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  1. 1
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  2. 2
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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    Final Year Project
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  3. 3
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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    Thesis
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  4. 4
    Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model

    Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model by Intan Azmira W.A.R., Ahmad A., Abidin I.Z., Yap K.S., Nasir M.N.M., Upkli W.R.

    Published 2023
    Conference Paper
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  5. 5
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…In this project, Support Vector Machines (SVM) is studied and experimented by the implementation ofa textual extractor. …”
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  6. 6
    Support Vector Machines (SVM) in Test Extraction

    Support Vector Machines (SVM) in Test Extraction by Ghazali, Nadirah

    Published 2006
    “…In this project, Support Vector Machines (SVM) is studied and experimented by the implementation ofa textual extractor. …”
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    Final Year Project
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  7. 7
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  8. 8
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  9. 9
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  10. 10
    Protein structure prediction using robust principal component analysis and support vector machine

    Protein structure prediction using robust principal component analysis and support vector machine by Zakaria, Nur Aini, Ali Shah, Zuraini, Kasim, Shahreen

    Published 2020
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    Article
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  11. 11
    Extreme learning machine classification of file clusters for evaluating content-based feature vectors

    Extreme learning machine classification of file clusters for evaluating content-based feature vectors by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

    Published 2018
    “…Consequently, an Extreme Learning Machine (ELM) neural network algorithm is used to evaluate the performance of the three methods in which it classifies the class label of the feature vectors to JPEG and Non-JPEG images for files in different file formats. …”
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  12. 12
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  13. 13
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
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  14. 14
    CHINESE CHARACTER RECOGNITION USING SUPPORT VECTOR MACHINE

    CHINESE CHARACTER RECOGNITION USING SUPPORT VECTOR MACHINE by Baharum A., Ismail R., Wahab S.H.A., Deris F.D., Noor N.A.M., Kasihmuddin M.S.M.

    Published 2023
    “…Therefore, this paper proposes recognizing Chinese characters using support vector machines. Support vector machines are a classification of two classes widely used in classification. …”
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  15. 15
    Batch mode heuristic approaches for efficient task scheduling in grid computing system

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…Many algorithms have been implemented to solve the grid scheduling problem. …”
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  16. 16
    Implementation of advanced techniques in production and manufacturing sectors through support vector machine algorithm with embedded system

    Implementation of advanced techniques in production and manufacturing sectors through support vector machine algorithm with embedded system by Rayner Alfred, Narender Chinthamu, T. Jayanthy, Elangovan Muniyandy, Tarun Kumar Dhiman, Nimmy John T.

    Published 2024
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  17. 17
    A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties

    A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties by Fatai Adesina, Anifowose, Jane, Labadin, Abdulazeez, Abdulraheem

    Published 2011
    “…This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model. …”
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    Proceeding
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  18. 18
    Modified word representation vector based scalar weight for contextual text classification

    Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

    Published 2024
    “…To bridge this gap, a five-phase research methodology is structured to propose and evaluate an algorithm enabling the external modification of LLM-generated word vectors using scalar values as the focus weightage. …”
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  19. 19
    A WEB-BASED SYSTEM FOR THE PREDICTION OF STUDENT PERFORMANCE IN UPCOMING PUBLIC EXAMS BASED ON ACADEMIC RECORDS

    A WEB-BASED SYSTEM FOR THE PREDICTION OF STUDENT PERFORMANCE IN UPCOMING PUBLIC EXAMS BASED ON ACADEMIC RECORDS by DELLON, NELSON BRUNNIE

    Published 2023
    “…Teachers will be able to precisely forecast their students' impending grades utilizing the system's web-based application integration and machine learning algorithms. The machine learning algorithms that will be used and compared are Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Linear Regression (LR). …”
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    Final Year Project Report / IMRAD
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  20. 20
    Hybrid Algorithm for Hand Gesture Recognition

    Hybrid Algorithm for Hand Gesture Recognition by Gupta, Shikha, Jaafar, J., Wan Ahmad, Wan Fatimah

    Published 2012
    “…For the classification purpose Support vector machine has been used as a classifier.…”
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