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Dissertations / Theses on the topic 'Convolution Neural Networks (CNN)'

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Published: 4 June 2025

Last updated: 15 July 2025

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1

Kapoor, Rishika. "Malaria Detection Using Deep Convolution Neural Network." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613749143868579.

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2

Hossain, Md Tahmid. "Towards robust convolutional neural networks in challenging environments." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/181882.

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Image classification is one of the fundamental tasks in the field of computer vision. Although Artificial Neural Network (ANN) showed a lot of promise in this field, the lack of efficient computer hardware subdued its potential to a great extent. In the early 2000s, advances in hardware coupled with better network design saw the dramatic rise of Convolutional Neural Network (CNN). Deep CNNs pushed the State-of-The-Art (SOTA) in a number of vision tasks, including image classification, object detection, and segmentation. Presently, CNNs dominate these tasks. Although CNNs exhibit impressive cla

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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the

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Ioannou, Yani Andrew. "Structural priors in deep neural networks." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278976.

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Deep learning has in recent years come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite breakthroughs in training deep networks, there remains a lack of understanding of both the optimization and structure of deep networks. The approach advocated by many researchers in the field has been to train monolithic networks with excess complexity, and strong regularization --- an approach that leaves much to desire in efficiency. Instead we propose that carefully designing networks in considerati

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5

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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Svensson, Göran, and Jonas Westlund. "Intravenous bag monitoring with Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148449.

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Drip bags are used in hospital environments to administerdrugs and nutrition to patients. Ensuring that they are usedcorrectly and are refilled in time are important for the safetyof patients. This study examines the use of a ConvolutionalNeural Network (CNN) to monitor the fluid levels of drip bagsvia image recognition to potentially form the base of an earlywarning system, and assisting in making medical care moreefficient. Videos of drip bags were recorded as they wereemptying their contents in a controlled environment and fromdifferent angles. A CNN was built to analyze the recordeddata in

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Li, Xile. "Real-time Multi-face Tracking with Labels based on Convolutional Neural Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36707.

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This thesis presents a real-time multi-face tracking system, which is able to track multiple faces for live videos, broadcast, real-time conference recording, etc. The real-time output is one of the most significant advantages. Our proposed tracking system is comprised of three parts: face detection, feature extraction and tracking. We deploy a three-layer Convolutional Neural Network (CNN) to detect a face, a one-layer CNN to extract the features of a detected face and a shallow network for face tracking based on the extracted feature maps of the face. The performance of our multi-face

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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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Habrman, David. "Face Recognition with Preprocessing and Neural Networks." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128704.

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Face recognition is the problem of identifying individuals in images. This thesis evaluates two methods used to determine if pairs of face images belong to the same individual or not. The first method is a combination of principal component analysis and a neural network and the second method is based on state-of-the-art convolutional neural networks. They are trained and evaluated using two different data sets. The first set contains many images with large variations in, for example, illumination and facial expression. The second consists of fewer images with small variations. Principal compon

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Ahlin, Björn, and Marcus Gärdin. "Automated Classification of Steel Samples : An investigation using Convolutional Neural Networks." Thesis, KTH, Materialvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209669.

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Automated image recognition software has earlier been used for various analyses in the steel making industry. In this study, the possibility to apply such software to classify Scanning Electron Microscope (SEM) images of two steel samples was investigated. The two steel samples were of the same steel grade but with the difference that they had been treated with calcium for a different length of time. To enable automated image recognition, a Convolutional Neural Network (CNN) was built. The construction of the software was performed with open source code provided by Keras Documentation, thus e

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Nikzad, Dehaji Mohammad. "Structural Improvements of Convolutional Neural Networks." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/410448.

