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Dissertations / Theses on the topic 'Modified convolutional neural network'

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Published: 2 June 2025

Last updated: 31 July 2025

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1

Ayoub, Issa. "Multimodal Affective Computing Using Temporal Convolutional Neural Network and Deep Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39337.

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Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and n

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Long, Cameron E. "Quaternion Temporal Convolutional Neural Networks." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565303216180597.

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Bylund, Andreas, Anton Erikssen, and Drazen Mazalica. "Hyperparameters impact in a convolutional neural network." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18670.

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Machine learning and image recognition is a big and growing subject in today's society. Therefore the aim of this thesis is to compare convolutional neural networks with different hyperparameter settings and see how the hyperparameters affect the networks test accuracy in identifying images of traffic signs. The reason why traffic signs are chosen as objects to evaluate hyperparameters is due to the author's previous experience in the domain. The object itself that is used for image recognition does not matter. Any dataset with images can be used to see the hyperparameters affect. Grid search

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Reiling, Anthony J. "Convolutional Neural Network Optimization Using Genetic Algorithms." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1512662981172387.

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DiMascio, Michelle Augustine. "Convolutional Neural Network Optimization for Homography Estimation." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1544214038882564.

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Embretsén, Niklas. "Representing Voices Using Convolutional Neural Network Embeddings." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261415.

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In today’s society services centered around voices are gaining popularity. Being able to provide the users with voices they like, to obtain and sustain their attention, is of importance for enhancing the overall experience of the service. Finding an efficient way of representing voices such that similarity comparisons can be performed is therefore of great use. In the field of Natural Language Processing great progress has been made using embeddings from Deep Learning models to represent words in an unsupervised fashion. These representations managed to capture the semantics of the words. This

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Tawfique, Ziring. "Tool-Mediated Texture Recognition Using Convolutional Neural Network." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303774.

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Vibration patterns can be captured by an accelerometer sensor attached to a hand-held device when it is scratched on various type of surface textures. These acceleration signals can carry relevant information for surface texture classification. Typically, methods rely on hand crafted feature engineering but with the use of Convolutional Neural Network manual feature engineering can be eliminated. A proposed method using modern machine learning techniques such as Dropout is introduced by training a Convolutional Neural Network to distinguish between 69 and 100 various surface textures. EHapNet,

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Winicki, Elliott. "ELECTRICITY PRICE FORECASTING USING A CONVOLUTIONAL NEURAL NETWORK." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2126.

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Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with l

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Cui, Chen. "Convolutional Polynomial Neural Network for Improved Face Recognition." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1497628776210369.

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Li, Chao. "WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/133.

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Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like fatigue for example. It also requires much of human intelligence, which either complex modeling or rich of welding experience. Therefore, weld penetration status identification has become the obstacle for intelligent welding system. In this dissertation, an innovative method has been proposed to detect the weld joint penetration status using machine-learning algorithms. A GTAW welding system is firstly built. Project a dot-structur

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Wang, Zhenyu. "A Digits-Recognition Convolutional Neural Network on FPGA." Thesis, Linköpings universitet, Datorteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161663.

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A convolutional neural network (CNN) is a deep learning framework that is widely used in computer vision. A CNN extracts important features of input images by perform- ing convolution and reduces the parameters in the network by applying pooling operation. CNNs are usually implemented with programming languages and run on central process- ing units (CPUs) and graphics processing units (GPUs). However in recent years, research has been conducted to implement CNNs on field-programmable gate array (FPGA). The objective of this thesis is to implement a CNN on an FPGA with few hardware resources an

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Jönsson, Jonatan, and Felix Stenbäck. "Fence surveillance with convolutional neural networks." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-37116.

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Broken fences is a big security risk for any facility or area with strict security standards. In this report we suggest a machine learning approach to automate the surveillance for chain-linked fences. The main challenge is to classify broken and non-broken fences with the help of a convolution neural network. Gathering data for this task is done by hand and the dataset is about 127 videos at 26 minutes length total on 23 diﬀerent locations. The model and dataset are tested on three performances traits, scaling, augmentation improvement and false rate. In these tests we concluded that nearest

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Keisala, Simon. "Designing an Artificial Neural Network for state evaluation in Arimaa : Using a Convolutional Neural Network." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143188.

