48 research outputs found
Identifying suitable profile joints in CAD using machine learning
Profile joints serve a critical role in connecting structural profiles, such as beams and columns. The process of selecting appropriate profile joints can be both complex and time-consuming for structural engineers. This thesis explores how machine learning techniques can be used to aid in the selection of profile joints.
Six different machine learning classifiers were implemented and compared. To train the classifiers, an experimental dataset was extracted from 80 professionally designed building models, with 14 informative features identified through multiple feature evaluation methods. Data collection proved to be a significant challenge, highlighting the importance of good data collection practices. This dataset had enough samples for only 20 different profile joints. As a result, the classifiers support only this limited number of joints, while the complete solution is required to classify up to 100 joints. Several evaluation methods were used to compare the implemented classifiers. The best classification accuracy of 98.5% was achieved with XGBoost classifier. Keras Tuner was used to build a neural network classifier that achieved a classification accuracy of 97.6%. This neural network was used in a proof-of-concept tool to assist the user in the target software.
As the amount of data was limited, a large effort was made to examine and understand the data available. Dimensionality reduction methods such as uniform manifold approximation and projection were used to visualize the data. In addition, the interpretability of the model was enhanced with a method to analyze how each feature contributes to the individual predictions.
This research contributes to the growing field of artificial intelligence-assisted building design by providing a foundation for future work on AI-based profile joint selection and offering a detailed analysis of various machine learning models in this context. The most pressing future work involves collecting more data and repeating the experiments with a larger dataset, as well as exploring alternative algorithms and feature engineering techniques to improve model performance and generalizability.Profiililiitokset ovat tärkeä osa rakennesuunnittelua. Ne yhdistävät profiileja, kuten palkkeja sekä pilareita. Sopivien profiililiitosten valitseminen voi olla monimutkaista ja aikaa vievää. Tässä diplomityössä tutkitaan erilaisten koneoppimistekniikoiden mahdollisuuksia helpottaa suunnittelijan työtä profiililiitosten valinnan osalta.
Työssä toteutettiin kuusi erilaista koneoppimiseen perustuvaa luokittelijaa. Niiden kouluttamista varten kerättiin 80 ammattimaisesti suunnitellusta rakennusmallista opetusdataa, josta tunnistettiin 14 informatiivista piirrettä useiden piirrevalintamenetelmien avulla. Datan kerääminen osoittautui merkittäväksi haasteeksi, mikä korostaa hyvien tiedonkeruukäytäntöjen tärkeyttä. Kerätyssä data-aineistossa oli riittävästi näytteitä 20 eri profiililiitoksen osalta. Tämän vuoksi toteutetut luokittelijat tukevat vain tätä rajallista määrää liitoksia, kun taas täyttä tukea varten luokittelijan tulisi pystyä tunnistamaan jopa 100 liitosta. Luokittelijoita vertailtiin käyttäen useita arviointimenetelmiä. Parhaan luokittelutarkkuuden 98,5 % saavutti XGBoost -luokittelija. Keras Tuner -ohjelmaa käytettiin rakentamaan neuroverkkoluokittelija, jolla saavutettiin 97,6 %:n luokittelutarkkuus. Tätä neuroverkkoa käytettiin kohdeohjelmistossa osana alustavaa työkalua, joka avustaa käyttäjää profiililiitosten valinnassa.
Käytettävissä olevan datan tutkimiseen ja ymmärtämiseen panostettiin erityisen paljon, koska data-aineiston määrä oli rajallinen. Datan visualisointiin käytettiin dimensionaalisuuden vähentämiskeinoja, kuten yhtenäisen moniston approksimointia ja projektointia. Lisäksi mallin tulkittavuutta parannettiin menetelmällä, jolla analysoidaan piirteiden arvojen vaikutusta yksittäisiin ennusteisiin.
