Search Results - (( java implication _ algorithm ) OR ( systematic implementation tree algorithm ))

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    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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    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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    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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    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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    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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