Дисертації з теми "SUPERVISED TECHNOLOGY"
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Persson, Travis. "Semi-Supervised Learning for Predicting Biochemical Properties." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447652.
Повний текст джерелаKola, Lokesh, and Vigneshwar Muriki. "A Comparison on Supervised and Semi-Supervised Machine Learning Classifiers for Diabetes Prediction." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21816.
Повний текст джерелаAboushady, Moustafa. "Semi-supervised learning with HALFADO: two case studies." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425888.
Повний текст джерелаRollenhagen, Svante. "Classification of social gestures : Recognizing waving using supervised machinelearning." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230678.
Повний текст джерелаI den har rapporten presenteras ett försök att göra gestigenkanning av gesterna vinkning samt handklappning med hjälp av ett verktyg som kan kanna igen ett antal punkter hos den mänskliga kroppen från videodata. At- tributen som användes är den maximala kovariansen från en sinus-anpassning till vinkeldata, samt det maximala och minimala värdet av anpassningen. En stodvektormaskin (Support Vector Machine) användes for inlärningen. Resultatet var en precision på 93% ± 4% där femdelad korsvalidering användes. Begränsningarna hos de använda metoderna diskuteras sedan, vilket inkluderar: brist på support for mer an en gest i video-datan, samt brister i generalitet nar det kommer till vilka attribut som anvandes. Slutligen ges förslag på framtida utvecklingar och förbättringar.
Eggertsson, Gunnar Atli. "Classification of Seismic Body Wave Phases Using Supervised Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-423977.
Повний текст джерелаElf, Sebastian, and Christopher Öqvist. "Comparison of supervised machine learning models forpredicting TV-ratings." Thesis, KTH, Hälsoinformatik och logistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278054.
Повний текст джерелаSammanfattningAtt manuellt förutsäga tittarsiffor för program- och annonsplacering kan vara kostsamt och tidskrävande om de är fel. Denna rapport utvärderar olika modeller som utnyttjar övervakad maskininlärning för att se om processen för att förutsäga tittarsiffror kan automatiseras med bättre noggrannhet än den manuella processen. Resultaten visar att av de två testade övervakade modellerna för maskininlärning, Random Forest och Support Vector Regression, var Random Forest den bättre modellen. Random Forest var bättre med båda de två mätningsmetoder, genomsnittligt absolut fel och kvadratiskt medelvärde fel, som används för att jämföra modellerna. Slutsatsen är att Random Forest, utvärderad med de data och de metoderna som används, inte är tillräckligt exakt för att ersätta den manuella processen. Även om detta är fallet, kan den fortfarande potentiellt användas som en del av den manuella processen för att underlätta de anställdas arbetsbelastning.Nyckelord Maskininlärning, övervakad inlärning, tittarsiffror, Support Vector Regression, Random Forest.
Pein, Raoul Pascal. "Semi-supervised image classification based on a multi-feature image query language." Thesis, University of Huddersfield, 2010. http://eprints.hud.ac.uk/id/eprint/9244/.
Повний текст джерелаPersson, Martin. "Semantic Mapping using Virtual Sensors and Fusion of Aerial Images with Sensor Data from a Ground Vehicle." Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2186.
Повний текст джерелаHussein, Abdul Aziz. "Identifying Crime Hotspot: Evaluating the suitability of Supervised and Unsupervised Machine learning." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1624914607243042.
Повний текст джерелаChetry, Roshan. "Web genre classification using feature selection and semi-supervised learning." Kansas State University, 2011. http://hdl.handle.net/2097/8855.
Повний текст джерелаDepartment of Computing and Information Sciences
Doina Caragea
As the web pages continuously change and their number grows exponentially, the need for genre classification of web pages also increases. One simple reason for this is given by the need to group web pages into various genre categories in order to reduce the complexities of various web tasks (e.g., search). Experts unanimously agree on the huge potential of genre classification of web pages. However, while everybody agrees that genre classification of web pages is necessary, researchers face problems in finding enough labeled data to perform supervised classification of web pages into various genres. The high cost of skilled manual labor, rapid changing nature of web and never ending growth of web pages are the main reasons for the limited amount of labeled data. On the contrary unlabeled data can be acquired relatively inexpensively in comparison to labeled data. This suggests the use of semi-supervised learning approaches for genre classification, instead of using supervised approaches. Semi-supervised learning makes use of both labeled and unlabeled data for training - typically a small amount of labeled data and a large amount of unlabeled data. Semi-supervised learning have been extensively used in text classification problems. Given the link structure of the web, for web-page classification one can use link features in addition to the content features that are used for general text classification. Hence, the feature set corresponding to web-pages can be easily divided into two views, namely content and link based feature views. Intuitively, the two feature views are conditionally independent given the genre category and have the ability to predict the class on their own. The scarcity of labeled data, availability of large amounts of unlabeled data, richer set of features as compared to the conventional text classification tasks (specifically complementary and sufficient views of features) have encouraged us to use co-training as a tool to perform semi-supervised learning. During co-training labeled examples represented using the two views are used to learn distinct classifiers, which keep improving at each iteration by sharing the most confident predictions on the unlabeled data. In this work, we classify web-pages of .eu domain consisting of 1232 labeled host and 20000 unlabeled hosts (provided by the European Archive Foundation [Benczur et al., 2010]) into six different genres, using co-training. We compare our results with the results produced by standard supervised methods. We find that co-training can be an effective and cheap alternative to costly supervised learning. This is mainly due to the two independent and complementary feature sets of web: content based features and link based features.
