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Dissertations / Theses on the topic 'Tensorflow I'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Persson, Ludvig, and Jesper Larsson. "Sarcasm Detection with TensorFlow." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229768.

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Sentiment analysis is the process of letting a computer guess the senti- ment of someone towards something based on a text. This can among other things be useful in marketing, for example in the case of the computer figuring out that a certain person likes a certain product it can present ads for similar products to the person. Sentiment analy- sis in social media is when the texts analyzed are from a social media context like comments or posts on Twitter, Facebook, etc. One prob- lematic aspect of these texts is sarcasm. People tend to be sarcastic very often in social media, with sarcasm bei

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Glass, Henrik, and Axel Swaretz. "Performance of TensorFlow : Examining what factors impact the performance of TensorFlow in distributed systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229776.

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This study aims to examine what factors affect the scalability and performance of a distributed TensorFlow program. We created a TensorFlow cluster consisting of 3 Raspberry Pi 3 model Bs functioning as workers and one Dell XPS 13 (9350) acting as a parameter server. We benchmarked the performance of our cluster for different sizes and compositions of the TensorFlow graph and for different network configurations between the computers in the cluster. From the results we conclude that both of the previously mentioned factors impact the performance of our distributed TensorFlow program.<br>Denna

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3

Adapa, Supriya. "TensorFlow Federated Learning: Application to Decentralized Data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks such as Google’s TensorFlow Federated that ease the process of acquiring data, training models, serving predictions, and refining future results. There are an estimated 3 billion smartphones in the world and 7 billion connected devices. These phones and devices are constantly generating new data. Traditional analytics and machine learning need that data to be centrally collected before it is processed to yield insights,

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Viklund, Alexander, and Emma Nimstad. "Character Recognition in Natural Images Utilising TensorFlow." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208385.

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Convolutional Neural Networks (CNNs) are commonly used for character recognition. They achieve the lowest error rates for popular datasets such as SVHN and MNIST. Usage of CNN is lacking in research about character classification in natural images regarding the whole English alphabet. This thesis conducts an experiment where TensorFlow is used to construct a CNN that is trained and tested on the Chars74K dataset, with 15 images per class for training and 15 images per class for testing. This is done with the aim of achieving a higher accuracy than the non-CNN approach by de Campos et al. [1],

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Sörell, Johan, and Elias Ågeby. "Inverse Diffusion by Proximal Optimization with TensorFlow." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239369.

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We present a method for implementing a large scale, proximal optimization algorithm in a machine learning framework to solve an inverse problem. The algorithm is based on a previously developed method for analyzing data of some imagebased immunoassays in the context of detecting diffused cells. By employing TensorFlow through it’s Python API, parallelized computations on graphical processing units, distributed processing, automatic gradient computation and computational efficiency are made available for implementation. Image processing methods are also utilized throughout the implementation, r

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Alsing, Oscar. "Mobile Object Detection using TensorFlow Lite and Transfer Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233775.

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With the advancement in deep learning in the past few years, we are able to create complex machine learning models for detecting objects in images, regardless of the characteristics of the objects to be detected. This development has enabled engineers to replace existing heuristics-based systems in favour of machine learning models with superior performance. In this report, we evaluate the viability of using deep learning models for object detection in real-time video feeds on mobile devices in terms of object detection performance and inference delay as either an end-to-end system or feature

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Karlsson, David. "Ljudklassificering med Tensorflow och IOT-enheter : En teknisk studie." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39331.

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Artificial Inteligens and machine learning has started to get established as reco- gnizable terms to the general masses in their daily lives. Applications such as voice recognicion and image recognicion are used widely in mobile phones and autonomous systems such as self-drivning cars. This study examines how one can utilize this technique to classify sound as a complement to videosurveillan- ce in different settings, for example a busstation or other areas that might need monitoring. To be able to do this a technique called Convolution Neural Ne- twork has been used since this is a popular ar

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Lind, Eric, and Velasquez Ävelin Pantigoso. "A performance comparison between CPU and GPU in TensorFlow." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260240.

