No description available.

Agricultural industry has started to rely more on data driven approaches to improve productivity and utilize their resources effectively. This thesis project was carried out in collaboration with Ljusgårda AB, it explores plant yield prediction using machine learning models and hyperparameter tweaking. This thesis work is based on data gathered from the company and the plant yield prediction is carried out on two scenarios whereby each scenario is focused on a different time frame of the growth stage. The first scenario predicts yield from day 8 to day 22 of DAT (Day After Transplant), while the second scenario predicts yield from day 1 to day 22 of DAT and three machine learning algorithms Support Vector Regression (SVR), Long Short Time Memory (LSTM) and Artificial Neural Network (ANN) were investigated. Machine learning model’s performances were evaluated using the metrics; Mean Square Error (MSE), Mean Absolute Error (MAE), and r-squared. The evaluation results showed that ANN performed best on MSE and r-squared with dataset 1, while SVR performed best on MAE with dataset 2. Thus, both ANN and SVR meets the objective of this thesis work. The hyperparameter tweaking experiment of the three models further demonstrated the significance of hyperparameter tuning in improving the models and making them more suitable to the available data.

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.

Environment perception is crucial to autonomous driving. Panoptic segmentation and objects tracking are two challenging tasks, and the combination of both, namely 4D panoptic segmentation draws researchers’ attention recently. In this work, we implement 4D panoptic LiDAR segmentation (4D-PLS) on Volvo datasets and provide a pipeline of data preparation, model building and model optimization. The main contributions of this work include: (1) building the Volvo datasets; (2) adopting an 4D-PLS model improved by Hyperparameter Optimization (HPO). We annotate point cloud data collected from Volvo CE, and take a supervised learning approach by employing a Deep Neural Network (DNN) to extract features from point cloud data. On the basis of the 4D-PLS model, we employ Bayesian Optimization to find the best hyperparameters for our data, and improve the model performance within a small training budget. / Miljöuppfattning är avgörande för autonom körning. Panoptisk segmentering och objektspårning är två utmanande uppgifter, och kombinationen av båda, nämligen 4D panoptisk segmentering, har nyligen uppmärksammat forskarna. I detta arbete implementerar vi 4D-PLS på Volvos datauppsättningar och tillhandahåller en pipeline av dataförberedelse, modellbyggande och modelloptimering. De huvudsakliga bidragen från detta arbete inkluderar: (1) bygga upp Volvos datauppsättningar; (2) anta en 4D-PLS-modell förbättrad av HPO. Vi kommenterar punktmolndata som samlats in från Volvo CE och använder ett övervakat lärande genom att använda en DNN för att extrahera funktioner från punktmolnsdata. På basis av 4D-PLS-modellen använder vi Bayesian Optimization för att hitta de bästa hyperparametrarna för vår data och förbättra modellens prestanda inom en liten utbildningsbudget.

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

No description available.

This project aimed to classified artificiality gener-ated,fake, and authentic,true, telecommunication signals, basedupon their frequency response, using methods from deep learn-ing. Another goal was to accomplish this with the least amountof dimension of data possible. The datasets used contained of anequal amount of measured, provided by Ericsson, and generated,by a WINNER II implementation in Matlab, frequency responses.It was determined that a normalized version of the absolute valueof the complex frequency response was enough information for afeedforward network to do a sufficient classification. To improvethe accuracy of the network we did a hyperparameter search,which allowed us to reach an accuracy of 90 percent on our testdataset. The results show that it is possible for neural networksto differentiate between true and fake telecommunication signalsbased on their frequency response, even if it is hard for a humanto tell the difference. / Målet med det här projektet var att klassificera artificiellt genererade signaler, falska, och riktiga, sanna, telekommunikation signaler med hjälp av signalernas frekvens- svar med djup inlärningsmetoder, deep learning. Ett annat mål med projektet var att klassificera signalerna med minsta möjliga antalet dimensioner av datan. Datasetet som användes bestod av till hälften av uppmät data som Ericsson har tillhandahållit, och till hälften av generad data ifrån en WINNER II modell implementerad i Matlab. En slutsats som kunde dras är att en normaliserad version av beloppet av det komplexa frekvenssvaret innehöll tillräckligt med information för att träna ett feedforward nätverk till att uppnå en hög klassificeringssäkerhet. För att vidare öka tillförlitligheten av nätverket gjordes en hyperparametersökning, detta ökade tillförligheten till 90 procent för testdataseten. Resultaten visar att det är möjligt för neurala nätverk att skilja mellan sanna och falska telekommunikations- signaler baserat på deras frekvenssvar, även om det är svårt för människor att skilja signalerna åt. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Notwithstanding the recent successes of deep convolutional neural networks for classification tasks, they are sensitive to the selection of their hyperparameters, which impose an exponentially large search space on modern convolutional models. Traditional hyperparameter selection methods include manual, grid, or random search, but these require expert knowledge or are computationally burdensome. Divergently, Bayesian optimization and evolutionary inspired techniques have surfaced as viable alternatives to the hyperparameter problem. Thus, an alternative hybrid approach that combines the advantages of these techniques is proposed. Specifically, the search space is partitioned into discrete-architectural, and continuous and categorical hyperparameter subspaces, which are respectively traversed by a stochastic genetic search, followed by a genetic-Bayesian search. Simulations on a prominent image classification task reveal that the proposed method results in an overall classification accuracy improvement of 0.87% over unoptimized baselines, and a greater than 97% reduction in computational costs compared to a commonly employed brute force approach. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

