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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
41

Taskfinder : Comparison of NLP techniques for textclassification within FMCG stores

Jensen, Julius January 2022 (has links)
Natural language processing has many important applications in today, such as translations, spam filters, and other useful products. To achieve these applications supervised and unsupervised machine learning models, have shown to be successful. The most important aspect of these models is what the model can achieve with different datasets. This article will examine how RNN models compare with Naive Bayes in text classification. The chosen RNN models are long short-term memory (LSTM) and gated recurrent unit (GRU). Both LSTM and GRU will be trained using the flair Framework. The models will be trained on three separate datasets with different compositions, where the trend within each model will be examined and compared with the other models. The result showed that Naive Bayes performed better on classifying short sentences than the RNN models, but worse in longer sentences. When trained on a small dataset LSTM and GRU had a better result then Naive Bayes. The best performing model was Naive Bayes, which had the highest accuracy score in two out of the three datasets.
42

Data Analysis of Minimally-Structured Heterogeneous Logs : An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes.

Liu, Chang January 2016 (has links)
Nowadays, the ideas of continuous integration and continuous delivery are under heavy usage in order to achieve rapid software development speed and quick product delivery to the customers with good quality. During the process ofmodern software development, the testing stage has always been with great significance so that the delivered software is meeting all the requirements and with high quality, maintainability, sustainability, scalability, etc. The key assignment of software testing is to find bugs from every test and solve them. The developers and test engineers at Ericsson, who are working on a large scale software architecture, are mainly relying on the logs generated during the testing, which contains important information regarding the system behavior and software status, to debug the software. However, the volume of the data is too big and the variety is too complex and unpredictable, therefore, it is very time consuming and with great efforts for them to manually locate and resolve the bugs from such vast amount of log data. The objective of this thesis project is to explore a way to conduct log analysis efficiently and effectively by applying relevant machine learning algorithms in order to help people quickly detect the test failure and its possible causalities. In this project, a method of preprocessing and clusering original logs is designed and implemented in order to obtain useful data which can be fed to machine learning algorithms. The comparable log analysis, based on two machine learning algorithms - Recurrent Neural Network and Naive Bayes, is conducted for detecting the place of system failures and anomalies. Finally, relevant experimental results are provided and analyzed.
43

Variant Detection Using Next Generation Sequencing Data

Pyon, Yoon Soo 08 March 2013 (has links)
No description available.
44

Exploring the Noise Resilience of Combined Sturges Algorithm

Agarwal, Akrita January 2015 (has links)
No description available.
45

A Massively Parallel Algorithm for Cell Classification Using CUDA

Schmidt, Samuel January 2015 (has links)
No description available.
46

Identifying Interesting Posts on Social Media Sites

Seethakkagari, Swathi, M.S. 21 September 2012 (has links)
No description available.
47

Filtering Social Tags for Songs based on Lyrics using Clustering Methods

Chawla, Rahul 21 July 2011 (has links)
In the field of Music Data Mining, Mood and Topic information has been considered as a high level metadata. The extraction of mood and topic information is difficult but is regarded as very valuable. The immense growth of Web 2.0 resulted in Social Tags being a direct interaction with users (humans) and their feedback through tags can help in classification and retrieval of music. One of the major shortcomings of the approaches that have been employed so far is the improper filtering of social tags. This thesis delves into the topic of information extraction from songs’ tags and lyrics. The main focus is on removing all erroneous and unwanted tags with help of other features. The hierarchical clustering method is applied to create clusters of tags. The clusters are based on semantic information any given pair of tags share. The lyrics features are utilized by employing CLOPE clustering method to form lyrics clusters, and Naïve Bayes method to compute probability values that aid in classification process. The outputs from classification are finally used to estimate the accuracy of a tag belonging to the song. The results obtained from the experiments all point towards the success of the method proposed and can be utilized by other research projects in the similar field.
48

System för att upptäcka Phishing : Klassificering av mejl

Karlsson, Nicklas January 2008 (has links)
Denna rapport tar en titt på phishing-problemet, något som många har råkat ut för med bland annat de falska Nordea eller eBay mejl som på senaste tiden har dykt upp i våra inkorgar, och ett eventuellt sätt att minska phishingens effekt. Fokus i rapporten ligger på klassificering av mejl och den huvudsakliga frågeställningen är: ”Är det, med hög träffsäkerhet, möjligt att med hjälp av ett klassificeringsverktyg sortera ut mejl som har med phishing att göra från övrig skräppost.” Det visade sig svårare än väntat att hitta phishing mejl att använda i klassificeringen. I de klassificeringar som genomfördes visade det sig att både metoden Naive Bayes och med Support Vector Machine kan hitta upp till 100 % av phishing mejlen. Rapporten pressenterar arbetsgången, teori om phishing och resultaten efter genomförda klassificeringstest. / This report takes a look at the phishing problem, something that many have come across with for example the fake Nordea or eBay e-mails that lately have shown up in our e-mail inboxes, and a possible way to reduce the effect of phishing. The focus in the report lies on classification of e-mails and the main question is: “Is it, with high accuracy, possible with a classification tool to sort phishing e-mails from other spam e-mails.” It was more difficult than expected to find phishing e-mails to use in the classification. The classifications that were made showed that it was possible to find up to 100 % of the phishing e-mails with both Naive Bayes and with Support Vector Machine. The report presents the work done, facts about phishing and the results of the classification tests made.
49