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The fast-growing field of Machine Learning has in the later years become more common, as it has gone from a restricted research area to actually be in general use. Frameworks such as TensorFlow have been developed to scale and analyze artificial neural networks, which are used in one of the areas in Machine Learning called Deep Learning. This paper will study how well the framework TensorFlow performs in regard to time and memory allocation on the processor units CPU and GPU since these are the factors that are often the restraining resources. Three neural networks have been used to measure ho

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Adlers, Jacob, and Gustaf Pihl. "Prediction of training time for deep neural networks in TensorFlow." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229423.

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Machine learning has gained a lot of interest over the past years and is now used extensively in various areas. Google has developed a framework called TensorFlow which simplifies the usage of machine learning without compromising the end result. However, it does not resolve the issue of neural network training being time consuming. The purpose of this thesis is to investigate with what accuracy training times can be predicted using TensorFlow. Essentially, how effectively one neural network in TensorFlow can be used to predict the training times of other neural networks, also in TensorFlow. I

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Marmayohan, Nivethan, and Abdirahman Farah. "Scene analysis using Tensorflow & YOLO algorithms on Raspberry pi 4." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45540.

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Objektdetektion är en av de viktigaste mjukvarukomponenterna i nästa generation trafikövervakning. Deep learnings-algoritmer för objektdetektion, exempelvis YOLO (You Only Look Once), är snabba och noggranna algoritmer i realtid. Realtidsdetektion och igenkänning av objekt är viktiga uppgifter för bildbehandling. I denna studie presenteras ett inbäddat system för detektion och igenkänning av objekt i normal videohastighet (realtid). Indata är följaktligen en videoström som härstammar från en trafikmiljö i Halmstad. Hårdvaran är Raspberry pi 4 i vilken programvarupaketen Tensorflow, YOLO sam

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Gedin, Sebastian, and Jakob Holm. "Benchmarking TensorFlow on a personal computer not specialised for machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229390.

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Many recent advancement of modern technologies can be attributed to the rapid growth of the machine learning field and especially deep learning. A big challenge for deep learning is that the learning process can be very time-consuming. TensorFlow is a framework which allows developers to make use of GPUs and other processing units in order to tackle this and other tasks involved in machine learning. In this study we benchmark and investigate the performance of TensorFlow in terms of images per second on a personal computer not specialised for machine learning. We investigate how the performanc

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Chien, Wei Der. "An Evaluation of TensorFlow as a Programming Framework for HPC Applications." Thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233795.

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In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and performance. At the core of these application is dense matrix-matrix multiplication. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabilities. In addition, specialized low-precision accelerators have emerged to specifically address Tensor operations. Software frameworks, such as TensorFlow have also emerged to increase the expressiveness of neural network model development. In TensorFlow comp

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Biswas, Rajarshi. "Benchmarking and Accelerating TensorFlow-based Deep Learning on Modern HPC Systems." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531827968620294.

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Furundzic, Bojan, and Fabian Mathisson. "Dataset Evaluation Method for Vehicle Detection Using TensorFlow Object Detection API." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43345.

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Recent developments in the field of object detection have highlighted a significant variation in quality between visual datasets. As a result, there is a need for a standardized approach of validating visual dataset features and their performance contribution. With a focus on vehicle detection, this thesis aims to develop an evaluation method utilized for comparing visual datasets. This method was utilized to determine the dataset that contributed to the detection model with the greatest ability to detect vehicles. The visual datasets compared in this research were BDD100K, KITTI and Udacity,

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Johansson, Tobias. "Managed Distributed TensorFlow with YARN : Enabling Large-Scale Machine Learning on Hadoop Clusters." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-248007.

