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Dissertations / Theses on the topic 'Temporary Convolution Neural Network'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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

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

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

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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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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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Yu, Xiafei. "Wide Activated Separate 3D Convolution for Video Super-Resolution." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39974.

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Video super-resolution (VSR) aims to recover a realistic high-resolution (HR) frame from its corresponding center low-resolution (LR) frame and several neighbouring supporting frames. The neighbouring supporting LR frames can provide extra information to help recover the HR frame. However, these frames are not aligned with the center frame due to the motion of objects. Recently, many video super-resolution methods based on deep learning have been proposed with the rapid development of neural networks. Most of these methods utilize motion estimation and compensation models as preprocessing to

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Liu, Ruixu. "Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948.

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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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Berti, Matteo. "Anomalous Activity Detection with Temporal Convolutional Networks in HPC Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/22185/.

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Detecting suspicious or unauthorized activities is an important concern for High-Performance Computing (HPC) systems administrators. Automatic classification of programs running on these systems could be a valuable aid towards this goal. This thesis proposes a machine learning model capable of classifying programs running on a HPC system into various types by monitoring metrics associated with different physical and architectural system components. As a specific case study, we consider the problem of detecting password-cracking programs that may have been introduced into the normal workload o

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Cranston, Daniel, and Filip Skarfelt. "Normalized Convolution Network and Dataset Generation for Refining Stereo Disparity Maps." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158449.

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Finding disparity maps between stereo images is a well studied topic within computer vision. While both classical and machine learning approaches exist in the literature, they frequently struggle to correctly solve the disparity in regions with low texture, sharp edges or occlusions. Finding approximate solutions to these problem areas is frequently referred to as disparity refinement, and is usually carried out separately after an initial disparity map has been generated. In the recent literature, the use of Normalized Convolution in Convolutional Neural Networks have shown remarkable results

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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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Lomonaco, Vincenzo. "Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9095/.

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In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind

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Maroli, John Michael. "Generating Comprehensible Equations from Unknown Discrete Dynamical Systems Using Neural Networks." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574760744876635.

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Pfundstein, Maximilian. "Human Age Prediction Based on Real and Simulated RR Intervals using Temporal Convolutional Neural Networks and Gaussian Processes." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165987.

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Electrocardiography (ECG) is a non-invasive method used in medicine to track the electrical pulses sent by the heart. The time between two subsequent electrical impulses and hence the heartbeat of a subject, is referred to as an RR interval. Previous studies show that RR intervals can be used for identifying sleep patterns and cardiovascular diseases. Additional research indicates that RR intervals can be used to predict the cardiovascular age of a subject. This thesis investigates, if this assumption is true, based on two different datasets as well as simulated data based on Gaussian Processe

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Yang, Ying. "Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1016.

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Human cognition involves dynamic neural activities in distributed brain areas. For studying such neural mechanisms, magnetoencephalography (MEG) and electroencephalography (EEG) are two important techniques, as they non-invasively detect neural activities with a high temporal resolution. Recordings by MEG/EEG sensors can be approximated as a linear transformation of the neural activities in the brain space (i.e., the source space). However, we only have a limited number sensors compared with the many possible locations in the brain space; therefore it is challenging to estimate the source neur

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Schembri, Massimo. "Anomaly Prediction in Production Supercomputer with Convolution and Semi-supervised autoencoder." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22379/.

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Un sistema HPC (High Performance Computing) è un sistema con capacità computazionali molto elevate adatto a task molto esigenti in termini di risorse. Alcune delle proprietà fondamentali di un sistema del genere sono certamente la disponibilità e l'affidabilità che possono essere messe a rischio da problemi hardware e software. In quest'attività di tesi si è realizzato e analizzato le performance di un sistema di anomaly detection in termini di capacità di rilevazione e predizione di un'anomalia su vari nodi di un sistema HPC, in particolare utilizzando i dati relativi al sistema MARCONI del c

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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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Karimi, Ahmad Maroof. "DATA SCIENCE AND MACHINE LEARNING TO PREDICT DEGRADATION AND POWER OF PHOTOVOLTAIC SYSTEMS: CONVOLUTIONAL AND SPATIOTEMPORAL GRAPH NEURAL NETWORK." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601082841477951.

