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Rozprawy doktorskie na temat „Deep learning technology”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Fourie, Aidan. ""Online Platform for Deep Learning Education"." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31381.

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My thesis is going to focus on the development of a standalone, web based, machine learning educational platform. This platform will have a specific focus on neural networks. This tool will have the primary intention to provide a theoretical background to the mathematics of neural networks and thereafter to allow users to train their own networks on regression problems of their own creation. This is so as to provide the user with both theoretical, and first-hand, experience in the applications and functions of artificial intelligence. The primary success metric of this project will be how info

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Backstad, Sebastian. "Federated Averaging Deep Q-NetworkA Distributed Deep Reinforcement Learning Algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149637.

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In the telecom sector, there is a huge amount of rich data generated every day. This trend will increase with the launch of 5G networks. Telco companies are interested in analyzing their data to shape and improve their core businesses. However, there can be a number of limiting factors that prevents them from logging data to central data centers for analysis. Some examples include data privacy, data transfer, network latency etc. In this work, we present a distributed Deep Reinforcement Learning (DRL) method called Federated Averaging Deep Q-Network (FADQN), that employs a distributed hierarc

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Lindström, Marcus, and Jahangir Jazayeri. "Deep reinforcement learning i distribuerad optimering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230707.

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Reinforcement learning has recently become a promising area of machine learning with significant achievements in the subject. Recent successes include surpassing human experts on Atari games and also AlphaGo becoming the first computer ranked on the highest professional level in the game Go, to mention a few. This project aims to apply Policy Gradient Methods (PGM) in a multi agent environment. PGM are widely regarded as being applicable to more problems than for instance Deep Q-Learning but have a tendency to converge upon local optimums. In this report we aim to explore if an optimal policy

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Ljunggren, Henrik. "Exploring the capabilities of deep learning in seasurveillance : Using deep learning to classify motion trajectories from AIS data." Thesis, KTH, Mekatronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217526.

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In this master thesis deep learning is proven to be applicable in the field of seasurveillance. Commercial ships using the AIS system have to report the type of thevessel such as fishing ship or cargo ship. A problem with AIS data is that it can beeasily manipulated and therefore deliberately or accidentally incorrect. This thesis will focus on detecting false ship types. To detect a false ship type 19 different methods were tested on the 1100 hour long AIS data set. Three of these methods were baseline methods using a more conventional approach to the sea surveillanceproblem. The testing show

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Örnberg, Oscar, and Jonas Nylund. "Incrementally Expanding Environment in Deep Reinforcement Learning." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230754.

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Tillräckligt snabb konvergens till optimala strategier är ett mål inom maskininlärning och speciellt förstärkande inlärning. Realtidslösningar till komplexa inlärningsproblem behövs för att expandera fältet till nya områden där maskininlärning tidigare varit en omöjlighet. I denna rapport introducerar vi en ny metod för att träna djupa Q-learning agenter i en miljö vars storlek är skalbar, i hopp om att förkorta inlärningstiden. I denna metod börjar agenten i en mycket liten miljö där den snabbt kan utforska olika situationer på en liten skala och lära sig att hantera dem. Miljön expanderar se

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Andersson, Gustav. "Classification of Heart Sounds with Deep Learning." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149699.

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Health care is becoming more and more digitalized and examinations of patients from a distance are closer to reality than fiction. One of these examinations would be to automatically classify a patient-recorded audiosegment of its heartbeats as healthy or pathological. This thesis examines how it can be achieved by examining different kinds of neural networks; convolutional neural networks (CNN) and long short-term memory networks (LSTM). The theory of artificial neural networks is explained. With this foundation, the feed forward CNN and the recurrent LSTM-network have their methods described

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Gao, Yuan. "Surround Vision Object Detection Using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231929.

