Littérature scientifique sur le sujet « CNN ALGORITHM »
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Articles de revues sur le sujet "CNN ALGORITHM"
Reddy, Y. Venkat Sai, G. Chandana, G. Chetan Redddy, Ayush Kumar, Suvarna Kumar et Dr Syed Siraj Ahmed. « Lung Cancer Detection using YOLO CNN Algorithm ». International Journal of Research Publication and Reviews 4, no 5 (juin 2023) : 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.
Texte intégralDiqi, Mohammad. « Waste Classification using CNN Algorithm ». International Conference on Information Science and Technology Innovation (ICoSTEC) 1, no 1 (26 février 2022) : 130–35. http://dx.doi.org/10.35842/icostec.v1i1.17.
Texte intégralTiancheng, Li, Ren Qing-dao-er-ji et Qiu Ying. « Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia ». Advances in Meteorology 2019 (6 décembre 2019) : 1–13. http://dx.doi.org/10.1155/2019/5176576.
Texte intégralBahaa, Ahmed, Abdalla Sayed, Laila Elfangary et Hanan Fahmy. « A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach ». PLOS ONE 17, no 12 (1 décembre 2022) : e0278493. http://dx.doi.org/10.1371/journal.pone.0278493.
Texte intégralRamekar, Aditya Dhanraj, Pooja Rajendra Sanas, Akshay Rajendra Ghodekar, Shailesh Ramesh et Prof S. S. Bhagat. « Crop Prediction Using CNN Algorithm ». International Journal for Research in Applied Science and Engineering Technology 10, no 4 (30 avril 2022) : 2714–19. http://dx.doi.org/10.22214/ijraset.2022.41873.
Texte intégralN, Krishnamoorthy. « TV Shows Popularity and Performance Prediction Using CNN Algorithm ». Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25 juillet 2020) : 1541–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202257.
Texte intégralQin, Jiangping, Yan Zhang, Huan Zhou, Feng Yu, Bo Sun et Qisheng Wang. « Protein Crystal Instance Segmentation Based on Mask R-CNN ». Crystals 11, no 2 (4 février 2021) : 157. http://dx.doi.org/10.3390/cryst11020157.
Texte intégralHUANG, Jiawei, Caixia BI, Jiayue LIU et Shaohua DONG. « Research on CNN-based intelligent recognition method for negative images of weld defects ». Journal of Physics : Conference Series 2093, no 1 (1 novembre 2021) : 012020. http://dx.doi.org/10.1088/1742-6596/2093/1/012020.
Texte intégralVitale, S., G. Ferraioli et V. Pascazio. « EDGE PRESERVING CNN SAR DESPECKLING ALGORITHM ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (4 novembre 2020) : 97–100. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-97-2020.
Texte intégralMehta, Jahangir Jepee, Furqaan Ahmad Wani, Aamir Ashraf Ahangar, Kanwaljeet Kaur et Najmusher H. « Leaf Disease Remedy Using CNN Algorithm ». International Journal for Research in Applied Science and Engineering Technology 10, no 4 (30 avril 2022) : 1148–51. http://dx.doi.org/10.22214/ijraset.2022.41468.
Texte intégralThèses sur le sujet "CNN ALGORITHM"
Shaif, Ayad. « Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors ». Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.
Texte intégralReiling, Anthony J. « Convolutional Neural Network Optimization Using Genetic Algorithms ». University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.
Texte intégralBrandt, Carl-Simon, Jonathan Kleivard et Andreas Turesson. « Convolutional, adversarial and random forest-based DGA detection : Comparative study for DGA detection with different machine learning algorithms ». Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20103.
Texte intégralEl-Shafei, Ahmed. « Time multiplexing of cellular neural networks ». Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365221.
Texte intégralMOREIRA, André Luis Cavalcanti. « An adaptable storage slicing algorithm for content delivery networks ». Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17331.