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Mocko, Štefan. "Využitie pokročilých segmentačných metód pre obrazy z TEM mikroskopov." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378145.

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Tato magisterská práce se zabývá využitím konvolučních neuronových sítí pro segmentační účely v oblasti transmisní elektronové mikroskopie. Také popisuje zvolenou topologii neuronové sítě - U-NET, použíté augmentační techniky a programové prostředí. Firma Thermo Fisher Scientific (dříve FEI Czech Republic s.r.o) poskytla obrazová data pro účely této práce. Získané segmentační výsledky jsou prezentovány ve formě křivek (ROC, PRC) a ve formě numerických hodnot (ARI, DSC, Chybová matice). Zvolená UNET topologie dosáhla excelentních výsledků v oblasti pixelové segmentace. S největší pravděpodobnos

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Hansen, Vedal Amund. "Comparing performance of convolutional neural network models on a novel car classification task." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213468.

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Recent neural network advances have lead to models that can be used for a variety of image classification tasks, useful for many of today’s media technology applications. In this paper, I train hallmark neural network architectures on a newly collected vehicle image dataset to do both coarse- and fine-grained classification of vehicle type. The results show that the neural networks can learn to distinguish both between many very different and between a few very similar classes, reaching accuracies of 50.8% accuracy on 28 classes and 61.5% in the most challenging 5, despite noisy images and lab

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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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Jackman, Simeon. "Football Shot Detection using Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157438.

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In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architecture

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Genestreti, Sara. "Impiego di una rete neurale convoluzionale per la decodifica di movimenti di arto superiore tramite segnali elettroencefalografici." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21807/.

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La possibilità di decodificare i movimenti delle varie parti del corpo a partire dai segnali elettroencefalografici (EEG) sta assumendo sempre più importanza: infatti, tale decodifica può consentire di comprendere meglio i correlati neurali alla base del movimento (e le loro alterazioni nelle patologie) e può contribuire alla realizzazione di Brain-Computer Interface. Tra le tecniche di intelligenza artificiale che appaiono più promettenti in questo ambito vi sono le reti neurali convoluzionali (CNN). In questo elaborato di tesi, è stata impiegata una CNN, già proposta in letteratura, per un n

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Sunesson, Albin. "Establishing Effective Techniques for Increasing Deep Neural Networks Inference Speed." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213833.

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Recent trend in deep learning research is to build ever more deep networks (i.e. increase the number of layers) to solve real world classification/optimization problems. This introduces challenges for applications with a latency dependence. The problem arises from the amount of computations that needs to be performed for each evaluation. This is addressed by reducing inference speed. In this study we analyze two different methods for speeding up the evaluation of deep neural networks. The first method reduces the number of weights in a convolutional layer by decomposing its convolutional kerne

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Кириченко, С. В. "Інформаційна технологія аналізу тональності текстів за допомогою згорткових нейронних мереж". Master's thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/72201.

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Розроблено алгоритм та інформаційну технологію для системи аналізу тональності текстів за допомогою згорткових нейронних мереж на мові Python та бібліотеками keras і Word2Vec. Обрано український корпус текстів з сервісу twitter.com/ukranews_com. Проведено аналіз ефективності алгоритму. Зроблено порівняння запропонованого методу з наївним баєсів класифікатором. Запропоновані заходи для покращення результатів даного метода.

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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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Coufal, Tomáš. "Deskriptor pro identifikaci osoby podle obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403177.

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Thesis provides an overview and discussion of current findings in the field of biometrics. In particular, it focuses on facial recognition subject. Special attention is payed to convolutional neural networks and capsule networks. Thesis then lists current approaches and state-of-the-art implementations. Based on these findings it provides insight into engineering a very own solution based of CapsNet architecture. Moreover, thesis discussed advantages and capabilitied of capsule neural networks for identification of a person by its face.

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Jelínek, Michael. "Umělá inteligence pro klasifikaci aplikačních služeb v síťové komunikaci." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442399.