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The thesis first develops an object detection framework for front view camera images in surround vision data set. And with the goal of reducing as much annotated data as possible, various domain adaptation methods are applied to train other camera images based on the pretraining of a baseline model. Relevant data analysis work is performed to reveal useful information in object distribution over all cameras. Regularization techniques involving dropout, weight decay, data augmentation are attempted to lower the complexity of training model. Also, the experiments of ratio reduction are carried o

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Oxenstierna, Johan. "Warehouse Vehicle Routing using Deep Reinforcement Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396853.

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In this study a Deep Reinforcement Learning algorithm, MCTS-CNN, is applied on the Vehicle Routing Problem (VRP) in warehouses. Results in a simulated environment show that a Convolutional Neural Network (CNN) can be pre-trained on VRP transition state features and then effectively used post-training within Monte Carlo Tree Search (MCTS). When pre-training works well enough better results on warehouse VRP’s were often obtained than by a state of the art VRP Two-Phase algorithm. Although there are a number of issues that render current deployment pre-mature in two real warehouse environments MC

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Johns, Rasmus Johns. "Intelligent Formation Control using Deep Reinforcement Learning." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-152687.

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In this thesis, deep reinforcement learning is applied to the problem of formation control to enhance performance. The current state-of-the-art formation control algorithms are often not adaptive and require a high degree of expertise to tune. By introducing reinforcement learning in combination with a behavior-based formation control algorithm, simply tuning a reward function can change the entire dynamics of a group. In the experiments, a group of three agents moved to a goal which had its direct path blocked by obstacles. The degree of randomness in the environment varied: in some experimen

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Geirsson, Gunnlaugur. "Deep learning exotic derivatives." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-430410.

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Monte Carlo methods in derivative pricing are computationally expensive, in particular for evaluating models partial derivatives with regard to inputs. This research proposes the use of deep learning to approximate such valuation models for highly exotic derivatives, using automatic differentiation to evaluate input sensitivities. Deep learning models are trained to approximate Phoenix Autocall valuation using a proprietary model used by Svenska Handelsbanken AB. Models are trained on large datasets of low-accuracy (10^4 simulations) Monte Carlo data, successfully learning the true model with

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Frisk, Martin. "Social robot learning with deep reinforcement learning and realistic reward shaping." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-395918.

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Deep reinforcement learning has been applied successfully to numerous robotic control tasks, but its applicability to social robot tasks has been comparatively limited. This work combines a spatial autoencoder and state-of-the-art deep reinforcement learning to train a simulated autonomous robot to perform group joining behavior. The resulting control policy uses only first-person camera images and the robot's speed as input. The behavior of the control policy was evaluated in a perceptual study, and was shown to be less rude, more polite, and more sociable when compared to the reference model

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Gybäck, Gustav, and Fredrik Röstlund. "Real-time System Control with Deep Reinforcement Learning." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230728.

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We reproduce the Deep Deterministic Policy Gradient algorithm presented in the paper Continuous Control With Deep Reinforcement Learning to verify its results. We also strive to explain the necessary machine learning framework needed to understand the algorithm. It is a model-free, actor-critic algorithm that implements target networks and mini batch learning from a replay buffer to increase stability. Batch normalisation is introduced to make the algorithm versatile and applicable to multiple environments with varying value ranges and physical units. We use neural networks as function approxi

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Dong, Shuzhi. "Deep Learning for Iceberg Detection in Satellite Images." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-436032.

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The application of satellite images for ship and iceberg monitoring is essential in many ways in Arctic waters. Even though the detection of ships and icebergs in images is well established using Geoscience techniques, the discrimination between those two target classes still represents a challenge for operational scenarios. This thesis project proposes the application of Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and SingleShot Detector (SSD) for ship-iceberg detection in satellite images. The CNN model is compared with SVM and SSD, and the final results indicate not o

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Valldor, Erik. "Person Detection in Thermal Images using Deep Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372089.

