Search Results - (( _ implication tree algorithm ) OR ( basic selection path algorithm ))

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  1. 1

    Integration of enchanced jump point search (JPS) algorithm with modified bresenham technique for path planning in virtual grid-based environment by Nurul Atikah Janis

    Published 2018
    “…Jump Point Search is one of the path finding algorithm with huge advantage of maintaining zero memory overhead as no preprocessing process involved. …”
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    Thesis
  2. 2

    Scheduling of batch process plant / Abdul Aziz Abu Bakar by Abu Bakar, Abdul Aziz

    Published 1995
    “…This research work is devoted to software development of scheduling and control algorithm of batch process plant using shortest path method developed by Dijkstra and K* shortest path method developed by Jin Y. …”
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    Student Project
  3. 3

    Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti by Abd Mokti, Nurnazifah

    Published 2024
    “…The project involves data collection, data preparation, and the implementation of the decision tree algorithm for price prediction. The decision tree's effectiveness and accuracy in predicting laptop prices are evaluated through rigorous testing and validation. …”
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    Thesis
  4. 4

    Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary by Mohd Zamary, Nurul Aida

    Published 2024
    “…The phase of this project is divided into data preprocessing, implementation of the decision tree algorithm, and evaluation of the algorithm and prototype. …”
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    Thesis
  5. 5

    Network analysis for GPS UTMnav system: preliminary stage by A. Majid, Siti Nuruljannah, Musa, Tajul Ariffin, Setan, Halim, Musliman, Ivin Amri

    Published 2008
    “…Each algorithm was reviewed and a simulation test was conducted to evaluate the selected algorithms. …”
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    Conference or Workshop Item
  6. 6

    IMPLEMENTATION OF BEHAVIOUR BASED NAVIGATION IN A PHYSICALLY CONFINED SITE by ABDUL RAZAK, NUSRAH

    Published 2017
    “…Selected basic behaviour-based algorithms such as wallfollower, obstacle avoidance, escape route and target detection are to be combined and its efficiency is measured. …”
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    Final Year Project
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    MOTION TARGET TRACKING OF MOBILE ROBOTS by NOOR RASHID, NOOR AIMAN

    Published 2015
    “…The main hardware and has been tested with basic programs based on the algorithms of the said methods. …”
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    Final Year Project
  11. 11

    Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

    Published 2023
    “…Practical implications In terms of managerial implications, the findings in this research help to frame the adoption of a more advanced analytical approach to forecasting, using a Machine Learning algorithm, in solving a newsvendor problem. …”
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    Book Section
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    Mitigating malicious nodes using trust and reputation based model in wireless sensor networks by Abdullah, Muhammad Daniel Hafiz

    Published 2018
    “…In order to achieve this, different sets of trust information including QoS, OSNs and ant colony system (ACS) algorithm are proposed to improve the selection of trustworthy node. …”
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    Thesis
  14. 14

    A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective by Ul haq, Riaz, Norrozila, Sulaiman, Muhammad, Alam

    Published 2013
    “…This survey paper is focused on the discussion of best optimal path routing algorithms in wireless sensor networks by using supervised machine learning approaches. …”
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    Conference or Workshop Item
  15. 15

    Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Ahmad Salihin, Samsudin, Amir Izzani, Mohamed, Mohd Mawardi, Saari

    Published 2025
    “…Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. …”
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    Article
  16. 16

    Enhanced Mobility Solution In Mobile Ipv6 Network by David, Adeniji Oluwashola

    Published 2008
    “…A framework based on policy based routing protocol was proposed for handling both the inbound and the outbound traffic on a mobile network, under specified policies which consider packet characteristics, current network situation and user preferences. The interface selection algorithm was based on NEMO implementation structure using a technique of mutihoming which was extended to MIPv6 concepts The outcome of this research work are: a designed policy protocol for policy messages communication between the Mobile Router and the Home Agent, the framework is simulated using Network Simulator (NS2) with an extension of mobiwan, the result shows the end to end delay, average end to end delay, overhead, optimal routing path, average inter-packet latency and throughput of the developed system. …”
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    Thesis
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    Prediction models of heritage building based on machine learning / Nur Shahirah Ja'afar by Ja'afar, Nur Shahirah

    Published 2021
    “…To overcome these limitations, this research has proposed five machine learning algorithms namely Linear Regression, Lasso, Ridge, Random Forest and Decision Tree. …”
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    Thesis
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    XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

    Published 2024
    “…Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM) have been employed to mitigate this challenge. …”
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    Article
  20. 20

    A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science by Balogun, A.-L., Tella, A., Baloo, L., Adebisi, N.

    Published 2021
    “…The study also revealed that machine learning algorithms such as random forest, gradient boosting machine, and classification and regression trees (CART) accurately predict air pollution hazard when integrated with spatial models. …”
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    Article