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Over the last decade, deep learning has demonstrated outstanding performance in almost every application domain. Among different types of deep frameworks, convolutional neural networks (CNNs), inspired by the biological process of the visual system, can learn to extract discriminative features from raw inputs without any prior manipulation. However, efficient information circulation and the ability to explore effective new features are still two key and challenging factors for a successful deep neural network. In this thesis, we aim at presenting novel structural improvements of the CNN framew

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LEONARDI, MARCO. "Image Collection Management using Convolutional Neural Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/365014.

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Al giorno d’oggi ormai quasi chiunque possiede uno smartphone dotato di una telecamera ad alta risoluzione. Negli ultimi decenni, i contenuti multimediali (immagini e video) stanno sempre più spesso diventando il principale mezzo di comunicazione. Dato il continuo calo dei prezzi dei dispositivi di archiviazione, il numero totale di immagini salvate sta aumentando notevolmente, andando così a creare collezioni di immagini sempre più grandi, a tal punto da essere una problema per chi vuole le vuole esplorare. Data una libreria di immagini, il processo di selezione di un gruppo di foto che rap

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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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Sung, Wei-Hong. "Investigating minimal Convolution Neural Networks (CNNs) for realtime embedded eye feature detection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281338.

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With the rapid rise of neural networks, many tasks that used to be difficult to complete in traditional methods can now be solved well, especially in the computer vision field. However, as the tasks we have to solve have become more and more complex, the neural networks we use are becoming deeper and larger. Therefore, although some embedded systems are powerful nowadays, most embedded systems still suffer from memory and computation limitations, which means it is hard to deploy our large neural networks on these embedded devices. This project aims to explore different methods to compress the

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Singh, Vineeta. "Understanding convolutional networks and semantic similarity." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593269596368388.

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Norén, Gustav. "Noise Robustness of Convolutional Autoencoders and Neural Networks for LPI Radar Classification." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273604.

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This study evaluates noise robustness of convolutional autoencoders and neural networks for classification of Low Probability of Intercept (LPI) radar modulation type. Specifically, a number of different neural network architectures are tested in four different synthetic noise environments. Tests in Gaussian noise show that performance is decreasing with decreasing Signal to Noise Ratio (SNR). Training a network on all SNRs in the dataset achieved a peak performance of 70.8 % at SNR=-6 dB with a denoising autoencoder and convolutional classifier setup. Tests indicate that the models have a dif

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Hodges, Jonathan Lee. "Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86364.

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Fire dynamics is a complex process involving multi-mode heat transfer, reacting fluid flow, and the reaction of combustible materials. High-fidelity predictions of fire behavior using computational fluid dynamics (CFD) models come at a significant computational cost where simulation times are often measured in hours, days, or even weeks. A new simulation method is to use a machine learning approach which uses artificial neural networks (ANNs) to represent underlying connections between data to make predictions of new inputs. The field of image analysis has seen significant advancements in ANN

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Carpani, Valerio. "CNN-based video analytics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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The content of this thesis illustrates the six months work done during my internship at TKH Security Solutions - Siqura B.V. in Gouda, Netherlands. The aim of this thesis is to investigate on convolutional neural networks possible usage, from two different point of view: first we propose a novel algorithm for person re-identification, second we propose a deployment chain, for bringing research concepts to product ready solutions. In existing works, the person re-identification task is assumed to be independent of the person detection task. In this thesis instead, we consider the two ta

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Khlif, Wafa. "Multi-lingual scene text detection based on convolutional neural networks." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS022.

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Cette thèse propose des approches de détection de texte par des techniques d'apprentissage profond pour explorer et récupérer des contenus faiblement structurés dans des images de scène naturelles. Ces travaux proposent, dans un premier temps, une méthode de détection de texte dans des images de scène naturelle basée sur une analyse multi-niveaux des composantes connexes (CC) et l'apprentissage des caractéristiques du texte par un réseau de neurones convolutionnel (CNN), suivie d'un regroupement des zones de texte détectées par une méthode à base de graphes. Les caractéristiques des composante

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Strömberg, Lucas. "Optimizing Convolutional Neural Networks for Inference on Embedded Systems." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444802.