Jönsson, Mattias, and Lucas Borg. "How to explain graph-based semi-supervised learning for non-mathematicians?" Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20339.
Повний текст джерелаThe large amount of available data on the web can be used to improve the predictions made by machine learning algorithms. The problem is that such data is often in a raw format and needs to be manually labeled by a human before it can be used by a machine learning algorithm. Semi-supervised learning (SSL) is a technique where the algorithm uses a few prepared samples to automatically prepare the rest of the data. One approach to SSL is to represent the data in a graph, also called graph-based semi-supervised learning (GSSL), and find similarities between the nodes for automatic labeling.Our goal in this thesis is to simplify the advanced processes and steps to implement a GSSL-algorithm. We will cover basic tasks such as setup of the developing environment and more advanced steps such as data preprocessing and feature extraction. The feature extraction techniques covered are bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Lastly, we present how to classify documents using Label Propagation (LP) and Multinomial Naive Bayes (MNB) with a detailed explanation of the inner workings of GSSL. We showcased the classification performance by classifying documents from the 20 Newsgroup dataset using LP and MNB. The results are documented using two different evaluation scores called F1-score and accuracy. A comparison between MNB and the LP-algorithm using two different types of kernels, KNN and RBF, was made on different amount of labeled documents. The results from the classification algorithms shows that MNB is better at classifying the data than LP.
Apprey-Hermann, Joseph Kwame. "Evaluating The Predictability of Pseudo-Random Number Generators Using Supervised Machine Learning Algorithms." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588805461290138.
Повний текст джерелаRodwell, David Alexander Richard. "Investigating perceptions of emerging technology in driver education." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132171/1/David_Rodwell_Thesis.pdf.
Повний текст джерелаLai, Daphne Teck Ching. "An exploration of improvements to semi-supervised fuzzy c-means clustering for real-world biomedical data." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14232/.
Повний текст джерелаPettersson, Ruiz Eric. "Combating money laundering with machine learning : A study on different supervised-learning algorithms and their applicability at Swedish cryptocurrency exchanges." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300375.
Повний текст джерелаEuropol (2018) uppskattade år 2018, att mer än 22 miljarder USD tvättades i Europa genom användning av kryptovalutor. Financial Action Task Force förklarar att penningtvättare kan byta deras olagligt förvärvade fiat-valutor mot kryptovaluta, tvätta kryptovalutan genom att fördela tillgångarna till ett flertal konton och sedan återväxla kryptovalutan tillbaka till fiat-valuta. Denna process, att växla valutor, görs genom en kryptovalutaväxlare, vilket ger växlaren en ideal position för att förhindra att tvättning sker eftersom de agerar som mellanhänder (FATF, 2021). Dock har de aktuella AMLansträngningarna vid dessa växlare visat sig vara föråldrade och i behov av förbättring. Dessutom hävdar Weber et al. (2019) att maskininlärning skulle kunna användas i denna strävan. Denna studies syfte är att undersöka hur maskininlärning kan användas för att bekämpa penningtvättaktiviteter där kryptovaluta används. Detta görs genom att utforska vilka maskininlärningsalgoritmer som är användbara för detta ändamål. Dessutom strävar undersökningen till att ge förståelse för tillämpligheten hos de undersökta algoritmerna genom att utforska deras lämplighet hos kryptovalutaväxlare. För att besvara frågeställningen har fyra supervised-learning algoritmer jämförts genom att använda Bitcoin Elliptic Dataset. För att kvantitativt förstå olikheterna i algoritmisk prestanda, har tre utvärderingsverktyg använts: F1-score, Precision och Recall. Slutligen, för att ytterligare förstå de undersökta algoritmernas tillämplighet, har två kompletterande kvalitativa intervjuer med svenska kryptovalutaväxlare gjorts. Studien kan inte dra slutsatsen att det finns en bästa algoritm för att upptäcka transaktioner som kan relateras till penningtvätt. Dock verkar tillämpbarheten hos decision tree algoritmen vara mer lovande vid de svenska kyptovalutaväxlarna än de tre andra algoritmerna.
Björk, Gabriella. "Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217859.
