Search Results - (( java implementation path algorithm ) OR ( systematic implementation tree 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
    Data mining techniques for disease risk prediction model: A systematic literature review

    Data mining techniques for disease risk prediction model: A systematic literature review by Ahmad W.M.T.W., Ghani N.L.A., Drus S.M.

    Published 2023
    Conference Paper
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  6. 6
    Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review

    Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review by Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.

    Published 2023
    Review
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  7. 7
    An object-based approach to detect tree stumps in a selective logging area using Unmanned Aerial Vehicle imagery

    An object-based approach to detect tree stumps in a selective logging area using Unmanned Aerial Vehicle imagery by Aisyah Marliza Muhmad Kamarulzaman, Wan Shafrina Wan Mohd Jaafar, Siti Nor Maizah Saad, Hamdan Omar, Mohd. Rizaludin Mahmud

    Published 2021
    “…This research implemented a technique for detecting, segmenting, classifying, and measuring tree stumps by using the Multiresolution Segmentation Algorithm method. …”
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  8. 8
    Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

    Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids by Chiaverini, Luca, Macdonald, David W., Hearn, Andrew J., Kaszta, Zaneta, Ash, Eric, Bothwell, Helen M., Can, Ozgun Emre, Channa, Phan, Clements, Gopalasamy Reuben *, Haidir, Iding Achmad, Kyaw, Pyae Phyoe, Moore, Jonathan H., Rasphone, Akchousanh, Tan, Cedric Kai Wei, Cushman, Samuel A.

    Published 2023
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  9. 9
    An improved diabetes risk prediction framework : An Indonesian case study

    An improved diabetes risk prediction framework : An Indonesian case study by Sutanto, Daniel Hartono

    Published 2018
    “…In conclusion,DRPF is implementable as prototype and has been highly accepted by Indonesian practitioners as aid for the diagnostics of diabetes.…”
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  10. 10
    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
    “…This study examines the utilization of different Machine Learning algorithms, such as Linear Regression, Decision Trees, Support Vector Machines (SVM), Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) Regression, and Particle Swarm Optimization (PSO), in the domain of predictive modeling and cost optimization in the field of construction project management. …”
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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
    DurianCare: optimising trunk disease detection and precision farming in a mobile application / Muhammad Haziq Azmi

    DurianCare: optimising trunk disease detection and precision farming in a mobile application / Muhammad Haziq Azmi by Azmi, Muhammad Haziq

    Published 2025
    “…Durian tree trunk diseases are threatening to reduce the productivity of farmers and therefore have economic consequences, such as lower yield and increased cost. …”
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  13. 13
    Species distribution and molecular variations in drogonflies (order: odonata) within the state of Selangor, Malaysia / Noorhidayah Binti Mamat

    Species distribution and molecular variations in drogonflies (order: odonata) within the state of Selangor, Malaysia / Noorhidayah Binti Mamat by Noorhidayah, Mamat

    Published 2013
    “…This was supported by the higher indices of richness (R) diversity (H’) and evenness (E) for TLR. The phylogenetic trees were constructed from 40 samples using the neighbor joining (NJ) and maximum parsimony (MP) algorithms which were implemented by MEGA 4.0.2. …”
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