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The master thesis focuses on the selection of a suitable algorithm for the classification of selected network traffic services and its implementation. The theoretical part describes the available classification approaches together with commonly used algorithms and selected network services. The practical part focuses on the preparation and preprocessing of the dataset, selection and optimization of the classification algorithm and verifying the classification capabilities of the algorithm in the various scenarios of the dataset.

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SACHDEVA, NITIN. "CYBERBULLYING DETECTION ON SOCIAL MEDIA USING DEEP LEARNING MODELS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18914.

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Application of deep learning models for cyberbullying detection in social media is an upcoming area for both researchers and practitioners for finding, exploring and analysing the extensibility of human-based expressions. Automated cyberbullying detection is typically a classification problem in natural language processing where the intent is to classify each abusive or offensive comment or post or message or image as either bullying or non-bullying. It needs high-level semantic analysis as well. Most of the earlier attempts on cyberbullying detection rely on manual feature extraction me

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Черногор, М. С. "Моделювання систем ідентифікації об'єктів методами комп'ютерного зору". Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/75670.

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JAFARI, MUHAMMAD REZA. "PERSIAN SIGN GESTURE TRANSLATION TO ENGLISH SPOKEN LANGUAGE ON SMARTPHONE." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18787.

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Hearing impaired and others with verbal challenges face difficulty to communicate with society; Sign Language represents their communication such as numbers or phrases. The communication becomes a challenge with people from other countries using different languages. Additionally, the sign language is different from one country to another. That is, learning one sign language doesn’t mean learning all sign languages. To translate a word from sign language to a spoken language is a challenge and to change a particular word from that language to another language is even a bigger challenge. I

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Sláma, Štěpán. "Pokročilá klasifikace poruch srdečního rytmu v EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413024.

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This work focuses on a theoretical explanation of heart rhythm disorders and the possibility of their automatic detection using deep learning networks. For the purposes of this work, a total of 6884 10-second ECG recordings with measured eight leads were used. Those recordings were divided into 5 groups according to heart rhythm into a group of records with atrial fibrillation, sinus rhythms, supraventricular rhythms, ventricular rhythms, and the last group consisted of the others records. Individual groups were unbalanced represented and more than 85 % of the total number of data are sinus rh

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BORDONE, MOLINI ANDREA. "Deep learning for inverse problems in remote sensing: super-resolution and SAR despeckling." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2903492.

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Amerineni, Rajesh. "BRAIN-INSPIRED MACHINE LEARNING CLASSIFICATION MODELS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1806.

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This dissertation focuses on the development of three classes of brain-inspired machine learning classification models. The models attempt to emulate (a) multi-sensory integration, (b) context-integration, and (c) visual information processing in the brain.The multi-sensory integration models are aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli. Two multimodal classification models are introduced: the feature integrating (FI) model and the decision integrating (DI) model. The FI model, inspired by multisensory integration in the subc

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Bertani, Federico. "Deep Learning methods for Portfolio Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24245/.

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Portfolio optimization is one of the most studied fields that have been researched with machine learning approaches because of its inherent demand for forecasting future market properties. In this thesis, it is shown how one can use deep neural networks with historical returns to do risk adjusted asset allocation. Unlike previous studies which set as target variable asset prices, the variable to predict here is represented by the best asset allocation strategy. Experiments performed on a time period of seven years show that temporal convolutional networks are superior to long short term memory

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Suchánek, Tomáš. "Detektor tempa hudebních nahrávek na bázi neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442576.

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This Master’s thesis deals with beat tracking systems, whose functionality is based on neural networks. It describes the structure of these systems and how the signal is processed in their individual blocks. Emphasis is then placed on recurrent and temporal convolutional networks, which by they nature can effectively detect tempo and beats in audio recordings. The selected methods, network architectures and their modifications are then implemented within a comprehensive detection system, which is further tested and evaluated through a cross-validation process on a genre-diverse data-set. The r

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Ghasemi, Ghaleh Bahmani Afsaneh. "Machine perception of human emotions using motion patterns." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104297/1/Afsaneh_Ghasemi%20Ghaleh%20Bahmani_Thesis.pdf.