Повний текст джерелаDet här masterexamensarbetet har utförts av en student från Kungliga Tekniska Högskolan i samarbete med Cybercom Group. Målet var att utvärdera och jämföra designstrategier för igenkänning av frukt i en skörderobot och prestandan av klassificerande maskininlärningsalgoritmer när de appliceras på det specifika problemet. Arbetet omfattar grunderna av dessa system; till vilket parametrar, begränsningar, krav och designbeslut har undersökts. Ramverket användes sedan som grund för implementationen av sensorsystemet, processerings- och klassifikationsalgoritmerna. En tomatplanta i pplast med frukter av varierande mognasgrad användes som bas för träning och validering av systemet, och en Kinect för Windows v2 utrustad med sensorer för högupplöst färg, djup, och infraröd data anvöndes för att erhålla bilder. Datan processerades i MATLAB med hjälp av mjukvaruutvecklingskit för Kinect tillhandahållandet av Windows, i syfte att extrahera egenskaper ifrån objekt på bilderna. Multipla vyer erhölls genom att låta tomatplantan rotera på en plattform, driven av en stegmotor Arduino Uno. De binära klassifikationsalgoritmer som testades var Support Vector MAchine, Decision Tree och k-Nearest Neighbor. Modellerna tränades och valideras med hjälp av en five fold cross validation i MATLABs Classification Learner applikation. Prestationsindikatorer som precision, återkallelse och F1- poäng beräknades för de olika modellerna. Resultatet visade bland annat att statiska modeller som k-NN och SVM presterade bättre för det givna problemet, och att den sistnömnda är mest lovande för framtida applikationer.
Rutter, Wilbur Cliff IV. "USING MACHINE LEARNING TO PREDICT ACUTE KIDNEY INJURIES AMONG PATIENTS TREATED WITH EMPIRIC ANTIBIOTICS." UKnowledge, 2018. https://uknowledge.uky.edu/pharmacy_etds/86.
Повний текст джерелаKorduner, Lars, and Mattias Sundquist. "Determining an optimal approach for human occupancy recognition in a study room using non-intrusive sensors and machine learning." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20632.
Повний текст джерелаHuman recognition with the use of sensors and machine learning is a field with many practical applications. There exists some commercial products that can reliably recognise humans with the use of video cameras. Video cameras often raises a concern about privacy though, by reading the related work one could argue that in some situations a video camera is not necessarily more reliable than low-cost, non-intrusive, ambient sensors. Human occupancy recognition in a small sized study/office room is one such situation. While there has been a lot of successful studies done on human occupancy recognition with various sensors and machine learning algorithms, a question about which combination of sensors and machine learning algorithms is more viable still remains. This thesis sets out to test five promising sensors in combination with six different machine learning algorithms to determine which combination outperformed the rest. To achieve this, an arduino prototype was built to collect and save the readings from all five sensors into a text file every second. The arduino, along with the sensors, was placed in a small study room at Malmö University to collect data on two separate occasions whilst students used the room as they would usually do. The collected data was then used to train and evaluate five machine learning classifier for each of the possible combinations of sensors and machine learning algorithms, for both occupancy detection and occupancy count. At the end of the experiment it was found that all algorithms could achieve an accuracy of at least 90% with usually more than one combination of sensors. The highest hit-rate achieved was 97%.
Anavberokhai, Isah. "Mapping land-use in north-western Nigeria (Case study of Dutse)." Thesis, University of Gävle, Department of Technology and Built Environment, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-143.
Повний текст джерелаThis project analyzes satellite images from 1976, 1985 and 2000 of Dutse, Jigawa state, in north-western Nigeria. The analyzed satellite images were used to determine land-use and vegetation changes that have occurred in the land-use from 1976 to 2000 will help recommend possible planning measures in order to protect the vegetation from further deterioration.
Studying land-use change in north-western Nigeria is essential for analyzing various ecological and developmental consequences over time. The north-western region of Nigeria is of great environmental and economic importance having land cover rich in agricultural production and livestock grazing. The increase of population over time has affected the land-use and hence agricultural and livestock production.
On completion of this project, the possible land use changes that have taken place in Dutse will be analyzed for future recommendation. The use of supervised classification and change detection of satellite images have produced an economic way to quantify different types of landuse and changes that has occurred over time.
The percentage difference in land-use between 1976 and 2000 was 37%, which is considered to be high land-use change within the period of study. The result in this project is being used to propose planning strategies that could help in planning sustainable land-use and diversity in Dutse.
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.
Повний текст джерелаNya neurala nätverksframsteg har lett till modeller som kan användas för en mängd olika bildklasseringsuppgifter, och är därför användbara många av dagens medietekniska applikationer. I detta projektet tränar jag moderna neurala nätverksarkitekturer på en nyuppsamlad bilbild-datasats för att göra både grov- och finkornad klassificering av fordonstyp. Resultaten visar att neurala nätverk kan lära sig att skilja mellan många mycket olika bilklasser, och även mellan några mycket liknande klasser. Mina bästa modeller nådde 50,8% träffsäkerhet vid 28 klasser och 61,5% på de mest utmanande 5, trots brusiga bilder och manuell klassificering av datasetet.
Nyman, David. "Injector diagnosis based on engine angular velocity pulse pattern recognition." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414967.