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Convolutional neural networks (CNN) are state of the art machine learning models used for various computer vision problems, such as image recognition. As these networks normally need a vast amount of parameters they can be computationally expensive, which complicates deployment on embedded hardware, especially if there are contraints on for instance latency, memory or power consumption. This thesis examines the CNN optimization methods pruning and quantization, in order to explore how they affect not only model accuracy, but also possible inference latency speedup. Four baseline CNN models, ba

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Ericsson, Andreas, and Kana Filip Döringer. "Convolutional Neural Networks for Classification of Metastatic Tissue in Lymph Nodes : How Does Cutout Affect the Performance of Convolutional Neural Networks for Biomedical Image Classification?" Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302529.

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One of every eight women will in their lifetime suffer from breast cancer, making it the most common type of cancer for women. A successful treatment is very much dependent on identifying metastatic tissue which is cancer found beyond the initial tumour. Using deep learning within biomedical analysis has become an effective approach. However, its success is very dependent on large datasets. Data augmentation is a way to enhance datasets without requiring more annotated data. One way of doing this is using the cutout method which masks parts of an input image. Our research focused on investigat

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Shuvo, Md Kamruzzaman. "Hardware Efficient Deep Neural Network Implementation on FPGA." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2792.

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In recent years, there has been a significant push to implement Deep Neural Networks (DNNs) on edge devices, which requires power and hardware efficient circuits to carry out the intensive matrix-vector multiplication (MVM) operations. This work presents hardware efficient MVM implementation techniques using bit-serial arithmetic and a novel MSB first computation circuit. The proposed designs take advantage of the pre-trained network weight parameters, which are already known in the design stage. Thus, the partial computation results can be pre-computed and stored into look-up tables. Then the

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Tunell, John. "Classification of offensive game-emblem drawings using CNN (convolutional neural networks) and transfer learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-348944.

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Convolutional neural networks (CNN) has become an important tool to solve many computer vision tasks of today. The technique is though costly, and training a network from scratch requires both a large dataset and adequate hardware. A solution to these shortcomings is to instead use a pre-trained network, an approach called transfer learning. Several studies have shown promising results applying transfer learning, but the technique requires further studies. This thesis explores the capabilities of transfer learning when applied to the task of filtering out offensive cartoon drawings in the game

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Pradels, Léo. "Efficient CNN inference acceleration on FPGAs : a pattern pruning-driven approach." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS087.

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Les modèles d'apprentissage profond basés sur les CNNs offrent des performances de pointe dans les tâches de traitement d'images et de vidéos, en particulier pour l'amélioration ou la classification d'images. Cependant, ces modèles sont lourds en calcul et en empreinte mémoire, ce qui les rend inadaptés aux contraintes de temps réel sur des FPGA embarqués. Il est donc essentiel de compresser ces CNNs et de concevoir des architectures d'accélérateurs pour l'inférence qui intègrent la compression dans une approche de co-conception matérielle et logicielle. Bien que des optimisations logicielles

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Leuzzi, Laura. "Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22516/.

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Studying Galaxy-Galaxy Strong Lensing events allows to tackle several problems, that include the reconstruction of the mass distribution of the lens galaxies and the estimation of the Hubble constant. Thousands of these systems are expected to be detected in upcoming imaging surveys, such as the one that will be carried out by the Euclid space telescope, but they will have to be identified among the billions of sources that will be observed. In this context, the development of automated and reliable techniques for the examination of large volumes of data is of crucial importance. Convolutional

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Lavenius, Axel. "Automatic identification of northern pike (Exos Lucius) with convolutional neural networks." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-418639.

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The population of northern pike in the Baltic sea has seen a drasticdecrease in numbers in the last couple of decades. The reasons for this are believed to be many, but the majority of them are most likely anthropogenic. Today, many measures are being taken to prevent further decline of pike populations, ranging from nutrient runoff control to habitat restoration. This inevitably gives rise to the problem addressed in this project, namely: how can we best monitor pike populations so that it is possible to accurately assess and verify the effects of these measures over the coming decades? Pike

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Hensman, Paulina. "Intra-prediction for Video Coding with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224197.

