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Relevant bibliographies by topics / Multiclass Sunflower Network

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Academic literature on the topic 'Multiclass Sunflower Network'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Multiclass Sunflower Network"

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Thilagavathi, T., and L. Arockiam. "Multi-Class Sunflower Disease Detection by Integrating Enhanced Jellyfish Search Algorithm." Indian Journal Of Science And Technology 17, no. 44 (2024): 4633–45. https://doi.org/10.17485/ijst/v17i44.2874.

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Objectives: This research aims to classify diseases observed in sunflower flowers and leaves using deep learning algorithms. The goal is to enhance agricultural output by addressing the significant issue of plant diseases, which negatively affect the security of the food supply. Methods: The study utilizes a feature selection approach to identify multi-class sunflower diseases. The Enhanced Jellyfish Search (EJFSOA) algorithm is improved in three ways: (i) optimization capabilities and convergence speed are enhanced through sine and cosine learning variables during Type B motion in the swarm,

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T, Thilagavathi, and Arockiam L. "Multi-Class Sunflower Disease Detection by Integrating Enhanced Jellyfish Search Algorithm." Indian Journal of Science and Technology 17, no. 44 (2024): 4633–45. https://doi.org/10.17485/IJST/v17i44.2874.

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Abstract <strong>Objectives:</strong> This research aims to classify diseases observed in sunflower flowers and leaves using deep learning algorithms. The goal is to enhance agricultural output by addressing the significant issue of plant diseases, which negatively affect the security of the food supply. <strong>Methods:</strong> The study utilizes a feature selection approach to identify multi-class sunflower diseases. The Enhanced Jellyfish Search (EJFSOA) algorithm is improved in three ways: (i) optimization capabilities and convergence speed are enhanced through sine and cos

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Zhang, Shuailing, Hailin Yu, Bingquan Tian, et al. "Combining UAV Multi-Source Remote Sensing Data with CPO-SVR to Estimate Seedling Emergence in Breeding Sunflowers." Agronomy 14, no. 10 (2024): 2205. http://dx.doi.org/10.3390/agronomy14102205.

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In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the

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Bonato, Jacopo, Francesco Pelosin, Luigi Sabetta, and Alessandro Nicolosi. "MIND: Multi-Task Incremental Network Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11105–13. http://dx.doi.org/10.1609/aaai.v38i10.28987.

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The recent surge of pervasive devices that generate dynamic data streams has underscored the necessity for learning systems to adapt continually to data distributional shifts. To tackle this challenge, the research community has put forth a spectrum of methodologies, including the demanding pursuit of class-incremental learning without replay data. In this study, we present MIND, a parameter isolation method that aims to significantly enhance the performance of replay-free solutions and achieve state-of-the-art results on several widely studied datasets. Our approach introduces two main contri

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You, Wu, Lee, and Liu. "Intelligent Neural Network Schemes for Multi-Class Classification." Applied Sciences 9, no. 19 (2019): 4036. http://dx.doi.org/10.3390/app9194036.

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Multi-class classification is a very important technique in engineering applications, e.g., mechanical systems, mechanics and design innovations, applied materials in nanotechnologies, etc. A large amount of research is done for single-label classification where objects are associated with a single category. However, in many application domains, an object can belong to two or more categories, and multi-label classification is needed. Traditionally, statistical methods were used; recently, machine learning techniques, in particular neural networks, have been proposed to solve the multi-class cl

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Wang, Ruxin, Jianping Fan, and Ye Li. "Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection." IEEE Journal of Biomedical and Health Informatics 24, no. 9 (2020): 2461–72. http://dx.doi.org/10.1109/jbhi.2020.2981526.

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Gonzalez-Barrios, Pablo, Marina Castro, Osvaldo Pérez, Diego Vilaró, and Lucía Gutiérrez. "Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency." Spanish Journal of Agricultural Research 15, no. 4 (2018): e0705. http://dx.doi.org/10.5424/sjar/2017154-11016.