Повний текст джерелаTovedal, Sofiea. "On The Effectiveness of Multi-TaskLearningAn evaluation of Multi-Task Learning techniques in deep learning models." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172257.
Повний текст джерелаGustafsson, Andreas. "Winner Prediction of Blood Bowl 2 Matches with Binary Classification." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20368.
Повний текст джерелаAndersson, Melanie, Arvola Maja, and Sara Hedar. "Sketch Classification with Neural Networks : A Comparative Study of CNN and RNN on the Quick, Draw! data set." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353504.
Повний текст джерелаBolstad, K. S. "Systematics of the Onychoteuthidae Gray, 1847 (Cephalopoda: Oegopsida) a thesis submitted to the Earth & Oceanic Sciences Research Institute, Auckland University of Technology in fulfilment of the requirements for the degree of Doctor of Philosophy, supervised by Dr Steve O'Shea, 2008." Click here to access this resource online, 2008. http://hdl.handle.net/10292/414.
Повний текст джерелаHunsche, Sandra. "PROFESSOR FAZEDOR DE CURRÍCULOS: DESAFIOS NO ESTÁGIO CURRICULAR SUPERVISIONADO EM ENSINO DE FÍSICA." Universidade Federal de Santa Maria, 2010. http://repositorio.ufsm.br/handle/1/6909.
Повний текст джерелаIn the face of current educational problems, indicated by research, as the propedeutic education and the separation between the "world of life" and the "world of school," the conception of science and technology neutral, among others, it is necessary to "make changes" in curriculum. Moreover, it is argued that teachers should be part of the process of curriculum reconfiguration. So, in this research, it is looked for identify and critically analyze challenges and potential faced by trainees in physics in the process of reconfiguring a curriculum guided by the approach of social themes marked by the Science and Technology. More specifically, focus on the elaboration and implementation of themes under the Supervised Internship Course in Teaching of Physics. The structure of these themes is referenced by an approximation of the presuppositions of the educator Paulo Freire and the Science-Technology-Society movement (CTS). The research problem is characterized by the following questions: 1) What dimensions the school context influences the execution of curriculum reconfigurations based on themes? 2) What are the constraints that the training of future teachers, has in the process of elaboration/implementation of themes? The research, entered on the Research Line called School Practice and Public Policies (PPGE/UFSM), is developed according to the dynamics of participative research. The instruments to obtain the "data" were used the Teacher's Journal, a semi-structured interviews and analysis of reportsfiled by the student teacher. The analysis was done using content analysis. The results were organized under four thematic categories: Training Fragmented; From Rigor" to the Curricular Flexibility; Real Problems and Epistemological Curiosity, and Student Problem or Curriculum Problem?.
Frente a atuais problemas educacionais, apontados pelas pesquisas, como o ensino propedêutico, a desvinculação entre o mundo da vida e o mundo da escola , a concepção de ciência e tecnologia neutras, entre outros, considera-se necessário mexer no currículo. Além disto, defende-se que os professores devem fazer parte do processo de reconfiguração curricular. Neste sentido, busca-se, nesta pesquisa, identificar e analisar criticamente desafios e potencialidades encontradas por estagiários de Física, no processo de uma reconfiguração curricular pautada pela abordagem de temas sociais marcados pela Ciência-Tecnologia. Mais especificamente, focalizar a elaboração e implementação de temáticas no âmbito do Estágio Curricular Supervisionado em Ensino de Física. A estruturação destas temáticas é referenciada por uma aproximação entre pressupostos do educador Paulo Freire e do movimento Ciência-Tecnologia-Sociedade (CTS). O problema de investigação é caracterizado pelas seguintes questões: 1) Em que dimensões o contexto escolar influencia a efetivação de reconfigurações curriculares baseadas em temáticas? 2) Quais os condicionamentos que a formação, destes futuros professores, exerce no processo de elaboração/implementação de temáticas? A pesquisa, inserida na Linha de Pesquisa Práticas Escolares e Políticas Públicas (PPGE/UFSM), é desenvolvida segundo a dinâmica da Pesquisa Participante. Como instrumentos, para a obtenção dos dados , foram utilizados o Diário do Professor, uma entrevista semi-estruturada e a análise dos relatórios entregues pelos estagiários. Em termos de análise, fez-se uso da análise de conteúdo. Os resultados foram sistematizados sob quatro categorias temáticas: Formação Fragmentada; Do Rigor à Flexibilidade Curricular; Problemas Reais e Curiosidade Epistemológica; e Aluno Problema ou Currículo Problema?.
Sörman, Paulsson Elsa. "Evaluation of In-Silico Labeling for Live Cell Imaging." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-180590.