Texte intégralMade available in DSpace on 2016-07-12T12:20:38Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Thesis - André Luis Cavalcanti Moreira.pdf: 3666881 bytes, checksum: 956e0e6be2bd9f076c0d30eea9d3ea25 (MD5) Previous issue date: 2015-08-28
Several works study the performance of Content Delivery Networks (CDNs) under various network infrastructure and demand conditions. Many strategies have been proposed to deal with aspects inherent to the CDN distribution model. Though mostly very effective, a traditional CDN approach of statically positioned elements often fails to meet quality of experience (QoE) requirements when network conditions suddenly change. CDN adaptation is a key feature in this process and some studies go even further and try to also deal with demand elasticity by providing an elastic infrastructure (cloud computing) to such CDNs. Each Content Provider (CP) gets served only the amount of storage space and network throughput that it needs and pays only for what has been used. Some IaaS providers offer simple CDN services on top of their infrastructure. However, in general, there is a lack of PaaS tools to create rapidly a CDN. There is no standard or open source software able to deliver CDN as a service for each tenant through well-known managers. A PaaS CDN should be able to implement content delivery service in a cloud environment, provision and orchestrate each tenant, monitor usage and make decisions on planning and dimensioning of resources. This work introduces a framework for the allocation of resources of a CDN in a multi-tenant environment. The framework is able to provision and orchestrate multi-tenant virtual CDNs and can be seen as a step towards a PaaS CDN. A simple dot product based module for network change detection is presented and a more elaborate multi-tenant resource manager model is defined. We solve the resulting ILP problem using both branch and bound as well as an efficient cache slicing algorithm that employs a three phase heuristic for orchestration of multi-tenant virtual CDNs. We finally show that a distributed algorithm with limited local information may be also offer reasonable resource allocation while using limited coordination among the different nodes. A self-organization behavior emerges when some of the nodes reach consensus.
Vários trabalhos estudam o desempenho de Redes de Distribuição de Conteúdo (CDN) em diferentes condições e demanda e de infraestrutura. Muitas estratégias têm sido propostas para lidar com aspectos inerentes ao modelo de distribuição de CDN. Embora essas técnicas sejam bastante eficazes, uma abordagem tradicional de elementos estaticamente posicionados numa CDN muitas vezes não consegue atender os requisitos de qualidade de experiência (QoE) quando as condições da rede mudam repentinamente. Adaptação CDN é uma característica fundamental neste processo e alguns estudos vão ainda mais longe e tentam lidar com a elasticidade da demanda, proporcionando uma infraestrutura elástica (computação em nuvem) para a CDN. Cada provedor de conteúdo obtém apenas a quantidade de armazenamento e de rede necessários, pagando apenas pelo efetivo uso. Alguns provedores IaaS oferecem serviços de CDN sobre suas estruturas. No entanto, em geral, não existe padrão ou softwares de código aberto capazes de entregar serviços de CDN por meio de gerenciadores. Uma CDN PaaS deve ser capaz de fornecer um serviço de entrega de conteúdo em um ambiente de nuvem, provisionar e orquestrar cada tenant, monitorar uso e tomar decisões de planejamento e dimensionamento de recursos. Este trabalho apresenta um framework para alocação de recursos de uma CDN em ambiente multi-tenant. O framework é capaz de provisionar e orquestrar CDNs virtuais e pode ser visto como um passo em direção a uma PaaS CDN. Um módulo baseado em simples produto escalar para detecção de mudanças na rede é apresentado, bem como um modelo mais elaborado de gerenciamento de recursos. Resolvemos o problema ILP resultante dessa abordagem por meio de um algoritmo de divisão de cache que emprega uma heurística em três fases para a orquestração de CDN virtuais. Por fim, mostramos uma outra abordagem com algoritmo distribuído que usa informação local e que também oferece uma alocação razoável usando coordenação limitada entre os diferentes nós. Um comportamento de auto-organização surge quando alguns desses nós chegam a um consenso.
Yavaş, Gökhan. « Algorithms for Characterizing Structural Variation in Human Genome ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1279345476.
Texte intégralTamaki, Suguru. « Improved Algorithms for CNF Satisfiability Problems ». 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/68895.
Texte intégralWathugala, Wathugala Gamage Dulan Manujinda. « Formal Modeling Can Improve Smart Transportation Algorithm Development ». Thesis, University of Oregon, 2017. http://hdl.handle.net/1794/22608.
Texte intégralEnsuring algorithms work accurately is crucial, especially when they drive safety critical systems like self-driving cars. We formally model a published distributed algorithm for autonomous vehicles to collaborate and pass thorough an intersection. Models are built and validated using the “Labelled Transition System Analyser” (LTSA). Our models reveal situations leading to deadlocks and crashes in the algorithm. We demonstrate two approaches to gain insight about a large and complex system without modeling the entire system: Modeling a sub system - If the sub system has issues, the super system too. Modeling a fast-forwarded state - Reveals problems that can arise later in a process. Some productivity tools developed for distributed system development are also presented. Manulator, our distributed system simulator, enables quick prototyping and debugging on a single workstation. LTSA-O, extension to LTSA, listens to messages exchanged in an execution of a distributed system and validates it against a model.
Pallotti, Davide. « Integrazione di dati di disparità sparsi in algoritmi per la visione stereo basati su deep-learning ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16633/.