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Apache Hadoop is the dominant open source platform for the storage and processing of Big Data. With the data stored in Hadoop clusters, it is advantageous to be able to run TensorFlow applications on the same cluster that holds the input data sets for training machine learning models. TensorFlow supports distributed executions where Deep Neural Networks can be trained utilizing a large amount of compute nodes. To configure and launch distributed TensorFlow applications manually is complex and impractical, and gets worse with more nodes. This project presents a framework that utilizes Hadoop’s

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Carlsson, David. "Tree trunk image classifier : Image classification of trees using Collaboratory, Keras and TensorFlow." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-98698.

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In the forestry industry tree trunks are currently classified manually. The object of this thesis is to answer whether it is possible to automate this using modern computer hardware and image-classification of tree-trunks using machine learning algorithms. The report concludes, based on results from controlled experiments that it is possible to achieve an accuracy above 90% across the genuses Birch, Pine and Spruce with a classification-time per tree shorter than 500 milli seconds. The report further compares these results against previous research and concludes that better results are probabl

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Кириченко, І. О. "Інтелектуальна технологія детектування стану трубопроводів з аугментацією даних в режимі екзамену". Master's thesis, Сумський державний університет, 2021. https://essuir.sumdu.edu.ua/handle/123456789/86859.

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Cпроектовано та розроблено класифікатор детектування стану трубопроводів. При цьому задача оцінки стану труб була розв’язана за допомогою підходу аугментації зображень, а сама технологія працює в режимі екзамену. Розроблений алгоритм реалізовано у формі програмного забезпечення, створеного за допомогою інструментального програмного середовища Python 3.0.

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Glandberger, Oliver, and Daniel Fredriksson. "Neural Network Regularization for Generalized Heart Arrhythmia Classification." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19731.

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Background: Arrhythmias are a collection of heart conditions that affect almost half of the world’s population and accounted for roughly 32.1% of all deaths in 2015. More importantly, early detection of arrhythmia through electrocardiogram analysis can prevent up to 90% of deaths. Neural networks are a modern and increasingly popular tool of choice for classifying arrhythmias hidden within ECG-data. In the pursuit of achieving increased classification accuracy, some of these neural networks can become quite complex which can result in overfitting. To combat this phenomena, a technique called r

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Antonini, Lorenzo. "Reinforcement Learning Middleware Solutions for Android-oriented Distributed Deployments." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Oggigiorno i dispositivi mobili sono l’artefatto tecnologico a maggior contatto con le persone. Pieni di sensori in grado di percepire l’ambiente circostante e dalla notevole potenza computazionale, risultano essere l’ambiente perfetto per creare applicazioni in grado di predire le azioni future e di evolvere in base alle continue scelte dell’utilizzatore. Negli ultimi anni si è fatto sempre più prorompente, nell’ambito dell’intelligenza artificiale, il Reinforcement Learning. Molte conoscenze matematiche e di programmazione sono necessarie per sfruttare al meglio questa famiglia di algoritmi

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Fahlén, Erik. "Androidapplikation för digitalisering av formulär : Minimering av inlärningstid, kostnad och felsannolikhet." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35623.

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This study was performed by creating an android application that uses custom object recognition to scan and digitalize a series of checkbox form for example to correct multiple-choice questions or collect forms in a spreadsheet. The purpose with this study was to see which dataset and hardware with the machine learning library TensorFlow was cheapest, price worthy, enough reliable and fastest. A dataset of filled example forms with annotated checkboxes was created and used in the learning process. The model that was used for the object recognition was Single Show MultiBox Detector, MobileNet v

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Kim, Soyoung. "Design and Experimental Evaluation of DeepMarket: An Edge Computing Marketplace with Distributed TensorFlow Execution Capability." PDXScholar, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/5120.

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There is a rise in demand among machine learning researchers for powerful computational resources to train complex machine learning models, e.g., deep learning models. In order to train these models in a reasonable amount of time, the training is often distributed among multiple machines; yet paying for such machines (either through renting them on cloud data centers or building a local infrastructure) is costly. DeepMarket attempts to reduce these costs by creating a marketplace that integrates multiple computational resources over a distributed TensorFlow framework. Instead of requiring user

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Payerl, Anders. "Tolka musiktecken från bilder : Optisk musikigenkänning med maskininlärning." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-34063.