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Agents being able to play board games such as Tic Tac Toe, Chess, Go and Arimaa has been, and still is, a major difficulty in Artificial Intelligence. For the mentioned board games, there is a certain amount of legal moves a player can do in a specific board state. Tic Tac Toe have in average around 4-5 legal moves, with a total amount of 255168 possible games. Both Chess, Go and Arimaa have an increased amount of possible legal moves to do, and an almost infinite amount of possible games, making it impossible to have complete knowledge of the outcome. This thesis work have created various Neu

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Buratti, Luca. "Visualisation of Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Le Reti Neurali, e in particolare le Reti Neurali Convoluzionali, hanno recentemente dimostrato risultati straordinari in vari campi. Purtroppo, comunque, non vi è ancora una chiara comprensione del perchè queste architetture funzionino così bene e soprattutto è difficile spiegare il comportamento nel caso di fallimenti. Questa mancanza di chiarezza è quello che separa questi modelli dall’essere applicati in scenari concreti e critici della vita reale, come la sanità o le auto a guida autonoma. Per questa ragione, durante gli ultimi anni sono stati portati avanti diversi studi in modo tale d

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Amaducci, Fabiola. "Reduced order modelling of combustion using convolutional neural network." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21409/.

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It is well known that CFD simulations of a complex combustion system, such as Moderate or Intense Low-oxygen Dilution (MILD) combustion, requires consid- erable computational resources. This precludes various applications including the use of CFD in real time control systems. The idea of a reduced order model (ROM) was born from the desire to overcome this obstacle. A ROM, if properly instructed, returns the output of a requested CFD simulation in extremely short time. This one is an ideal mechanism with two basic gears: the input size reduction technique and the interpolation method. This pro

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Chen, Tairui. "Going Deeper with Convolutional Neural Network for Intelligent Transportation." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/144.

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Over last several decades, computer vision researchers have been devoted to find good feature to solve different tasks, object recognition, object detection, object segmentation, activity recognition and so forth. Ideal features transform raw pixel intensity values to a representation in which these computer vision problems are easier to solve. Recently, deep feature from covolutional neural network(CNN) have attracted many researchers to solve many problems in computer vision. In the supervised setting, these hierarchies are trained to solve specific problems by minimizing an objective functi

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Franke, Cameron. "Autonomous Driving with a Simulation Trained Convolutional Neural Network." Scholarly Commons, 2017. https://scholarlycommons.pacific.edu/uop_etds/2971.

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Autonomous vehicles will help society if they can easily support a broad range of driving environments, conditions, and vehicles. Achieving this requires reducing the complexity of the algorithmic system, easing the collection of training data, and verifying operation using real-world experiments. Our work addresses these issues by utilizing a reflexive neural network that translates images into steering and throttle commands. This network is trained using simulation data from Grand Theft Auto V~\cite{gtav}, which we augment to reduce the number of simulation hours driven. We then validate our

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Samuelsson, Elin. "A Confidence Measure for Deep Convolutional Neural Network Regressors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273967.

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Deep convolutional neural networks can be trained to estimate gaze directions from eye images. However, such networks do not provide any information about the reliability of its predictions. As uncertainty estimates could enable more accurate and reliable gaze tracking applications, a method for confidence calculation was examined in this project. This method had to be computationally efficient for the gaze tracker to function in real-time, without reducing the quality of the gaze predictions. Thus, several state-of-the-art methods were abandoned in favor of Mean-Variance Estimation, which use

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Matuh, Delic Senad. "A Convolutional Neural Network for predicting HIV Integration Sites." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279796.

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Convolutional neural networks are commonly used when training deep networks with time-independent data and have demonstrated positive results in predicting DNA binding sites for DNA-binding proteins. Based upon the success of convolutional neural networks in predicting DNA binding sites of proteins, this project intends to determine if a convolutional neural network could predict possible HIV-B provirus integration sites. When exploring existing research, little information was found regarding DNA sequences targeted by HIV for integration, few, if any, have attempted to use artificial neural n

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Rochford, Matthew. "Visual Speech Recognition Using a 3D Convolutional Neural Network." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2109.

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Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN ar

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Qian, Songyue. "Using convolutional neural network to generate neuro image template." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546620227038248.

