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11	Data-Driven Traffic Forecasting for Completed Vehicle Simulation: : A Case Study with Volvo Test Trucks Shahrokhi, Samaneh January 2023 (has links) This thesis offers a thorough investigation into the application of machine learning algorithms for predicting the presence of vehicles in a traffic setting. The research primarily focuses on enhancing vehicle simulation by employing data-driven traffic prediction methods. The study approaches the problem as a binary classification task. Various supervised learning algorithms, including Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and Logistic Regression (LogReg) were evaluated and tested. The thesis encompasses six distinct implementations, each involving different combinations of algorithms, feature engineering, hyperparameter tuning, and data splitting. The performance of each model was assessed using metrics such as accuracy, precision, recall, and F1-score, and visualizations like ROC-AUC curves were used to gain insights into their discrimination capabilities. While the RF model achieved the highest accuracy at 97%, the AUC score of Combination 2 (RF+GB) suggests that this ensemble model could strike a better balance between high accuracy (86%) and effective class separation (99%). Ultimately, the study identifies an ensemble model as the preferred choice, leading to significant improvements in prediction accuracy. The research also explores working on the problem as a time-series prediction task, exploring the use of Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (Auto-ARIMA) models. However, we found that this approach was impractical due to the dataset’s discrete and non-sequential nature. This research contributes to the advancement of vehicle simulation and traffic forecasting, demonstrating the potential of machine learning in addressing complex real-world scenarios. supervised machine learning traffic forecasting vehicle presence prediction binary classification ensemble learning feature engineering hyperparameter tuning data-driven analysis Information Systems
12	Neonatal Sepsis Detection Using Decision Tree Ensemble Methods: Random Forest and XGBoost Al-Bardaji, Marwan, Danho, Nahir January 2022 (has links) Neonatal sepsis is a potentially fatal medical conditiondue to an infection and is attributed to about 200 000annual deaths globally. With healthcare systems that are facingconstant challenges, there exists a potential for introducingmachine learning models as a diagnostic tool that can beautomatized within existing workflows and would not entail morework for healthcare personnel. The Herlenius Research Teamat Karolinska Institutet has collected neonatal sepsis data thathas been used for the development of many machine learningmodels across several papers. However, none have tried to studydecision tree ensemble methods. In this paper, random forestand XGBoost models are developed and evaluated in order toassess their feasibility for clinical practice. The data contained24 features of vital parameters that are easily collected througha patient monitoring system. The validation and evaluationprocedure needed special consideration due to the data beinggrouped based on patient level and being imbalanced. Theproposed methods developed in this paper have the potentialto be generalized to other similar applications. Finally, usingthe measure receiver-operating-characteristic area-under-curve(ROC AUC), both models achieved around ROC AUC= 0.84.Such results suggest that the random forest and XGBoost modelsare potentially feasible for clinical practice. Another gainedinsight was that both models seemed to perform better withsimpler models, suggesting that future work could create a moreexplainable model. / Nenatal sepsis är ett potentiellt dödligt‌‌‌ medicinskt tillstånd till följd av en infektion och uppges globalt orsaka 200 000 dödsfall årligen. Med sjukvårdssystem som konstant utsätts för utmaningar existerar det en potential för maskininlärningsmodeller som diagnostiska verktyg automatiserade inom existerande arbetsflöden utan att innebära mer arbete för sjukvårdsanställda. Herelenius forskarteam på Karolinska Institet har samlat ihop neonatal sepsis data som har använts för att utveckla många maskininlärningsmodeller över flera studier. Emellertid har ingen prövat att undersöka beslutsträds ensemble metoder. Syftet med denna studie är att utveckla och utvärdera random forest och XGBoost modeller för att bedöma deras möjligheter i klinisk praxis. Datan innehör 24 attribut av vitalparameterar som enkelt samlas in genom patientövervakningssystem. Förfarandet för validering och utvärdering krävde särskild hänsyn med tanke på att datan var grupperad på patientnivå och var obalanserad. Den föreslagna metoden har potential att generaliseras till andra liknande tillämpningar. Slutligen, genom att använda receiveroperating-characteristic area-under-curve (ROC AUC) måttet kunde vi uppvisa att båda modellerna presterade med ett resultat på ROC AUC= 0.84. Sådana resultat föreslår att både random forest och XGBoost modellerna kan potentiellt användas i klinisk praxis. En annan insikt var att båda modellerna verkade prestera bättre med enklare modeller vilket föreslår att ete skulle kunna vara att skapa en mer förklarlig skininlärningsmodell. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm Machine Learning Sepsis Neonatal Sepsis Random Forest XGBoost Imbalanced Data Binary Classification Cross-Validation Hyperparameter Tuning Elektroteknik och elektronik
