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    Prediction of customer churn for ABC Multistate Bank using machine learning algorithms / Hui Shan Hon ... [et al.] by Hui, Shan Hon, Khai, Wah Khaw, XinYing, Chew, Wai, Peng Wong

    Published 2023
    “…Customer churn is defined as the tendency of customers to cease doing business with a company in a given period. ABC Multistate Bank faces the challenges to hold clients. The purpose of this study is to apply machine learning algorithms to develop the most effective model for predicting bank customer churn. …”
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    Article
  2. 2

    They are different: molecular approach on Tirathaba pest infesting on oil palm and coconut tree by Tan, Calvin Zhe Khai, Su, Chong Ming, King, Patricia Jie Hung

    Published 2018
    “…The DNA sequences were analyzed with other Tirathaba sp. sequences available in Gene bank using phylogenetic tree constructed with Neighbor-Joining (NJ) and genetic distance analysis algorithms. …”
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  3. 3

    Evaluating Machine Learning Algorithms for Fake Currency Detection by Keerthana, S.N, Chitra, K.

    Published 2024
    “…In this study, we evaluate the effectiveness of six supervised machine learning algorithms—K-Nearest Neighbor, Decision Trees, Support Vector Machine, Random Forests, Logistic Regression, and Naive Bayes—in detecting the authenticity of banknotes. …”
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    Design and performance analysis of a fast 4-way set associative cache controller using Tree Pseudo Least Recently Used algorithm by Hazlan, Mohamed Alfian Al-Zikry, Gunawan, Teddy Surya, Yaacob, Mashkuri, Kartiwi, Mira, Arifin, Fatchul

    Published 2023
    “…A key feature of this design is the incorporation of the Tree Pseudo Least Recently Used (PLRU) algorithm for cache replacement, a strategic choice aimed at optimizing cache performance. …”
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  7. 7

    A simultaneous spam and phishing attack detection framework for short message service based on text mining approach by Mohd Foozy, Cik Feresa

    Published 2017
    “…There are five (5) Classification techniques used such as Naive Bayes, K-NN, Decision Tree, Random Tree and Decision Stump. The result of Hybrid Feature accuracy using Rapidminer and Naive Bayes technique is 77.47%, for K-NN: 78.56%, Decision Tree: 57.16%, Random Tree: 57.24% and Decision Stump: 57.16%. …”
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