Search Results - (( java interactive data algorithm ) OR ( problem application learning algorithm ))

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  1. 1
    A random search based effective algorithm for pairwise test data generation

    A random search based effective algorithm for pairwise test data generation by Sabira, Khatun, K. F., Rabbi, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

    Published 2011
    “…Java program has been used to test the performance of the algorithm. …”
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  2. 2
    EasyA: Easy and effective way to generate pairwise test data

    EasyA: Easy and effective way to generate pairwise test data by Rabbi, Khandakar Fazley, Sabira, Khatun, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

    Published 2013
    “…Java program has been used to test the performance of the algorithm. …”
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  3. 3
    Web-based RIG performance reporting system using interactive visualization techniques / Amir Hambaly Nasaruddin

    Web-based RIG performance reporting system using interactive visualization techniques / Amir Hambaly Nasaruddin by Nasaruddin, Amir Hambaly

    Published 2019
    “…The system architecture which consists of a user interface, web framework and storage data. D3.js is used as a technology to visualize the result in interactive form or dynamic visualization which is a JavaScript library and Python is a language to program the system. …”
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  4. 4
    Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems

    Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems by Abdul Hamid, Norhamreeza, Mohd Nawi, Nazri, Ghazali, Rozaida, Mohd Salleh, Mohd Najib

    Published 2011
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  5. 5
    The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

    The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement by Hosseini E., Al-Ghaili A.M., Kadir D.H., Daneshfar F., Gunasekaran S.S., Deveci M.

    Published 2025
    “…In the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. …”
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  6. 6
    An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective

    An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective by Mousavi, Seyed Mohsen, Sadeghi R., Kiarash, Lee, Lai Soon

    Published 2023
    “…However, there needs to be more research examining the role of interactive methods in multiobjective optimisation problems. To integrate machine learning and human interactions, this paper develops a new three-stage interactive algorithm in business analytics, called the interactive Nautilus-based algorithm, to address complex problems. …”
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  7. 7
    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments

    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

    Published 2006
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  8. 8
    Opposition-based learning simulated kalman filter for Numerical optimization problems

    Opposition-based learning simulated kalman filter for Numerical optimization problems by Mohd Falfazli, Mat Jusof

    Published 2016
    “…The SKF is however subjected to premature convergence problem. In this research, opposition-based learning is employed to solve the premature convergence problem in SKF. …”
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  9. 9
    Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

    Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection by Tubishat, Mohammad, Idris, Norisma, Shuib, Liyana, Abushariah, Mohammad A.M., Mirjalili, Seyedali

    Published 2020
    “…These problems will occur because these fields are mainly used machine learning classifiers. …”
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  10. 10
    Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering

    Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

    Published 2024
    “…Although the swmcan algorithm solves the noise problem in multi-view data, its initial and final graphs are independent and cannot learn from each other. …”
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  11. 11
    An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators

    An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Muhammad Ikram, Mohd Rashid

    Published 2023
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  12. 12
    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider

    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider by Omer Fadlelmola Elkhider, Samih

    Published 2011
    “…In this dissertation a mobile cloud will be under consideration when designing the prototype, it is proven that mobile processors are tiny processors and could only handle a limited resources, thus the ability of making the applications processes in server, leaving the mobile clients a small part of only accessing data and interact with it. Such methodology can be achieved by java programming language for mobile clients J2ME, and by using a second generation mobile phones. …”
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  13. 13
    A machine learning approach to tourism recommendations system

    A machine learning approach to tourism recommendations system by Chia, An

    Published 2025
    “…This project aims to develop a tourism attractions recommendation system by integrating machine learning recommendation algorithms. The main problem encountered when developing a powerful recommendation system is cold start problem, data sparsity and scalability problems. …”
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  14. 14
    Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic

    Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic by Yap, Soon Teck

    Published 2004
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  15. 15
    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023)

    Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018?2023) by Hosseini E., Al-Ghaili A.M., Kadir D.H., Gunasekaran S.S., Ahmed A.N., Jamil N., Deveci M., Razali R.A.

    Published 2025
    “…This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. …”
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  16. 16
    Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying

    Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying by Liu , Zongying

    Published 2019
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  17. 17
    Rice yield prediction - a comparison between enhanced back propagation learning algorithms

    Rice yield prediction - a comparison between enhanced back propagation learning algorithms by Saad, Puteh, Jamaludin, Nor Khairah, Rusli, Nursalasawati, Bakri, Aryati, Kamarudin, Siti Sakira

    Published 2004
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  18. 18
    Hybrid data regression model based on the generalized adaptive resonance theory neural network

    Hybrid data regression model based on the generalized adaptive resonance theory neural network by Wong S.Y., Yap K.S., Lim C.P., Lee E.W.M.

    Published 2023
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  19. 19
    Automated bilateral negotiation with incomplete information in the e-marketplace.

    Automated bilateral negotiation with incomplete information in the e-marketplace. by Jazayeriy, Hamid

    Published 2011
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  20. 20
    Multi-Backpropagation network

    Multi-Backpropagation network by Wan Ishak, Wan Hussain, Siraj, Fadzilah, Othman, Abu Talib

    Published 2002
    “…The learning mechanism for Neural Network is its learning algorithm. …”
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