Relevant bibliographies by topics / Dati neurali

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dati neurali'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Dati neurali"

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AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.
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Trenz, O., J. Šťastný, and V. Konečný. "Agricultural data prediction by means of neural network." Agricultural Economics (Zemědělská ekonomika) 57, No. 7 (2011): 356–61. http://dx.doi.org/10.17221/108/2011-agricecon.

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The contribution deals with the prediction of crop yield levels, using an artificial intelligence approach, namely a multi-layer neural network model. Subsequently, we are contrasting this approach with several non-linear regression models, the usefulness of which has been tested and published several times in the specialized periodicals. The main stress is placed on judging the accuracy of the individual methods and of the implementation. A neural network simulation device is that which enables the user to set an adequate configuration of the neural network vis á vis the required t
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Harliman, Rheza, and Kaoru Uchida. "Data- and Algorithm-Hybrid Approach for Imbalanced Data Problems in Deep Neural Network." International Journal of Machine Learning and Computing 8, no. 3 (2018): 208–13. http://dx.doi.org/10.18178/ijmlc.2018.8.3.689.

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Teh, Chee Siong, Ming Leong Yii, and Chwen Jen Chen. "Dimensional Reduction and Data Visualization Using Hybrid Artificial Neural Networks." International Journal of Machine Learning and Computing 5, no. 5 (2015): 420–25. http://dx.doi.org/10.7763/ijmlc.2015.v5.545.

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Liu, Bin, Lirong He, Yingming Li, Shandian Zhe, and Zenglin Xu. "NeuralCP: Bayesian Multiway Data Analysis with Neural Tensor Decomposition." Cognitive Computation 10, no. 6 (2018): 1051–61. http://dx.doi.org/10.1007/s12559-018-9587-4.

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IATAN, Iuliana. "DEALING THE NONLINEARITY ASSOCIATED WITH THE DATA USING ARTIFICIAL NEURAL NETWORKS." Review of the Air Force Academy 15, no. 2 (2017): 15–22. http://dx.doi.org/10.19062/1842-9238.2017.15.2.2.

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Ito, Toshio. "Supervised Learning Methods of Bilinear Neural Network Systems Using Discrete Data." International Journal of Machine Learning and Computing 6, no. 5 (2016): 235–40. http://dx.doi.org/10.18178/ijmlc.2016.6.5.604.

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Vogt, Siegfried, and Daniel Sacher. "A neural network method for wind estimation using wind profiler data." Meteorologische Zeitschrift 10, no. 6 (2001): 479–87. http://dx.doi.org/10.1127/0941-2948/2001/0010-0479.

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Ouadfeul, Sid-Ali, and Leila Aliouane. "Noise Attenuation from GPR Data Using Wavelet Transform and Artificial Neural Network." International Journal of Applied Physics and Mathematics 4, no. 6 (2014): 426–33. http://dx.doi.org/10.17706/ijapm.2014.4.6.426-433.

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Yu, Youhao, and Richard M. Dansereau. "Fast Reconstruction of 1D Compressive Sensing Data Using a Deep Neural Network." International Journal of Signal Processing Systems 8, no. 1 (2020): 26–31. http://dx.doi.org/10.18178/ijsps.8.1.26-31.

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Dissertations / Theses on the topic "Dati neurali"

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Avena, Anna. "Tecniche di data mining applicate alla decodifica di dati neurali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14800/.