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Deep learning has achieved unprecedented results in many image analysis tasks. Long-wave infrared (thermal) images is still a little- explored area of application, and is the main subject of investigation in this thesis. To this end, a case study is performed where the goal is to detect persons in infrared images using deep learning. Two different deep learning based approaches are implemented and benchmarked against a baseline cascaded classifier. Due to the large amount of unlabelled data available, an autoencoder setup is used to pretrain the deep learning based detectors. One of the detect

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Andersson, Gabriel, and Martti Yap. "Att spela 'Breakout' med hjälp av 'Deep Q-Learning'." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255799.

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I denna rapport implementerar vi en reinforcement learning (RL) algoritm som lär sig spela Breakout på 'Atari Learning Environment'. Den dator drivna spelaren (Agenten) har tillgång till samma information som en mänsklig spelare och vet inget om spelet och dess regler på förhand. Målet är att reproducera tidigare resultat genom att optimera agenten så att den överträffar den typiska mänskliga medelpoängen. För att genomföra detta formaliserar vi problemet som en 'Markov decision Process'. VI applicerar 'Deep Q-learning' algoritmen med 'action masking' för att uppnå en optimal strategi. Vi finn

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Holmberg, Joakim. "Targeting the zebrafish eye using deep learning-based image segmentation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-428325.

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Researchers studying cardiovascular and metabolic disease in humans commonly usecomputer vision techniques to segment internal structures of the zebrafish animalmodel. However, there are no current image segmentation methods to target theeyes of the zebrafish. Segmenting the eyes is essential for accurate measurement ofthe eyes' size and shape following the experimental intervention. Additionally,successful segmentation of the eyes functions as a good starting point for futuresegmentation of other internal organs. To establish an effective segmentation method,the deep learning neural network a

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Lanaras, Filippos Petros. "Reducing data path from storage to GPUs for Deep Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372092.

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Deep learning is an emerging workload in the field of HPC. This powerful method of resolution is able to tackle problems which have been out of reach for traditional algorithmic approaches. However, before being able to solve an instance of the problem, Deep Learning has to go through a learning phase involving a huge volume of data. From the computational standpoint the profile of Deep Learning applications is specific enough to be run almost exclusively on dedicated architectures such as GPUs. Most of the research in the field of deep learning has been focused in the numerical side and archi

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Kalliomäki, Roger. "Real-time object detection for autonomous vehicles using deep learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393999.

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Self-driving systems are commonly categorized into three subsystems: perception, planning, and control. In this thesis, the perception problem is studied in the context of real-time object detection for autonomous vehicles. The problem is studied by implementing a cutting-edge real-time object detection deep neural network called Single Shot MultiBox Detector which is trained and evaluated on both real and virtual driving-scene data. The results show that modern real-time capable object detection networks achieve their fast performance at the expense of detection rate and accuracy. The Single

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Yokobori, Sävö Andreas. "User Plane Selection for Core Networks using Deep Reinforcement Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265780.

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Allocating service functions to a core network upon users’ various demands isof importance in 5G networks. In this thesis work, we have studied reinforcementlearning models to solve this allocation problem. More precisely, 1) webuild a simple version of an MDP model for allocation in 5G core networks,2) we train an agent using a family of deep-Q learning (DQN) algorithms.When the number of nodes in the core network is large, one critical challengeis overcoming the sampling inefficiency due to a high dimensional actionspace, i.e., most of the exploratory allocations made by the agent gives zero

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Neverova, Natalia. "Deep learning for human motion analysis." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI029/document.

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L'objectif de ce travail est de développer des méthodes avancées d'apprentissage pour l’analyse et l'interprétation automatique du mouvement humain à partir de sources d'information diverses, telles que les images, les vidéos, les cartes de profondeur, les données de type “MoCap” (capture de mouvement), les signaux audio et les données issues de capteurs inertiels. A cet effet, nous proposons plusieurs modèles neuronaux et des algorithmes d’entrainement associés pour l’apprentissage supervisé et semi-supervisé de caractéristiques. Nous proposons des approches de modélisation des dépendances te

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Larsson, Hannes. "Deep Reinforcement Learning for Cavity Filter Tuning." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354815.

