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Relevant bibliographies by topics / Hybrid deep learning / Dissertations / Theses

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Dissertations / Theses on the topic 'Hybrid deep learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Singh, Amarjot. "ScatterNet hybrid frameworks for deep learning." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/285997.

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Image understanding is the task of interpreting images by effectively solving the individual tasks of object recognition and semantic image segmentation. An image understanding system must have the capacity to distinguish between similar looking image regions while being invariant in its response to regions that have been altered by the appearance-altering transformation. The fundamental challenge for any such system lies within this simultaneous requirement for both invariance and specificity. Many image understanding systems have been proposed that capture geometric properties such as shapes

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2

Yin, Yuan. "Physics-Aware Deep Learning and Dynamical Systems : Hybrid Modeling and Generalization." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS161.

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L'apprentissage profond a fait des progrès dans divers domaines et est devenu un outil prometteur pour modéliser les phénomènes dynamiques physiques présentant des relations hautement non linéaires. Cependant, les approches existantes sont limitées dans leur capacité à faire des prédictions physiquement fiables en raison du manque de connaissances préalables et à gérer les scénarios du monde réel où les données proviennent de dynamiques multiples ou sont irrégulièrement distribuées dans le temps et l'espace. Cette thèse vise à surmonter ces limitations dans les directions suivantes: améliorer

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Kabore, Raogo. "Hybrid deep neural network anomaly detection system for SCADA networks." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0190.

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Les systèmes SCADA sont de plus en plus ciblés par les cyberattaques en raison de nombreuses vulnérabilités dans le matériel, les logiciels, les protocoles et la pile de communication. Ces systèmes utilisent aujourd'hui du matériel, des logiciels, des systèmes d'exploitation et des protocoles standard. De plus, les systèmes SCADA qui étaient auparavant isolés sont désormais interconnectés aux réseaux d'entreprise et à Internet, élargissant ainsi la surface d'attaque. Dans cette thèse, nous utilisons une approche deep learning pour proposer un réseau de neurones profonds hybride efficace pour l

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4

ISAKSSON, LARS JOHANNES. "HYBRID DEEP LEARNING AND RADIOMICS MODELS FOR ASSESSMENT OF CLINICALLY RELEVANT PROSTATE CANCER." Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946529.

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Precision medicine holds the potential to revolutionize healthcare by providing every patient with personalized treatments and decisions tailored to his or her individual needs. This might be enabled by the large influx of potentially diagnostic information from new sources such as genetics and modern imaging techniques, provided the relevant information can be extracted. One such framework that has started to demonstrate promise in radiology, especially in the assessment of cancer, is radiomics; the practice of characterizing images by extracting a substantial amount of quantitative mathemati

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Déchelle-Marquet, Marie. "Deep learning based physical-statistics modeling of ocean dynamics." Electronic Thesis or Diss., Sorbonne université, 2023. https://theses.hal.science/tel-04166816.

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La modélisation des phénomènes dynamiques en géophysique repose sur une compréhension de la physique sous-jacente, décrite sous la forme d'EDP, et sur leur résolution par des modèles numériques. Le nombre croissant d'observations de systèmes physiques, l'essor récent de l'apprentissage profond et l'énorme puissance de calcul requise par les solveurs numériques, qui entrave la résolution des modèles existants, suggèrent que l'avenir des modèles physiques pourrait être orienté données. Mais pour cela, l'apprentissage profond doit relever plusieurs défis, tels que l'interprétabilité et la cohéren

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Theobald, Claire. "Bayesian Deep Learning for Mining and Analyzing Astronomical Data." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0081.