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Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, esti

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Yang, Hai, and Hai-Jun Huang. "The multi-class, multi-criteria traffic network equilibrium and systems optimum problem." Transportation Research Part B: Methodological 38, no. 1 (2004): 1–15. http://dx.doi.org/10.1016/s0191-2615(02)00074-7.

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Liu, Jin, Chenkai Gu, Jin Wang, Geumran Youn, and Jeong-Uk Kim. "Multi-scale multi-class conditional generative adversarial network for handwritten character generation." Journal of Supercomputing 75, no. 4 (2017): 1922–40. http://dx.doi.org/10.1007/s11227-017-2218-0.

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Raovic, Nevena, Otto Anker Nielsen, and Carlo Giacomo Prato Carlo Giacomo Prato. "DYNAMIC QUEUING TRANSMISSION MODEL FOR DYNAMIC NETWORK LOADING." Transport 32, no. 2 (2015): 146–59. http://dx.doi.org/10.3846/16484142.2015.1062417.

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This paper presents a new macroscopic multi-class dynamic network loading model called Dynamic Queuing Transmission Model (DQTM). The model utilizes ‘good’ properties of the Dynamic Queuing Model (DQM) and the Link Transmission Model (LTM) by offering a DQM consistent with the kinematic wave theory and allowing for the representation of multiple vehicle classes, queue spillbacks and shock waves. The model assumes that a link is split into a moving part plus a queuing part, and p that traffic dynamics are given by a triangular fundamental diagram. A case-study is investigated and the DQTM is co

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Dissertations / Theses on the topic "Multiclass Sunflower Network"

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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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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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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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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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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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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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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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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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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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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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Book chapters on the topic "Multiclass Sunflower Network"

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Legato, Pasquale, and Rina Mary Mazza. "Class Aggregation for Multi-class Queueing Networks with FCFS Multi-server Stations." In Queueing Theory and Network Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27181-7_14.

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Legato, Pasquale, and Rina Mary Mazza. "Correction to: Class Aggregation for Multi-class Queueing Networks with FCFS Multi-server Stations." In Queueing Theory and Network Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27181-7_24.

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Tiwary, Shubhranshu Kumar, Jagadish Pal, and Chandan Kumar Chanda. "Multi-Class Classification of Power Network States Using Multi-Dimensional Neural Network." In Recent Advances in Energy Systems, Power and Related Smart Technologies. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29586-7_16.

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Bishop, J. M., P. R. Minchinton, and R. J. Mitchell. "Multi Class Pattern Association Using Digital N-Tuple Networks." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_132.

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Morolong, Mamoqenelo P., Fungai Bhunu Shava, and Attlee M. Gamundani. "Multi-class Classification on Network Traffic Data: Evaluation with a Multi-class APT Dataset." In Learning and Analytics in Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9855-1_49.

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Schetinin, Vitaly, Joachim Schult, Burkhart Scheidt, and Valery Kuriakin. "Learning Multi-class Neural-Network Models from Electroencephalograms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_23.

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Akhila Naz, K. A., R. S. Jeena, and P. Niyas. "Deep Neural Network Based Multi-class Arrhythmia Classification." In Intelligent Systems, Technologies and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0730-1_15.

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Xue, Mengqi, Jie Song, Li Sun, and Mingli Song. "Tree-Like Branching Network for Multi-class Classification." In Intelligent Computing & Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93247-3_18.

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Ameli, Mostafa, Jean-Patrick Lebacque, and Ludovic Leclercq. "Multi-Attribute, Multi-Class, Trip-Based, Multi-Modal Traffic Network Equilibrium Model: Application to Large-Scale Network." In Traffic and Granular Flow '17. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11440-4_53.

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Huang, Xi, Keyu Wu, Gang Hu, and Jie Shao. "Multi-class Human Body Parsing with Edge-Enhancement Network." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_51.

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Conference papers on the topic "Multiclass Sunflower Network"

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Wang, Yu, Anhui Tan, and Shenming Gu. "Dynamic Graph Convolution Network for Multi-Label Class Incremental Learning." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762492.

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Otake, Mamoru, Shun Miura, Hiroyuki Kusaka, Nambara Takahiro, Yuichiro Kunai, and Masahiro Kashiwagi. "Multi-class object detection by an optical neural network implementation." In AI and Optical Data Sciences VI, edited by Masaya Notomi and Tingyi Zhou. SPIE, 2025. https://doi.org/10.1117/12.3042688.

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Islam, M. D. Samiul, and Irene Cheng. "PLANET: Multi-Class Patch Layer Adaptive Network for Satellite Image Segmentation." In 2024 IEEE Space, Aerospace and Defence Conference (SPACE). IEEE, 2024. http://dx.doi.org/10.1109/space63117.2024.10668387.

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Liu, Jiachao, Pablo Guarda, Koichiro Niinuma, and Sean Qian. "Enhancing Multi-Class Mesoscopic Network Modeling with High-Resolution Satellite Imagery." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919613.

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Liu, Zhitong, Zhiqiang Zhang, Mengqing Cheng, Haonan Tan, Xinyao Tan, and Le Wang. "SLX:A Multi-class Network Intrusion Detection Method on Stacking Boosting Algorithm." In 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC). IEEE, 2024. https://doi.org/10.1109/dsc63484.2024.00023.

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Khademi, Sadaf, Zohreh Hajiakhondi-Meybodi, Golnaz Vaseghi, Nizal Sarrafzadegan, and Arash Mohammadi. "FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning Network." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715254.

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Xu, Jianhang, Anhui Tan, and Shenming Gu. "Global and Local Graph Convolution Network for Multi-Label Class Incremental Learning." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762125.

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Wang, Yunlan, Qing Wang, Tianhai Zhao, YongKuo Hu, JianHua Gu, and ZhengXiong Hou. "Stochastic Network Calculus Based Quality of Service Guarantee for Multi-class Traffic." In 2024 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 2024. https://doi.org/10.1109/ipccc59868.2024.10850266.

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Zhao, Peng, Ruicong Wang, Xueyi Zhang, Mingrui Lao, and Siqi Cai. "Binary-Temporal Convolutional Neural Network for Multi-Class Auditory Spatial Attention Detection." In 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024. https://doi.org/10.1109/iscslp63861.2024.10800319.

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Duan, Yalin, Lixia Xue, Juan Yang, and Ronggui Wang. "MCANet: A Multi-Scale Class-Related Attention Network for Few-Shot Learning." In 2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2025. https://doi.org/10.1109/cacml64929.2025.11010976.

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Reports on the topic "Multiclass Sunflower Network"

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Mahat, Marian, Guy Morrow, Brian Long, Siew Fang Law, Amy Gullickson, and Chengxin Guo. Developing an impact framework for Science Gallery Network: Final report. University of Melbourne, 2022. http://dx.doi.org/10.46580/124372.

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The aim of this project was to develop an impact framework for the Science Gallery Network (SGN). This work was commissioned by the Science Gallery International (SGI). The SGN has eight member organisations across four continents: Dublin, London, Melbourne, Bengaluru, Detroit, Rotterdam, Atlanta and Berlin. Whilst the network consistently sees unprecedented levels of accomplishment by its members, a testimony to their capacity, innovation and vision, the SGN does not have a systematic way to measure and monitor this impact. An impact framework that can assist with understanding and reporting

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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping University Electronic Press, 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were test

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Saltus, Christina, Richard Johansen, Molly Reif, Weston Nowlin, Benjamin Schwartz, and Joshuah Perkins. Next Generation Ecological Models - Central Texas Watersheds: Geospatial Layers and Related Tables. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47608.

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Relevant geospatial data layers are required for developing next generation ecological response models for specific reaches of 5 rivers in Central Texas: Colorado, Concho, San Saba, Llano, and Pedernales Rivers. Therefore, a collaborative effort between Engineer Research and Development Center (ERDC), Texas State University, and Texas A&M University was undertaken to acquire and curate a collection of biological and physical datasets to be utilized as inputs for next generation ecological response models at various spatial scales (watershed, river buffer, and point). The objective was to a

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