Search Results - (( java implementation path algorithm ) OR ( waste selection means 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
    A hybrid P-graph and WEKA approach in decision-making: waste conversion technologies selection

    A hybrid P-graph and WEKA approach in decision-making: waste conversion technologies selection by Ali, Rabiatul Adawiyah, Nik Ibrahim, Nik Nor Liyana, Wan Abdul Karim Ghani, Wan Azlina, Sani, Nor Samsiah, Hon, Loong Lam

    Published 2022
    “…The focus of user interface for selection of waste conversion technologies. As a result, the model can be used to determine the best municipal solid waste conversion technology.…”
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  6. 6
    Wind power forecasting with metaheuristic-based feature selection and neural networks

    Wind power forecasting with metaheuristic-based feature selection and neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Mohd Mawardi, Saari, Mohammad Fadhil, Abas

    Published 2024
    “…Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. …”
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  7. 7
    Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm

    Development of effluent removal prediction model efficiency in septic sludge treatment plant through clonal selection algorithm by Ting S.C., Ismail A.R., Malek M.A.

    Published 2023
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  8. 8
    Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots

    Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots by Shojaei, Taha Roodbar, Mohd Salleh, Mohamad Amran, Mobli, Hossein, Aghbashlo, Mortaza, Tabatabaei, Meisam

    Published 2019
    “…To find the optimum model, ANN was trained by using different algorithms. Then, the generated models were statistically assessed and subsequently, the capability of the selected model for predicting the mean diameter size of the nanoparticles was verified. …”
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  9. 9
    Development of soft computing prediction model for the influent physicochemical characteristics of sewage treatment plants / Mozafar Ansari

    Development of soft computing prediction model for the influent physicochemical characteristics of sewage treatment plants / Mozafar Ansari by Mozafar , Ansari

    Published 2021
    “…The best algorithm for each parameter was selected based on these criteria. …”
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  10. 10
    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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  11. 11
    Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management

    Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management by almahameed, Bader aldeen, Bisharah, Majdi

    Published 2024
    “…Evaluation metrics such as Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and R-squared are commonly employed in the assessment of Machine Learning models' performance. …”
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  12. 12
    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
    “…To evaluate the EANN model, 19 samples of experimental data from Zainol on the regression modelling of biogas production from banana stem waste were selected. Thirteen samples were used for training (70%) and six samples were used for testing (30%). …”
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  13. 13
    Artificial neural network: physico-chemical and macronutrients in an aquaponic system / Qistina Khadijah Abd Rahman

    Artificial neural network: physico-chemical and macronutrients in an aquaponic system / Qistina Khadijah Abd Rahman by Abd Rahman, Qistina Khadijah

    Published 2020
    “…Therefore, this paper proposed ANN model to evaluate graph comparison between the performances of the actual data from aquaponics activity and forecast data from simulated artificial neural network (ANN). Then, the best algorithms will be selected in a variety of neuron numbers of the ANN’s model. …”
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  14. 14
    Artificial neural network: physico-chemical and macronutrients parameters in an aquaponic system / Qistina Khadijah Abd Rahman, T.s Mohamed Syazwan Osman and Dr Samsul Setumin

    Artificial neural network: physico-chemical and macronutrients parameters in an aquaponic system / Qistina Khadijah Abd Rahman, T.s Mohamed Syazwan Osman and Dr Samsul Setumin by Abd Rahman, Qistina Khadijah, Osman, Mohamed Syazwan, Setumin, Samsul

    Published 2020
    “…Therefore, this paper proposed ANN model to evaluate graph comparison between the performances of the actual data from aquaponics activity and forecast data from simulated artificial neural network (ANN). Then, the best algorithms will be selected in a variety of neuron numbers of the ANN’s model. …”
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  15. 15
    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar

    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar by Abd Ghafar, Muhamad Azfar

    Published 2015
    “…In this paper, the energy and mean features are been selected. The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). …”
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  16. 16
    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar

    Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar by Abd Ghafar, Muhamad Azfar

    Published 2015
    “…In this paper, the energy and mean features are been selected. The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). …”
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  17. 17
    Mobile tour guide application with attraction recognition for UTAR Kampar campus

    Mobile tour guide application with attraction recognition for UTAR Kampar campus by Lee, Mun Hong

    Published 2021
    “…UTAR Kampar Campus has a lot of unique and beautiful buildings and structures but many people seldom get the chance to know the histories and stories behind these buildings and structures. This is such a waste because most of the buildings in UTAR Kampar Campus is built with meanings and the designs are based on some unique ideas such as the Ling Liong Sik Hall, which resembles the Forbidden City Palace in China. …”
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  18. 18
    Modification and characterization of phytase for animal feed production

    Modification and characterization of phytase for animal feed production by Noorbatcha, Ibrahim Ali, Samsudin, Nurhusna, Mohd. Salleh, Hamzah

    Published 2009
    “…Due to the importance of, microbial sources for the commercial production of phytases, we have selected waste water bacterium phytase as the subject of interest in this study. …”
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  19. 19
    PREDICTIVE MODELING OF DIMENSIONAL ACCURACIES IN 3D PRINTING USING ARTIFICIAL NEURAL NETWORK

    PREDICTIVE MODELING OF DIMENSIONAL ACCURACIES IN 3D PRINTING USING ARTIFICIAL NEURAL NETWORK by Sivaraos, Kumaran K., Dharsyanth R., Amran M., Shukor S.M., Pujari S., Ramasamy D., Vatesh U.K., Mahdi Al-Obaidi A.S.H., Ramesh S., Lee K.Y.S.

    Published 2024
    “…Additive manufacturing, particularly Fused Deposition Modeling (FDM) using three-dimensional (3D) printing, has revolutionized the manufacturing industry by offering design flexibility, customization options, affordability, and high printing speed. However, improper selection of process parameters in FDM can lead to suboptimal surface efficiency, defective mechanical properties, increased waste, and higher production costs. …”
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  20. 20
    Predictive modeling of dimensional accuracies in 3D printing using artificial neural network

    Predictive modeling of dimensional accuracies in 3D printing using artificial neural network by Subramonian, Sivarao, Kumaran, K., Dharsyanth, R., Md Ali, Mohd Amran, Salleh, Mohd Shukor, Pujari, Satish, Ramasamy, Devarajan, Vatesh, Umesh Kumar, Mahdi Al-Obaidi, Abdulkareem Sh, Ramesh, S., Lee, Kit Yee Sara

    Published 2023
    “…Additive manufacturing, particularly Fused Deposition Modeling (FDM) using three-dimensional (3D) printing, has revolutionized the manufacturing industry by offering design flexibility, customization options, affordability, and high printing speed. However, improper selection of process parameters in FDM can lead to suboptimal surface efficiency, defective mechanical properties, increased waste, and higher production costs. …”
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