A contribution to topological learning and its application in Social Networks / Une contribution à l'apprentissage topologique et son application dans les réseaux sociaux

Ezzeddine, Diala 01 October 2014 (has links)
L'Apprentissage Supervisé est un domaine populaire de l'Apprentissage Automatique en progrès constant depuis plusieurs années. De nombreuses techniques ont été développées pour résoudre le problème de classification, mais, dans la plupart des cas, ces méthodes se basent sur la présence et le nombre de points d'une classe donnée dans des zones de l'espace que doit définir le classifieur. Á cause de cela la construction de ce classifieur est dépendante de la densité du nuage de points des données de départ. Dans cette thèse, nous montrons qu'utiliser la topologie des données peut être une bonne alternative lors de la construction des classifieurs. Pour cela, nous proposons d'utiliser les graphes topologiques comme le Graphe de Gabriel (GG) ou le Graphes des Voisins Relatifs (RNG). Ces dernier représentent la topologie de données car ils sont basées sur la notion de voisinages et ne sont pas dépendant de la densité. Pour appliquer ce concept, nous créons une nouvelle méthode appelée Classification aléatoire par Voisinages (Random Neighborhood Classification (RNC)). Cette méthode utilise des graphes topologiques pour construire des classifieurs. De plus, comme une Méthodes Ensemble (EM), elle utilise plusieurs classifieurs pour extraire toutes les informations pertinentes des données. Les EM sont bien connues dans l'Apprentissage Automatique. Elles génèrent de nombreux classifieurs à partir des données, puis agrègent ces classifieurs en un seul. Le classifieur global obtenu est reconnu pour être très eficace, ce qui a été montré dans de nombreuses études. Cela est possible car il s'appuie sur des informations obtenues auprès de chaque classifieur qui le compose. Nous avons comparé RNC à d'autres méthodes de classification supervisées connues sur des données issues du référentiel UCI Irvine. Nous constatons que RNC fonctionne bien par rapport aux meilleurs d'entre elles, telles que les Forêts Aléatoires (RF) et Support Vector Machines (SVM). La plupart du temps, RNC se classe parmi les trois premières méthodes en terme d'eficacité. Ce résultat nous a encouragé à étudier RNC sur des données réelles comme les tweets. Twitter est un réseau social de micro-blogging. Il est particulièrement utile pour étudier l'opinion à propos de l'actualité et sur tout sujet, en particulier la politique. Cependant, l'extraction de l'opinion politique depuis Twitter pose des défis particuliers. En effet, la taille des messages, le niveau de langage utilisé et ambiguïté des messages rend très diffcile d'utiliser les outils classiques d'analyse de texte basés sur des calculs de fréquence de mots ou des analyses en profondeur de phrases. C'est cela qui a motivé cette étude. Nous proposons d'étudier les couples auteur/sujet pour classer le tweet en fonction de l'opinion de son auteur à propos d'un politicien (un sujet du tweet). Nous proposons une procédure qui porte sur l'identification de ces opinions. Nous pensons que les tweets expriment rarement une opinion objective sur telle ou telle action d'un homme politique mais plus souvent une conviction profonde de son auteur à propos d'un mouvement politique. Détecter l'opinion de quelques auteurs nous permet ensuite d'utiliser la similitude dans les termes employés par les autres pour retrouver ces convictions à plus grande échelle. Cette procédure à 2 étapes, tout d'abord identifier l'opinion de quelques couples de manière semi-automatique afin de constituer un référentiel, puis ensuite d'utiliser l'ensemble des tweets d'un couple (tous les tweets d'un auteur mentionnant un politicien) pour les comparer avec ceux du référentiel. L'Apprentissage Topologique semble être un domaine très intéressant à étudier, en particulier pour résoudre les problèmes de classification...... / Supervised Learning is a popular field of Machine Learning that has made recent progress. In particular, many methods and procedures have been developed to solve the classification problem. Most classical methods in Supervised Learning use the density estimation of data to construct their classifiers.In this dissertation, we show that the topology of data can be a good alternative in constructing classifiers. We propose using topological graphs like Gabriel graphs (GG) and Relative Neighborhood Graphs (RNG) that can build the topology of data based on its neighborhood structure. To apply this concept, we create a new method called Random Neighborhood Classification (RNC).In this method, we use topological graphs to construct classifiers and then apply Ensemble Methods (EM) to get all relevant information from the data. EM is well known in Machine Learning, generates many classifiers from data and then aggregates these classifiers into one. Aggregate classifiers have been shown to be very efficient in many studies, because it leverages relevant and effective information from each generated classifier. We first compare RNC to other known classification methods using data from the UCI Irvine repository. We find that RNC works very well compared to very efficient methods such as Random Forests and Support Vector Machines. Most of the time, it ranks in the top three methods in efficiency. This result has encouraged us to study the efficiency of RNC on real data like tweets. Twitter, a microblogging Social Network, is especially useful to mine opinion on current affairs and topics that span the range of human interest, including politics. Mining political opinion from Twitter poses peculiar challenges such as the versatility of the authors when they express their political view, that motivate this study. We define a new attribute, called couple, that will be very helpful in the process to study the tweets opinion. A couple is an author that talk about a politician. We propose a new procedure that focuses on identifying the opinion on tweet using couples. We think that focusing on the couples's opinion expressed by several tweets can overcome the problems of analysing each single tweet. This approach can be useful to avoid the versatility, language ambiguity and many other artifacts that are easy to understand for a human being but not automatically for a machine.We use classical Machine Learning techniques like KNN, Random Forests (RF) and also our method RNC. We proceed in two steps : First, we build a reference set of classified couples using Naive Bayes. We also apply a second alternative method to Naive method, sampling plan procedure, to compare and evaluate the results of Naive method. Second, we evaluate the performance of this approach using proximity measures in order to use RNC, RF and KNN. The expirements used are based on real data of tweets from the French presidential election in 2012. The results show that this approach works well and that RNC performs very good in order to classify opinion in tweets.Topological Learning seems to be very intersting field to study, in particular to address the classification problem. Many concepts to get informations from topological graphs need to analyse like the ones described by Aupetit, M. in his work (2005). Our work show that Topological Learning can be an effective way to perform classification problem.
50