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Gli studi sulla decodifica dell'attività neuronale permettono di mappare gli impulsi elettrici della corteccia cerebrale in segnali da inviare a determinati dispositivi per poterli monitorare. È su questo tema che la ricerca scientifica si sta concentrando, al fine di aiutare le persone affette da gravi lesioni fisiche ad ottenere un maggiore grado di autonomia nelle piccole azioni di tutti i giorni. In questo elaborato, sono stati analizzati dati derivanti da attività neuronali raccolti da esperimenti effettuati su primati non umani, eseguiti dal gruppo di ricerca della professoressa Patrizi
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Borra, Davide. "Sviluppo ed applicazione di reti neurali convoluzionali con dati di neuroimaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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La malattia di Alzheimer (AD) è un disordine neurodegenerativo che rappresenta la forma più comune di demenza negli adulti sopra i 65 anni, mentre la compromissione cognitiva lieve (MCI) è una condizione che in alcuni casi può rappresentare una fase prodromica della malattia di Alzheimer, mentre in altri, è comune in pazienti con la malattia dei piccoli vasi cerebrali (SVD). In questo elaborato sono state sviluppate due reti neurali convoluzionali 2-D, NeuroNet-1 e NeuroNet-2 (o NeuroNet), ed applicate alla classificazione a 2 vie di: a) MCI con SVD (40 pazienti in totale) con dati di diffusio
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Di, Ielsi Luca. "Analisi di serie temporali riguardanti dati energetici mediante architetture neurali profonde." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10504/.

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Il presente lavoro di tesi riguarda lo studio e l'impiego di architetture neurali profonde (nello specifico stacked denoising auto-encoder) per la definizione di un modello previsionale di serie temporali. Il modello implementato è stato applicato a dati industriali riguardanti un impianto fotovoltaico reale, per effettuare una predizione della produzione di energia elettrica sulla base della serie temporale che lo caratterizza. I risultati ottenuti hanno evidenziato come la struttura neurale profonda contribuisca a migliorare le prestazioni di previsione di strumenti statistici classici come
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Maffei, Nicola. "Reti neurali e modelli fisico-predittivi: Dati clinici e analisi di trattamenti in tomotherapy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6621/.

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Il lavoro è parte integrante di un progetto di ricerca del Ministero della Salute ed è stato sviluppato presso la Fisica Sanitaria ed il reparto di Radioterapia Oncologica dell’Azienda Ospedaliero Universitaria di Modena. L’obiettivo è la realizzazione di modelli predittivi e di reti neurali per tecniche di warping in ambito clinico. Modifiche volumetrico-spaziali di organi a rischio e target tumorali, durante trattamenti tomoterapici, possono alterare la distribuzione di dose rispetto ai constraints delineati in fase di pianificazione. Metodologie radioterapiche per la valutazione di organ
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Fabbri, Alessandro. "Reti neurali in ambito finanziario." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19593/.

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In particolare in questo lavoro cercherò di analizzare l’utilizzo di reti neurali in ambito economico-finanziario in quanto alcuni dei temi che si riscontrano in economia ben si prestano ad un’analisi attraverso le reti neurali. In particolare nel primo capitolo di questo elaborato descriverò le origini delle reti neurali e alcuni criteri attraverso i quali oggi si classificano le reti stesse. Nel secondo capitolo mi occuperò invece di approfondire quali sono i passaggi da seguire al fine di costruire una rete neurale concentrandomi sulla risoluzione di problemi legati all’ambito economico-fi
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Cesaroni, Maurizio. "Armonizzazione dei dati per l’addestramento di reti neurali ricorrenti: applicazione per la gestione delle promozioni nel settore retail." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23194/.

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All'interno dei dati processati da un'azienda è solito trovare errori dovuti a varie motivazioni, ad esempio cattive misurazioni. Questo può comportare interpretazioni errate e di conseguenza scelte future che non portano il profitto sperato. Il processo di correzione di tali errori in letteratura viene chiamato Data Harmonization, e permette alle aziende di poter ripulire i dati sfruttando regole euristiche proprie del business di competenza. Lavorare con set di dati coerenti alle dinamiche del mercato e allo stesso tempo solidi, è fondamentale per i modelli di tipo predittivo di cui sempre p
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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/.