[pt] Este trabalho busca usar o comitê de algoritmos de aprendizado por reforço profundo (deep reinforcement learning) sob uma nova perspectiva. Na literatura, a técnica de comitê é utilizada para melhorar o desempenho, mas, pela primeira vez, esta pesquisa visa utilizar comitê para minimizar a dependência do desempenho de aprendizagem por reforço profundo no ajuste fino de hiperparâmetros, além de tornar o aprendizado mais preciso e robusto. Duas abordagens são pesquisadas; uma considera puramente a agregação de ação, enquanto que a outra também leva em consideração as funções de valor. Na primeira abordagem, é criada uma estrutura de aprendizado online com base no histórico de escolha de ação contínua do comitê com o objetivo de integrar de forma flexível diferentes métodos de ponderação e agregação para as ações dos agentes. Em essência, a estrutura usa o desempenho passado para combinar apenas as ações das melhores políticas. Na segunda abordagem, as políticas são avaliadas usando seu desempenho esperado conforme estimado por suas funções de valor. Especificamente, ponderamos as funções de valor do comitê por sua acurácia esperada, calculada pelo erro da diferença temporal. As funções de valor com menor erro têm maior peso. Para medir a influência do esforço de ajuste do hiperparâmetro, grupos que consistem em uma mistura de diferentes quantidades de algoritmos bem e mal parametrizados foram criados. Para avaliar os métodos, ambientes clássicos como o pêndulo invertido, cart pole e cart pole duplo são usados como benchmarks. Na validação, os ambientes de simulação Half Cheetah v2, um robô bípede, e o Swimmer v2 apresentaram resultados superiores e consistentes demonstrando a capacidade da técnica de comitê em minimizar o esforço necessário para ajustar os hiperparâmetros dos algoritmos. / [en] This work seeks to use ensembles of deep reinforcement learning algorithms from a new perspective. In the literature, the ensemble technique is used to improve performance, but, for the first time, this research aims to use ensembles to minimize the dependence of deep reinforcement learning performance on hyperparameter fine-tuning, in addition to making it more precise and robust. Two approaches are researched; one considers pure action aggregation, while the other also takes the value functions into account. In the first approach, an online learning framework based on the ensemble s continuous action choice history is created, aiming to flexibly integrate different scoring and aggregation methods for the agents actions. In essence, the framework uses past performance to only combine the best policies actions. In the second approach, the policies are evaluated using their expected performance as estimated by their value functions. Specifically, we weigh the ensemble s value functions by their expected accuracy as calculated by the temporal difference error. Value functions with lower error have higher weight. To measure the influence on the hyperparameter tuning effort, groups consisting of a mix of different amounts of well and poorly parameterized algorithms were created. To evaluate the methods, classic environments such as the inverted pendulum, cart pole and double cart pole are used as benchmarks. In validation, the Half Cheetah v2, a biped robot, and Swimmer v2 simulation environments showed superior and consistent results demonstrating the ability of the ensemble technique to minimize the effort needed to tune the the algorithms.

The increasing size and complexity of datasets have accelerated the development of machine learning models and exposed the need for more scalable solutions. This thesis explores challenges associated with large-scale machine learning under data privacy constraints. With the growth of machine learning models, traditional privacy methods such as data anonymization are becoming insufficient. Thus, we delve into alternative approaches, such as differential privacy. Our research addresses the following core areas in the context of scalable privacy-preserving machine learning: First, we examine the implications of data dimensionality on privacy for the application of medical image analysis. We extend the classification algorithm Private Aggregation of Teacher Ensembles (PATE) to deal with high-dimensional labels, and demonstrate that dimensionality reduction can be used to improve privacy. Second, we consider the impact of hyperparameter selection on privacy. Here, we propose a novel adaptive technique for hyperparameter selection in differentially gradient-based optimization. Third, we investigate sampling-based solutions to scale differentially private machine learning to dataset with a large number of records. We study the privacy-enhancing properties of importance sampling, highlighting that it can outperform uniform sub-sampling not only in terms of sample efficiency but also in terms of privacy. The three techniques developed in this thesis improve the scalability of machine learning while ensuring robust privacy protection, and aim to offer solutions for the effective and safe application of machine learning in large datasets. / Den ständigt ökande storleken och komplexiteten hos datamängder har accelererat utvecklingen av maskininlärningsmodeller och gjort behovet av mer skalbara lösningar alltmer uppenbart. Den här avhandlingen utforskar tre utmaningar förknippade med storskalig maskininlärning under dataskyddskrav. För stora och komplexa maskininlärningsmodeller blir traditionella metoder för integritet, såsom datananonymisering, otillräckliga. Vi undersöker därför alternativa tillvägagångssätt, såsom differentiell integritet. Vår forskning behandlar följande utmaningar inom skalbar och integitetsmedveten maskininlärning: För det första undersöker vi hur hög data-dimensionalitet påverkar integriteten för medicinsk bildanalys. Vi utvidgar klassificeringsalgoritmen Private Aggregation of Teacher Ensembles (PATE) för att hantera högdimensionella etiketter och visar att dimensionsreducering kan användas för att förbättra integriteten. För det andra studerar vi hur valet av hyperparametrar påverkar integriteten. Här föreslår vi en ny adaptiv teknik för val av hyperparametrar i gradient-baserad optimering med garantier på differentiell integritet. För det tredje granskar vi urvalsbaserade lösningar för att skala differentiellt privat maskininlärning till stora datamängder. Vi studerar de integritetsförstärkande egenskaperna hos importance sampling och visar att det kan överträffa ett likformigt urval av sampel, inte bara när det gäller effektivitet utan även för integritet. De tre teknikerna som utvecklats i denna avhandling förbättrar skalbarheten för integritetsskyddad maskininlärning och syftar till att erbjuda lösningar för effektiv och säker tillämpning av maskininlärning på stora datamängder. / <p>QC 20231101</p>