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Kaster, Joshua M. "Training Convolutional Neural Network Classifiers Using Simultaneous Scaled Supercomputing." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588973772607826.

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Nyrönen, P. (Pekka). "Convolutional neural network based super-resolution for mobile devices." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201812083250.

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Super-resolution is a challenging problem of restoring details lost to diffraction in the image capturing process. Degradations from the environment and the imaging device increase its difficulty, and they are strongly present in mobile phone cameras. The latest promising approaches involve convolutional neural networks, but little testing has been done on degraded images. Also, sizes of neural networks raise a question of their applicability on mobile devices. A wide review of published super-resolution neural networks is done. Four of the network architectures are chosen, and their TensorFlo

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Lopes, André Teixeira. "Facial expression recognition using deep learning - convolutional neural network." Universidade Federal do Espírito Santo, 2016. http://repositorio.ufes.br/handle/10/4301.

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Made available in DSpace on 2016-08-29T15:33:24Z (GMT). No. of bitstreams: 1 tese_9629_dissertacao(1)20160411-102533.pdf: 9277551 bytes, checksum: c18df10308db5314d25f9eb1543445b3 (MD5) Previous issue date: 2016-03-03<br>CAPES<br>O reconhecimento de expressões faciais tem sido uma área de pesquisa ativa nos últimos dez anos, com uma área de aplicação em crescimento como animação de personagens e neuro-marketing. O reconhecimento de uma expressão facial não é um problema fácil para métodos de aprendizagem de máquina, dado que pessoas diferentes podem variar na forma com que mostram suas exp

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Khasgiwala, Anuj. "Word Recognition in Nutrition Labels with Convolutional Neural Network." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7101.

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Nowadays, everyone is very busy and running around trying to maintain a balance between their work life and family, as the working hours are increasing day by day. In such hassled life people either ignore or do not give enough attention to a healthy diet. An imperative part of a healthy eating routine is the cognizance and maintenance of nourishing data and comprehension of how extraordinary sustenance and nutritious constituents influence our bodies. Besides in the USA, in many other countries, nutritional information is fundamentally passed on to consumers through nutrition labels (NLs) whi

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Vikström, Joel. "Training a Convolutional Neural Network to Evaluate Chess Positions." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263062.

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Convolutional neural networks are typically applied to image analysis problems. We investigate whether a simple convolutional neural network can be trained to evaluate chess positions by means of predicting Stockfish (an existing chess engine) evaluations. Publicly available data from lichess.org was used, and we obtained a final MSE of 863.48 and MAE of 12.18 on our test dataset (with labels ranging from -255 to +255). To accomplish better results, we conclude that a more capable model architecture must be used.<br>Konvolutionella neuronnät används ofta för bildanalys. Vi undersöker om ett en

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SAH, BIKASH KUMAR. "A NOVEL CONVOLUTIONAL NEURAL NETWORK FOR AIR POLLUTION FORECASTING." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18792.

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Air pollution was a global problem a few decades back. It is still a problem and will continue to be a problem if not solved appropriately.Various machine learning and deep learining approaches have been purposed for accurate prediction, estimation and analysis of the air polution. We have purposed a novel five layer one-dimensional convolution neural network architecture to forecast the PM2.5 concentration. It is a deep learning approach. We have used the five year air pollution dataset from 2010 to 2014 recorded by the US embassy in Beijing, China taken from the database from UCI machi

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Zhang, Huizhen. "Alpha Matting via Residual Convolutional Grid Network." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39467.

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Alpha matting is an important topic in areas of computer vision. It has various applications, such as virtual reality, digital image and video editing, and image synthesis. The conventional approaches for alpha matting perform unsatisfactorily when they encounter complicated background and foreground. It is also difficult for them to extract alpha matte accurately when the foreground objects are transparent, semi-transparent, perforated or hairy. Fortunately, the rapid development of deep learning techniques brings new possibilities for solving alpha matting problems. In this thesis, we pro

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Martell, Patrick Keith. "Hierarchical Auto-Associative Polynomial Convolutional Neural Networks." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

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Plouet, Erwan. "Convolutional and dynamical spintronic neural networks." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP120.