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In this Master's thesis the option of using deep reinforcement learning for cavity filter tuning has been explored. Several reinforcement learning algorithms have been explained and discussed, and then the deep deterministic policy gradient algorithm has been used to solve a simulated filter tuning problem. Both the filter environment and the reinforcement learning agent were implemented, with the filter environment making use of existing circuit models. The reinforcement learning agent learned how to tune filters with four poles and one transmission zero, or eight tune-able screws in total. A

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Berthold, Gabriel, and Jonas Kinnvall. "Anomaly detection on factory lathe using audio analysis and deep learning." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162471.

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This paper presents a master’s thesis project in which a system for anomaly detection on sound of a factory lathe has been developed and evaluated. The audio has been recorded with a microphone on site and has been analyzed using Fourier transforms and a Gated Recurrent Unit, developed to detect when this machine is running. An autoencoder has been used to determine if the gathered audio contains anomalies and thus indicates an error with the machine. The Gated Recurrent Unit has been evaluated using the metrics Precision, Recall and F1 score along with ROC curves and AUC, which has been used

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Engström, Isak. "Automated Gait Analysis : Using Deep Metric Learning." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178139.

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Sectors of security, safety, and defence require methods for identifying people on the individual level. Automation of these tasks has the potential of outperforming manual labor, as well as relieving workloads. The ever-extending surveillance camera networks, advances in human pose estimation from monocular cameras, together with the progress of deep learning techniques, pave the way for automated walking gait analysis as an identification method. This thesis investigates the use of 2D kinematic pose sequences to represent gait, monocularly extracted from a limited dataset containing walking

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Khays, Samir. "Motion Prediction of Surrounding Vehicles in Highway Scenarios With Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254408.

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Anticipating the future positions of the surrounding vehicles is a crucial task foran autonomous vehicle in order to drive safely. To foresee complex manoeuvresfor longer time horizons, a framework that relies on high-level properties ofmotion and is able to incorporate, e.g. contextual features, is needed. In thisthesis, the problem of predicting the trajectories of the surrounding vehicles ona highway is tackled by using machine learning. The objective is to evaluate theperformance of recurrent neural networks for trajectory prediction, specificallylong-short term memory neural networks. Mor

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Saeidian, Sara. "Deep Reinforcement Learning for Downlink Power Control in Dense 5G Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265675.

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This thesis examines the problem of downlink power allocation in dense 5Gnetworks, and attempts to develop a data-driven solution by employing deepreinforcement learning. We train and test multiple reinforcement learningagents using the deep Q-networks (DQN) algorithm, and the so-called Rainbowextensions of DQN. The performance of each agent is tested on 5G UrbanMacro simulation scenarios, and is benchmarked against a fixed power allocationapproach. Our test results show that the DQN models are successful atimproving data rates at cell-edge, while generalizing well to previously unseensimulati

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Lidberg, Love. "Object Detection using deep learning and synthetic data." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150555.

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This thesis investigates how synthetic data can be utilized when training convolutional neural networks to detect flags with threatening symbols. The synthetic data used in this thesis consisted of rendered 3D flags with different textures and flags cut out from real images. The synthetic data showed that it can achieve an accuracy above 80% compared to 88% accuracy achieved by a data set containing only real images. The highest accuracy scored was achieved by combining real and synthetic data showing that synthetic data can be used as a complement to real data. Some attempts to improve the ac

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Purmonen, Sami. "Predicting Game Level Difficulty Using Deep Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217140.

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We explored the usage of Monte Carlo tree search (MCTS) and deep learning in order to predict game level difficulty in Candy Crush Saga (Candy) measured as number of attempts per success. A deep neural network (DNN) was trained to predict moves from game states from large amounts of game play data. The DNN played a diverse set of levels in Candy and a regression model was fitted to predict human difficulty from bot difficulty. We compared our results to an MCTS bot. Our results show that the DNN can make estimations of game level difficulty comparable to MCTS in substantially shorter time.<br>

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Chen, Yiqiang. "Person re-identification in images with deep learning." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI074/document.