Повний текст джерелаAraujo, Izabel Cristina de 1966. "Desenvolvimento de uma proposta didático-pedagógica para ambiente virtual de aprendizagem assistida por computador." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/319168.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Educação
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Resumo: Essa pesquisa tem como objetivo geral desenvolver uma proposta didático-pedagógica para ambiente virtual de aprendizagem assistida por computador (AAC). Os objetivos específicos situam-se em: identificar referenciais didático-pedagógicos junto à literatura e especialistas da área, construir um quadro indicativo dos referenciais didático-pedagógicos de ambiente virtual de AAC e sistematizar os referenciais encontrados, agrupando-os em unidades de análise para composição das diretrizes norteadoras do desenvolvimento da proposta didático-pedagógica. O problema da pesquisa apresentou-se em: Como referenciais didático-pedagógicos podem nortear ações educativas em ambientes virtuais de AAC? Realizamos trabalho de campo com levantamento e revisão da literatura, anotações em diário de campo advindas da observação em campo e entrevista semi-estruturada. Tivemos a participação de 36 pesquisadores de diferentes universidades americanas, asiáticas, europeias e da Oceania. A análise dos dados de predominância qualitativa norteou as conclusões, quais sejam: que o investimento em pesquisa na área de educação com inovação tecnológica proporciona resultados práticos, impactando na formulação de políticas públicas e na formação de professores; a relevância da autoria do professor na ação educativa em ambiente virtual de AAC; o professor-autor como mediador da aprendizagem; as destrezas e os conhecimentos necessários para utilizar os materiais de ambiente de AAC se apresentam mais eficazes se autores e coautores contarem com formação básica para a utilização das ferramentas tecnológicas. Daí a importância de fazê-lo gradualmente para que sejam capazes de aumentar seu nível de autonomia frente a sua própria aprendizagem.Essa pesquisa tem como objetivo geral desenvolver uma proposta didático-pedagógica para ambiente virtual de aprendizagem assistida por computador (AAC). Os objetivos específicos situam-se em: identificar referenciais didático-pedagógicos junto à literatura e especialistas da área, construir um quadro indicativo dos referenciais didático-pedagógicos de ambiente virtual de AAC e sistematizar os referenciais encontrados, agrupando-os em unidades de análise para composição das diretrizes norteadoras do desenvolvimento da proposta didático-pedagógica. O problema da pesquisa apresentou-se em: Como referenciais didático-pedagógicos podem nortear ações educativas em ambientes virtuais de AAC? Realizamos trabalho de campo com levantamento e revisão da literatura, anotações em diário de campo advindas da observação em campo e entrevista semi-estruturada. Tivemos a participação de 36 pesquisadores de diferentes universidades americanas, asiáticas, europeias e da Oceania. A análise dos dados de predominância qualitativa norteou as conclusões, quais sejam: que o investimento em pesquisa na área de educação com inovação tecnológica proporciona resultados práticos, impactando na formulação de políticas públicas e na formação de professores; a relevância da autoria do professor na ação educativa em ambiente virtual de AAC; o professor-autor como mediador da aprendizagem; as destrezas e os conhecimentos necessários para utilizar os materiais de ambiente de AAC se apresentam mais eficazes se autores e coautores contarem com formação básica para a utilização das ferramentas tecnológicas. Daí a importância de fazê-lo gradualmente para que sejam capazes de aumentar seu nível de autonomia frente a sua própria aprendizagem
Abstract: The overall goal of this research work is to develop a didactic proposal for Computer Assisted Learning (CAL) environments. The specific goals are: to identify the didactic requirements from both the literature and the specialists in the field; to build a conceptual framework of the didactic requirements of a virtual CAL environment; and to organize the requirements found by creating categories based on units of analysis so as to build up the main requirements of the development of a teaching proposal. The main concern was: How can pedagogical criteria set the foundations for sound educational actions in computer-assisted learning environments? 36 researchers from different universities in America, Asia, Europe and Australia participated in the research. The data analysis led to the following conclusions: a) the investment in research in the field of technology-enhanced teaching and learning leads to practical results which in turn foster the emergence of public policies and the improvement of teacher training; b) the relevance of teacher authorship in education in virtual CAL environments should be pointed out, the starting point being the social and cultural environment of the school where the teaching-learning process takes place; c) the role of the teacher-author as a mediator in learning should be borne in mind; d) the skills and knowledge needed when using the materials of a CAL environment become more effective when used by authors and co-authors trained in the use of technological tools. This means that it is important to provide authors and co-authors with the opportunity to develop those competences gradually so as to increase their level of autonomy regarding their own learning process
Doutorado
Ciencias Sociais na Educação
Doutora em Educação
Heidfors, Filip, and Elias Moltedo. "Maskininlärning: avvikelseklassificering på sekventiell sensordata. En jämförelse och utvärdering av algoritmer för att klassificera avvikelser i en miljövänlig IoT produkt med sekventiell sensordata." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20742.