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Cette thèse aborde le développement de composants spintroniques pour le calcul neuromorphique, une approche novatrice visant à réduire la consommation énergétique significative des applications d'intelligence artificielle (IA). L'adoption généralisée de l'IA, y compris des très grands modèles de langage tels que ChatGPT, a entraîné une augmentation des besoins énergétiques, les centres de données consommant environ 1 à 2 de l'énergie mondiale, avec une projection de doublement d'ici 2030. Les architectures hardware traditionnelles, qui séparent la mémoire et les unités de traitement, ne sont p

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Vi, Margareta. "Object Detection Using Convolutional Neural Network Trained on Synthetic Images." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153224.

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Training data is the bottleneck for training Convolutional Neural Networks. A larger dataset gives better accuracy though also needs longer training time. It is shown by finetuning neural networks on synthetic rendered images, that the mean average precision increases. This method was applied to two different datasets with five distinctive objects in each. The first dataset consisted of random objects with different geometric shapes. The second dataset contained objects used to assemble IKEA furniture. The neural network with the best performance, trained on 5400 images, achieved a mean averag

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Fasth, Niklas, and Rasmus Hallblad. "Air Reconnaissance Analysis using Convolutional Neural Network-based Object Detection." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48422.

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The Swedish armed forces use the Single Source Intelligent Cell (SSIC), developed by Saab, for analysis of aerial reconnaissance video and report generation. The analysis can be time-consuming and demanding for a human operator. In the analysis workflow, identifying vehicles is an important part of the work. Artificial Intelligence is widely used for analysis in many industries to aid or replace a human worker. In this paper, the possibility to aid the human operator with air reconnaissance data analysis is investigated, specifically, object detection for finding cars in aerial images. Many st

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Li, Yi-Hsiu, and 李易修. "Face Alignment Based on Modified Deep Convolutional Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v79j68.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>106<br>Many face-related applications including face recognition, emotion detection, and medical cosmetology, rely on accurate facial features information. Manual labeling are inefficient, unstable, and subjective, and therefore an efficient automatic facial landmarking technique has been a crucial research topic. Current automatic facial feature extraction techniques based on deep learning networks are mostly applied to frontal facial landmarking. This thesis explores the method of deep convolutional neural networks for detecting 21 features on profile faces. Three

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Lai, Chen Yao, and 賴晨堯. "Using Convolution Neural Network to Identify the Modified Regions of Seam Carving." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107CGU05392010%22.&searchmode=basic.

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"Color Labeling via Convolutional Neural Network." 2016. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1292310.

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色彩，在計算機視覺里的很多領域扮演著非常重要的角色。然而，由於光照條件、觀察角度以及物體表面反射性質的多種多樣，要準確而真實地描述現實生活中捕捉到的色彩卻不是一件容易的事情。因此，將失真的色彩恢復過來加以描述，繼而加以利用，是一個重要的課題。在本次研究中，我們將探討如何實現顏色的恆常性描述。要達到這個目的，我們提出了一個端到端、像素到像素的卷積神經網絡，來將顏色值映射到預定義的顏色標籤上。我們將會描述應用這一網絡模型的兩個具體例子。<br>首先，我們想要解決具挑戰性的物體顏色命名問題。我們專注解決公共環境中的行人顏色描述，其非常容易受不同光照與觀察角度的影響。為了解決這一問題，我們提出要為屬於同一個表面區域的像素生成一致的色彩命名。在此研究中我們有兩點貢獻：（1）我們構建了一個大型的行人顏色命名數據集，包含了14213張人工標註的圖。（2）我們將提出的卷積神經網絡適應到基於區域的行人顏色命名任務中來。我們發現行人顏色命名卷積神經網絡要優於現有的方法，其可以為現實生活中的行人提供一致的顏色命名。此外，在行人再鑒證任務中，我們展現了我們提取的顏色描述的有效性。另外，我們提出了一個嶄新的應用，其可以用手工素描的簡易圖像來匹配購物網站中的衣服配飾，這個任務將由我們提取的顏色描述來完成。<br>此外，我們對彩色二維碼的顏色恢復問題很感興趣。彩色二維碼用多種顏色來表示不同數據組合。由於色彩失

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Xiao, Bin, and 肖彬. "Epilepsy prediction with convolutional neural network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/emhyjx.