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Dans cette thèse, nous abordons le problème de la confiance que nous pouvons avoir en des systèmes prédictifs de type réseaux profonds selon deux directions de recherche complémentaires. Le premier axe s'intéresse à la capacité d'une IA à estimer de la façon la plus juste possible son degré d'incertitude liée à sa prise de décision. Le second axe quant à lui se concentre sur l'explicabilité de ces systèmes, c'est-à-dire leur capacité à convaincre l'utilisateur humain du bien fondé de ses prédictions. Le problème de l'estimation des incertitudes est traité à l'aide de l'apprentissage profond ba

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Buvari, Sebastian, and Kalle Pettersson. "A Comparison on Image, Numerical and Hybrid based Deep Learning for Computer-aided AD Diagnostics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279977.

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Alzheimer’s disease (AD) is the most common form of dementia making up 60- 70% of the 50 million active cases worldwide and is a degenerative disease which causes irreversible damage to the parts of the brain associated with the ability of thinking and memorizing. A lot of time and effort has been put towards diagnosing and detecting AD in its early stages and a field showing great promise in aiding with early stage detection is deep learning. The main issues with deep learning in the field of AD detection is the lack of relatively big datasets that are typically needed in order to train an ac

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Benkirane, Fatima Ezzahra. "Integration of contextual knowledge in deep Learning modeling for vision-based scene analysis." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCA002.

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La vision par ordinateur a connu une évolution importante, passant des méthodes traditionnelles aux modèles d'apprentissage profond. L’un des principaux objectifs des tâches de vision par ordinateur est d’émuler la perception humaine. En effet, le processus classique effectué par les modèles d’apprentissage profond dépend entièrement des caractéristiques visuelles, reflétant simplement la manière dont les humains perçoivent visuellement leur environnement. Cependant, pour que les humains comprennent l’environnement qui les entoure, leur raisonnement dépend non seulement de leurs capacités visu

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Chaulagain, Dewan. "Hybrid Analysis of Android Applications for Security Vetting." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1555608766287613.

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Duong, Nam duong. "Hybrid Machine Learning and Geometric Approaches for Single RGB Camera Relocalization." Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0008.

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Au cours des dernières années, la relocalisation de la caméra à base d'images est devenue un enjeu important de la vision par ordinateur appliquée à la réalité augmentée, à la robotique ainsi qu'aux véhicules autonomes. La relocalisation de la caméra fait référence à la problématique de l'estimation de la pose de la caméra incluant à la fois la translation 3D et la rotation 3D. Dans les systèmes de localisation, le composant de relocalisation de la caméra est nécessaire pour récupérer la pose de la caméra après le suivi perdu, plutôt que de redémarrer la localisation à partir de zéro.Cette thè

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Awan, Ammar Ahmad. "Co-designing Communication Middleware and Deep Learning Frameworks for High-Performance DNN Training on HPC Systems." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587433770960088.

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12

Bai, Kang Jun. "Moving Toward Intelligence: A Hybrid Neural Computing Architecture for Machine Intelligence Applications." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103711.

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Rapid advances in machine learning have made information analysis more efficient than ever before. However, to extract valuable information from trillion bytes of data for learning and decision-making, general-purpose computing systems or cloud infrastructures are often deployed to train a large-scale neural network, resulting in a colossal amount of resources in use while themselves exposing other significant security issues. Among potential approaches, the neuromorphic architecture, which is not only amenable to low-cost implementation, but can also deployed with in-memory computing strategy

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Torregrosa, jordan Sergio. "Approches Hybrides et Méthodes d'Intelligence Artificielle Basées sur la Simulation Numérique pour l'Optimisation des Systèmes Aérodynamiques Complexes." Electronic Thesis or Diss., Paris, HESAM, 2024. http://www.theses.fr/2024HESAE002.