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The objective of the project was to examine the possibility to use machine lear- ning without prior knowledge of machine learning or of mathematics and if that is possible also explore the possibility to use machine learning to interpret a picture of a piece of sheet music. The capacity of detecting notes from images of sheet music in the produced model was then compared to an existing pro- gram called Audiveris. The result became a model later used in a comparison with the program Audiveris. The comparison resulted in Audiveris finding al- most 100% of the notes but the new model only being a

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Bediako, Peter Ken. "Long Short-Term Memory Recurrent Neural Network for detecting DDoS flooding attacks within TensorFlow Implementation framework." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-66802.

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Distributed Denial of Service (DDoS) attacks is one of the most widespread security attacks to internet service providers. It is the most easily launched attack, but very difficult and expensive to detect and mitigate. In view of the devastating effect of DDoS attacks, there has been the increase on the adaptation of a network detection technique to reveal the presence of DDoS attack before huge traffic buildup to prevent service availability. Several works done on DDoS attack detection reveals that, the conventional DDoS attack detection methods based on statistical divergence is useful, howe

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Della, Chiesa Enrico. "Implementazione Tensorflow di Algoritmi di Anomaly Detection per la Rilevazione di Intrusioni Mediante Signals of Opportunity (SoOP)." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In questo elaborato viene presentata l’implementazione di algoritmi di machine learning di tipo supervised e unsupervised attraverso Python e Tensorflow. In particolare viene affrontato come caso di studio l’implementazione di algoritmi di Anomaly Detection. Nel Capitolo 1 vengono presentati gli algoritmi di machine learning implementati. Nel Capitolo 2 viene presentato e analizzato l’ambiente di sviluppo utilizzato, costituito da Python e Tensoflow. Infine è presentata l’implementazione degli algoritmi descritti al capitolo 1. Nel Capitolo 3 sono implementati come caso di studio due algoritm

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Yin, Jiaqi. "Measurement of machine learning performance with different condition and hyperparameter." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587693436870594.

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Alsalehy, Ahmad, and Ghada Alsayed. "Scenanalys av trafikmiljön." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44936.

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Antalet vägtrafikanter ökar varje år, och med det ökar trängseln. Man har därför gjort undersökningar med hjälp av objektdetektionsalgoritmer på videoströmmar. Genom att analysera data resultat är det möjligt att bygga en bättre infrastruktur, för att minska trafikstockning samt olyckor. Data som analyseras kan till exempel vara att räkna hur många trafikanter som vistas på en viss väg (Slottsbron i Halmstad) under en viss tid. Detta examensarbete undersöker teoretiskt hur en YOLO algoritm samt TensorFlow kan användas för att detektera olika trafikanter. Utvärderingsmetoder som användes i proj

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Ferm, Oliwer. "Real-time Object Detection on Raspberry Pi 4 : Fine-tuning a SSD model using Tensorflow and Web Scraping." Thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39455.

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Edge AI is a growing area. The use of deep learning on low cost machines, such as the Raspberry Pi, may be used more than ever due to the easy use, availability, and high performance. A quantized pretrained SSD object detection model was deployed to a Raspberry Pi 4 B to evaluate if the throughput is sufficient for doing real-time object recognition. With input size of 300x300, an inference time of 185 ms was obtained. This is an improvement as of the previous model; Raspberry Pi 3 B+, 238 ms with a input size of 96x96 which was obtained in a related study. Using a lightweight model is for the

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Manousian, Jonathan. "Digitalisering av handskrivna siffror på fysiska formulär : Utvärdering av tillförlitlighet och träningstid." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39343.