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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>104<br>Epilepsy is one of the most common brain diseases, which can break out at anytime, anywhere. The unpredictability of seizure is often considered the most problematic aspect of epilepsy by the patients. A good epilepsy seizure predictor can help patients reduce the burden of unpredictability and improve patients’ life quality greatly. Therefore, a central theme in epilepsy treatments is to predict epilepsy seizure, so that patients can get a warning before epilepsy seizures take place. Electroencephalograms (EEGs) are recordings of the electrical potentials

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Liu, Yu-Cheng, and 劉又誠. "Action Recognition Using Convolutional Neural Network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/30475793234292847224.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>104<br>Multimedia plays an important role in human daily life. Hundreds of thousands videos are uploaded on the Internet. Some hot topic such as basketball and baseball games are with high click through rate so information retrieval techniques become important. Human action detection can be further applied to detect abnormal events and analyze activity. In this thesis, the dataset we use in experiments contains the human body action and interaction with objects like jumping, clapping, drinking. In the thesis, we first uses convolutional neural network (CNN) to train

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Lee, Ssu-Rui, and 李思叡. "Image Denoising by Convolutional Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/he56yv.

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碩士<br>國立清華大學<br>資訊系統與應用研究所<br>107<br>Removing noise from the images to improve image quality is the main challenge in image processing. Especially as the ubiquitous spread of computers, smartphones, the Internet, and social networks, image denoising becomes more and more important. In this work, we extend upon the results of Ulyanov et al.~\cite{Ulyanov_2018_CVPR} and introduce a competitive image denoising method based on the structure characteristic of convolutional neural networks (CNNs). Different from most CNN-based methods which need a large-scale dataset for training, our method only l

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WANG, SHENG-YUAN, and 王聖淵. "Convolutional Neural Network for Image Deblurring." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/gxr5xp.

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碩士<br>國立中央大學<br>資訊工程學系<br>106<br>In recent years, along with the rise of deep learning in academia and industry. There will be striking deep learning achievements and works every few months. It also proves that deep learning technology application has many great effects in the image. In this paper, the convolution neural network is used as the main method to restore out of focus images or blurred images to clear images. This paper proposes three network architectures: Auto_deblur, S-Net and AGDNet. In the case that the image is slightly damaged and blurred, it is better to select S-Net, becaus

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CHEN, HUNG-PEI, and 陳虹霈. "Integrating Convolutional Neural Network and Recurrent Neural Network for Automatic Text Classification." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4jqh8z.

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碩士<br>東吳大學<br>數學系<br>108<br>With the rapid development of huge data research area, the demand for processing textual information is increasing. Text classification is still a hot research in the field of natural language processing. In the traditional text mining process, we often use the "Bag-of-Words" model, which discards the order of the words in the sentence, mainly concerned with the frequency of occurrence of the words. TF-IDF (term frequency–inverse document frequency) is one of the techniques for feature extraction commonly used in text exploration and classification. Therefore, we co

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(5931110), Durvesh Pathak. "Compressed Convolutional Neural Network for Autonomous Systems." Thesis, 2019.

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The word “Perception” seems to be intuitive and maybe the most straightforward problem for the human brain because as a child we have been trained to classify images, detect objects, but for computers, it can be a daunting task. Giving intuition and reasoning to a computer which has mere capabilities to accept commands and process those commands is a big challenge. However, recent leaps in hardware development, sophisticated software frameworks, and mathematical techniques have made it a little less daunting if not easy. There are various applications built around to the concept of “Perception

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Pathak, Durvesh. "Compressed convolutional neural network for autonomous systems." Thesis, 2018. http://hdl.handle.net/1805/17921.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>The word “Perception” seems to be intuitive and maybe the most straightforward problem for the human brain because as a child we have been trained to classify images, detect objects, but for computers, it can be a daunting task. Giving intuition and reasoning to a computer which has mere capabilities to accept commands and process those commands is a big challenge. However, recent leaps in hardware development, sophisticated software frameworks, and mathematical techniques have made it a little less daunting if not easy. There

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Reis, Afonso de Sá. "Accelerating the training of convolutional neural network." Master's thesis, 2019. https://hdl.handle.net/10216/122196.