13	Modelling and Run-Time Control of Localization System for Resource-Constrained Devices / Modellering och Realtidsreglering av Lokaliseringssystem på Enheter med Begränsade Resurser Mosskull, Albin January 2022 (has links) As resource-constrained autonomous vehicles are used for more and more applications, their ability to achieve the lowest possible localization error without expending more power than needed is crucial. Despite this, the parameter settings of the localization systems, both for the platform and the application, are often set arbitrarily. In this thesis, we propose a model-based controller that adapts the parameters of the localization system during run-time by observing conditions in the environment. The test-bed used for experiments consists of maplab, a visual-inertial localization framework, that we execute on the Nvdia Jetson AGX platform. The results show that the linear velocity is the single most important environmental attribute to base the decision of when to update the parameters upon. We also found that while it was not possible to find a direct connection between certain parameters and environmental conditions, a connection could be found between sets of configuration parameters and conditions. Based on these conclusions, we compare model-based controller setups based on three different models: Finite Impulse Response (FIR), AutoRegressive eXogenous input (ARX) and Multi-Layer Perceptron (MLP). The FIR-based controller performed the best. This FIR-based controller is able to select configurations at the appropriate times to keep the error lower than it would be to randomly guess which set of configuration parameters is best. The proposed solution requires offline profiling before it can be implemented on new localization systems, but it can help to reduce the error and power consumption and thus enable more uses of resource-constrained devices. / Användningen av autonoma fordon med begränsade resurser ökar allt mer, vilket i sin tur ökar vikten av att dessa kan lokalisera med lägsta möjliga fel utan att förbruka mer effekt. Trots detta bestäms parametrarna för både hårdvara och i algoritmerna ofta godtyckligt för dessa lokaliseringssystem. I detta examensarbete presenterar vi en lösning till detta, i form av en modellbaserad regulator som anpassar parametrarna baserat på vad den detekterar i omgivningen. Vår testuppställning består av maplab, ett lokaliseringsramverk, som vi exekverar på Nvida Jetson AGX plattformen. Resultaten visar att den linjära hastigheten är den viktigaste miljövariabeln att detektera och använda för att anpassa parametrarna i lokaliseringssystemet. Resultaten visar även att det går att hitta kopplingar mellan konfigurationer och miljövariabler, även om det inte går att hitta mellan specifika konfigurationsparameterar och miljövariabler. Den regulator som presterar bäst visar sig vara en som är baserad på en Finite Impulse Response modell, med en optimeringshorisont på 5 sekunder. Denna presterar bättre än både AutoRegressive eXogenous input baserad regulator och en Multi-Layer Perceptron baserad regulator. Finite Impulse Response regulatorn åstadkommer ett fel som är lägre än slumpmässig gissning, på data den inte sett förut. Lösningen som uppvisas i detta projekt kräver optimering offline för att fungera, men om det utförs kan den reducera både lokaliseringsfelet och effektförbrukningen och genom det skapa nya användningsområden för resursbegränsade enheter. Cross-layer optimization Resource-constrained devices Automatic hyperparameter tuning Runtime control Visual-inertial localization Korsskiktsoptimering Resursbegränsade enheter Automatisk hyperparameterreglering Körtidsreglering Visuell-tröghets lokalisering Elektroteknik och elektronik
14	Využití umělé inteligence v technické diagnostice / Utilization of artificial intelligence in technical diagnostics Konečný, Antonín January 2021 (has links) The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.
15	ML implementation for analyzing and estimating product prices / ML implementation för analys och estimation av produktpriser Kenea, Abel Getachew, Fagerslett, Gabriel January 2024 (has links) Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. This project aims to use machine learning to assist in the pricing of products by estimating the prices to be inserted. Multiple machine learning models have been tested, and an artificial neural network has been implemented for estimating prices effectively. Through additional experimentation, the design of the network was fine-tuned to make it compatible with the project’s needs. The libraries used for implementing and managing the machine learning models are mainly ScikitLearn and TensorFlow. As a result, the trained model has been saved into a file and integrated with an API for accessibility. Machine Learning ML Regression Deep Learning Artificial Neural Network ANN TensorFlow ScikitLearn CUDA cuDNN Estimation Prediction AI Artificial Intelligence Price Tracking Price Logging Price Estimation Supervised Learning Random Forest Decision Trees Batch Learning Hyperparameter Tuning Linear Regression Multiple Linear Regression Maskininlärning Djup lärning Artificiellt Neuralt Nätverk Regression TensorFlow SciktLearn ML ANN Estimation Uppskattning CUDA cuDNN AI Artificiell Intelligens pris loggning pris estimation prisspårning Batchinlärning Hyperparameterjustering Linjär Regression Multipel Linjär Regression Supervised Learning Random Forest Decision Trees Computer Engineering Datorteknik

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