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Dissertations / Theses on the topic 'Multi­class Sunflower Network'

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Published: 7 June 2025
Last updated: 2 August 2025

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1

Camynta-Baezie, Gylbet. "Multi-class pseudo-dynamic traffic assignment in a signalized urban road network." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313502.

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2

Shan, Liang. "Joint Gaussian Graphical Model for multi-class and multi-level data." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81412.

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Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are considered as higher level variables and others are nested in these higher level variables,
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3

Mele, Matteo. "Convolutional Neural Networks for the Classification of Olive Oil Geographical Origin." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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This work proposed a deep learning approach to a multi-class classification problem. In particular, our project goal is to establish whether there is a connection between olive oil molecular composition and its geographical origin. To accomplish this, we implement a method to transform structured data into meaningful images (exploring the existing literature) and developed a fine-tuned Convolutional Neural Network able to perform the classification. We implement a series of tailored techniques to improve the model.
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4

Phillips, Adon. "Melanoma Diagnostics Using Fully Convolutional Networks on Whole Slide Images." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36929.

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Semantic segmentation as an approach to recognizing and localizing objects within an image is a major research area in computer vision. Now that convolutional neural networks are being increasingly used for such tasks, there have been many improve- ments in grand challenge results, and many new research opportunities in previously untennable areas. Using fully convolutional networks, we have developed a semantic segmentation pipeline for the identification of melanocytic tumor regions, epidermis, and dermis lay- ers in whole slide microscopy images of cutaneous melanoma or cutaneous metastati
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5

Tuma, Matthias Paul [Verfasser], Tobias [Gutachter] Glasmachers, and Christian [Gutachter] Igel. "Optimization of online multi-class support vector machines and applications to the classification of passive-acoustic remote sensing data from the verification network of the comprehensive nuclear-test-ban treaty / Matthias Paul Tuma ; Gutachter: Tobias Glasmachers, Christian Igel ; Fakultät für Elektrotechnik und Informationstechnik." Bochum : Ruhr-Universität Bochum, 2019. http://d-nb.info/1197305475/34.

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6

Slunský, Tomáš. "Vícetřídá segmentace 3D lékařských dat pomocí hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400891.

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Master's thesis deals with multiclass image segmentation using convolutional neural networks. The theoretical part of the Master's thesis focuses on image segmentation. There are basics principles of neural networks and image segmentation with more types of approaches. In practical part the Unet architecture is choosen and is described for image segmentation more. U-net was applied for medicine dataset. There is processing procedure which is more described for image proccesing of three-dimmensional data. There are also methods for data preproccessing which were applied for image multiclass seg
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7

Liang, Hong-Yi, and 梁弘一. "Multi-class Vehicle Type Detection and Classification based on Lightweight Convolutional Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5yzvaw.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>106<br>According to the statisitcs from Ministry of Transportation and Communications, there are currently about 7 million vehicles and more than 14 million motorbikes in Taiwan. The numbers of deaths per years in traffic accidents is about 2,000 and about 200,000 are injured. 77% of accidents are caused by driver’s mistakes, so vehicle identification is very important in ADAS, which can accurately identify the objects that may appear on the road, not only helps the driver understand the traffic conditions, but also improves driving safety. This thesis mainly studies
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8

Kouvatsos, Demetres D., Irfan U. Awan, and Khalid Al-Begain. "Performance Modelling of GPRS with Bursty Multi-class Traffic." 2003. http://hdl.handle.net/10454/3279.

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No<br>An analytic framework is devised, based on the principle of maximum entropy (ME), for the performance modelling and evaluation of a wireless GSM/GPRS cell supporting bursty multiple class traffic of voice calls and data packets under complete partitioning (CPS), partial sharing (PSS) and aggregate sharing (ASS) traffic handling schemes. Three distinct open queueing network models (QNMS) under CPS, PSS and ASS, respectively, are described, subject to external compound Poisson traffic processes and generalised exponential (GE) transmission times under a repetitive service blocking mechanis
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9

LIU, HSIN-CHIAO, and 柳馨喬. "Research on Application of RST and Artificial Neural Network in Multi-class Package Product Classification." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/52759303862131687215.

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碩士<br>國立勤益科技大學<br>工業工程與管理系<br>97<br>The semiconductor industry is an important industry in Taiwan. The IC design, fabrication and package are all focused on light-and-thin style. Since customer orders may be various, and there are numerous product types and applications, the purchase-order to production-order process requires more labors to respond to the demands. However, the current operational process is human communication. Therefore, it is very important to provide package information to designer efficiently. Only by doing so could the subsequent design method and fabrication process of I
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10

Hung, Hsiu-Chun, and 洪修淳. "An Application of Multi-Class Online Transfer Learning on 4G/LTE Network Traffic Data Analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/aq8xbs.

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碩士<br>國立交通大學<br>統計學研究所<br>106<br>Propose another framework of Online Transfer Learning that can solve the multi-class classification task. To transfer the knowledge of a source domain to a target domain, we combine the source classifier and the online target classifier by allocating different weights. We introduce a concept of possibility vector and combine the possibility vectors of two classifiers to make the prediction. Then we develop a new mechanism for updating these allocation weights. We also provide theoretical analysis to guarantee the performance of the framework will not be too bad
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11

Philipp, Stefan [Verfasser]. "Design and implementation of a multi-class network architecture for hardware neural networks / presented by Stefan Philipp." 2008. http://d-nb.info/989561755/34.

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12

Lai, Yi-Fang, and 賴怡芳. "A Multi-Class Users Emergency Rescue and Evacuation Network Reconstruction Model for Large-Scale Natural Disasters with a Supernetwork Structure." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/20013696618407788950.

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碩士<br>國防管理學院<br>國防決策科學研究所<br>95<br>Since natural disasters usually go with the large-scale destruction of the transportation network system, how to rapidly restore the infrastructure of the transportation network is the primary topic of some rescue-related and evacuation-related researches. However, using multi-class users in network to analyze the reality of rescue and evacuation will improve some rescue and evacuation network reconstructive plans based on the single vehicle. It can reflect flow patterns of real network more properly and make rescue-related and evacuation-related network desi
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13

Dubey, Abhishek. "Multimodal Deep Learning for Multi-Label Classification and Ranking Problems." Thesis, 2015. http://etd.iisc.ac.in/handle/2005/3681.

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In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better features, which further improves many supervised tasks. These models not only automate the feature extraction process but also provide with robust features for various machine learning tasks. But the unsupervised pretraining and feature extraction using multi-layered networks are restricted only to the input features and not to the output. The performance of many supervised lear
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14

Dubey, Abhishek. "Multimodal Deep Learning for Multi-Label Classification and Ranking Problems." Thesis, 2015. http://etd.iisc.ernet.in/2005/3681.

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In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better features, which further improves many supervised tasks. These models not only automate the feature extraction process but also provide with robust features for various machine learning tasks. But the unsupervised pretraining and feature extraction using multi-layered networks are restricted only to the input features and not to the output. The performance of many supervised lear
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15

BALZOTTI, CATERINA. "Second order traffic flow models on road networks and real data applications." Doctoral thesis, 2021. http://hdl.handle.net/11573/1538080.

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This thesis concerns macroscopic traffic models and data-driven models. In the first part we deal with the extension of Generic Second Order Models (GSOM) for traffic flow to road networks. We define a Riemann Solver at the junction based on a priority rule, providing an iterative algorithm able to build the solution to junctions with n incoming and m outgoing roads. The logic underlying our solver is the following: the flow is maximised respecting the priority rule, but the latter can be modified if the outgoing road supply exceeds the demand of the road with higher priority. We provide bound
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