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We propose novel, intelligent facial expression recognition systems as well as unique encoding of the dynamics of facial actions, enabling machines to have the ability to sense and understand human visual interaction signals. Various methodologies have been proposed to address the existing limitations in performance as well as the computing resource constraints, to advance the field of automatic human emotion recognition.

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Munasinghe, Kankanamge Sarasi Madushika. "Facial analysis models for face and facial expression recognition." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/118197/1/Sarasi%20Madushika_Munasinghe%20Kankanamge_Thesis.pdf.

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This thesis examines the research and development of new approaches for face and facial expression recognition within the fields of computer vision and biometrics. Expression variation is a challenging issue in current face recognition systems and current approaches are not capable of recognizing facial variations effectively within human-computer interfaces, security and access control applications. This thesis presents new contributions for performing face and expression recognition simultaneously; face recognition in the wild; and facial expression recognition in challenging environments. T

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Dvořák, Martin. "Anticurtaining - obrazový filtr pro elektronovou mikroskopii." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445537.

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Tomographic analysis produces 3D images of examined material in nanoscale by focus ion beam (FIB). This thesis presents new approach to elimination of the curtain effect by machine learning method. Convolution neuron network is proposed for elimination of damaged imagine by the supervised learning technique. Designed network deals with features of damaged image, which are caused by wavelet transformation. The outcome is visually clear image. This thesis also designs creation of synthetic data set for training the neuron network which are created by simulating physical process of the creation

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Martinek, Václav. "Rozpoznávání hudebních coververzí pomocí technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442587.

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This master’s thesis deals with designs and implementation of systems for music cover recognition. The introduction part is devoted to the calculation parameters from audio signal using Music Information Retrieval techniques. Subsequently, various forms of cover versions and musical aspects that cover versions share are defined. The thesis also deals in detail with the creation and distribution of a database of cover versions. Furthermore, the work presents methods and techniques for comparing and processing the calculated parameters. Attention is then paid to the OTI method, CSM calculation a

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Чапалюк, Богдан Володимирович. "Системи автоматичної медичної комп’ютерної дiагностики з використанням методiв штучного iнтелекту". Doctoral thesis, Київ, 2020. https://ela.kpi.ua/handle/123456789/39677.

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Мета даного дисертацiйного дослiдження полягає в детальному розглядi, розробцi та удосконаленнi систем автоматичної комп’ютерної дiагностики раку легень використовуючи методи штучного iнтелекту, зокрема застосовуючи та удосконалюючи останнi досягнення в областi глибинного навчання. Для дiагностування раку легенiв в сучасних медичних закладах використовують комп’ютерну томографiю, що представляє собою тривимiрне зображення легенiв пацiєнта, отримане за допомогою рентгенiвського променю, що пошарово та поступово проходить через тканини людського тiла в рiзних напрямках, з рiзних кутiв та по

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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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Yang, Xuecan. "Approximate computing for embedded machine learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT005.

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Les réseaux de neurones convolutifs (CNN) ont été largement utilisés dans de nombreux domaines tels que la reconnaissance d’image, le traitement vidéo et le traitement du langage naturel. Cependant, les CNN sont toujours gourmands en calculs et en ressources. Ils sont souvent limités par les performances et la mémoire limitées lorsqu’ils sont déployés sur des systèmes embarqués. Ce projet de recherche doctorale vise à proposer des CNNs à faibles besoins en ressources informatiques et en mémoire, qui sont plus adaptés aux systèmes embarqués. En plus de la revue de la littérature, trois méthodes

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Enan, Muhammad Esmat. "Deep learning for studying urban water bodies´ spatio-temporal transformation: a study of Chittagong City, Bangladesh." Master's thesis, 2021. http://hdl.handle.net/10362/113704.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies<br>Water has been playing a key role in human life since the dawn of civilization. It is an integral part of our lives. In recent years, water bodies specially, urban water bodies are in a poor state due to climate change and rapid urban expansion. Though some cities have become aware of this poor state of water bodies, many cities around the world are not contemplating this issue. Because less research has been conducted on water bodies than other land covers in

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HSIEH, PO-FENG, and 謝柏鋒. "Visualization of Convolution Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qna29g.