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La conception industrielle d'un composant est un processus complexe, long et coûteux, contraint par des spécifications physiques, stylistiques et de développement précises en fonction de ses conditions et de son environnement d'utilisation futurs. En effet, un composant industriel est défini et caractérisé par de nombreux paramètres qui doivent être optimisés pour satisfaire au mieux toutes ces spécifications. Cependant, la complexité de ce problème d'optimisation multiparamétrique sous contraintes est telle que sa résolution analytique est compromise.Dans le passé, un tel problème était résol

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Moulouel, Koussaila. "Hybrid AI approaches for context recognition : application to activity recognition and anticipation and context abnormalities handling in Ambient Intelligence environments." Electronic Thesis or Diss., Paris Est, 2023. http://www.theses.fr/2023PESC0014.

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Les systèmes d'intelligence ambiante (AmI) visent à fournir aux utilisateurs des services d'assistance destinés à améliorer leur qualité de vie en termes d'autonomie, de sécurité et de bien-être. La conception de systèmes AmI capables d'une reconnaissance précise, fine et cohérente du contexte spatial et/ou temporel de l'utilisateur, en tenant compte de l'incertitude et de l'observabilité partielle des environnements AmI, pose plusieurs défis pour permettre une meilleure adaptation des services d'assistance au contexte de l'utilisateur. L'objectif de cette thèse est de proposer un ensemble de

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Nassar, Alaa S. N. "A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/16917.

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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal

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Budaraju, Sri Datta. "Unsupervised 3D Human Pose Estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291435.

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The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. The method does not use images, heatmaps, 3D pose annotations, paired/unpaired 2Dto3D skeletons, 3D priors, synthetic 2D skeletons, multiview or temporal information in any shape or form. The 2D skeleton input is taken by a VAE that encodes it in a latent space and then decodes that latent represe

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Mazzieri, Diego. "Machine Learning for combinatorial optimization: the case of Vehicle Routing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24688/.

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The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimization problems in the Operations Research (OR) community. Its relevance is not only related to the various real-world applications it deals with, but to its inherent complexity being an NP-hard problem. From its original formulation more than 60 years ago, numerous mathematical models and algorithms have been proposed to solve VRP. The most recent trend is to leverage Machine Learning (ML) in conjunction with these traditional approaches to enhance their performance. In particular, this work investi

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Panten, Niklas [Verfasser]. "Deep Reinforcement Learning zur Betriebsoptimierung hybrider industrieller Energienetze / Niklas Panten." Düren : Shaker, 2019. http://d-nb.info/1200808231/34.

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Nyberg, Selma. "Video Recommendation Based on Object Detection." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-351122.

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In this thesis, various machine learning domains have been combined in order to build a video recommender system that is based on object detection. The work combines two extensively studied research fields, recommender systems and computer vision, that also are rapidly growing and popular techniques on commercial markets. To investigate the performance of the approach, three different content-based recommender systems have been implemented at Spotify, which are based on the following video features: object detections, titles and descriptions, and user preferences. These systems have then been

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Zhao, Zhou. "Heart Segmentation and Evaluation of Fibrosis." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS003.

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La fibrillation auriculaire est la maladie du rythme cardiaque la plus courante. En raison d'un manque de compréhension des structures atriales sous-jacentes, les traitements actuels ne sont toujours pas satisfaisants. Récemment, avec la popularité de l'apprentissage profond, de nombreuses méthodes de segmentation basées sur l'apprentissage profond ont été proposées pour analyser les structures auriculaires, en particulier à partir de l'imagerie par résonance magnétique renforcée au gadolinium tardif. Cependant, deux problèmes subsistent : 1) les résultats de la segmentation incluent le fond d

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Mohy, El Dine Kamal. "Control of robotic mobile manipulators : application to civil engineering." Thesis, Université Clermont Auvergne‎ (2017-2020), 2019. http://www.theses.fr/2019CLFAC015/document.

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Malgré le progrès de l'automatisation industrielle, les solutions robotiques ne sont pas encore couramment utilisées dans le secteur du génie civil. Plus spécifiquement, les tâches de ponçage, telles que le désamiantage, sont toujours effectuées par des opérateurs humains utilisant des outils électriques et hydrauliques classiques. Cependant, avec la diminution du coût relatif des machines par rapport au travail humain et les réglementations sanitaires strictes applicables à des travaux aussi risqués, les robots deviennent progressivement des alternatives crédibles pour automatiser ces tâches

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Haykal, Vanessa. "Modélisation des séries temporelles par apprentissage profond." Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4019.

