Search Results - (( intelligence model cloud algorithm ) OR ( intelligence based tree algorithm ))

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    Modeling approach of cloud 4D printing service composition optimization based on non-dominated sorting genetic algorithm III by Liu, Jiajia, Zainudin, Edi Syams, As'arry, Azizan, Ismail, Mohd Idris Shah

    Published 2024
    “…This research provides valuable insights for the advancement of intelligent cloud-based 4D printing systems, paving the way for future developments in this field.…”
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    Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea by Sim, Doreen Ying Ying

    Published 2018
    “…This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. …”
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    Thesis
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    E2IDS: an enhanced intelligent intrusion detection system based on decision tree algorithm by Bouke, Mohamed Aly, Abdullah, Azizol, ALshatebi, Sameer Hamoud, Abdullah, Mohd Taufik

    Published 2022
    “…The model design is Decision Tree (DT) algorithm-based, with an approach to data balancing since the data set used is highly unbalanced and one more approach for feature selection. …”
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    User authentication in public cloud computing through adoption of electronic personal synthesis behavior by Mohanaad Talal Shakir

    Published 2023
    “…The problem is in multi-factor authentication with public cloud computing, the performance of user authentication in password-based authentication needs to move from traditional security processes to intelligent security processes. …”
    text::Thesis
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    Intelligent cooperative web caching policies for media objects based on decision tree supervised machine learning algorithm by Ibrahim, Hamidah, Yasin, Waheed, Abdul Hamid, Nor Asilah Wati, Udzir, Nur Izura

    Published 2014
    “…Moreover, cache pollution is a drawback of traditional web caching policies such as Least Frequently Used (LFU), Least Recently Used (LRU), and Greedy Dual Size (GDS) where web objects that are stored in the cache are not visited frequently. In this work, new intelligent cooperative web caching approaches based on decision tree supervised machine learning algorithm are presented. …”
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    Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy by Rahman, Sam Matiur, Ali, Md. Asraf, Altwijri, Omar, Alqahtani, Mahdi, Ahmed, Nasim, Ahamed, Nizam Uddin

    Published 2020
    “…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
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    Intelligent cooperative web caching policies for media objects based on J48 decision tree and naïve Bayes supervised machine learning algorithms in structured peer-to-peer systems by Ibrahim, Hamidah, Mohammed, Waheed Yasin, Udzir, Nur Izura, Abdul Hamid, Nor Asilah Wati

    Published 2016
    “…Moreover, traditional web caching policies such as Least Recently Used (LRU), Least Frequently Used (LFU), and Greedy Dual Size (GDS) suffer from caching pollution (i.e. media objects that are stored in the cache are not frequently visited which negatively affects on the performance of web proxy caching). In this work, intelligent cooperative web caching approaches based on J48 decision tree and Naïve Bayes (NB) supervised machine learning algorithms are presented. …”
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    Quantitative and intelligent risk models in risk management for constructing software development projects: A review by Burairah, Hussin, Abdelrafe, Elzamly

    Published 2016
    “…Indeed, this area needs more effort from scholars and researchers in quantitative and intelligent risk models to mitigate risks. As future work, we will use these hybrid models of quantitative and intelligent for mitigating software risks in cloud computing such as neural network, genetic algorithm and others artificial intelligence techniques.…”
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