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Intra-prediction is a method for coding standalone frames in video coding. Until now, this has mainly been done using linear formulae. Using an Artificial Neural Network (ANN) may improve the prediction accuracy, leading to improved coding efficiency. In this degree project, Fully Connected Networks (FCN) and Convolutional Neural Networks (CNN) were used for intra-prediction. Experiments were done on samples from different image sizes, block sizes, and block contents, and their effect on the results were compared and discussed. The results show that ANN methods have the potential to perform be

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Ekman, Carl. "Traffic Sign Classification Using Computationally Efficient Convolutional Neural Networks." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157453.

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Traffic sign recognition is an important problem for autonomous cars and driver assistance systems. With recent developments in the field of machine learning, high performance can be achieved, but typically at a large computational cost. This thesis aims to investigate the relation between classification accuracy and computational complexity for the visual recognition problem of classifying traffic signs. In particular, the benefits of partitioning the classification problem into smaller sub-problems using prior knowledge in the form of shape or current region are investigated. In the experime

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Truzzi, Stefano. "Event classification in MAGIC through Convolutional Neural Networks." Doctoral thesis, Università di Siena, 2022. http://hdl.handle.net/11365/1216295.

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The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes are able to detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic known objects, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts. Gamma rays and cosmic rays are detected by imaging the Cherenkov light produced by the charged superluminal leptons in the extended air shower originated when the primary particle interacts with the atmosphere. These Cherenkov flashes brighten the night sky for short times in the nanosecond scale. From the image topology an

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Sörsäter, Michael. "Active Learning for Road Segmentation using Convolutional Neural Networks." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-152286.

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In recent years, development of Convolutional Neural Networks has enabled high performing semantic segmentation models. Generally, these deep learning based segmentation methods require a large amount of annotated data. Acquiring such annotated data for semantic segmentation is a tedious and expensive task. Within machine learning, active learning involves in the selection of new data in order to limit the usage of annotated data. In active learning, the model is trained for several iterations and additional samples are selected that the model is uncertain of. The model is then retrained on ad

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Linder, Johannes. "Modeling the intronic regulation of Alternative Splicing using Deep Convolutional Neural Nets." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172327.

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This paper investigates the use of deep Convolutional Neural Networks for modeling the intronic regulation of Alternative Splicing on the basis of DNA sequence. By training the CNN on massively parallel synthetic DNA libraries of Alternative 5'-splicing and Alternatively Skipped exon events, the model is capable of predicting the relative abundance of alternatively spliced mRNA isoforms on held-out library data to a very high accuracy (R2 = 0.77 for Alt. 5'-splicing). Furthermore, the CNN is shown to generalize alternative splicing across cell lines efficiently. The Convolutional Neural Net is

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Thörnberg, Jesper. "Combining RGB and Depth Images for Robust Object Detection using Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174137.

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We investigated the advantage of combining RGB images with depth data to get more robust object classifications and detections using pre-trained deep convolutional neural networks. We relied upon the raw images from publicly available datasets captured using Microsoft Kinect cameras. The raw images varied in size, and therefore required resizing to fit our network. We designed a resizing method called "bleeding edge" to avoid distorting the objects in the images. We present a novel method of interpolating the missing depth pixel values by comparing to similar RGB values. This method proved sup

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Adhikari, Aakriti. "Skin Cancer Detection using Generative Adversarial Networkand an Ensemble of deep Convolutional Neural Networks." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1574383625473665.

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Sundström, Johan. "Sentiment analysis of Swedish reviews and transfer learning using Convolutional Neural Networks." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-339066.