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Inom arbetslivet finns situationer i vilka vi kan utnyttja digitalisering för att förenkla och effektivisera arbetet. Ett exempel är den analoga hanteringen av fysiska formulär. Oftast överförs data från fysiska formulär till datorn manuellt. Syftet med detta projekt är att effektivisera den generella hanteringen av pappersformulär genom inskanning. Detta kan göras genom att utnyttja en beskärningsfunktion vid inskanningen. Beskärningen används för att beskära bort irrelevant data från formuläret och därmed framhävs det som ska skannas in. Därefter kan objektigenkänning användas för att känna

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Achilli, Mattia. "Rilevamento di mascherine facciali sanitarie: sperimentazione e porting di una soluzione allo stato dell'arte su Android con TensorFlow Lite." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21567/.

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La pandemia COVID-19 attualmente in corso denominata anche "coronavirus" ha destabilizzato la nostra società e il nostro modo di vivere, l’utilizzo di mascherine mediche e il distanziamento sociale sono diventati essenziali per evitare la diffusione del virus. Questo elaborato di tesi ha lo scopo di individuare se le persone indossano le mascherine mediche o meno grazie al supporto del machine learning e in particolare del deep learning, con lo scopo di limitare i contagi tra le persone. Da internet è possibile reperire un gran numero di immagini contenenti persone che indossano una mascherina

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Michelini, Mattia. "Barcode detection by neural networks on Android mobile platforms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21080/.

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Lo scopo di questa esperienza di tesi è stato quello di fare un confronto sul tema delle reti neurali e, in particolare, sul modo di portare la potenza inferenziale di questi modelli nel mondo mobile. Il caso di studio interessa i codici a barre e, nello specifico, l’obiettivo è stato quello di riuscire a identificare la zona in cui questi si trovavano, in modo poi da avere una zona minore da indagare con un altro algoritmo specifico per la decodifica (cosa che esula dallo scopo della tesi). Questo è chiaramente un problema di object detection e, per risolverlo, ho esplorato due differenti tip

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Kirik, Engin. "Tolkning av handskrivna siffror i formulär : Betydelsen av datauppsättningens storlek vid maskininlärning." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41291.

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Forskningen i denna studie har varit att tag fram hur mycket betydelse storleken på datauppsättningen har för inverkan på resultat inom objektigenkänning. Forskningen implementerades i att träna en modell inom datorseende som skall kunna identifiera och konvertera handskrivna siffror från fysisk-formulär till digitaliserad-format. Till denna process användes två olika ramverk som heter TensorFlow och PyTorch. Processen tränades inom två olika miljöer, ena modellen tränades i CPU-miljö och den andra i Google Clouds GPU-miljö. Tanken med studien är att förbättra resultat från tidigare examensarb

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Козак, Антон Володимирович, та Anton Kozak. "Проєктування інформаційної системи для виявлення і запобігання масової дезінформації із застосуванням ООП-мови Python та фреймворків Scikit-learn та TensorFlow". Master's thesis, ТНТУ ім. І Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/36810.

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Для досягнення поставленої мети необхідно виконати наступні завдання: • виконати аналіз існуючих алгоритмів та методів комп'ютерної лінгвістики та машинного навчання для класифікації текстових потоків даних та виявлення елементів дезінформації; • розробити алгоритм первинної обробки тексту для збільшення точності визначення елементів дезінформації; • розробити метод виявлення елементів дезінформації в текстових потоках даних; • виконати програмну реалізацію розробленого методу виявлення елементів дезінформації в текстових потоках даних; • провести аналіз отриманих результатів для оцінки я

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Хитров, О. Б. "Інформаційна технологія тестування робастності моделей штучного інтелекту до змагальних атак". Master's thesis, Сумський державний університет, 2021. https://essuir.sumdu.edu.ua/handle/123456789/86708.

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Fong, Vivian Lin. "Software Requirements Classification Using Word Embeddings and Convolutional Neural Networks." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1851.