Tämä työ edesauttaa kasvavaa tekoälyavusteista rakennussuunnittelua luomalla perustan tekoälypohjaisen profiililiitosten valinnan jatkokehitykselle ja tarjoamalla yksityiskohtaisen analyysin erilaisten koneoppimismallien hyödyntämisestä tässä asiayhteydessä. Tärkein jatkokehityksen kohde on kokeiden toistaminen suuremmalla data-aineistolla. Lisäksi tulisi tutkia vaihtoehtoisia algoritmeja sekä piirteiden käsittelytekniikoita mallin suorituskyvyn ja yleistettävyyden parantamiseksi
Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods
Hate speech refers to verbal expression or communication that aims to provoke or discriminate against individuals. The Ministry of Communication and Information of Indonesia has encountered and dealt with 3,640 cases of hate speech transmitted through digital channels between 2018 and 2021. Particularly in South Kalimantan, hate speech in the local language, Banjarese has become increasingly prevalent in recent years. Surprisingly, there is a lack of research on using machine learning to detect hate speech in the Banjarese language, specifically on Instagram. Therefore, this study aimed to address this gap by constructing a dataset of Banjarese language hate speech and comparing various feature extraction and machine learning models to detect Banjarese language hate speech effectively. Thisresearch used several feature extraction techniques and machine learning methods to detect Banjareselanguage hate speech. The feature extraction methods used were Word N-Gram, Term Frequency- Inverse Document Frequency (TF-IDF), a combination of Word N-Gram and TF-IDF, Word2Vec, and Glove, while the machine learning methods used were Support Vector Machine (SVM), Na¨ıve Bayes, and Decision Tree. The results of this study revealed that the combination of TF-IDF for feature extraction and SVM as the model achieves exceptional performance. The average Recall, Precision, Accuracy, and F1-Score score exceeded 90%, demonstrating the model’s ability to identify Banjarese hate speech accurately
Sentiment analysis in context: Investigating the use of BERT and other techniques for ChatBot improvement
openIn an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent.
Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks.
The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction.
The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself.
This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis.
Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all.
Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task.
During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis.
Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production.
The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment.In an increasingly digitized world, where large amounts of data are generated daily, its efficient analysis has become more and more stringent.
Natural Language Processing (NLP) offers a solution by exploiting the power of artificial intelligence to process texts, to understand their content and to perform specific tasks.
The thesis is based on an internship at Pat Srl, a company devoted to create solutions to support digital innovation, process automation, and service quality with the ultimate goal of improving leadership and customer satisfaction.
The primary objective of this thesis is to develop a sentiment analysis model in order to improve the customer experience for clients using the ChatBot system created by the company itself.
This task has gained significant attention in recent years as it can be applied to different fields, including social media monitoring, market research, brand monitoring or customer experience and feedback analysis.
Following a careful analysis of the available data, a comprehensive evaluation of various models was conducted. Notably, BERT, a large language model that has provided promising results in several NLP tasks, emerged among all.
Different approaches utilizing the BERT models were explored, such as the fine-tuning modality or the architectural structure. Moreover, some preprocessing steps of the data were emphasized and studied, due to the particular nature of the sentiment analysis task.
During the course of the internship, the dataset underwent revisions aimed to mitigate the problem of inaccurate predictions. Additionally, techniques for data balancing were tested and evaluated, enhancing the overall quality of the analysis.
Another important aspect of this project involved the deployment of the model. In a business environment, it is essential to carefully consider and balance resources before transitioning to production.
The model distribution was carried out using specific tools, such as Docker and Kubernetes. These specialized technologies played a pivotal role in ensuring efficient and seamless deployment
Data, deep learning and depression: can artificial neural networks learn risk factors for depression from genetic variants and radiology reports
Major Depressive Disorder (MDD) is a psychiatric disorder characterised by persistent
low mood and loss of enjoyment or interest. MDD affects around 1 in 8
people worldwide and is one of the leading causes of global disability. Studies have
found both genetic and environmental risk factors. In this thesis automated and
scalable models using artificial neural networks are used to analyse two sources of
data where risk factors can be found and quantified.
A number of genes have a small effect size on MDD, making MDD a polygenic
disease. To investigate polygenic diseases, we can analyse Single Nucleotide Polymorphisms
(SNPs), base pairs in DNA that commonly differ between individuals.
Genome wide association studies (GWAS) are used to quantify the association
between SNPs and MDD. From modelling these associations in combination, a
Polygenic Risk Score (PRS) can be devised, which quantifies an individual’s genetic
risk of MDD.
Through scanning the brain using CT or MRI, we can find evidence of disease,
including stroke and small vessel disease. A number of brain diseases have been
linked to subsequent development of MDD, and combined with genetics could give
a better overall risk prediction of developing MDD than either in isolation.
This thesis focuses on these two key biological disciplines in MDD research (genetics
and imaging) where deep learning, in the form of artificial neural networks,
might provide improvement on key problems in these fields. . Specific problems
are chosen due to their tractable nature and the ability to benchmark the new
techniques against the current state-of-the-art methods.
The first project of this thesis uses artificial neural networks that take as input
SNP genotypes and output a polygenic risk score for MDD. A number of hyperparameters
are tested, as well as different architectures. The best of these models,
as chosen by performance (measured using AUC) on a validation set, is then
compared on a held-out test set to existing methods including p-value threshold
and clump, SBayesR, and LDPred2.