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Sentiment analysis is a field within machine learning that focus on determine the contextual polarity of subjective information. It is a technique that can be used to analyze the "voice of the customer" and has been applied with success for the English language for opinionated information such as customer reviews, political opinions and social media data. A major problem regarding machine learning models is that they are domain dependent and will therefore not perform well for other domains. Transfer learning or domain adaption is a research field that study a model's ability of transferring k

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Ďuriš, Denis. "Detekce ohně a kouře z obrazového signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412968.

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This diploma thesis deals with the detection of fire and smoke from the image signal. The approach of this work uses a combination of convolutional and recurrent neural network. Machine learning models created in this work contain inception modules and blocks of long short-term memory. The research part describes selected models of machine learning used in solving the problem of fire detection in static and dynamic image data. As part of the solution, a data set containing videos and still images used to train the designed neural networks was created. The results of this approach are evaluated

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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify are

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Mele, Matteo. "Convolutional Neural Networks for the Classification of Olive Oil Geographical Origin." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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This work proposed a deep learning approach to a multi-class classification problem. In particular, our project goal is to establish whether there is a connection between olive oil molecular composition and its geographical origin. To accomplish this, we implement a method to transform structured data into meaningful images (exploring the existing literature) and developed a fine-tuned Convolutional Neural Network able to perform the classification. We implement a series of tailored techniques to improve the model.

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Maragno, Alessandro. "Programmazione di Convolutional Neural Networks orientata all'accelerazione su FPGA." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12476/.

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Attualmente la Computer Vision, disciplina che consente di estrarre informazioni a partire da immagini digitali, è uno dei settori informatici più in fermento. Grazie alle recenti conquiste e progressi, tale settore ha raggiunto uno stato di maturità tale da poter essere applicato in svariati ambiti, a partire da quello industriale, fino ad arrivare ad applicazioni più vicine alla vita quotidiana. In particolare, si è raggiunto uno stato dell'arte sempre più solido nel campo del riconoscimento di oggetti (object detection) grazie allo sviluppo delle Convolutional Neural Networks (CNN): sistemi

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Näslund, Anton, and Charlie Jeansson. "Robust Speech Activity Detection and Direction of Arrival Using Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297756.

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Social robots are becoming more and more common in our everyday lives. In the field of conversational robotics, the development goes towards socially engaging robots with humanlike conversation. This project looked into one of the technical aspects when recognizing speech, videlicet speech activity detection (SAD). The presented solution uses a convolutional neural network (CNN) based system to detect speech in a forward azimuth area. The project used a dataset from FestVox, called CMU Artic and was complimented by adding recorded noises. A library called Pyroomacoustics were used to simulate

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Castelli, Filippo Maria. "3D CNN methods in biomedical image segmentation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18796/.

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A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most interesting and looked-forward goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its use as a purely assistive tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedic

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El, Ahmar Wassim. "Head and Shoulder Detection using CNN and RGBD Data." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39448.

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Alex Krizhevsky and his colleagues changed the world of machine vision and image processing in 2012 when their deep learning model, named Alexnet, won the Im- ageNet Large Scale Visual Recognition Challenge with more than 10.8% lower error rate than their closest competitor. Ever since, deep learning approaches have been an area of extensive research for the tasks of object detection, classification, pose esti- mation, etc...This thesis presents a comprehensive analysis of different deep learning models and architectures that have delivered state of the art performances in various machi

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Liberatore, Lorenzo. "Introduction to geometric deep learning and graph neural networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25339/.

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This thesis proposes an introduction to the fundamental concepts of supervised deep learning. Starting from Rosemblatt's Perceptron we will discuss the architectures that, in recent years, have revolutioned the world of deep learning: graph neural networks, which led to the formulation of geometric deep learning. We will then give a simple example of graph neural network, discussing the code that composes it and then test our architecture on the MNISTSuperpixels dataset, which is a variation of the benchmark dataset MNIST.