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La visione stereo consiste nell’estrarre informazioni di profondità da una scena a partire da una vista sinistra e una vista destra. Il problema si riduce a determinare punti corrispondenti nelle due immagini, che nel caso di immagini rettificate risultano traslati solo orizzontalmente, di una distanza detta disparità. Tra gli algoritmi stereo tradizionali spiccano SGM e la sua implementazione rSGM. SGM minimizza una funzione di costo definita su un volume dei costi, che misura la somiglianza degli intorni di potenziali punti omologhi per numerosi valori di disparità. L’abilità delle reti ne
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Portas, Daniela. "Studio, implementazione e confronto di diverse tecniche di deep learning per la valutazione morfologica dell'atrio sinistro da dati LGE-MRI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19938/.

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La definizione di algoritmi in grado di effettuare un’accurata segmentazione dell’atrio sinistro si è rilevata di fondamentale importanza nel trattamento di alcune aritmie cardiache come, per esempio, la fibrillazione atriale. I recenti sviluppi nel campo delle reti neurali convoluzionali hanno permesso lo studio di modelli che forniscono una segmentazione atriale automatica. Attualmente la rete U-Net sembra essere la più adatta a svolgere questo compito anche se presenta alcuni aspetti negativi che spingono a ricercare soluzioni alternative e a testare delle condizioni che possano migliorare
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Escuain, i. Poole Lara Sofia. "Data-driven neural mass modelling." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/666615.

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The brain is a complex organ whose activity spans multiple scales, both spatial and temporal. The computational unit of the brain is thought to be the neurone. At the microscopic level, neurones communicate via action potentials. These may be observed experimentally by means of precise techniques that work with a small number of these cells and their interactions, and that can be modelled mathematically in a variety of ways. Other techniques consider the averaged activity of large groups of neurones in the mesoscale, or cortical columns; theoretical models of these signals also abound. The pr
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Rahman, Sardar Muhammad Monzurur, and mrahman99@yahoo com. "Data Mining Using Neural Networks." RMIT University. Electrical & Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080813.094814.

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Data mining is about the search for relationships and global patterns in large databases that are increasing in size. Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable pat
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Books on the topic "Dati neurali"

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Kass, Robert E., Uri T. Eden, and Emery N. Brown. Analysis of Neural Data. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9602-1.

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Cirrincione, Giansalvo. Neural based orthogonal data fitting: The EXIN neural networks. Wiley, 2010.

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Cirrincione, Giansalvo, and Maurizio Cirrincione. Neural-Based Orthogonal Data Fitting. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470638286.

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Canale, Antonio, Daniele Durante, Lucia Paci, and Bruno Scarpa, eds. Studies in Neural Data Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00039-4.

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Rzempoluck, Edward J. Neural Network Data Analysis Using SimulnetTM. Springer New York, 1998.

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Watkins, Bruce E. Data compression using artificial neural networks. Naval Postgraduate School, 1991.

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Rzempoluck, Edward J. Neural Network Data Analysis Using Simulnet™. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1746-6.

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Turner, Brandon M., Birte U. Forstmann, and Mark Steyvers. Joint Models of Neural and Behavioral Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03688-1.

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Neural and brain modeling. Academic Press, 1987.

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Zhang, Xiang-Sun. Neural networks in optimization. Kluwer Academic Publishers, 2000.

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Book chapters on the topic "Dati neurali"

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Silipo, Rosaria. "Neural Networks." In Intelligent Data Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03969-4_7.

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Nokeri, Tshepo Chris. "Neural Networks." In Data Science Revealed. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6870-4_12.

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Lopes, Noel, and Bernardete Ribeiro. "Neural Networks." In Studies in Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06938-8_3.

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García, Alberto Luis. "Neural Networks." In Encyclopedia of Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-32001-4_148-1.

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Curto, Carina Pamela, and Nora Youngs. "Neural Ring Homomorphisms and Maps Between Neural Codes." In Topological Data Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43408-3_7.

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Evgniou, Theodoros, and Massimiliano Pontil. "Learning Preference Relations from Data." In Neural Nets. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45808-5_2.

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Shadbolt, Jimmy. "Data Selection." In Perspectives in Neural Computing. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0151-2_5.

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Huguenard, Brian R., and Deborah J. Ballou. "Neural Net Tutorial." In Analytics and Data Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58097-5_18.

