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Dissertations / Theses on the topic 'Neural network classification algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Albarakati, Noor. "FAST NEURAL NETWORK ALGORITHM FOR SOLVING CLASSIFICATION TASKS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2740.

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Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is famous for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In the thesis, we performed experiments by using three different NN structures in order to find the best MLP neural network structure for performing the nonlinear classification of multiclass data sets. A developed learning algorithm used here is the ba

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2

Rimer, Michael Edwin. "Improving Neural Network Classification Training." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2094.pdf.

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Vasilic, Slavko. "Fuzzy neural network pattern recognition algorithm for classification of the events in power system networks." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/436.

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This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combin

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4

Labarge, Isaac E. "Neural Network Pruning for ECG Arrhythmia Classification." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2136.

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Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase inter

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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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Alaskar, Haya Mohmmad. "Dynamic self-organised neural network inspired by the immune algorithm for financial time series prediction and medical data classification." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4562/.

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Artificial neural networks have been proposed as useful tools in time series analysis in a variety of applications. They are capable of providing good solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables of data are related to the time dimension and are highly correlated. The main aim of this research work is to investigate and develop efficient dynamic neural networks in order to deal with data analysis issues. This research work proposes a novel dynamic self-organised multilayer neura

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PACIFICI, FABIO. "Novel neural network-based algorithms for urban classification and change detection from satellite imagery." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2010. http://hdl.handle.net/2108/1239.

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L`attività umana sta cambiando radicalmente l`ecosistema ambientale, unito anche alla rapida espansione demografica dei sistemi urbani. Benche` queste aree rappresentano solo una minima frazione della Terra, il loro impatto sulla richiesta di energia, cibo, acqua e materiali primi, e` enorme. Per cui, una informazione accurata e tempestiva risulta essere essenziale per gli enti di protezione civile in caso, ad esempio, di catastrofi ambientali. Negli ultimi anni il forte sviluppo di sistemi satellitari, sia dal punto di vista della risoluzione spaziale che di quella radiometrica e temporale,

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Du, Zekun. "Algorithm Design and Optimization of Convolutional Neural Networks Implemented on FPGAs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254575.

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Deep learning develops rapidly in recent years. It has been applied to many fields, which are the main areas of artificial intelligence. The combination of deep learning and embedded systems is a good direction in the technical field. This project is going to design a deep learning neural network algorithm that can be implemented on hardware, for example, FPGA. This project based on current researches about deep learning neural network and hardware features. The system uses PyTorch and CUDA as assistant methods. This project focuses on image classification based on a convolutional neural netwo

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Nune, Rakesh. "Path Prediction and Path Diversion Identifying Methodologies for Hazardous Materials Transported by Malicious Entities." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/36238.

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Safe and secure transportation of hazardous materials (hazmat) is a challenging issue in terms of optimizing risk to society and simultaneously making the shipment delivery economical. The most important safety concern of hazardous material transportation is accidents causing multiple causalities. The potential risk to society from hazmat transportation has led to the evolution of a new threat from terrorism. Malicious entities can turn hazmat vehicles into weapons causing explosions in high profile locations. The present research is divided into two parts. First, a neural network model is d

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10

Galáž, Zoltán. "Analýza ručně psaného projevu u pacientů postižených neurologickými onemocněními." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-221083.

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The master‘s thesis deals with the analysis of the hand-written text. There is a design and a realization of a system for the purpose of diagnosing a Parkinson’s desease based on the analysis of hand-written text. The system consists from several modules and it is programmed in the programming environment of MATLAB. The first module provides pre-processing of the records to adjust records to the form suitable for the segmentation. Afterwards, the records are divided into those with signals onto the surface of the tablet and those with the signals above the surface of the tablet. In the next mo

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11

Zavaroni, Sofia. "Modulation Classification with Deep Learning Algorithms." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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L'attività di tesi è inquadrata nell'ambito del Deep Learning (DL), una potente tecnica di classificazione che ha un grande successo in molti domini applicativi. Tuttavia, il suo utilizzo nei sistemi di comunicazione non è stato ancora ben esplorato. Viene affrontato il problema dell'utilizzo di DL nei sistemi di comunicazione, in particolare per la classificazione della modulazione. Nello specifico, la rete neurale convoluzionale (CNN) viene utilizzata per completare l'attività di classificazione. Le reti wireless di nuova generazione devono essere in grado di supportare comunicazioni ultra

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12

Hari, Vijaya. "Empirical Investigation of CART and Decision Tree Extraction from Neural Networks." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1235676338.

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TARTAGLIONE, ENZO. "From Statistical Physics to Algorithms in Deep Neural Systems." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2743283.

