Search Results - (( java application mining algorithm ) OR ( parameter segmentation clustering algorithm ))
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Individual-tree segmentation and extraction based on LiDAR point cloud data
Published 2024“…Utilizing LiDAR point cloud data and ground-measured data from 30 plots, we examined the sensitivity of individual tree segmentation to key parameters by varying the grid values of the point cloud distance discriminant clustering algorithm and adjusting the canopy height resolution (CHR) of the watershed algorithm. …”
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Direct approach for mining association rules from structured XML data
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AUTOMATED SCORING FOR SCALINESS OF PSORIASIS LESIONS USING CLUSTER SEGMENTATION
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Final Year Project -
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…The second segmentation algorithm combines Delaunay triangulation clustering in the spatial domain and Particle Swarm Optimization (PSO). …”
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Model-based hybrid variational level set method applied to lung cancer detection
Published 2024“…Initially, the VLSM introduces a scale-adaptive fast level-set image segmentation algorithm to address the inefficiency of low gray scale image segmentation. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Cauchy density-based algorithm for VANETs clustering in 3D road environments
Published 2022“…Clustering algorithms for VANETs operate in a decentralized mode, which requires incorporating additional stages before deciding the clustering decisions and might create sub-optimality due to the local nature of the decentralized approach. …”
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Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
Published 2011“…To estimate the bi - modal background-foreground distribution mixture parameters, Expectation-Maximization (EM) algorithm is applied and the images are clustered statistically and linearly. …”
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Data stream clustering by divide and conquer approach based on vector model
Published 2016“…The continuous effort on data stream clustering method has one common goal which is to achieve an accurate clustering algorithm. …”
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Model-based hybrid variational level set method applied to object detection in grey scale images
Published 2024“…In industrial inspection, segmentation algorithms detect product defects, cracks, or anomalies for quality control and safety. …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Hence, the algorithm must overcome the problem of dynamic updates in the internal parameters or counter the concept drift. …”
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Extracting crown morphology with a low-cost mobile LiDAR scanning system in the natural environment
Published 2021“…After using the RANSAC algorithm to remove the map ground, the European clustering algorithm is used for point cloud segmentation. …”
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Mining Sequential Patterns using I-PrefixSpan
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Digital segmentation of skin diseases / Hadzli Hashim and Razali Abdul Hadi
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A coverage path planning approach for autonomous radiation mapping with a mobile robot
Published 2023Article -
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The framework uses machine learning methods, including classification, clustering, feature selection, and parameter tuning, to improve accuracy and reliability. …”
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