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Mucherino, Antonio, Petraq J. Papajorgji, and Panos M. Pardalos. "Artificial Neural Networks." In Data Mining in Agriculture. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-88615-2_5.

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Di Gesù, Vito, and Jerome H. Friedman. "New Similarity Rules for Mining Data." In Neural Nets. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731177_26.

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Conference papers on the topic "Dati neurali"

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Yang, Zhun, Adam Ishay, and Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.

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We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a n
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Dakovic, Milos, Tijana Ruzic, Tanja Rogac, Milos Brajovic, and Budimir Lutovac. "Neural networks application to Neretva basin hydro-meteorological data." In 2016 13th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2016. http://dx.doi.org/10.1109/neurel.2016.7800126.

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Zunic, Emir, Harun Hindija, Admir Besirevic, Kerim Hodzic, and Sead Delalic. "Improving Performance of Vehicle Routing Algorithms using GPS Data." In 2018 14th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018. http://dx.doi.org/10.1109/neurel.2018.8586982.

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Cisija, Merima, Emir Zunic, and Dzenana Donko. "Collection and Sentiment Analysis of Twitter Data on the Political Atmosphere." In 2018 14th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018. http://dx.doi.org/10.1109/neurel.2018.8586980.

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Gavrovska, Ana M., Goran J. Zajic, Milan S. Milivojevic, and Irini S. Reljin. "Machine-learning based Blind Visual Quality Assessment with Content-aware Data Partitioning." In 2018 14th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018. http://dx.doi.org/10.1109/neurel.2018.8587018.

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Horvath, Andras, Michael Hillmer, Qiuwen Lou, X. Sharon Hu, and Michael Niemier. "Cellular neural network friendly convolutional neural networks — CNNs with CNNs." In 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2017. http://dx.doi.org/10.23919/date.2017.7926973.

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Hudec, Miroslav. "Fuzzy data in traditional relational databases." In 2014 12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2014). IEEE, 2014. http://dx.doi.org/10.1109/neurel.2014.7011504.

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Krawczyk, Bartosz, and Gerald Schaefer. "Ensemble fusion methods for medical data classification." In 2012 11th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2012). IEEE, 2012. http://dx.doi.org/10.1109/neurel.2012.6419993.

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Gilou, S., C. Dimitrousis, A. Zogkas, et al. "Artificial neural networks and statistical classification applied to Electrical Impedance Spectroscopy data for Melanoma diagnosis in Dermatology (DermaSense)." In 2018 14th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018. http://dx.doi.org/10.1109/neurel.2018.8586995.

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Nastac, Dumitru Iulian, and Paul Dan Cristea. "A modified adaptive retraining procedure for data forecasting." In 2012 11th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2012). IEEE, 2012. http://dx.doi.org/10.1109/neurel.2012.6419995.

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Reports on the topic "Dati neurali"

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Yaworsky, Paul S., and James M. Vaccaro. Neural Networks, Reliability and Data Analysis. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada262354.

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M. Kamrunnahar and M. Urquidi-Macdonald. DATA MINING OF EXPERIMENTAL CORROSION DATA USING NEURAL NETWORK. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/861072.

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Hawkins, Rupert S., K. F. Heideman, and Ira G. Smotroff. Cloud Data Set for Neural Network Classification Studies. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada256181.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada259120.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada251567.

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Armstrong, Derek Elswick, and Joseph Gabriel Gorka. Using Deep Neural Networks to Extract Fireball Parameters from Infrared Spectral Data. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1623398.

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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. W
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Puttanapong, Nattapong, Arturo M. Martinez Jr, Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Predicting Poverty Using Geospatial Data in Thailand. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200434-2.

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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yiel
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Levitan, Herbert. Microcomputer-Based Data Acquisition, Analysis and Control of Information Processing by Neural Networks. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177170.

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Fitch, J. The radon transform for data reduction, line detection, and artificial neural network preprocessing. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6874873.

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