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Tomášek, Michal. "Genetický návrh klasifikátoru s využítím neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255398.

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The aim of this work is the genetic design of neural networks, which are able to classify within various classification tasks. In order to create these neural networks, algorithm called NeuroEvolution of Augmenting Topologies (also known as NEAT) is used. Also the idea of preprocessing, which is included in implemented result, is proposed. The goal of preprocessing is to reduce the computational requirements for processing of benchmark datasets for classification accuracy. The result of this work is a set of experiments conducted over a data set for cancer cells detection and a database of han

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Anani-Manyo, Nina K. "Computer Vision and Building Envelopes." Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1619539038754026.

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Crossen, Samantha Lokelani. "Investigation of Variability in Cognitive State Assessment based on Electroencephalogram-derived Features." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316025164.

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Šimíček, Ondřej. "Akcelerace neuronových sítí s využitím GPU." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234921.

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The thesis deals with the acceleration of backpropagation neural networks using graphics chips. To solve this problem it was used the OpenCL technology that allows work with graphics chips from different manufacturers. The main goal was to accelerate the time-consuming learning process and classification process. The acceleration was achieved by training a large amount of neural networks simultaneously. The speed gain was used to find the best settings and topology of neural network for a given task using genetic algorithm.

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Al-Shammaa, Mohammed. "Granular computing approach for intelligent classifier design." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13686.

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Granular computing facilitates dealing with information by providing a theoretical framework to deal with information as granules at different levels of granularity (different levels of specificity/abstraction). It aims to provide an abstract explainable description of the data by forming granules that represent the features or the underlying structure of corresponding subsets of the data. In this thesis, a granular computing approach to the design of intelligent classification systems is proposed. The proposed approach is employed for different classification systems to investigate its effici

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Thames, John Lane. "Advancing cyber security with a semantic path merger packet classification algorithm." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45872.

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This dissertation investigates and introduces novel algorithms, theories, and supporting frameworks to significantly improve the growing problem of Internet security. A distributed firewall and active response architecture is introduced that enables any device within a cyber environment to participate in the active discovery and response of cyber attacks. A theory of semantic association systems is developed for the general problem of knowledge discovery in data. The theory of semantic association systems forms the basis of a novel semantic path merger packet classification algorithm. The theo

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Li, Jim. "An Automated Malignant Tumour Localization Algorithm for Prostate Cancer Detection in Trans-rectal Ultrasound Images." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/886.

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The goal of this thesis is to design, implement and evaluate an automated algorithm to detect cancerous tissues and segment the malignant tumour in ultrasound images of the prostate. To accomplish this goal, first, the important image features which would lead to the optimal segmentation are identified. This work focuses on the local texture feature and spatial features. Various approaches to extract the local texture feature are explored, including grey-level co-occurrence matrix (GLCM), recurrent random-pulsed neural networks (RNN), and a novel wavelet-based filter. The spatial featu

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Chakik, Fadi El. "Maximum d'entropie et réseaux de neurones pour la classification." Grenoble INPG, 1998. http://www.theses.fr/1998INPG0091.

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Cette these s'inscrit dans le cadre de la classification. Elle porte particulierement sur l'etude des methodes basees sur le principe du maximum d'entropie (maxent). Ces approches ont ete utilisees dans le laboratoire leibniz, par exemple, pour apprendre des comportements a un robot autonome. Le but du travail a ete de comparer cette approche a celles basees sur des reseaux de neurones. Une analyse theorique de la classification a permis de montrer qu'il existe une equivalence entre le maxent et l'apprentissage hebbien des reseaux neuronaux. Apprendre les valeurs des poids de ces derniers est

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22

Karas, Kristián. "Softwarové možnosti nasazení algoritmů metod umělé inteligence v průmyslu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444961.

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The work is focused on the use of artificial intelligence techniques in the industry and in systems for monitoring machines. In the practical part, the work focuses on the construction of a convolutional neural network and its testing on real data for diagnosing the state of the machine.

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Nakisa, Bahareh. "Emotion classification using advanced machine learning techniques applied to wearable physiological signals data." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/129875/9/Bahareh%20Nakisa%20Thesis.pdf.

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This research contributed to the development of advanced feature selection model, hyperparameter optimization and temporal multimodal deep learning model to improve the performance of dimensional emotion recognition. This study adopts different approaches based on portable wearable physiological sensors. It identified best models for feature selection and best hyperparameter values for Long Short-Term Memory network and how to fuse multi-modal sensors efficiently for assessing emotion recognition. All methods of this thesis collectively deliver better algorithms and maximize the use of miniatu

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Petřík, Patrik. "Predikce vývoje akciového trhu prostřednictvím technické a psychologické analýzy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222507.

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This work deals with stock market prediction via technical and psychological analysis. We introduce theoretical resources of technical and psychological analysis. We also introduce some methods of artificial intelligence, specially neural networks and genetic algorithms. We design a system for stock market prediction. We implement and test a part of system. In conclusion we discuss results.

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Gabrielsson, Patrick. "Evolvering av Biologiskt Inspirerade Handelsalgoritmer." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16886.

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One group of information systems that have attracted a lot of attention during the past decade are financial information systems, especially systems pertaining to financial markets and electronic trading. Delivering accurate and timely information to traders substantially increases their chances of making better trading decisions.Since the dawn of electronic exchanges the trading community has seen a proliferation of computer-based intelligence within the field, enabled by an exponential growth of processing power and storage capacity due to advancements in computer technology. The financial b

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Benatia, Mohamed Amin. "Optimisation multi-objectives d’une infrastructure réseau dédiée aux bâtiments intelligents." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0024/document.

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Au cours de cette thèse, nous avons étudié le problème de déploiement des Réseaux de Capteurs Sans-Fil (RCSF) pour des applications indoor tel que le bâtiment intelligent. Le but de notre travail était de développer un outil de déploiement capable d'assister les concepteurs de RCSF lors de la phase de déploiement de ces derniers. Nous avons commencé cette thèse par la modélisation de tous les paramètres qui interviennent lors du déploiement des RCSF, à savoir : coût, connectivité, couverture et durée de vie. Par la suite, nous avons implémenté cinq algorithmes d'optimisation, dont trois multi-

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Herec, Jan. "Principy a aplikace neuroevoluce." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385879.

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The theoretical part of this work deals with evolutionary algorithms (EA), neural networks (NN) and their synthesis in the form of neuroevolution. From a practical point of view, the aim of the work is to show the application of neuroevolution on two different tasks. The first task is the evolutionary design of the convolutional neural network (CNN) architecture that would be able to classify handwritten digits (from the MNIST dataset) with a high accurancy. The second task is the evolutionary optimization of neurocontroller for a simulated Falcon 9 rocket landing. Both tasks are computational

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Duncan, Andrew Paul. "The analysis and application of artificial neural networks for early warning systems in hydrology and the environment." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.

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Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warni

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Wyganowski, Mateusz. "Classification algorithms on the cell processor /." Online version of thesis, 2008. http://hdl.handle.net/1850/7767.

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Balnys, Mantas. "Genetinių algoritmų pritaikymo klasifikavimo uždaviniams spręsti tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2004. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2004~D_20040528_155652-64676.

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Neural networks are one of the most efficient classifier methods. One of such classifying neural networks we are trying to teach in this work by using genetic algorithms. In this work we test two types of genetic algorithms. One may be called parameterized genetic algorithm. It is built on the basic ideas of genetic algorithms. The other one is called parameter less genetic algorithm. It was presented by F. G. Lobo and D. E. Goldberg. Both genetic algorithms are tested and compared to the other well known optimization methods such as Bayes and Monte Carlo search. Experiments show the relevance

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Салтиш, О. І. "Інформаційна технологія класифікації жанрів музичних творів". Master's thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/72192.

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Побудовано штучну нейронну мережу для класифікації жанрів музичних творів, що навчається з допомогою генетичного алгоритму. Розроблено автоматизовану систему, що реалізує обраний метод. Виконано випробування розробленої системи

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Hanittinan, Wichai. "Resilient modulus prediction using neural network algorithm." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1190140082.

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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level

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Bailer-Jones, Coryn Andreas. "Neural network classification of stellar spectra." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627444.

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Harris, David Anthony. "Neural network studies of lithofacies classification." Thesis, University of Edinburgh, 1994. http://hdl.handle.net/1842/14001.

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Exploration for hydrocarbons and other resources requires that large amounts of data be interpreted and used to infer the geology of extensive regions. Many different types of data are used. They are interpreted by geologists and sedimentologists in the light of experience. Artificial neural network models implemented on computers provide a powerful means of performing tasks such as pattern classification. Such tasks are difficult to perform using rule based methods, as we often do not know how to specify appropriate rules. We show that artificial neural networks can be used to discriminate be

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Ahmad, Jamil. "A novel learning algorithm for feedforward neural network." Thesis, King's College London (University of London), 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404310.

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Kaylani, Assem. "AN ADAPTIVE MULTIOBJECTIVE EVOLUTIONARY APPROACH TO OPTIMIZE ARTMAP NEURAL NETWORKS." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2538.

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This dissertation deals with the evolutionary optimization of ART neural network architectures. ART (adaptive resonance theory) was introduced by a Grossberg in 1976. In the last 20 years (1987-2007) a number of ART neural network architectures were introduced into the literature (Fuzzy ARTMAP (1992), Gaussian ARTMAP (1996 and 1997) and Ellipsoidal ARTMAP (2001)). In this dissertation, we focus on the evolutionary optimization of ART neural network architectures with the intent of optimizing the size and the generalization performance of the ART neural network. A number of researchers have foc

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Lim, Chee Peng. "Autonomous neural network systems for pattern classification." Thesis, University of Sheffield, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242285.

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Shu, Xingliang. "Electrocardiograph Signal Classification By Using Neural Network." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592395089900722.

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Hirotsu, Kenichi. "Neural network hardware with random weight change learning algorithm." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/15765.

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Tong, Dong Ling. "Genetic algorithm-neural network : feature extraction for bioinformatics data." Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.

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With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of variou

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Pierrot, Henri Jan, and n/a. "Artificial intelligence architectures for classifying conjoined data." Swinburne University of Technology, 2007. http://adt.lib.swin.edu.au./public/adt-VSWT20070426.102059.

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This thesis is concerned with the development of novel methods of classifying data that is not inherently clustered. The performance of these novel algorithms in finding classifications in this data will be compared with that of existing artificial intelligence methods.

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San, Pedro Miguel G. "Signal classification using the mean separator neural network." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA377744.

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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, March 2000.<br>Thesis advisor(s): Fargues, Monique P. ; Hippenstiel, Ralph D. "March 2000." Includes bibliographical references (p. 193-194). Also available online.

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au, P. Kraipeerapun@murdoch edu, and Pawalai Kraipeerapun. "Neural network classification based on quantification of uncertainty." Murdoch University, 2009. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20090526.100525.

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This thesis deals with feedforward backpropagation neural networks and interval neutrosophic sets for the binary and multiclass classification problems. Neural networks are used to predict true and false output values. These results together with the uncertainty of type error and vagueness occurred in the prediction are then represented in the form of interval neutrosophic sets. Each element in an interval neutrosophic set consists of three membership values: truth, indeterminacy, and false. These three membership values are then used in the classification process. For binary classificatio

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Wichert, Terry S., and Daniel Joseph Collins. "Feature based neural network acoustic transient signal classification." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/24169.

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Jamal, Md Ashraf. "Analysis and classification of EEG signals using mixture of features and committee neural network." Thesis, 2012. http://ethesis.nitrkl.ac.in/4075/1/thesis-4-june.pdf.

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Electroencephalography signal is the recording of electrical activity of brain, provides valuable information of the brain function and neurological disorder. this paper proposed committee neural network for classification of EEG signals. Committee neural network consists of different neural network that used multilayer perceptron back propagation algorithm. The number of input node and hidden node selection for artificial neural network remains an important issues, as over parametrized ANN gets trapped in local minima resulting non convergence of ANN structure during training. Redundant featu

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Wu, Chen-Ling, and 吳晨翎. "A Novel Classification Algorithm Using Random Back-Propagation Neural Networks." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/5bc47r.

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Oguntala, George A., Yim Fun Hu, Ali A. S. Alabdullah, Raed A. Abd-Alhameed, Muhammad Ali, and D. K. Luong. "Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living." 2021. http://hdl.handle.net/10454/18418.

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Yes<br>IEEE Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework tha

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Chou, Po-sheng, and 周柏昇. "Fast MPEG-2 to H.264/AVC Video Transcoding Based on Neural-Network Classification Algorithm." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/57528945978423783666.

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碩士<br>國立中正大學<br>電機工程所<br>96<br>In this paper, we propose a neural-classifier-based block mode decision to improve the efficiency of a MPEG-2 to H.264/AVC video transcoder. The work is based on a modified Pixel-Domain Cascade Transcoding (PDCT) architecture. By accepting the decoded information from MPEG-2 bit stream (e.g., motion vectors and DCT coefficients) and calculating related image features, the neural classifier is capable of determining candidates of the most probable block-size partitions, which hence alleviates the most consuming load in H.264/AVC encoding. Experimentally, the overa

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Roussinov, Dmitri G., and Hsinchun Chen. "A Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation." 1998. http://hdl.handle.net/10150/106141.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>The rapid proliferation of textual and multimedia online databases, digital libraries, Internet servers, and intranet services has turned researchers' and practitioners' dream of creating an information-rich society into a nightmare of info-gluts. Many researchers believe that turning an info-glut into a useful digital library requires automated techniques for organizing and categorizing large-scale information. This paper presents research in which we sought to develop a scaleable textual classification and categoriz

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