The second project uses graph based neural networks, which introduce an additional
layer involving a graph, to add structure to the network computation.
This structure allows use of existing biological information, in this case data detailing
which SNPs act as expression quantitative trait loci (eQTL) for specific
genes. A number of graph networks are designed and tested, with the best of
these compared to the methods in the first project. Across both the first and second
project, the neural network models achieve an AUC, accuracy and Nagelkerke
R2 that are comparable to the best of the current methods tested. Additionally,
when using ensemble modelling the best performing models included both a neural
network based model as well as a summary statistics Bayesian model (LDPred2 or
SBayesR). This indicates the neural network models find information not used by
the best existing methods, and that an ensemble of models provides the highest
performance as defined using the above mentioned metrics.
The final project uses neuroradiology reports, which are written reports that
accompany radiology scans such as CT or MRI scans, and are used to describe
abnormalities that indicate disease. There is evidence that some of the diseases
observable in these scans are risk factors for MDD. Part of the processing of the reports
needed for further analysis is negation detection, which is the task of deciding
if a mention of disease (such as ischaemic stroke) indicates either presence of the
disease or lack of presence. An artificial neural network (NN) is developed for this
task, and its predictions are assessed against a gold standard labelled by domain
experts. The performance of the NN, measured using F1 score, is then compared
against that of a rule-based model developed on the same datasets as the NN, and
two state-of-the-art rule-based models developed on different datasets. The NN
achieves similar performance to the other models, and outperforms the rule-based
models not developed on our datasets. Neural networks have previously shown
a greater adaptability to new datasets than rule-based methods, thereby demonstrating
a potential advantage over rule-based models in transferability between
data sources, such as different health boards or studies.
The work on this final project has contributed to enabling the automatic annotation
of a much larger dataset with increased accuracy. Using this larger dataset
further analysis has linked hypertension with increased risk of stroke, as well as
baseline depression with increased risk of cerebral small vessel disease. Additionally,
approval for access to electronic health records for the entire Scottish population
has been granted, and this has been made possible because of the utility and
effectiveness of the machine learning approaches.
Overall, the deep learning (artificial neural networks) models developed in this
thesis are stronger on the negation detection task than the polygenic risk scoring
task, performing well against all the models tested and proving useful for processing
large datasets for future work.
The models developed for assessing genetic risk of MDD currently have more
limited use, but deliver results that are comparable to current methods, particularly
when summary statistics aren’t available. Additionally, the performance,
using AUC and Nagelkerke R2, of the ensemble models indicates the NN models
find information in the data unused by the other methods, indicating potential
for providing future mechanistic insights. While there are a number of challenges
preventing improvements in the predictive performance of NN models, larger samples
of individuals with MDD with contemporaneous imaging and genetic data are
likely to lead to improvements for these models when used for predictive analytics.
This thesis represents a beginning of the work possible with deep learning for
MDD research, and these experiments are just a subset of the potential problems
where deep learning may provide benefit. The methods used here have the potential
to lead to more accurate prediction, further mechanistic insights, and better
automation of dataset processing and creation for a number of other problems and
challenges in MDD research
Multivariate Statistical Machine Learning Methods for Genomic Prediction
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool
Performance of Gaussian Naïve Bayes for classification with dependencies from Archemedian copula
Master's Project (M.S.) University of Alaska Fairbanks, 2022Naive Bayes is an application of Bayes theorem in which the likelihood
function is factored into marginals by making the assumption that the
variables are independent. Naive Bayes is typically used for classification
problems in which the goal is to find the class with the largest probability
given the data on hand. When the data on hand are continuous real
numbers we can further assume they are class conditionally normally
distributed, which is a particular version of Naive Bayes called Gaussian
Naive Bayes. This paper explores when Gaussian Naive Bayes classification
problems work well vs when they do not. Typically when assumptions
are not valid, valid conclusions cannot be drawn. However, Naive Bayes
is known to be robust even when the independence assumption is not
met. We show using simulations that binary classification accuracy of
Naive Bayes is much more sensitive to differences in the class conditional
marginal distributions than the correlation between predictors. Additionally
we show that Naive Bayes completely fails when predictors are generated
using a Gumbel copula and compare results with a general Bayes classifier
and the K-Nearest Neighbors classifier
Protectbot: A Chatbot to Protect Children on Gaming Platforms