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Ramesh, Shreyas. "Deep Learning for Taxonomy Prediction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/89752.

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The last decade has seen great advances in Next-Generation Sequencing technologies, and, as a result, there has been a rise in the number of genomes sequenced each year. In 2017, there were as many as 10,000 new organisms sequenced and added into the RefSeq Database. Taxonomy prediction is a science involving the hierarchical classification of DNA fragments up to the rank species. In this research, we introduce Predicting Linked Organisms, Plinko, for short. Plinko is a fully-functioning, state-of-the-art predictive system that accurately captures DNA - Taxonomy relationships where other state

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Meechan-Maddon, Ailsa. "The effect of noise in the training of convolutional neural networks for text summarisation." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384607.

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In this thesis, we work towards bridging the gap between two distinct areas: noisy text handling and text summarisation. The overall goal of the paper is to examine the effects of noise in the training of convolutional neural networks for text summarisation, with a view to understanding how to effectively create a noise-robust text-summarisation system. We look specifically at the problem of abstractive text summarisation of noisy data in the context of summarising error-containing documents from automatic speech recognition (ASR) output. We experiment with adding varying levels of noise (erro

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Julin, Fredrik. "Vision based facial emotion detection using deep convolutional neural networks." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-42622.

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Emotion detection, also known as Facial expression recognition, is the art of mapping an emotion to some sort of input data taken from a human. This is a powerful tool to extract valuable information from individuals which can be used as data for many different purposes, ranging from medical conditions such as depression to customer feedback. To be able to solve the problem of facial expression recognition, smaller subtasks are required and all of them together form the complete system to the problem. Breaking down the bigger task at hand, one can think of these smaller subtasks in the form of

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Sparr, Henrik. "Object detection for a robotic lawn mower with neural network trained on automatically collected data." Thesis, Uppsala universitet, Datorteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444627.

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Machine vision is hot research topic with findings being published at a high pace and more and more companies currently developing automated vehicles. Robotic lawn mowers are also increasing in popularity but most mowers still use relatively simple methods for cutting the lawn. No previous work has been published on machine learning networks that improved between cutting sessions by automatically collecting data and then used it for training. A data acquisition pipeline and neural network architecture that could help the mower in avoiding collision was therefor developed. Nine neural networks

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Caldera, Shehan. "Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2170.

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Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from co

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Agatensi, Luca. "Studio e Sperimentazione di Character-Level Convolutional Neural Networks per la Sentiment Classification." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14652/.

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L'obiettivo di questa tesi progettuale è quello di utilizzare una particolare tipologia di reti neurali, definite Char-CNN ovvero Character-Level Convolutional Neural Networks, per classificare e testare una serie di recensioni di prodotti relativi a differenti contesti commerciali e con differenti linguaggi, in modo tale che il sistema sia poi in grado di riconoscere la classe delle recensioni future. Gli esperimenti sono stati sviluppati sia In-Domain che Cross-Domain, ciò significa che il sistema addestrato è stato valutato sia con recensioni appartenenti allo stesso dominio utilizzato pe

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Pizzigati, Lorenzo. "Anomaly Prediction with Temporal Convolutional Networks for HPC Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20182/.

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Per far fronte a esigenze computazionali elevate, necessarie per la risoluzione di problemi complessi, la scienza e le industrie fanno spesso uso di sistemi di calcolo di enormi dimensioni e potenza. I sistemi HPC (High Performance Computing) sono identificabili come un insieme di tanti computer cooperanti e connessi tra loro, chiamati singolarmente “nodi”. I costi da sostenere per l’acquisto o la costruzione di questi sistemi ammontano a svariate decine di milioni di euro. Per questo motivo viene spesso affittata la potenza di calcolo di questi sistemi in modalità on-demand, grazie alla tecno

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Viswavarapu, Lokesh Kumar. "Real-Time Finger Spelling American Sign Language Recognition Using Deep Convolutional Neural Networks." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404616/.

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This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition an

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