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The objective of this report is to implement a Convolutional Neural Network (CNN) in an FPGA, with a main focus on accelerating the training, using Maxeler technology as a way to compile higher level code directly into hardware.Neural Networks are one of the most commonly used models used in all sorts of tasks in Machine Learning. This type of network is mostly used for image recognition/generation, since a few layers ( convolutional, pooling) can be viewed as image operations to find features, which are then combined in the fully connected layer(s) and used to produce the output.

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Chen, Jun-Hao, and 陳俊豪. "Predict FX via Convolutional Neural Network (CNNs)." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2n2fqy.

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碩士<br>國立臺灣大學<br>經濟學研究所<br>105<br>Deep learning is an effective approach to solve image recognition problems. People like to think intuitively from the trading chart. This study used the characteristics of deep learning to train computers how to imitate people''s thinking from the trading chart. We have three steps as follows: 1. Before training, we need to pre-process our input data from quantitative data to images. 2. We use Convolutional-Neural-Network (CNN), which is a kind of the deep learning, to train our trading model. 3. We evaluate the model performance by the accuracy of

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Lee, Heng, and 李亨. "Convolutional Neural Network Accelerator with Vector Quantization." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/w7kr56.

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碩士<br>國立臺灣大學<br>電子工程學研究所<br>107<br>Deep neural networks (DNNs) have demonstrated impressive performance in many edge computer vision tasks, causing the increasing demand for DNN accelerator on mobile and internet of things (IoT) devices. However, the massive power consumption and storage requirement make the hardware design challenging. In this paper, we introduce a DNN accelerator based on a model compression technique vector quantization (VQ), which can reduce the network model size and computation cost simultaneously. Moreover, a specialized processing element (PE) is designed with various

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Lin, Tian-Yi, and 林天翼. "Manga Character Clustering using Convolutional Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5s46b5.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>106<br>As reading habits change, more and more manga books are digitized and can be read on tablet or smartphone. However, most of them are just scanned from the printed version and treated as normal image files. There are only few studies focus on extracting information from manga pages automatically. In this thesis, we try to cluster faces in manga books based on their identities. We proposed a method using convolutional neural network and clustering algorithm. We designed and trained a CNN network to extract features from manga character faces, and used the extra

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Yang, Shu-Sian, and 楊恕先. "Speech Recognition by Using Convolutional Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/szuwyc.

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碩士<br>國立中央大學<br>電機工程學系<br>107<br>The thesis developed a speech recognition method for automatic speech recognition. In this speech recognition method, we obtained the speech feature parameters through Mel frequency cepstral coefficients and input a Convolutional Neural Network. The main difference between this Convolutional Neural Network speech recognition method and traditional speech recognition method is that it does not need to establish an acoustic model. For example, in Chinese, it saved a lot of time without establishing a large number of consonant and vowel models. After obtaining the

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Huang, Tzu-Hsuan, and 黃子軒. "A Convolutional Neural Network for Face Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/b7g3ew.

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碩士<br>國立中央大學<br>數學系<br>107<br>Motivated by the work of Viola and Jones [10] for object detection, in this thesis we propose a new convolutional neural network model for face detection which is based on the study by Krizhevsky et al. [5]. The proposed convolutional neural network model for face detection is not limited to the black-white images and fixed face size. This approach combines Keras with OpenCV to construct a neural network framework consisting of several convolutional layers and fully connected layers. We use a training set which contains about 150,000 color images, cited from the C

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Wang, Shi-Hao, and 王士豪. "Applying Convolutional Neural Network for Malware Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/98tyh5.

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博士<br>國立中山大學<br>資訊管理學系研究所<br>106<br>Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type an

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Liou, Jhao-Yu, and 劉昭雨. "Using Convolutional Neural Network on Technical Analysis Indicators." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/32289485607677466547.

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碩士<br>國立東華大學<br>資訊工程學系<br>105<br>Deep learning is a state of the art artificial intelligence technology, and the convolutional neural networks have been widely used in image recognition competitions. In the financial stock market, we often use the linear graph of various technical indexes to predict the trend. This paper uses the convolution neural network's excellent image recognition ability, combining the linear graph of various technical indicators, to predict the stock price as a classification problem, and to predict the results. The single stock's history data is too small. This paper u

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