Повний текст джерелаA company has developed a environment-friendly IoT device with sequential sensor data and want to use machine learning to classify anomalies in their data. Throughout the years, several well working algorithms for classifications have been developed. However, there is no optimal algorithm for every problem. The purpose of this work was therefore to investigate, compare and evaluate different classifiers within supervised machine learning to find out which classifier that gives the best accuracy to classify anomalies in the kind of IoT device that the company has developed. With a literature review we first wanted to find out which classifiers that are commonly used and have worked well in related work for similar purposes and applications. We concluded to further compare and evaluate Random Forest, Naïve Bayes and Support Vector Machines. We created a dataset of 513 examples that we used for training and evaluation for each classifier. The result showed that Random Forest had superior accuracy with 95.7% compared to Naïve Bayes (81.5%) and Support Vector Machines (78.6%). The conclusion for this work is that Random Forest, with 95.7%, gives a high enough accuracy for the company to have good use of the machine learning model. The result also indicates that Random Forest, for this thesis specific classification problem, is the best classifier within supervised machine learning but that there is a potential possibility to get even higher accuracy with other techniques such as unsupervised machine learning or semi-supervised machine learning.
Ferrer, Martínez Claudia. "Machine Learning for Solar Energy Prediction." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27423.
Повний текст джерелаBosc, Guillaume. "Anytime discovery of a diverse set of patterns with Monte Carlo tree search." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI074/document.
Повний текст джерелаThe discovery of patterns that strongly distinguish one class label from another is still a challenging data-mining task. Subgroup Discovery (SD) is a formal pattern mining framework that enables the construction of intelligible classifiers, and, most importantly, to elicit interesting hypotheses from the data. However, SD still faces two major issues: (i) how to define appropriate quality measures to characterize the interestingness of a pattern; (ii) how to select an accurate heuristic search technique when exhaustive enumeration of the pattern space is unfeasible. The first issue has been tackled by Exceptional Model Mining (EMM) for discovering patterns that cover tuples that locally induce a model substantially different from the model of the whole dataset. The second issue has been studied in SD and EMM mainly with the use of beam-search strategies and genetic algorithms for discovering a pattern set that is non-redundant, diverse and of high quality. In this thesis, we argue that the greedy nature of most such previous approaches produces pattern sets that lack diversity. Consequently, we formally define pattern mining as a game and solve it with Monte Carlo Tree Search (MCTS), a recent technique mainly used for games and planning problems in artificial intelligence. Contrary to traditional sampling methods, MCTS leads to an any-time pattern mining approach without assumptions on either the quality measure or the data. It converges to an exhaustive search if given enough time and memory. The exploration/exploitation trade-off allows the diversity of the result set to be improved considerably compared to existing heuristics. We show that MCTS quickly finds a diverse pattern set of high quality in our application in neurosciences. We also propose and validate a new quality measure especially tuned for imbalanced multi-label data
Buttar, Sarpreet Singh. "Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-77201.
Повний текст джерелаHelle, Valeria, Andra-Stefania Negus, and Jakob Nyberg. "Improving armed conflict prediction using machine learning : ViEWS+." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354845.
Повний текст джерелаI detta projekt, vilket vi valt att benämna ViEWS+, har vi förbättrat olika aspekter av ViEWS (Violence Early-Warning System), ett system som med maskinlärning försöker förutsäga var i världen väpnade konflikter kommer uppstå. Målet med ViEWS är att kunna förutsäga sannolikheten för konflikter så långt som 36 månader i framtiden. Målet med att förutsäga sannoliketen för konflikter är att politiker och beslutsfattare ska kunna använda dessa kunskaper för att förhindra dem. Indata till systemet är konfliktdata med ett stort antal egenskaper, så som tidigare konflikter, barnadödlighet och urbanisering. Dessa är av varierande användbarhet, vilket skapar ett behov för att sålla ut de som inte är användbara för att förutsäga framtida konflikter. Innan vårt projekt har forskarna som använder ViEWS valt ut egenskaper för hand, vilket blir allt svårare i och med att fler introduceras. Forskargruppen hade även ingen formell metodik för att välja parametervärden till de maskinlärningsfunktioner de använder. De valde parametrar baserat på erfarenhet och känsla, något som kan leda till onödigt långa exekveringstider och eventuellt sämre resultat beroende på funktionen som används. Våra mål med projektet var att förbättra systemets produktivitet, i termer av exekveringstid och säkerheten i förutsägelserna. För att uppnå detta utvecklade vi analysverktyg för att försöka lösa de existerande problemen. Vi har utvecklat ett verktyg för att välja ut färre, mer användbara, egenskaper från datasamlingen. Detta gör att egenskaper som inte tillför någon viktig information kan sorteras bort vilket sparar exekveringstid. Vi har även jämfört prestandan hos olika maskinlärningsfunktioner, för att identifiera de bäst lämpade för konfliktprediktion. Slutligen har vi implementerat ett verktyg för att analysera hur resultaten från funktionerna varierar efter valet av parametrar. Detta gör att man systematiskt kan bestämma vilka parametervärden som bör väljas för att garantera bra resultat samtidigt som exekveringstid hålls nere. Våra resultat visar att med våra förbättringar sänkes exekveringstiden med en faktor av omkring nio och förutsägelseförmågorna höjdes med en faktor av tre. Vi hoppas att vårt arbete kan leda till säkrare föutsägelser och vilket i sin tur kanske leder till en fredligare värld.