Texte intégralEssink, Wesley. « CNC milling toolpath generation using genetic algorithms ». Thesis, University of Bath, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715245.
Texte intégralLivres sur le sujet "CNN ALGORITHM"
Quadflieg, Sven, Klaus Neuburg et Simon Nestler, dir. (Dis)Obedience in Digital Societies. Bielefeld, Germany : transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839457634.
Texte intégralMunerman, Viktor, Vadim Borisov et Aleksandra Kononova. Mass data processing. Algebraic models and methods. ru : INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1906037.
Texte intégral1944-, Webb William, dir. Cake-cutting algorithms : Be fair if you can. Natick, Mass : A.K. Peters, 1998.
Trouver le texte intégralDubanov, Aleksandr. Simulation of pursuit and parallel approach methods in pursuit problems. ru : Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02071-5.
Texte intégralGdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru : INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.
Texte intégralP, Banks Stephen. Can Perceptrons find Lyapunov functions ? : An algorithmic approach to systems stability. Sheffield : University of Sheffield, Dept. of Control Engineering, 1989.
Trouver le texte intégralDapporto, Paolo, Paola Paoli, Patrizia Rossi et Annalisa Guerri. The UTN program. Florence : Firenze University Press, 2001. http://dx.doi.org/10.36253/88-8453-032-6.
Texte intégralvan Es, Karin, et Nanna Verhoeff. Situating Data. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland : Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463722971.
Texte intégralUlyanina, Olga, Azalia Zinatullina et Elena Lyubka. Countering terrorism : psychological assistance to students and the formation of a safe type of personality. ru : Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02048-7.
Texte intégralCevelev, Aleksandr. Strategic development of railway transport logistics. ru : INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1194747.
Texte intégralChapitres de livres sur le sujet "CNN ALGORITHM"
Su, Te-Jen, Yi Hui, Chiao-Yu Chuang et Wen-Pin Tsai. « MCSA-CNN Algorithm for Image Noise Cancellation ». Dans Lecture Notes in Electrical Engineering, 209–20. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74935-8_15.
Texte intégralDi, Lei, Hongzhong Ma, Yi Luo et Zhiru Li. « A CNN-Based Information Network Attack Detection Algorithm ». Dans Advances in Wireless Communications and Applications, 155–61. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3486-5_19.
Texte intégralWu, Jin, Lei Wang et Yu Wang. « An Improved CNN-LSTM Model Compression Pruning Algorithm ». Dans Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 727–36. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_75.
Texte intégralWan, Yanchen, Yu Liu, Yuan Li et Puhong Zhang. « p-Faster R-CNN Algorithm for Food Detection ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 132–42. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00916-8_13.
Texte intégralNandhini, S., R. Suganya, K. Nandhana, S. Varsha, S. Deivalakshmi et Senthil Kumar Thangavel. « Automatic Detection of Leaf Disease Using CNN Algorithm ». Dans Machine Learning for Predictive Analysis, 237–44. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_24.
Texte intégralVardhani, P. Ragha, Y. Indira Priyadarshini et Y. Narasimhulu. « CNN Data Mining Algorithm for Detecting Credit Card Fraud ». Dans Soft Computing and Medical Bioinformatics, 85–93. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0059-2_10.
Texte intégralWang, Ying, Aili Wang et Changyu Hu. « A Novel Airplane Detection Algorithm Based on Deep CNN ». Dans Communications in Computer and Information Science, 721–28. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2203-7_60.
Texte intégralYu, Wenjun, Sumi Kim, Fei Chen et Jaeho Choi. « Pedestrian Detection Based on Improved Mask R-CNN Algorithm ». Dans Advances in Intelligent Systems and Computing, 1515–22. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51156-2_176.
Texte intégralGuo, Yuwen, Xin Zhang, Qi Yang et Hong Guo. « A Novel Image Recognition Method Based on CNN Algorithm ». Dans Lecture Notes in Computer Science, 281–91. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74717-6_29.
Texte intégralYan, XinQing, YuHan Yang et GuiMing Lu. « A Target Detection Algorithm Based on Faster R-CNN ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 502–9. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69066-3_44.
Texte intégralActes de conférences sur le sujet "CNN ALGORITHM"
Mody, Mihir, Chaitanya Ghone, Manu Mathew et Jason Jones. « Efficient frequency domain CNN algorithm ». Dans 2017 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2017. http://dx.doi.org/10.1109/icce-asia.2017.8307846.
