Search Results - (( java implementation path algorithm ) OR ( waste active learning algorithm ))

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  1. 1
    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer

    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

    Published 2017
    “…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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  2. 2
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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  3. 3
    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment

    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

    Published 2021
    “…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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  4. 4
    Smart appointment organizer for mobile application / Mohd Syafiq Adam

    Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

    Published 2009
    “…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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  5. 5
    Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms

    Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms by Ayodele B.V., Mustapa S.I., Kanthasamy R., Zwawi M., Cheng C.K.

    Published 2023
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  6. 6
    Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations

    Data-Driven Approach to Modeling Biohydrogen Production from Biodiesel Production Waste: Effect of Activation Functions on Model Configurations by Hossain, S.K.S., Ayodele, B.V., Alhulaybi, Z.A., Alwi, M.M.A.

    Published 2022
    “…In this study, a data-driven machine-learning approach is employed to model the prediction of biohydrogen from waste glycerol. …”
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  7. 7
    A review of Artificial Intelligence application of sustainable solid waste management practices in Western Asia

    A review of Artificial Intelligence application of sustainable solid waste management practices in Western Asia by Nagimeldin, Olla, Ahmad Tajuddin, Husna, Jami, Mohammed Saedi

    Published 2022
    “…Over the past few years, Machine-learning algorithms and Artificial intelligence models have demonstrated great ability to optimize and automate critical solid waste and waste management complications. …”
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  8. 8
    An intelligent risk management framework for monitoring vehicular engine health

    An intelligent risk management framework for monitoring vehicular engine health by Rahim, Md. Abdur, Rahman, Md. Arafatur, Rahman, Md. Mustafizur, Zaman, Nafees, Moustafa, Nour, Razzak, Imran

    Published 2022
    “…The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. …”
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  9. 9
    Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm

    Modelling of biogas production process with evolutionary artificial neural network and genetic algorithm by Fakharudin, Abdul Sahli

    Published 2017
    “…In recent years, several researchers have actively pursued the application of machine learning to biogas production processes. …”
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  10. 10
    Mixed waste classification based on vision inspection / Hassan Mehmood Khan

    Mixed waste classification based on vision inspection / Hassan Mehmood Khan by Hassan Mehmood , Khan

    Published 2022
    “…Classification of dry waste garbage is crucial since incorrect labelling of dry waste types may contribute huge loss to waste industry. …”
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  11. 11
    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs

    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

    Published 2019
    “…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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  12. 12
    Graphical user interface test case generation for android apps using Q-learning / Husam N. S. Yasin

    Graphical user interface test case generation for android apps using Q-learning / Husam N. S. Yasin by Husam , N. S. Yasin

    Published 2021
    “…The computation time complexity of the Q-Learning-based test coverage algorithm was also analyzed. …”
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  13. 13
    Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes

    Artificial Neural Network-Forecasted Compression Strength of Alkaline-Activated Slag Concretes by Yi, Xuan Tang, Yeong, Huei Lee, Mugahed, Amran, Roman, Fediuk, Nikolai, Vatin, Beng, Ahmad Hong Kueh, Yee, Yong Lee

    Published 2022
    “…To remedy this, an environmentally friendly concrete, alkaline-activated slag concrete (AASC), where OPC is completely replaced by ground granulated blast-furnace slag (GGBFS) industrial waste, is one of the currently pursued research interests. …”
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