Search Results - (( parameter optimization swarm algorithm ) OR ( using vectorization learning algorithm ))

Refine Results
  1. 1

    Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method by Abbaszadeh M., Soltani-Mohammadi S., Ahmed A.N.

    Published 2023
    “…Copper deposits; Deposits; Geology; Learning algorithms; Mineralogy; Static Var compensators; Support vector machines; Three dimensional computer graphics; Alteration zones; Grid search; Grid-search method; Mineralization zone; Model Selection; Particle swarm optimization algorithm; Penalty parameters; Performance; Support vector classifiers; Support vectors machine; Particle swarm optimization (PSO); accuracy assessment; algorithm; classification; computer simulation; copper; geological survey; mineral alteration; mineralization; numerical model; ore deposit; parameterization; performance assessment; porphyry; resource assessment; support vector machine; three-dimensional modeling; Iran…”
    Article
  2. 2

    Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique by Lee, Zhong Zhen, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah, Abraham, Ajith

    Published 2013
    “…The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optim ization of the gam ma and large margin parameters are done using the PSO algorithm. …”
    Get full text
    Get full text
    Conference or Workshop Item
  3. 3

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
    “…Optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm , ant colony algorithm, and many other algorithms are used along with classifiers to improve the work of these classifiers in detecting intrusion and to increase the performance of these classifiers. …”
    Get full text
    Get full text
    Get full text
    Article
  4. 4

    Face Recognition Approach using an Enhanced Particle Swarm Optimization and Support Vector Machine by Saad, Wasan Kadhim, Jabbar, Waheb A., Abbas, Ahmed Abdul Rudah

    Published 2019
    “…Though, there is an important issue that can affects the whole classification process which is picking the optimum parameters of SVM. Recently, Particle Swarm Optimization (PSO) is used to discover the optimal parameters of SVM and many versions of PSO are used for this purpose, like: PSO-SVM technique, opposition PSO and SVM which called (OPSO-SVM) technique and AAPSO-SVM technique which represents adaptive acceleration PSO and SVM. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  5. 5

    Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui by Yang , Dong Rui

    Published 2019
    “…One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. …”
    Get full text
    Get full text
    Get full text
    Thesis
  6. 6
  7. 7

    Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting by Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira

    Published 2014
    “…The importance of optimizing machine learning control parameters has motivated researchers to investigate for proficient optimization techniques.In this study, a Swarm Intelligence approach, namely artificial bee colony (ABC) is utilized to optimize parameters of least squares support vector machines.Considering critical issues such as enriching the searching strategy and preventing over fitting, two modifications to the original ABC are introduced. …”
    Get full text
    Get full text
    Article
  8. 8
  9. 9

    Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

    Published 2019
    “…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
    Get full text
    Get full text
    Article
  10. 10

    Prediction of device performance in SnO2 based inverted organic solar cells using machine learning framework by Aidil Zulkafli, Nadhirah, Elyca Anak Bundak, Caceja, Amiruddin Abd Rahman, Mohd, Chin Yap, Chi, Chong, Kok-Keong, Tee Tan, Sin

    Published 2024
    “…The device performance of the SnO2 prepared using different spinning rates was used as the training data for machine learning prediction. …”
    Get full text
    Get full text
    Get full text
    Article
  11. 11

    Prediction of solar irradiance using grey Wolf optimizer least square support vector machine by Yasin Z.M., Salim N.A., Aziz N.F.A., Mohamad H., Wahab N.A.

    Published 2023
    “…In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). …”
    Article
  12. 12

    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

    Published 2024
    “…In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  13. 13

    Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model by Mohammed Adam, Kunna Azrag

    Published 2021
    “…In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. …”
    Get full text
    Get full text
    Thesis
  14. 14

    Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

    Published 2018
    “…The segmentation process was optimized using Fuzzy-based Segmentation Parameter. …”
    Get full text
    Get full text
    Get full text
    Thesis
  15. 15

    OPTIMIZATION OF PID CONTROLLER PARAMETERS USING ARTIFICIAL FISH SWARM ALGORITHM by SOOMRO, WAFA ALI SOOMRO

    Published 2013
    “…This Final Year Project is preceded on the topic named “The Optimization of PID Control Parameters Using Artificial Fish Swarm Algorithm”. …”
    Get full text
    Get full text
    Final Year Project
  16. 16
  17. 17

    Application of Particle Swarm Optimization in Optimizing Stereo Matching Algorithm’s Parameters for Star Fruit Inspection System by Nasroddin, Saidatul Nizan, Mohd Mokji, Musa, Tan, Kok, Zainal Abidin, Amar Faiz, Amirulah, Rahman, Nordin, Nur Anis, Hasim, Saipol Hadi, Zakaria, Hamzah, Hassan, Jefery, Jaafar, Hazriq Izzuan, Khairuddin, Osman

    Published 2014
    “…This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. …”
    Get full text
    Get full text
    Conference or Workshop Item
  18. 18
  19. 19
  20. 20

    Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm by Ehteram M., Othman F.B., Yaseen Z.M., Afan H.A., Allawi M.F., Malek M.B.A., Ahmed A.N., Shahid S., Singh V.P., El-Shafie A.

    Published 2023
    “…Decision making; Disaster prevention; Floods; Routing algorithms; Water resources; Absolute deviations; Bat algorithms; Comparative analysis; Computational time; Flood routing; Muskingum models; Particle swarm optimization algorithm; Swarm algorithms; Particle swarm optimization (PSO); accuracy assessment; algorithm; comparative study; decision making; flood; flood forecasting; flood routing; numerical method; optimization; parameter estimation; water resource; United Kingdom; United States…”
    Article