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La prévision des séries temporelles est un problème qui est traité depuis de nombreuses années. Dans cette thèse, on s’est intéressé aux méthodes issues de l’apprentissage profond. Il est bien connu que si les relations entre les données sont temporelles, il est diﬃcile de les analyser et de les prévoir avec précision en raison des tendances non linéaires et du bruit présent, spéciﬁquement pour les séries ﬁnancières et électriques. A partir de ce contexte, nous proposons une nouvelle architecture de réduction de bruit qui modélise des séries d’erreurs récursives pour améliorer les prévisions.

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HU, YUN-CHENG, and 胡允誠. "Real-Time Facial Expression Recognition Using Hybrid Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/u32gwv.

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Chen, Guan-Ren, and 陳冠任. "A Hybrid Deep Learning Method of Fast Object Detection for Embedded System." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/9f9dgt.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>106<br>Nowadays, there are a plenty of the computer vision applications on embedded system. However, it is not good to run computer vision applications on embedded systems which will take much computation and power consumption. This study aims to measure the power consumption, the accuracy and the performance of different algorithm running on different platform to give the suggestion of choosing algorithm on the platform so to meet the request of the performance and power consumption for different applications in different situation. In this thesis, we build a 3D

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TAREKEGN, GETANEH BERIE, and Getaneh Berie Tarekegn. "DFOPS: Fingerprinting Outdoor Positioning Scheme in Hybrid Networks: A Deep Learning Approach." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9jzm29.

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碩士<br>國立臺北科技大學<br>電資國際專班<br>107<br>Recently, Location Based Services (LBSs) are becoming a key technology for enhancing the applicability of Internet-of-Things (IoT) to offer seamless, intelligent and adaptive services in academia and industry to create smart world due to the growth of multiple built-in sensors on mobile devices and wireless technology. Satellite-based positioning (e.g., GPS) do not work well for urban and suburban outdoor positioning for the success of IoT deployment because of Line of Sight (LoS) problems and it requiring much power. Besides, most satellite-based positioning

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Pan, Hsiang-Hua, and 潘香樺. "A Deep Learning Framework with Region Features and Hybrid Regression for Age Estimation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dazdp6.

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碩士<br>國立臺灣科技大學<br>機械工程系<br>106<br>We propose the Region-based Hybrid Framework (RHF) with moving segmentation and soft-boundary regression for age estimation. The RHF is an ensemble of VGG networks, and each VGG net considers a specific facial region as input. The VGG is selected from a comparison of pretrained facial models originally designed for face recognition, but trained again for age estimation by transfer learning. To improve the accuracy of RHF, we implement two schemes, the moving segmentation and soft boundary regression. The moving segmentation better determines the boundary ages

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WEI, JING-LING, and 魏敬玲. "A Hybrid Model using Deep Learning for Crowding Status Prediction at Emergency Departments." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/448qrt.

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碩士<br>國立中正大學<br>資訊工程研究所<br>106<br>Over the last two decades, the number of patients at emergency departments in Taiwan has grown significantly. According to the statistics, in a year, nearly 220,000 patients visit the emergency department of the hospital which has the largest number of visits. An average of 600 patients pour into the emergency department each day. The increasing number of emergency department visits and the emergency department crowding have become major public health problems globally. The ability to accurately predict the level of demand (i.e., predict patient flow) has cons

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Wang, Wei-Yi, and 王瑋逸. "Deep Learning Assisted Low Density Parity Check Decoder with Hybrid Hidden Layer Architecture." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/us596n.

