by Moghbel, Mehrdad

    Published 2017
    “…The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. …”
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  11. 11
    Using web questionnaire for web-based course evaluation: advantages and disadvantages

    Using web questionnaire for web-based course evaluation: advantages and disadvantages by Fariborzi, Elham, Abu Bakar, Kamariah, Kasa, Zakaria, Abu Samah, Bahaman, Abdullah, Muhammad Taufik

    Published 2017
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  12. 12
    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman

    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman by Azman, Alya Kauthar

    Published 2025
    “…Data for the study was collected from university records, and algorithm performance was tested against predefined scheduling criteria. …”
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  13. 13
    Virtual reality in algorithm programming course: practicality and implications for college students

    Virtual reality in algorithm programming course: practicality and implications for college students by Dewi, Ika Parma, Ambiyar, Mursyida, Lativa, Effendi, Hansi, Giatman, Muhammad, Efrizon, Hanafi, Hafizul Fahri, Ali, Siti Khadijah

    Published 2024
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  14. 14
    Prediction of MUET result based on KNN algorithm / Siti Fatimah Azzahra Hamrizan

    Prediction of MUET result based on KNN algorithm / Siti Fatimah Azzahra Hamrizan by Hamrizan, Siti Fatimah Azzahra

    Published 2021
    “…The exam results from the ELC can be the features to help the students to know if they can pass the MUET exam and the lecturers can figure out which language skills need to be improved to help preparing the students to sit for MUET. The study aims to explore the K-Nearest Neighbour (KNN) algorithm in solving the MUET result prediction problem, to develop a prototype of MUET result prediction based on the KNN algorithm and to evaluate the accuracy of the KNN algorithm in MUET result prediction. …”
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  15. 15
    Recommendation system in selecting course of public university in Malaysia using K-nearest neighbour

    Recommendation system in selecting course of public university in Malaysia using K-nearest neighbour by Thoi, Wen Bin

    Published 2018
    “…The objectives of this project are to study the current algorithm and technique in recommendation systems for selecting courses; to implement k-Nearest Neighbour in the recommendation system; and to evaluate the application of the recommendation system. …”
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    Undergraduates Project Papers
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  16. 16
    Graph theory approach for managing lecturers’ schedule using graph colouring method / Siti Nor Ba Basri, Nur Su’aidah Khozaid and Farhana Hazwani Ismail

    Graph theory approach for managing lecturers’ schedule using graph colouring method / Siti Nor Ba Basri, Nur Su’aidah Khozaid and Farhana Hazwani Ismail by Basri, Siti Nor Ba, Khozaid, Nur Su’aidah, Ismail, Farhana Hazwani

    Published 2023
    “…Different colours are allocated to each vertex using graph colouring techniques such as the vertices algorithm or the edges algorithm, ensuring that clashing courses and lecturers are assigned different time slots. …”
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    Student Project
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  17. 17
    CSC649 - Special Topics in Computer Science / College of Computing, Informatics and Media

    CSC649 - Special Topics in Computer Science / College of Computing, Informatics and Media by UiTM, College of Computing, Informatics and Media

    Published 2022
    “…The course will cover specialized technology and described in terms of frameworks and problem formulations, standard models, methods, computational tools, algorithms, as well as methodologies to evaluate the system and select optimal models. …”
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  18. 18
    CSC649: Special Topics in Computer Science / College of Computing, Informatics and Mathematics

    CSC649: Special Topics in Computer Science / College of Computing, Informatics and Mathematics by UiTM, College of Computing, Informatics and Mathematics

    Published 2018
    “…The course will cover specialized technology and described in terms of frameworks and problem formulations, standard models, methods, computational tools, algorithms, as well as methodologies to evaluate the system and select optimal models. …”
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  19. 19
    Integrating of web 2.0 technologies for interactive courseware : data structure and algorithm as case study

    Integrating of web 2.0 technologies for interactive courseware : data structure and algorithm as case study by Mohd Nurhafeezi, Nordin

    Published 2013
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    Final Year Project Report / IMRAD
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  20. 20
    Tracking student performance in introductory programming by means of machine learning

    Tracking student performance in introductory programming by means of machine learning by Khan I., Al Sadiri A., Ahmad A.R., Jabeur N.

    Published 2023
    Conference Paper
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