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Software requirements classification, the practice of categorizing requirements by their type or purpose, can improve organization and transparency in the requirements engineering process and thus promote requirement fulfillment and software project completion. Requirements classification automation is a prominent area of research as automation can alleviate the tediousness of manual labeling and loosen its necessity for domain-expertise. This thesis explores the application of deep learning techniques on software requirements classification, specifically the use of word embeddings for documen

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Alpire, Adam. "Predicting Solar Radiation using a Deep Neural Network." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215715.

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Simulating the global climate in fine granularity is essential in climate science research. Current algorithms for computing climate models are based on mathematical models that are computationally expensive. Climate simulation runs can take days or months to execute on High Performance Computing (HPC) platforms. As such, the amount of computational resources determines the level of resolution for the simulations. If simulation time could be reduced without compromising model fidelity, higher resolution simulations would be possible leading to potentially new insights in climate science resear

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Enerstrand, Simon. "Klassificering av kvitton med hjälp av maskininlärning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261144.

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Maskininlärning nyttjas inom fler och fler områden. Det har potential att ersätta många repetitiva arbetsuppgifter, eller åtminstone förenkla dem. Dokumenthantering inom ekonomisystem är ett område maskininlärning kan hjälpa till med. Det behövs ofta mycket manuell input i olika fält genom att avläsa fakturor eller kvitton. Målet med projektet är att skapa en applikation som nyttjar maskininlärning åt företaget Centsoft AB. Applikationen ska ta emot OCR-tolkad textmassa från en bild på ett kvitto och sedan, med hög säkerhet, kunna avgöra vilken kategori kvittot tillhör. Den här rapporten syfta

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Olmucci, Poddubnyy Oleksandr. "Investigating Single Translation Function CycleGANs." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16126/.

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With the advent of Deep Learning, we have been able to find solutions to many problems which didn't have an algorithmic solution, among these the image-to-image translation problem. One approach to solve it is by using the CycleGAN framework, which allows to learn mappings between two given image categories (or classes) that aren't necessarily paired. In this dissertation we present some attempts that were done in order to use the CycleGAN approach to perform image translations between more than two image classes at a time.

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Kashif, Muhammad. "Analysis and Evaluation of Tiny Machine Learning applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection

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Черемський, І. А., та Олена Петрівна Черних. "Дослідження сучасних методів машинного перекладу". Thesis, Національний університет харчових технологій, 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/48362.

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Kuvik, Michal. "Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400878.

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The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. S

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Dvonč, Tomáš. "Automatická detekce událostí ve fotbalových zápasech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412993.

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This diploma thesis describes methods suitable for automatic detection of events from video sequences focused on football matches. The first part of the work is focused on the analysis and creation of procedures for extracting informations from available data. The second part deals with the implementation of selected methods and neural network algorithm for corner kick detection. Two experiments were performed in this work. The first captures static information from one image and the second is focused on detection from spatio-temporal data. The output of this work is a program for automatic ev

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Ersson, Sara, and Oskar Dahl. "Specialization of an Existing Image Recognition Service Using a Neural Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232072.

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To help combat the environmental impacts caused by humans this project is about investigating one way to simplify the waste management process. The idea is to use image recognition to identify what material the recyclable object is made of. A large data set containing labeled images of trash, called Trashnet, was analyzed using Google Cloud Vision. Since this API is not written for material detection specifically, a feed forward neural network was created using Tensorflow and trained with the output from Google Cloud Vision. Thus, the network learned how different word combinations from Google

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Gilljam, Daniel, and Mario Youssef. "Jämförelse av artificiella neurala nätverksalgoritmerför klassificering av omdömen." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230660.