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碩士<br>國立清華大學<br>電機工程學系<br>107<br>In this thesis, we propose a novel belief propagation for decoding the low density parity check code (LDPC) with the assistance of deep learning method. With long enough girth, the belief propagation (BP) has been shown with the powerful ability to reduce the complexity of decoding the LDPC, and yields nice error correction performance which is close to the maximum likelihood (ML) method. However, the equal weights on the Tanner graph is faced by ”double counting effect”. The messages passed on the edge have different reliability due to the structure of the par

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Rioflorido, Christian Lian Paulo Perez, and 戴瑞翔. "A Hybrid Deep Learning-Based Network for 24-Hour Ahead Wind Power Forecasting." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9ugerr.

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碩士<br>中原大學<br>電機工程研究所<br>106<br>Wind power generation is always associated with some uncertainties as a result of the fluctuating value of the wind speed. It is well known that both developed and developing countries are seeking new energy for their economy. Wind power and other natural resources are considered as next-generation energy. They are clean without pollution. They can be renewed without limitations as they are available anywhere. Accurate predictions are important for efficient operation of power systems. This thesis presents a hybrid deep learning neural network approach for 1-hou

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EVANDER, RICHARD, and RICHARD EVANDER. "A Hybrid Deep Machine Learning Model For Soil Classification of Compressed Earth Block." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9x3u39.

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碩士<br>國立臺灣科技大學<br>營建工程系<br>107<br>Classifying or predicting soil type for Compressed Earth Block (CEB) construction using machine learning model is an important technique to replace laboratory tests which are time and cost consuming. The previous study has established soil classification using Artificial Neural Network (ANN). Nonetheless, gradient-based learning on ANN face several issues like overfitting and trapped in local minima due to poor generalization performance. In the further development of the neural network model, Extreme Learning Machine (ELM) has been developed with faster learn

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Huang, Shen-Hang, and 黃慎航. "Online Structural Break Detection for Pairs Trading using Wavelet Transform and Hybrid Deep Learning Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5kkcxr.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>108<br>With the mature development in the financial market, numerous people study in arbitrage strategies. Pairs trading is one of the common statistical arbitrage strategies. It first supervises two stocks that move similarly and form a stationary equilibrium with certain weights, and then makes arbitrage when the pair deviates from the stable value. The time point that the stationary relationship between two stocks does not exist any longer is called a structural break, and detecting structural breaks is important to pairs trading. There are some traditional met

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Farzad, Amir. "Log message anomaly detection using machine learning." Thesis, 2021. http://hdl.handle.net/1828/13085.

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Log messages are one of the most valuable sources of information in the cloud and other software systems. These logs can be used for audits and ensuring system security. Many millions of log messages are produced each day which makes anomaly detection challenging. Automating the detection of anomalies can save time and money as well as improve detection performance. In this dissertation, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for log message anomaly detection. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB. The f

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HSIEH, YI-LIN, and 謝易霖. "High Dimensional Deep Learning of Real-Time Stock Price Forecasting Model by Hybrid Dimension Reduction Method." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/c9y4ec.

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碩士<br>輔仁大學<br>統計資訊學系應用統計碩士班<br>106<br>Nowadays in Taiwan people find themselves hard to pay living expenses just by their salaries, and stocks became a popular choice to gain wealth. Stock Price varies with many unpredictable messages or some unperceivable complicated relations, so there are many variables to consider about. If there are ways good enough to reduce dimensions and get features that really changes stock price, it will be able to determine trends and get more remuneration. So, this research uses real time information of Taiwanese stock market, western and some Asian index along wi

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Chen, Yi-Sheng, and 陳宜陞. "A Grammatical Error Correction System based on the Integration of Deep Learning and Hybrid N-grams." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2u77h7.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>More than half of English-speaking users are non-native English speakers. For these people, how to quickly and effectively check whether there are grammatical errors in their articles is quite important. Natural Language Processing has always been a very important topic in the field of computer science. Grammatical Error Correction is one of the main research topics. Over the past few years, different approaches to grammatical error correction have been proposed. Each approach has its own advantages and disadvantages. This thesis tries to combine deep learning

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Chen-YuanChang and 張振遠. "A Hybrid Deep Learning Network for Basketball Referee Signal Recognition Based on Multi-channel IMU Sensors." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/yyg7gc.