Twittersentimentanalys : Jämförelse av klassificeringsmodeller tränade på olika datamängder. / Twitter Sentiment Analysis : Comparison of classification models trained on different data sets.

Bandgren, Johannes, Selberg, Johan January 2018 (has links)
Twitter är en av de populäraste mikrobloggarna, som används för att uttryckatankar och åsikter om olika ämnen. Ett område som har dragit till sig mycketintresse under de senaste åren är twittersentimentanalys. Twittersentimentanalyshandlar om att bedöma vad för sentiment ett inlägg på Twitter uttrycker, om detuttrycker någonting positivt eller negativt. Olika metoder kan användas för attutföra twittersentimentanalys, där vissa lämpar sig bättre än andra. De vanligastemetoderna för twittersentimentanalys använder maskininlärning.Syftet med denna studie är att utvärdera tre stycken klassificeringsalgoritmerinom maskininlärning och hur märkningen av en datamängd påverkar en klassifi-ceringsmodells förmåga att märka ett twitterinlägg korrekt för twittersentimenta-nalys. Naive Bayes, Support Vector Machine och Convolutional Neural Network ärklassificeringsalgoritmerna som har utvärderats. För varje klassificeringsalgoritmhar två klassificeringsmodeller tagits fram, som har tränats och testats på två se-parata datamängder: Stanford Twitter Sentiment och SemEval. Det som skiljer detvå datamängderna åt, utöver innehållet i twitterinläggen, är märkningsmetodenoch mängden twitterinlägg. Utvärderingen har gjorts utefter vilken prestanda deframtagna klassificeringmodellerna uppnår på respektive datamängd, hur lång tidde tar att träna och hur invecklade de var att implementera.Resultaten av studien visar att samtliga modeller som tränades och testades påSemEval uppnådde en högre prestanda än de som tränades och testades på Stan-ford Twitter Sentiment. Klassificeringsmodellerna som var framtagna med Convo-lutional Neural Network uppnådde bäst resultat över båda datamängderna. Dockär ett Convolutional Neural Network mer invecklad att implementera och tränings-tiden är betydligt längre än Naive Bayes och Support Vector Machine. / Twitter is one of the most popular microblogs, which is used to express thoughtsand opinions on different topics. An area that has attracted much interest in recentyears is Twitter sentiment analysis. Twitter sentiment analysis is about assessingwhat sentiment a Twitter post expresses, whether it expresses something positiveor negative. Different methods can be used to perform Twitter sentiment analysis.The most common methods of Twitter sentiment analysis use machine learning.The purpose of this study is to evaluate three classification algorithms in ma-chine learning and how the labeling of a data set affects classification models abilityto classify a Twitter post correctly for Twitter sentiment analysis. Naive Bayes,Support Vector Machine and Convolutional Neural Network are the classificationalgorithms that have been evaluated. For each classification algorithm, two classi-fication models have been trained and tested on two separate data sets: StanfordTwitter Sentiment and SemEval. What separates the two data sets, in addition tothe content of the twitter posts, is the labeling method and the amount of twitterposts. The evaluation has been done according to the performance of the classifi-cation models on the respective data sets, training time and how complicated theywere to implement.The results show that all models trained and tested on SemEval achieved ahigher performance than those trained and tested on Stanford Twitter Sentiment.The Convolutional Neural Network models achieved the best results over both datasets. However, a Convolutional Neural Network is more complicated to implementand the training time is significantly longer than Naive Bayes and Support VectorMachine.

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