Lannge, Jakob, and Ali Majed. "Classifying human activities through machine learning." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20115.
Повний текст джерелаClassifying Activities of daily life (ADL) can be used in a system that monitor people’s activities for different purposes. For example, in emergency systems. Machine learning is a way to classify ADL with high accuracy, using wearable sensors as an input. In this paper, a proof-of-concept system consisting of three different machine learning algorithms is evaluated and compared between tree different datasets, one publicly available at (Ugulino, et al., 2012), and two collected in this paper using an android device’s accelerometer and gyroscope sensor. The algorithms are: Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Neural Network. The two sensors used are an accelerometer and a gyroscope. The result shows how a system can be implemented using Azure Machine Learning Studio, and how three different algorithms performs when classifying three different datasets. One algorithm achieves a higher accuracy compared to the machine learning model initially used with the Ugolino data set.
Na, Li. "Combination of supervised and unsupervised classifiers based on belief functions." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S041.
Повний текст джерелаLand cover relates to the biophysical cover of the Earth’s terrestrial surface, identifying vegetation, water, bare soil, or impervious surfaces, etc. Identifying land cover is essential for planning and managing natural resources (e.g. development, protection), understanding the distribution of habitats, and for modeling environmental variables. Identification of land cover types provides basic information for the generation of other thematic maps and establishes a baseline for monitoring activities. Therefore, land cover classification using satellite data is one of the most important applications of remote sensing. A great deal of ground information (e.g. labeled samples) is usually required to generate high-quality land cover classification. However, in complex natural areas, collecting information on the ground can be time-consuming and extremely expensive. Nowadays, multiple sensor technologies have gained great attention in land cover classification. They bring different and complementary information—spectral characteristics that may help to overcome the limitations caused by inadequate ground information. In our research, we focus on the fusion of heterogeneous information from different sources. The combination system aims to solve the problems caused by limited labeled samples and can thus be used in land cover classification for hard-to-access areas. These mantic labels for the land cover classification from each sensor can be different, and may not corresponds to the final scheme of labels that users await. For instance, land cover classification methods of different sensors provide semantic labels for the ground. However, based on these land cover maps, an accessibility map is supposed to be generated to meet users’ needs. Therefore, another objective of the combination is to provide an interface with a final scheme probably different from the input land cover maps
Abou, El Houda Zakaria. "Security Enforcement through Software Defined Networks (SDN)." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0023.
Повний текст джерелаThe original design of Internet did not take into consideration security aspects of the network; the priority was to facilitate the process of communication. Therefore, many of the protocols that are part of the Internet infrastructure expose a set of vulnerabilities that can be exploited by attackers to carry out a set of attacks. Distributed Denial-of-Service (DDoS) represents a big threat and one of the most devastating and destructive attacks plaguing network operators and Internet service providers (ISPs) in stealthy way. Software defined networks (SDN) is an emerging technology that promises to solve the limitations of the conventional network architecture by decoupling the control plane from the data plane. On one hand, the separation of the control plane from the data plane allows for more control over the network and brings new capabilities to deal with DDoS attacks. On the other hand, this separation introduces new challenges regarding the security of the control plane. This thesis aims to deal with DDoS attacks while protecting the resources of the control plane. In this thesis, we contribute to the mitigation of both intra-domain and inter-domain DDoS attacks, and we contribute to the reinforcement of security aspects in SDN
O’Neil, Kason, Jennifer M. Krause, and Scott Douglas. "University Supervisor Perceptions of Live Remote Supervision in Physical Education Teacher Education." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/4048.
Повний текст джерелаBrorson, Erik. "Classifying Hate Speech using Fine-tuned Language Models." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352637.
Повний текст джерелаMathonat, Romain. "Rule discovery in labeled sequential data : Application to game analytics." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI080.
Повний текст джерелаIt is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of rules covering sets of objects having interesting properties, e.g., they characterize a given target class. Though many subgroup discovery algorithms have been proposed for both transactional and numerical data, discovering rules within labeled sequential data has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. In this thesis, we propose to apply bandit models and Monte Carlo Tree Search to explore the search space of possible rules using an exploration-exploitation trade-off, on different data types such as sequences of itemset or time series. For a given budget, they find a collection of top-k best rules in the search space w.r.t chosen quality measure. They require a light configuration and are independent from the quality measure used for pattern scoring. To the best of our knowledge, this is the first time that the Monte Carlo Tree Search framework has been exploited in a sequential data mining setting. We have conducted thorough and comprehensive evaluations of our algorithms on several datasets to illustrate their added-value, and we discuss their qualitative and quantitative results. To assess the added-value of one or our algorithms, we propose a use case of game analytics, more precisely Rocket League match analysis. Discovering interesting rules in sequences of actions performed by players and using them in a supervised classification model shows the efficiency and the relevance of our approach in the difficult and realistic context of high dimensional data. It supports the automatic discovery of skills and it can be used to create new game modes, to improve the ranking system, to help e-sport commentators, or to better analyse opponent teams, for example
Benammar, Riyadh. "Détection non-supervisée de motifs dans les partitions musicales manuscrites." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI112.