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Syu, Miao-hua, and 徐妙華. "Biomimetic fiber-based hybrid sensor for Multifunctional Pressure Sensing and human gesture identification via Deep Learning Method." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ps6hw3.

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碩士<br>國立中央大學<br>機械工程學系<br>107<br>Within this paper, Near-field electrospinning (NFES) technological employed to deposit your nano/micro fibers for the different starting, and a new nanogenerator (NG)/deformation sensor ended up being fabricated. Within this study, polyvinylidene fluoride (PVDF), a polymer product with substantial piezoelectric components, was lodged and properly arranged with a flexible substrate by direct-write process using near-field electrospinning technological and XY detail motion stage as being a piezoelectric nano-generator. One of the research use of flexible printed

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(8747079), Nicholas S. Schultz. "A Hybrid Method for Distributed Multi-Agent Mission Planning System." Thesis, 2020.

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<div>The goal of this research is to develop a method of control for a team of unmanned aerial and ground robots that is resilient, robust, and scalable given both complete and incomplete information of the environment. The method presented in this paper integrates approximate and optimal methods of path planning integrated with a market-based task allocation strategy. Further work presents a solution to unmanned ground vehicle path planning within the developed mission planning system framework under incomplete information. Deep reinforcement learning is proposed to solve movement through unk

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Partovi, Tahmineh. "3D Building Model Reconstruction from Very High Resolution Satellite Stereo Imagery." Doctoral thesis, 2019. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201910022067.

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Automatic three-dimensional (3D) building model reconstruction using remote sensing data is crucial in applications which require large-scale and frequent building model updates, such as disaster monitoring and urban management, to avoid huge manual efforts and costs. Recent advances in the availability of very high-resolution satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for 3D building model reconstructions. In this dissertation, a novel multistage hybrid automatic 3D building model reconstruction approach is

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(9187466), Bharath Kumar Comandur Jagannathan Raghunathan. "Semantic Labeling of Large Geographic Areas Using Multi-Date and Multi-View Satellite Images and Noisy OpenStreetMap Labels." Thesis, 2020.

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<div>This dissertation addresses the problem of how to design a convolutional neural network (CNN) for giving semantic labels to the points on the ground given the satellite image coverage over the area and, for the ground truth, given the noisy labels in OpenStreetMap (OSM). This problem is made challenging by the fact that -- (1) Most of the images are likely to have been recorded from off-nadir viewpoints for the area of interest on the ground; (2) The user-supplied labels in OSM are frequently inaccurate and, not uncommonly, entirely missing; and (3) The size of the area covered on the gro

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Lopes, Tiago Miguel Dias da Gama Lobo de Sousa. "Como construir um modelo híbrido de previsão para o S&P500 usando um modelo VECM com um algoritmo LSTM?" Master's thesis, 2021. http://hdl.handle.net/10071/23512.

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A previsão de séries financeiras faz parte do processo de decisão das políticas monetárias por parte dos bancos centrais. Mendes, Ferreira e Mendes (2020) propõem um modelo híbrido que junta um VECM (modelo vetorial corretor de erro) com um algoritmo de aprendizagem profunda o LSTM (memória de longo curto-prazo) para uma previsão multivariada do índice acionista norte-americano S&P500, utilizando-se as séries do Nasdaq, Dow Jones e as taxas de juro dos bilhetes do tesouro americano a 3 meses no mercado secundário, com dados semanais, entre 19/04/2019 e 17/04/2020. Nesta dissertação, replicou-s

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