Online gaming no longer has limited access, as it has become available to a high percentage of children in recent years. Consequently, children are exposed to multifaceted threats, such as cyberbullying, grooming, and sexting. The online gaming industry is taking concerted measures to create a safe environment for children to play and interact with, such efforts remain inadequate and fragmented. Different approaches utilizing machine learning (ML) techniques to detect child predatory behavior have been designed to provide potential detection and protection in this context. After analyzing the available AI tools and solutions it was observed that the available solutions are limited to the identification of predatory behavior in chat logs which is not enough to avert the multifaceted threats. In this thesis, we developed a chatbot Protectbot to interact with the suspect on the gaming platform. Protectbot leveraged the dialogue generative pre-trained transformer (DialoGPT) model which is based on Generative Pre-trained Transformer 2 (GPT-2). To analyze the suspect\u27s behavior, we developed a text classifier based on natural language processing that can classify the chats as predatory and non-predatory. The developed classifier is trained and tested on Pan 12 dataset. To convert the text into numerical vectors we utilized fastText. The best results are obtained by using non-linear SVM on sentence vectors obtained from fastText. We got a recall of 0.99 and an F_0.5-score of 0.99 which is better than the state-of-the-art methods. We also built a new dataset containing 71 predatory full chats retrieved from Perverted Justice. Using sentence vectors generated by fastText and KNN classifier, 66 chats out of 71 were correctly classified as predatory chats
Classificação de Scrap de Capply com recurso a metodologias Deep Learning
A nível industrial, o processo de controlo de qualidade de produtos é realizado com diferentes técnicas e especificações, adequadas a cada processo. No entanto, por vezes, as técnicas utilizadas têm falhas que se traduzem num prejuízo para as empresas. Os avanços tecnológicos seguem num ritmo acelerado e o mercado tem vindo a absorver as inovações nos seus mais diversos setores. Numa procura pela transformação digital, as empresas passaram a investir mais em soluções que gerem um diferencial competitivo frente à concorrência. Nesse sentido, o conceito de Inteligência Artificial (IA) ganhou bastante importância. Nos últimos anos temos assistido a uma adoção acelerada do Machine Learning (ML) como parte integrante da Indústria 4.0, na qual a digitalização está refazendo a indústria. Essa última onda de iniciativas é marcada pela introdução de sistemas inteligentes e autónomos, alimentados por grandes quantidades de dados e por Deep Learning (DL). Uma poderosa geração de IA que promove a inspeção de qualidade no chão de fábrica. Este trabalho visa investigar e implementar técnicas supervisionadas de Deep Learning, aliadas à visão computacional, para a implementação de um sistema de classificação automático de imperfeições de Capply. Para esse efeito, inicialmente, foi realizada uma revisão bibliográfica sobre o estado da arte, passando de seguida à implementação e comparação do desempenho de várias arquiteturas utilizando as métricas adequadas. Para a execução desta tarefa foi necessário recolher e fazer um pré-processamento dos dados (imagens de bobines de capply). Foi ainda, desenvolvida uma aplicação Web que permite testar e avaliar os resultados e por último, foi também desenvolvido e implementado um sistema de classificação em contexto real. Resumidamente, os resultados deste trabalho demonstraram o grande potencial das metodologias de Deep Learning aplicadas ao controlo de qualidade na indústria.At an industrial level, the product quality control process is carried out using different techniques and specifications, suitable for each process. However, sometimes the techniques used have flaws that translate into a loss for companies. Technological advances continue at an accelerated pace and the market has been absorbing innovations in its most diverse sectors. In a search for digital transformation, companies began to invest more in solutions that generate a competitive edge against the competition. In this sense, the concept of Artificial Intelligence (AI) has gained a lot of importance. In recent years we have seen an accelerated adoption of Machine Learning (ML) as an integral part of Industry 4.0, in which digitalization is remaking the industry. This latest wave of initiatives is marked by the introduction of intelligent and autonomous systems, powered by large amounts of data and by Deep Learning (DL). A powerful generation of AI that drives quality inspection on the shop floor. This work aims to investigate and implement supervised Machine Learning techniques, combined with computer vision, for the implementation of an automatic classification system for Capply imperfections. For this purpose, initially, a bibliographic review was carried out on the state of the art, followed by the implementation and comparison of the performance of several architectures using the appropriate metrics. To perform this task, it was necessary to collect and preprocess the data (capply reel images). A web application was also developed that allows testing and evaluating the results and finally, a classification system in real context was also developed and implemented. Briefly, the results of this work demonstrated the great potential of Machine Learning methodologies applied to quality control in the industry
Big Data in Bioeconomy
This edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen. With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources