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Relevant bibliographies by topics / Text Generation Using Neural Networks / Dissertations / Theses

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Dissertations / Theses on the topic 'Text Generation Using Neural Networks'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Zaghloul, Waleed A. Lee Sang M. "Text mining using neural networks." Lincoln, Neb. : University of Nebraska-Lincoln, 2005. http://0-www.unl.edu.library.unl.edu/libr/Dissertations/2005/Zaghloul.pdf.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2005.<br>Title from title screen (sites viewed on Oct. 18, 2005). PDF text: 100 p. : col. ill. Includes bibliographical references (p. 95-100 of dissertation).

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2

Wang, Run Fen. "Semantic Text Matching Using Convolutional Neural Networks." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362134.

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Semantic text matching is a fundamental task for many applications in NaturalLanguage Processing (NLP). Traditional methods using term frequencyinversedocument frequency (TF-IDF) to match exact words in documentshave one strong drawback which is TF-IDF is unable to capture semanticrelations between closely-related words which will lead to a disappointingmatching result. Neural networks have recently been used for various applicationsin NLP, and achieved state-of-the-art performances on many tasks.Recurrent Neural Networks (RNN) have been tested on text classificationand text matching, but it d

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3

Shishani, Basel. "Segmentation of connected text using constrained neural networks." Thesis, Queensland University of Technology, 1997.

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4

Lameris, Harm. "Homograph Disambiguation and Diacritization for Arabic Text-to-Speech Using Neural Networks." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446509.

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Pre-processing Arabic text for Text-to-Speech (TTS) systems poses major challenges, as Arabic omits short vowels in writing. This omission leads to a large number of homographs, and means that Arabic text needs to be diacritized to disambiguate these homographs, in order to be matched up with the intended pronunciation. Diacritizing Arabic has generally been achieved by using rule-based, statistical, or hybrid methods that combine rule-based and statistical methods. Recently, diacritization methods involving deep learning have shown promise in reducing error rates. These deep-learning methods

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5

Casini, Luca. "Automatic Music Generation Using Variational Autoencoders." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16137/.

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The aim of the thesis is the design and evaluation of a generative model based on deep learning for creating symbolic music. Music, and art in general, pose interesting problems from a machine learning standpoint as they have structure and coherence both locally and globally and also have semantic content that goes beyond the mere structural problems. Working on challenges like those can give insight on other problems in the machine learning world. Historically algorithmic music generation focused on structure and was achieved through the use of Markov models or by defining, often manually,

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6

Kullmann, Emelie. "Speech to Text for Swedish using KALDI." Thesis, KTH, Optimeringslära och systemteori, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189890.

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The field of speech recognition has during the last decade left the re- search stage and found its way in to the public market. Most computers and mobile phones sold today support dictation and transcription in a number of chosen languages. Swedish is often not one of them. In this thesis, which is executed on behalf of the Swedish Radio, an Automatic Speech Recognition model for Swedish is trained and the performance evaluated. The model is built using the open source toolkit Kaldi. Two approaches of training the acoustic part of the model is investigated. Firstly, using Hidden Markov Model

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7

AbuRa'ed, Ahmed Ghassan Tawfiq. "Automatic generation of descriptive related work reports." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669975.

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A related work report is a section in a research paper which integrates key information from a list of related scientific papers providing context to the work being presented. Related work reports can either be descriptive or integrative. Integrative related work reports provide a high-level overview and critique of the scientific papers by comparing them with each other, providing fewer details of individual studies. Descriptive related work reports, instead, provide more in-depth information about each mentioned study providing information such as methods and results of the cited works. In

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8

Stein, Roger Alan. "An analysis of hierarchical text classification using word embeddings." Universidade do Vale do Rio dos Sinos, 2018. http://www.repositorio.jesuita.org.br/handle/UNISINOS/7624.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2019-03-07T14:41:05Z No. of bitstreams: 1 Roger Alan Stein_.pdf: 476239 bytes, checksum: a87a32ffe84d0e5d7a882e0db7b03847 (MD5)<br>Made available in DSpace on 2019-03-07T14:41:05Z (GMT). No. of bitstreams: 1 Roger Alan Stein_.pdf: 476239 bytes, checksum: a87a32ffe84d0e5d7a882e0db7b03847 (MD5) Previous issue date: 2018-03-28<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Efficient distributed numerical word representation models (word embeddings) combined with modern machine learning algorithms have recently

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9

Bengtsson, Fredrik, and Adam Combler. "Automatic Dispatching of Issues using Machine Learning." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162837.

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Many software companies use issue tracking systems to organize their work. However, when working on large projects, across multiple teams, a problem of finding the correctteam to solve a certain issue arises. One team might detect a problem, which must be solved by another team. This can take time from employees tasked with finding the correct team and automating the dispatching of these issues can have large benefits for the company. In this thesis, the use of machine learning methods, mainly convolutional neural networks (CNN) for text classification, has been applied to this problem. For na

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Nord, Sofia. "Multivariate Time Series Data Generation using Generative Adversarial Networks : Generating Realistic Sensor Time Series Data of Vehicles with an Abnormal Behaviour using TimeGAN." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302644.

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Large datasets are a crucial requirement to achieve high performance, accuracy, and generalisation for any machine learning task, such as prediction or anomaly detection, However, it is not uncommon for datasets to be small or imbalanced since gathering data can be difficult, time-consuming, and expensive. In the task of collecting vehicle sensor time series data, in particular when the vehicle has an abnormal behaviour, these struggles are present and may hinder the automotive industry in its development. Synthetic data generation has become a growing interest among researchers in several fie

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Bustos, Aurelia. "Extraction of medical knowledge from clinical reports and chest x-rays using machine learning techniques." Doctoral thesis, Universidad de Alicante, 2019. http://hdl.handle.net/10045/102193.

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This thesis addresses the extraction of medical knowledge from clinical text using deep learning techniques. In particular, the proposed methods focus on cancer clinical trial protocols and chest x-rays reports. The main results are a proof of concept of the capability of machine learning methods to discern which are regarded as inclusion or exclusion criteria in short free-text clinical notes, and a large scale chest x-ray image dataset labeled with radiological findings, diagnoses and anatomic locations. Clinical trials provide the evidence needed to determine the safety and effectiveness o

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12

Barrère, Killian. "Architectures de Transformer légères pour la reconnaissance de textes manuscrits anciens." Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0017.

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En reconnaissance d’écriture manuscrite, les architectures Transformer permettent de faibles taux d’erreur, mais sont difficiles à entraîner avec le peu de données annotées disponibles. Dans ce manuscrit, nous proposons des architectures Transformer légères adaptées aux données limitées. Nous introduisons une architecture rapide basée sur un encodeur Transformer, et traitant jusqu’à 60 pages par seconde. Nous proposons aussi des architectures utilisant un décodeur Transformer pour inclure l’apprentissage de la langue dans la reconnaissance des caractères. Pour entraîner efficacement nos archit

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13

WANG, YU-XIANG, and 王鈺翔. "Text Generation Using Sequence GAN Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5553dn.

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碩士<br>國立雲林科技大學<br>資訊工程系<br>107<br>In recent years, GAN (Generative Adversarial Network) has been very popular, for example, it has been very successful in the application of continuous data such as images, but applications for discrete data (such as text data) still face some difficulties. For this purpose, this thesis applies the Policy Gradient method of reinforcement learning to the conditional sequence generation (Conditional Sequence GAN, CSeqGAN) model to implement text generation technology. Since CSeqGAN can produce more diverse sentences, we use it to implement ordering systems and ma

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14

Almeida, Rodrigo de Matos Pires Tavares de. "3D terrain generation using neural networks." Master's thesis, 2020. http://hdl.handle.net/10071/22222.

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With the increase in computation power, coupled with the advancements in the field in the form of GANs and cGANs, Neural Networks have become an attractive proposition for content generation. This opened opportunities for Procedural Content Generation algorithms (PCG) to tap Neural Networks generative power to create tools that allow developers to remove part of creative and developmental burden imposed throughout the gaming industry, be it from investors looking for a return on their investment and from consumers that want more and better content, fast. This dissertation sets out to dev

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You, Yu-Jhen, and 尤鈺臻. "Grating Profile Generation using Artificial Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/td4yg7.

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碩士<br>國立中央大學<br>光電科學與工程學系<br>107<br>Neural networks have been successfully applied in many applications. With appropriate training data and fine design of the artificial neural networks structure, the neural networks can be trained to carry out specific tasks. In literature, to design the grating profile for specific diffraction efficiencies requires to solve the inverse Maxwell’s equation with the optimization methods such as the genetic algorithm. The optimization process is time-consuming. By using the neural networks method to perform the learning and testing processes, we could obtain the

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16

Brown, Calvin James. "Modelling locomotor pattern generation using artificial neural networks." 1994. http://hdl.handle.net/1993/17840.