Texte intégralVelmurugadass, P., Ancha Rohith, Vamshi Krishna B, Harish M et Bharath Reddy G. « Dentalcariesdetectionsystem Using R-CNN Algorithm ». Dans 2023 4th International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2023. http://dx.doi.org/10.1109/iciem59379.2023.10165754.
Texte intégralWang, Yifeng, Yang Wang, Hongyi Li, Zhuoxi Cai, Xiaohan Tang et Yin Yang. « CNN Hyperparameter Optimization Based on CNN Visualization and Perception Hash Algorithm ». Dans 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2020. http://dx.doi.org/10.1109/dcabes50732.2020.00029.
Texte intégralGahiwad, Prasad, Nilesh Deshmane, Sachet Karnakar, Sujit Mali et Rohini Pise. « Brain Stroke Detection Using CNN Algorithm ». Dans 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126125.
Texte intégralReiling, Anthony, William Mitchell, Stefan Westberg, Eric Balster et Tarek Taha. « CNN Optimization with a Genetic Algorithm ». Dans NAECON 2019 - IEEE National Aerospace and Electronics Conference. IEEE, 2019. http://dx.doi.org/10.1109/naecon46414.2019.9058307.
Texte intégralVitale, S., G. Ferraioli et V. Pascazio. « Edge Preserving Cnn Sar Despeckling Algorithm ». Dans 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). IEEE, 2020. http://dx.doi.org/10.1109/lagirs48042.2020.9165559.
Texte intégralM, Nivethaa, Pavithra N, Priyanka et Palaniappan Sambandam. « Speech Emotional Recognition Using CNN Algorithm ». Dans 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2021. http://dx.doi.org/10.1109/conecct52877.2021.9622714.
Texte intégralJagadeesh, Mandala, P. Chitra, K. Srilatha, M. Sumathi et I. Rexiline Sheeba. « Brain Tumour Classification using CNN Algorithm ». Dans 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9752096.
Texte intégralPesaru, Swetha, K. Sucharitha, R. Lahari et P. Prakash. « Music Recommedation System Using CNN Algorithm ». Dans 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917811.
Texte intégralFauzi, Fauzi, Adhistya Erna Permanasari et Noor Akhmad Setiawan. « Butterfly Image Classification Using Convolutional Neural Network (CNN) ». Dans 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA). IEEE, 2021. http://dx.doi.org/10.1109/icera53111.2021.9538686.
Texte intégralRapports d'organisations sur le sujet "CNN ALGORITHM"
Baader, Franz, Jan Hladik et Rafael Peñaloza. PSpace Automata with Blocking for Description Logics. Aachen University of Technology, 2006. http://dx.doi.org/10.25368/2022.157.
Texte intégralLewis, Dustin, dir. A Compilation of Materials Apparently Reflective of States’ Views on International Legal Issues pertaining to the Use of Algorithmic and Data-reliant Socio-technical Systems in Armed Conflict. Harvard Law School Program on International Law and Armed Conflict, décembre 2020. http://dx.doi.org/10.54813/cawz3627.
Texte intégralBaader, Franz, Oliver Fernández Gil et Barbara Morawska. Hybrid Unification in the Description Logic EL. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.197.
Texte intégralAllende López, Marcos, Diego López, Sergio Cerón, Antonio Leal, Adrián Pareja, Marcelo Da Silva, Alejandro Pardo et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, juin 2021. http://dx.doi.org/10.18235/0003313.
Texte intégralBaader, Franz, Stefan Borgwardt et Barbara Morawska. Unification in the Description Logic EL w.r.t. Cycle-Restricted TBoxes. Technische Universität Dresden, 2011. http://dx.doi.org/10.25368/2022.183.
Texte intégralEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak et Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, juillet 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Texte intégralGoldberg, L. A., P. D. MacKenzie et D. S. Greenberg. Network congestion can be controlled : Routing algorithms in optical networks and Ethernets. Office of Scientific and Technical Information (OSTI), décembre 1997. http://dx.doi.org/10.2172/565650.
Texte intégralYan, Yujie, et Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, mai 2021. http://dx.doi.org/10.17760/d20410114.
Texte intégralBaader, Franz, Stefan Borgwardt et Barbara Morawska. A Goal-Oriented Algorithm for Unification in ELHR+ w.r.t. Cycle-Restricted Ontologies. Technische Universität Dresden, 2012. http://dx.doi.org/10.25368/2022.189.
Texte intégralHorrocks, Ian, Ulrike Sattler et Stephan Tobies. A Description Logic with Transitive and Converse Roles, Role Hierarchies and Qualifying Number Restrictions. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.94.
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