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Vid stor mängd data i form av kundomdömen kan det vara ett relativt tidskrävande arbeteatt bedöma varje omdömes sentiment manuellt, om det är positivt eller negativt laddat. Denna avhandling har utförts för att automatiskt kunna klassificera kundomdömen efter positiva eller negativa omdömen vilket hanterades med hjälp av maskininlärning. Tre olika djupa neurala nätverk testades och jämfördes med hjälp av två olika ramverk, TensorFlow och Keras, på både större och mindre datamängder. Även olika inbäddningsmetoder testades med de neurala nätverken. Den bästa kombination av neuralt nätverk, ramve

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Camborata, Caterina. "Capsule networks: a new approach for brain imaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18127/.

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Nel campo delle reti neurali per il riconoscimento immagini, una delle più recenti e promettenti innovazioni è l’utilizzo delle Capsule Networks (CapsNet). Lo scopo di questo lavoro di tesi è studiare l'approccio CapsNet per l'analisi di immagini, in particolare per quelle neuroanatomiche. Le odierne tecniche di microscopia ottica, infatti, hanno posto sfide significative in termini di analisi dati, per l'elevata quantità di immagini disponibili e per la loro risoluzione sempre più fine. Con l'obiettivo di ottenere informazioni strutturali sulla corteccia cerebrale, nuove proposte di segmenta

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Бабич, Іван Русланович. "Технологія розробки програмної системи для генерування музичних творів". Магістерська робота, Хмельницький національний університет, 2020. http://elar.khnu.km.ua/jspui/handle/123456789/9419.

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Сворення моделі створення музики на основі вивчення штучного інтелекту теорії музики та аналізу музичних творів та удосконалення методу врахування введення даних користувача для генерування музичних творів із використанням штучного інтелекту.

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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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Тарасов, О. Є. "Інтелектуальна система автоматичного керування автомобілем у віртуальній моделі навколишнього середовища". Thesis, Чернігів, 2021. http://ir.stu.cn.ua/123456789/25133.

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Тарасов, О. Є. Інтелектуальна система автоматичного керування автомобілем у віртуальній моделі середовища : випускна кваліфікаційна робота : 121 "Інженерія програмного забезпечення" / О. Є. Тарасов ; керівник роботи О. В. Трунова ; НУ "Чернігівська політехніка", кафедра технологій та програмної інженерії. – Чернігів, 2021. – 82 с.<br>Кваліфікаційна робота передбачає дослідження сучасного стану розвитку галузі безпілотних автомобілів, а також основних технологій, які в ній застосовуються; дослідження можливостей використання віртуальних середовищ для перевірки ефективності роботи систем автомат

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Černil, Martin. "Automatická detekce ovládacích prvků výtahu zpracováním digitálního obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-444987.

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This thesis deals with the automatic detection of elevator controls in personal elevators through digital imaging using computer vision. The theoretical part of the thesis goes through methods of image processing with regards to object detection in image and research of previous solutions. This leads to investigation into the field of convolutional neural networks. The practical part covers the creation of elevator controls image dataset, selection, training and evaluation of the used models and the implementation of a robust algorithm utilizing the detection of elevator controls. The concluss

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Nordqvist, My. "Identifiera löv i skogar – Att lära en dator känna igen löv med ImageAI." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36454.

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A current field of research today is machine learning because it can simplify everyday life for human beings. A functioning system that has learned specific tasks can make it easier for companies in both cost and time. A company who want to use machine learning is SCA, who owns and manages forests to produce products. They have a need to automate forest classification. In order to evaluate forests, and to plan forestry measures, the proportion of leafy tree that is not used in production must be determined. Today, manual work is required of people who have to investigate aerial photos to class

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Clase, Christian. "Maskininlärning och bildtolkning för ökad tillförlitlighet i strömavtagarlarm." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27766.

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This master´s degree project is carried out by Trafikverket and concerns machine learning and image detection of defective pantographs on trains. Today, Trafikverket has a system for detecting damages of the coal rail located on the pantograph. This coal rail lies against the contact wire and may become worn in such a way that damages are formed in the coal rail, which results in a risk of demolition of the contact wire which causes major interference and high costs. Today, approximately 10 demolitions of contact wire occur annually due to missed detection. Today's system is called KIKA2, de

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