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17

Lau, Chuang-Yeong, and 劉全勇. "An Automatic Melody Generation Using Fuzzy Neural Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/68017460390309814549.

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碩士<br>國立中央大學<br>通訊工程研究所<br>100<br>The generated music from automatic music composition is not completely match the rule of music theory in the past research. This thesis proposed using fuzzy neural network (FNN) to training a repeating pattern melody which called refrain in pop music. A refrain usually repeats many times in the music objects. The proposed learning algorithm is based on fuzzy back propagation algorithm (FBP). The main goal of a fuzzy inference system is to model composer decision making within conceptual as the process of composing music. The music theory knowledge of consonanc

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18

YANG, YI-XUN, and 楊貽勛. "On The Text-To-Image Synthesis Using Conditional GAN Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8976n3.

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碩士<br>國立雲林科技大學<br>資訊工程系<br>107<br>It is an interesting topic to give a textual narrative to synthesize a realistic picture, which can be achieved by designing a Sequence to Sequence Model. In recent years, the Generative Adversarial Network（GAN）has been successfully used to train good generators. The idea of generating a confrontational network is to jointly train two models: a generating model and a discriminant model. In the given text sequence, the generative model generates a sequence of pictures, and the discriminative model is used to distinguish the true and false of the generated pictu

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19

Matos, Pedro Ferreira de. "Recognition of genetic mutations in text using deep learning." Master's thesis, 2018. http://hdl.handle.net/10773/25972.

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Deep learning is a sub-area of automatic learning that attempts to model complex structures in the data through the application of different neural network architectures with multiple layers of processing. These methods have been successfully applied in areas ranging from image recognition and classification, natural language processing, and bioinformatics. In this work we intend to create methods for named-entity recognition (NER) in text using techniques of deep learning in order to identify genetic mutations.<br>Deep Learning é uma subárea de aprendizagem automática que tenta modelar estrut

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20

Tang, Kai-Yu, and 湯凱喻. "Biped Robot Gait Generation Using Recurrent Neural Networks Optimized Through A Multi-objective Optimization Algorithm." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/e8ppff.

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碩士<br>國立中興大學<br>電機工程學系所<br>107<br>The thesis uses a fully connected neural network (FCRNN) as a kernel controller to control the forward walking gait of a biped robot, the NAO, through the multi-objective modified continuous ant colony optimization (MO-MCACO) algorithm. We control the five joints in each leg, and there are a total of ten degrees of freedom of control. After optimizing the FCRNN, we use it to control the hip pitch, hip roll, and knee pitch angle of one leg. The other seven angles are obtained through the symmetry of the forward walking posture. The FCRNN has two types. One is n

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Huang, Jiang-Kai, and 黃江凱. "Short-term Load Forecasting of Distribution Feeders with Renewable Energy Generation by Using Artificial Neural Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/67714625628095508980.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>103<br>Load forecasting plays the extremely important role in power dispatch operations. Accurate load forecasting can provide the more accurate unit commitment and planning in order to improve quality of power supply. Especially, after a lot of renewable energy generations were integrated into power systems, improving the precision of load forecasting is important for increasing safety of system operation and reducing the cost. Although renewable energy is inexhaustible, it is difficult for stable and sustained supply. The power dispatch will be considerable uncerta

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(11393945), Shaben Kayamboo. "Proactive Fault Detection using Machine Learning to Aid Predictive Maintenance in Photovoltaic Systems." Thesis, 2024. https://figshare.com/articles/thesis/Proactive_Fault_Detection_using_Machine_Learning_to_Aid_Predictive_Maintenance_in_Photovoltaic_Systems/26548420.

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In recent history, photovoltaic (PV) systems as a means of energy generation have risen in popularity due to the world’s decreasing reliance on fossil fuels and a stronger focus on combating the adverse effects of climate change. While PV systems have immense potential, their vulnerability to faults substantially threatens their efficiency and reliability, potentially reducing their positive impact on the environment and the world economy. Current PV system maintenance strategies are either reactive or preventive, with a limited focus on predictive methods that leverage advanced machine learni

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(5931020), Babak Bahrami Asl. "FUTURISTIC AIR COMPRESSOR SYSTEM DESIGN AND OPERATION BY USING ARTIFICIAL INTELLIGENCE." Thesis, 2020.

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<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profi

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