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La vidéosurveillance est d’une grande valeur pour la sécurité publique. En tant que l’un des plus importantes applications de vidéosurveillance, la ré-identification de personnes est définie comme le problème de l’identification d’individus dans des images captées par différentes caméras de surveillance à champs non-recouvrants. Cependant, cette tâche est difficile à cause d’une série de défis liés à l’apparence de la personne, tels que les variations de poses, de point de vue et de l’éclairage etc. Pour régler ces différents problèmes, dans cette thèse, nous proposons plusieurs approches basé

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Albert, Florea George, and Filip Weilid. "Deep Learning Models for Human Activity Recognition." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.

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AMI Meeting Corpus (AMI) -databasen används för att undersöka igenkännande av gruppaktivitet. AMI Meeting Corpus (AMI) -databasen ger forskare fjärrstyrda möten och naturliga möten i en kontorsmiljö; mötescenario i ett fyra personers stort kontorsrum. För attuppnågruppaktivitetsigenkänninganvändesbildsekvenserfrånvideosoch2-dimensionella audiospektrogram från AMI-databasen. Bildsekvenserna är RGB-färgade bilder och ljudspektrogram har en färgkanal. Bildsekvenserna producerades i batcher så att temporala funktioner kunde utvärderas tillsammans med ljudspektrogrammen. Det har visats att inkluder

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Azumah, Sylvia w. "Deep Learning -Based Anomaly Detection System for Guarding Internet of Things Devices." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624917874580953.

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Wang, Wei. "Image Segmentation Using Deep Learning Regulated by Shape Context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-227261.

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In recent years, image segmentation by using deep neural networks has made great progress. However, reaching a good result by training with a small amount of data remains to be a challenge. To find a good way to improve the accuracy of segmentation with limited datasets, we implemented a new automatic chest radiographs segmentation experiment based on preliminary works by Chunliang using deep learning neural network combined with shape context information. When the process was conducted, the datasets were put into origin U-net at first. After the preliminary process, the segmented images were

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Herman, Andreas. "Visualizing Important Areas for Facial Verification A Deep Learning Evaluation with ArtificialNoise Injection." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-337660.

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Facial verification is a growing area of research, where heavily trained neural network models obtain superhuman performance on recognizing individuals. However, because of network models' complex nature, it is hard to know what areas of the image are used. This thesis introduces three visualization techniques, to better understand the importance of different facial areas when performing verification. A method for measuring the visualizations' hierarchical correctness was established, evaluating the model's ability to preserve and damage the verification performance, when treated as artificial

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Omar, Ali Nasra. "A Comparative study of cancer detection models using deep learning." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20468.

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Leukemi är en form av cancer som kan vara en dödlig sjukdom. För att rehabilitera och behandla sjukdomen krävs det en korrekt och tidig diagnostisering. För att minska väntetiden för testresultaten har de ordinära metoderna transformerats till automatiserade datorverktyg som kan analyser och diagnostisera symtom.I detta arbete, utfördes det en komparativ studie. Det man jämförde var två olika metoder som detekterar leukemia. Den ena metoden är en genetisk sekvenserings metod som är en binär klassificering och den andra metoden en bildbehandlings metod som är en fler-klassad klassificeringsmod

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Toghiani-Rizi, Babak. "Evaluation of Deep Learning Methods for Creating Synthetic Actors." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324756.

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Recent advancements in hardware, techniques and data availability have resulted in major advancements within the field of Machine Learning and specifically in a subset of modeling techniques referred to as Deep Learning. Virtual simulations are common tools of support in training and decision making within the military. These simulations can be populated with synthetic actors, often controlled through manually implemented behaviors, developed in a streamlined process by domain doctrines and programmers. This process is often time inefficient, expensive and error prone, potentially resulting in

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Abdelhalim, Mohamed Ammar Ahmed. "Identifying Shooting Tweets with Deep Learning and Keywords Filtering: Comparative Study." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105265912086.