Повний текст джерелаThis thesis is part of the data mining applied to ancient handwritten music scores and aims at a search for frequent melodic or rhythmic motifs defined as repetitive note sequences with characteristic properties. There are a large number of possible variations of motifs: transpositions, inversions and so-called "mirror" motifs. These motifs allow musicologists to have a level of in-depth analysis on the works of a composer or a musical style. In a context of exploring large corpora where scores are just digitized and not transcribed, an automated search for motifs that verify targeted constraints becomes an essential tool for their study. To achieve the objective of detecting frequent motifs without prior knowledge, we started from images of digitized scores. After pre-processing steps on the image, we exploited and adapted a model for detecting and recognizing musical primitives (note-heads, stems...) from the family of Region-Proposal CNN (RPN) convolution neural networks. We then developed a primitive encoding method to generate a sequence of notes without the complex task of transcribing the entire manuscript work. This sequence was then analyzed using the CSMA (Constraint String Mining Algorithm) approach designed to detect the frequent motifs present in one or more sequences, taking into account constraints on their frequency and length, as well as the size and number of gaps allowed within the motifs. The gap was then studied to avoid recognition errors produced by the RPN network, thus avoiding the implementation of a post-correction system for transcription errors. The work was finally validated by the study of musical motifs for composers identification and classification
Diedericks, Elsabé. "Flourishing of employees in the information technology industry in South Africa / Elsabé Diedericks." Thesis, North-West University, 2012. http://hdl.handle.net/10394/10278.
Повний текст джерелаPhD, Labour relations management, North-West University, Vaal Triangle Campus, 2012
Sköld, Martin. "Employee perspective on communication and engagement : A case study in a manufacturing organisation." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75209.
Повний текст джерелаDovgalecs, Vladislavs. "Indoor location estimation using a wearable camera with application to the monitoring of persons at home." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14384/document.
Повний текст джерелаVisual lifelog indexing by content has emerged as a high reward application. Enabled by the recent availability of miniaturized recording devices, the demand for automatic extraction of relevant information from wearable sensors generated content has grown. Among many other applications, indoor localization is one challenging problem to be addressed.Many standard solutions perform unreliably in indoors conditions or require significant intervention. In this thesis we address from the perspective of wearable video camera sensors using an image-based approach. The key contribution of this work is the development and the study of a location estimation system composed of diverse modules, which perform tasks ranging from low-level visual information extraction to final topological location estimation with the aid of automatic indexing algorithms. Within this framework, important contributions have been made by efficiently leveraging information brought by multiple visual features, unlabeled image data and the temporal continuity of the video.Early and late data fusion were considered, and shown to take advantage of the complementarities of multiple visual features describing the images. Due to the difficulty in obtaining annotated data in our context, semi-supervised approaches were investigated, to use unlabeled data as additional source of information, both for non-linear data-adaptive dimensionality reduction, and for improving classification. Herein we have developed a time-aware co-training approach that combines late data-fusion with the semi-supervised exploitation of both unlabeled data and time information. Finally, we have proposed to apply transformation invariant learning to adapt non-invariant descriptors to our localization framework.The methods have been tested on controlled publically available datasets to evaluate the gain of each contribution. This work has also been applied to the IMMED project, dealing with activity recognition and monitoring of the daily living using a wearable camera. In this context, the developed framework has been used to estimate localization on the real world IMMED project video corpus, which showed the potential of the approaches in such challenging conditions
Frankenius, Joakim. "Från synt till drama : En kvalitativ fallstudie om den licensierade musikens narrativafunktioner i film." Thesis, Högskolan Dalarna, Ljud- och musikproduktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:du-27280.
Повний текст джерелаWesterman, Christelle. "Work-related wellness of information technology professionals in South Africa / C. Westerman." Thesis, North-West University, 2005. http://hdl.handle.net/10394/2458.
Повний текст джерелаVoisin, Aurélie. "Classification supervisée d'images d'observation de la Terre à haute résolution par utilisation de méthodes markoviennes." Phd thesis, Université de Nice Sophia-Antipolis, 2012. http://tel.archives-ouvertes.fr/tel-00747906.
Повний текст джерелаΜαυρουδή, Σεφερίνα. "Combination of unsupervised and supervised learning for complex biomedical applications." Thesis, 2000. http://nemertes.lis.upatras.gr/jspui/handle/10889/3203.
Повний текст джерелаKarmali, Tejan. "Landmark Estimation and Image Synthesis Guidance using Self-Supervised Networks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5899.
Повний текст джерелаLúcio, Simão Lopes. "Supervised Record Linkage For Banking Reconciliations." Master's thesis, 2020. https://hdl.handle.net/10216/133580.
Повний текст джерелаLúcio, Simão Lopes. "Supervised Record Linkage For Banking Reconciliations." Dissertação, 2020. https://hdl.handle.net/10216/133580.
Повний текст джерела