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碩士<br>國立臺北科技大學<br>資訊工程系<br>107<br>In recent years, convolutional neural networks have had many groundbreaking developments. This paper's goal is to analyze the recent YOLO (You Only Look Once) that has a very good performance classification for object detection technology. This paper is a simple way to explain the operation of the convolutional neural network. Present the process which can make the general public more aware of the way machine learning works, and also make it convenient for experts to analyze the structure of it. The ability to quickly improve the original architecture and acce

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Checg, Chung-Sheng, and 鄭仲勝. "An Accelerative Convolution Neural Network Model." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/2tycsb.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>106<br>Machine learning is a technology that allows computers to learn the rules through vast amounts of information and correct their mistakes themselves. It show the superiority to conventional artificial methods. However in shallow learning, the capability of modelling complex functions is limited in the case of finite samples. Thus, shallow learning models are not enough to simulate human brains in solving difficult problems. Until recently, deep learning was proposed to model complex functions that shallow learning cannot achieve and automatically extract dat

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KANG, NAN-RAN, and 康乃人. "Speaker Verification using Convolution Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/c8e3qe.

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碩士<br>逢甲大學<br>資訊工程學系<br>106<br>Biometric system is no longer a new thing in daily life, and it has become more and more popular in recent years, fingerprint recognition, iris recognition, voiceprint recognition, and I-phone's Face ID are all biometric system, and speaker verification is one of them. Speaker recognition can be divided into two parts: feature extraction and classification. In the past, the two parts were solved by different methods, due to the rapid development of deep learning, the neural network for speaker recognition has gained breadth of development. In the part of the spea

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Hu, Yi-Chun, and 胡依淳. "Analysis and Comparison of Convolution Layer in Deep Convolution Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/686x7u.

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碩士<br>國立暨南國際大學<br>電機工程學系<br>106<br>With the rapid development of information technology, big data has become mainstream, and many identification systems have been greatly affected. Therefore, deep learning requires a large database learning model and thus becomes the mainstream. Deep learning can take advantage of the characteristics of robots to automatically learn to task objectives, and thus deep learning of this architecture has become a very popular technology in academics. Nowadays, neural networks are popular in the field of visual imaging. The best performing model is the convolutional

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Sun, Tzu-Chun, and 孫梓鈞. "Fruit Recognition Using Deep Convolution Neural Network." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/76315586416634324332.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>102<br>This thesis focuses on developing a fruit recognition method. It can be used to improve life convenience by shortening the supermarket checkout time. Existing methods for fruit recognition use handcrafted image features, such as the texture, the color, and the shape of a fruit, for fruit recognition. However, image features extracted with a set of specific algorithms do not necessarily provide enough information for pattern recognition. In this work, we use deep convolution neural network (DCNN) to learn discriminative fruit features automatically. In order

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WEI, TSUNG-HSIN, and 魏崇訓. "Video Super-resolution via Convolution Neural Network." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/823kfa.

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碩士<br>國立高雄應用科技大學<br>資訊工程系<br>104<br>Nowadays, people might need super resolution to have more effective and clear information. The technology of image processing becomes better and better, and there are more and more people present their research in this field. Super resolution algorithm enhances high frequent information (texture or edges) to improve the image quality. We can do more things with super resolution, such as road surveillance system. The view might be influence by illumination, angle, distance, and other conditions, so these might not be good for us to recognize the number of lic

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Chang, Yao-Ren, and 張耀仁. "Convolution neural network on WIFI indoor localization." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/s7dhny.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>106<br>The mobile payment has been growing very quickly in these year, our life has become more and more convenient. Once we can locate user’s position precisely, we can broadcast the advertisement to the user to increase sales performance. For example: when you walk into the restaurant, the system sent you the coupon of this restaurant immediately, when you walk into the apparel store, the system list all of the clothes you might like, when you are leaving parking lot, the system auto-debiting your parking fee. In the past, WIFI localization system is based on RFID

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