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Fridberg, Mats, and Adam Hoflin. "Deep Learning to Detect Snow and Water in Construction Planning using Remote Sensing Images." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48706.

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Ryan, Christian. "On Optimization of Sequential Decision-Making in Customer Relationship Management using Deep Reinforcement Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261711.

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Customer relationship management (CRM) is a fickle but pivotal elementto the success of any business. Used correctly, it can not only yield higherrevenue and lower operational costs, but significantly boost customersatisfaction. Nonetheless, it can also be mismanaged—sacrificing thewell-being of customers for profitability. Industries have thereby beenflooded with a range of different heuristic strategies that aim to optimizeCRM. This thesis aims to instead study and optimize CRM using a datadrivenapproach, and present a framework that can readily incorporatecustomer well-being into the optimi

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Kudaka, Ganesh Chaitanya. "Survival Analysis using Deep Learning for Predictive Aging Models of Batteries in Electric Vehicles." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163993.

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With the growing EV market, predictive maintenance of batteries is one of the key challenges faced by the car industry. Since this technology is still nascent, most of the battery data obtained is through simulations which may not give an accurate estimate of the battery behavior in the real world. The goal of this thesis is to predict the probability of occurrence of an event in the future when we have incomplete information about the life cycle of the battery parameters from the entire fleet of cars in operation in real world conditions. We achieve this through Survival Analysis. This techni

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Nordlund, Tobias. "Jämförelse av upplösning i lastprediktering med Deep Learning : GPU-optimerad träning." Thesis, Karlstads universitet, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-85194.

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Yadati, Naganand. "Deep Learning over Hypergraphs." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5560.

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Graphs have been extensively used for modelling real-world network datasets, however, they are restricted to pairwise relationships, i.e., each edge connects exactly two vertices. Hypergraphs relax the notion of edges to connect arbitrary numbers of vertices. Hypergraphs provide a mathematical foundation for understanding and learning from large amounts of real-world data. State-of-the-art techniques for learning vertex representations from graph data with pairwise relationships use graph-based deep models such as graph neural networks. A prominent observation that inspires this thesis i

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Lu, Yu-Hsin, and 呂昱昕. "Mathematic Formula Generator Base On Deep Learning Technology." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/pgb2n7.

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Han, Chen, and 陳涵. "Lung Nodule Detection Based on Deep Learning Technology." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5dhd73.

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碩士<br>中華大學<br>資訊工程學系<br>106<br>With the advancement of artificial intelligence, computer aided detection (CAD) has been continuously developed to improve the diagnosis rate of diseases and reduce the burden on doctors. The mortality rate of lung cancer is one of the highest in cancer. If it can detect early malignant nodules in the lungs, it can effectively reduce the mortality of lung cancer. Because the lungs are a large organ in the human body, and the diameter of the tumor is very small, the detection is difficult because of the low detection rate and excessive nodule misjudgment. With the

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Vivek, B. S. "Towards Learning Adversarially Robust Deep Learning Models." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4488.

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Deep learning models have shown impressive performance across a wide spectrum of computer vision applications, including medical diagnosis and autonomous driving. One of the major concerns that these models face is their susceptibility to adversarial samples: samples with small, crafted noise designed to manipulate the model’s prediction. A defense mechanism named Adversarial Training (AT) shows promising results against these attacks. This training regime augments mini-batches with adversaries. However, to scale this training to large networks and datasets, fast and simple methods (e.g.

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Tatou, Kevin, and Liam Wolter. "Nätmobbning och Deep Learning : Att spåra och förebygga nätmobbning med hjälp av Deep Learning." Thesis, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14585.

