Search Results - (( java implementation plan algorithm ) OR ( framework implementation tree algorithm ))
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Quantum Processing Framework And Hybrid Algorithms For Routing Problems
Published 2010“…The framework is used to increase the implementation performance of quantum algorithms. …”
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2
Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea
Published 2017“…The improved algorithms were implemented in OSA dataset and UCI online databases for comparisons. …”
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3
Edge assisted crime prediction and evaluation framework for machine learning algorithms
Published 2022“…In particular, this study proposes a crime prediction and evaluation framework for machine learning algorithms of the network edge. …”
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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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A web-based implementation of k-means algorithms
Published 2022“…The project was planned using the Kanban development methodology, and was built using HTML, CSS, JavaScript, Django, NumPy and pandas. …”
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Final Year Project / Dissertation / Thesis -
6
Disparity map algorithm using hierarchical of bitwise pixel differences and segment-tree from stereo image
Published 2024“…The final step of the proposed algorithm framework is refinement stage. At this stage, Bilateral filter (BF) and Weighted Median (WM) filter are utilized. …”
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Smart appointment organizer for mobile application / Mohd Syafiq Adam
Published 2009“…The development of this project is based on a framework of 5 main stages: planning, analysis, design, implementation and testing. …”
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8
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…This methodology consist of Requirement and Planning, Design, Construction, Testing and Implementation. …”
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Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer
Published 2017“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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11
Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment
Published 2021“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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Implementation of RIO in UMJaNetSim / Chan Chin We
Published 2004“…In this project, I will do some research on the recent TCP congestion control and avoidance mechanisms and implement one of the mechanisms into the Java Network Simulator (JaNetSim). …”
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13
Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…It is organized into three phases: preliminary investigation, implementation and analysis, and validation. The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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14
Songs Search Using Human Humming Voice
Published 2007“…The scopes for this project are human humming voice, voice capture in WA V format, songs database, and MIDI file comparing algorithm. Methodologies used in this system are based on system analysis and design methodology comprising planning, analysis, design and implementation. …”
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Final Year Project -
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An improved diabetes risk prediction framework : An Indonesian case study
Published 2018“…However,there is the issue of noisy dataset detected as incomplete data and the outlier class problem that affects sampling bias.Existing frameworks were deemed difficult in identifying the critical risk factors of diabetes;some of which were considerably inaccurate and consume substantial computation time.The purpose of this study is to develop a suitable framework for predicting diabetes risks.From a complete blood test,the framework can predict and classify the output of either having diabetes risk or no diabetes risk.A Diabetes Risk Prediction Framework (DRPF) was developed from the literature review and case studies were afterwards conducted in three private hospitals in Semarang.Analyses were conducted to find a suitable component of the framework—due to lack of comparison and analysis on the combination of feature selection and classification algorithm.DRPF comprises four main sections: pre-processing,outlier detection,risk weighting,and learning. …”
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16
Improved random forest for feature selection in writer identification
Published 2015“…Discretization process was proven to increase the classification performances and improved the identification performance in WI. An algorithm and framework of Improved Random Forest (IRF) tree was applied for feature selection process. …”
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A conceptual model for the E-commerce application recommendation framework using exploratory search
Published 2024journal::journal article -
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Car dealership web application
Published 2022“…Tree SHAP, which was implemented by third-party SHAP Python library, were used in model monitoring and made the models interpretable. …”
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Final Year Project / Dissertation / Thesis -
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms
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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