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Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
Published 2013“…Using 10-fold cross validation for each algorithm, it was found that decision tree was the best algorithm with 83.6944% correctness. …”
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Classification Algorithms to Determine Students’ Specialization in a Higher Education Institution
Published 2023“…After that, we looked at how Python programming classification algorithms like Support Vector Machine (SVM), Naïve Bayes, Random Forest, and Decision Tree performed in classifying the areas of specialization of the students. …”
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Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm
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Rapid software framework for the implementation of machine learning classification models
Published 2021“…The machine learning model in the two platforms were tested on breast cancer and tax avoidance datasets with Decision Tree algorithm. The results indicated that although the software framework is easier than the programming platform for implementing the machine learning model, the results from the software framework were highly accurate and reliable. …”
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Enhancing understanding of programming concepts through physical games
Published 2017“…We produced in total 10 lesson games to illustrate variables, swapping, arrays, sorting algorithm particularly bubble sort, quicksort, selection sort, graph theory, dynamic programming, amortized analysis and trees. …”
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Mobile Learning: An Application Prototype for AVL Tree Learning Object
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Modeling of static and dynamic components of bio-nanorobotic systems
Published 2012“…In addition, a graph algorithm based on greedy methods is employed to compute a new set of optimal weighted electronic properties of the fullerenes via computing their Minimum Weight Spanning Trees (MWSTs). …”
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Energy-aware task scheduling for streaming applications on NoC-based MPSoCs
Published 2024“…Our approach is supported by a set of novel techniques, which include constructing an initial schedule based on a list scheduling where the priority of each task is its approximate successor-tree-consistent deadline such that the workload across all the processors is balanced, a retiming heuristic to transform intraperiod dependencies into inter-period dependencies for enhancing parallelism, assigning an optimal discrete frequency for each task and each message using a Non-Linear Programming (NLP)-based algorithm and an Integer-Linear Programming (ILP)-based algorithm, and an incremental approach to reduce the memory usage of the retimed schedule in case of memory size violations. …”
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Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with Orange Data Mining
Published 2025“…Next, machine learning (ML) methods (i.e. decision tree, gradient boosting, random forest, stacking, and neural network) were used to classify FLS of soybean. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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