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Syftet med detta kandidatarbetet är att hitta ett sätt att spåra och förebygga nätmobbning med hjälp av självinlärningssystem, webbteknologier och antimobbningsmetoder. Uppsatsen tar även upp problematiken kring mobbning i skolor, nätmobbning och ansvarstagande inom utvecklande av webbapplikationer. Denna studien går vidare in på olika typer av nätmobbning, dess effekter, fall, system som motverkar nätmobbning och generellt om självinlärningssystem. Studien går även in på djupet vad det gäller metoder vi har använt för att spåra och förebygga nätmobbning, också vidare in på hur vi har arbetat

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LIN, YI-TING, and 林奕廷. "Pedestrian Recognition based on Multi-stream Deep Learning technology." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/jmcxmf.

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碩士<br>中華大學<br>資訊工程學系<br>106<br>The conventional pedestrian recognition system can’t provide high recognition accuracy and efficiency for real-time application. Recently, many deep learning technologies are proposed to improve the pedestrian recognition accuracy. However, many DNN architectures are too complex to make the real-time application feasible. Hence, a new multi-stream deep learning architecture is proposed in this paper to improve both the accuracy and efficiency of the pedestrian recognition system. The first deep learning stream is used to recognize pedestrians of normal and large

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Mehrotra, Akshay. "Deep Learning Models for Few-shot and Metric Learning." Thesis, 2018. http://etd.iisc.ac.in/handle/2005/4275.

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Deep neural network-based models have achieved unprecedented performance levels over many tasks in the traditional supervised setting and scale well with large quantities of data. On the other hand, improving performance in the low-data regime is understudied and the sheer number of parameters versus small amounts of data makes it a challenging task. The problem of learning from a few instances of data is known as few-shot learning. A related problem is that of metric learning over disjoint training and testing classes, where the task is to learn a function that maps a data point to a low-dime

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Chang, Liang-Yu, and 張良宇. "A Study on Navigation Robot Technology via Deep Reinforcement Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3h639f.

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碩士<br>國立雲林科技大學<br>電機工程系<br>107<br>Autonomous robotic navigation in a complex environment has been one of the most practical applications in real life. To this end, this thesis proposes a feasible solution reflective of deep reinforcement learning that enables a robot with sufficient training to navigate a given space in an autonomous manner. Deep reinforcement learning is poised to advance the field of machine learning by relieving conventional techniques of formidable state explosion problems, i.e., settings with high-dimensional state and action spaces. As a step toward constructing autonomo

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Gupta, Rahul. "Deep Learning for Bug Localization and Program Repair." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4350.

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In this thesis, we focus on the problem of program debugging and present novel deep learning based techniques for bug-localization and program repair. Deep learning techniques have been successfully applied to a variety of tasks in natural language processing over the years. Although natural language text and programs are similar to some extent, the latter have procedural interpretation and richer structure. Applying deep learning techniques to programs presents many novel challenges, which arise due to these differences. We address some of these challenges in this thesis. Most of the e

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Larsson, Emil. "Evaluation of Pretraining Methods for Deep Reinforcement Learning." Thesis, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-346599.

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In recent years, Machine Learning research has made notable progress using Deep Learning methods. Deep Learning leverages deep convolutional neural networks to extract features from data, and has been able to reinstate interest in Reinforcement Learning, a Machine Learning method for modeling behaviour. It is well suited for game type environments and Deep Reinforcement Learning broke headlines teaching a computer to play Atari games only from pixel inputs. The Swedish Defence Agency is conducting research to incorporate Deep Reinforcement Learning for creating virtual actors in military train

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SU, YI-SYUAN, and 蘇奕璇. "Removing Embedded Text in an Image by Using Deep Learning Technology." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/342bb3.

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碩士<br>嶺東科技大學<br>資訊科技系碩士班<br>107<br>The most commonly used medium on the Internet is the combination of image and text. The most common form of media on the flat media is the use of image embedded text. For example, netizens will attach watermarks to their published images, or create some interesting snippets (memes) for use in many places. But these added words are easy to destroy the beauty of the image itself. Therefore, the main purpose of this study is to use a deep learning technique to design a system that recognizes embedded text in an image and automatically removes and inpaints or res

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