Search Results - (( visualization implementation tree algorithm ) OR ( java applications optimisation algorithm ))
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Article -
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Fast clock tree generation using exact zero skew clock routing algorithm
Published 2009“…A few Perl scripts and a new visual basic based routing tool have been developed to support the methodology implementation. …”
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Zero skew clock routing for fast clock tree generation
Published 2008“…A few Perl scripts and a new Visual Basic based routing tool have been developed to support the methodology implementation. …”
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Proceeding Paper -
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The comparison of interactive 3D visualization between static and animated approaches for learning binary tree topic / Mohd Zulhisam Yaakub
Published 2016“…This shows that both 3D visualization methods implemented in this study can increase the student learning achievements and spatial abilities. …”
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Thesis -
5
Comparison Of Phylogenetic Trees Using Difference Distance Function Method
Published 2005“…The pre-processing is implemented using the Microsoft Visual C++. The phylogenetic tree is build using the PHYLlP (the PHYlogeny Inference Package), a package of programs for inferring phylogenies (evolutionary trees). …”
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Final Year Project Report / IMRAD -
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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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Edge assisted crime prediction and evaluation framework for machine learning algorithms
Published 2022“…The complete study and implementation process have shown a visual representation of crime in various areas of country. …”
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Conference or Workshop Item -
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Using modification of Prim's algorithm and GNU Octave and to solve the multiperiods installation problem
Published 2020“…, and that name is chosen because it is like Unix but differs from Unix because it is free and contains no Unix code. Those algorithms were implemented using 300 randomly generated problems. …”
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Analysis of hyperspectral reflectance for disease classification of soybean frogeye leaf spot using Knime analytics
Published 2023“…This analysis involved the implementation of machine learning (ML) algorithms, including decision trees, random forests, and stacking, to classify soybean FLS severity levels. …”
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Dynamic scene occlusion culling in architectural scenes based on dynamic bounding volume
Published 2006“…This algorithm is implemented using portal occlusion culling and kD-tree, which is suitable for indoor and architectural scenes. …”
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Thesis -
11
Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Thesis -
12
Enhancement Of Medical Image Compression Algorithm In Noisy WLANS Transmission
Published 2018“…For image reconstruction,decoding steps based the developed quantization architecture are examined.The EICA is particularly useful where the size of the transmitted data needs to be reduced to minimize the image transmission time.For data compression with suitable and effective performance,enhanced independent components analysis (EICA) is proposed as an algorithm for compression and decompression of medical data.A comparative analysis is performed based on existing data compression techniques:discrete cosine transform (DCT), set partitioning in hierarchical trees (SPIHT),and Joint Photographic Experts Group (JPEG 2000).Three main modules,namely,compression segment (CS),transceiver segment (TRS),and outcome segment (OTS) modules,are developed to realize a fully computerized simulation tool for medical data compression with suitable and effective performance.To compress medical data using algorithms,CS module involves four different approaches which are DCT, SPIHT,JPEG 2000 and EICA.TRS module is processed by low-cost WLANs with low-bandwidth transmission.Finally,OTS is used for data decompression and visualization result.In terms of compression module,results show the benefits of applying EICA in medical data compression and transmission.While for system design,the developed system displays favorable outcomes in compressing and transmitting medical data.In conclusion,all three modules (CS,TRS,and OTS) are integrated to yield a computerized prototype named as Medical Data Simulation System(Medata-SIM) computerized system that includes medical data compression and transceiver for visualization to aid medical practitioners in carrying out rapid diagnoses.…”
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13
Non-photorealistic outdoor scene rendering : techniques and application
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Book Section -
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Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization
Published 2023“…Additionally, descriptive analytics techniques were employed to visualize the data, extracting valuable insights and understanding the correlations among the features. …”
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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“…Furthermore, reliefF-Gradient boosting and random forest algorithms achieved promising overall accuracy of 97.4% and 96.9%, respectively after implementing filtering and feature selection techniques. …”
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DurianCare: optimising trunk disease detection and precision farming in a mobile application / Muhammad Haziq Azmi
Published 2025“…Durian tree trunk diseases are threatening to reduce the productivity of farmers and therefore have economic consequences, such as lower yield and increased cost. …”
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Gene Selection for Cancer Classification Based on XGBoost
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