Brandväggar är en kritisk komponent i nätverkssäkerhet som kontrollerar och filtrerar nätverkstrafik för att skydda mot obehörig åtkomst och cyberhot. Effektiv hantering av brandväggsregler är avgörande för att säkerställa att ett nätverk fungerar smidigt och säkert. I stora företagsnätverk som Scania kan hanteringen av dessa regler bli komplex och resurskrävande, vilket kan leda till duplicerade och överlappande regler som försämrar systemets prestanda.Detta examensarbete undersöker tillämpningen av generativ artificiell intelligens (GAI) och maskininlärning för att hantera och optimera brandväggsregler, med fokus på identifiering och hantering av duplicerade och överlappande regler. Problemställningen adresserar de växande utmaningarna med att underhålla effektiva brandväggsregler i stora företagsnätverk som Scania. Genom att implementera och utvärdera en prototyp baserad på XGBoost, utforskar arbetet potentialen hos AI-tekniker för att förbättra hanteringen och säkerheten av nätverkstrafik. Resultaten visar att AI kan spela en kritisk roll i automatiseringen av processer för upptäckt och korrigering av felaktiga regler, vilket bidrar till ökad nätverkssäkerhet och optimerad resursanvändning. Studien bekräftar att användningen av AI inom brandväggshantering erbjuder betydande fördelar, men lyfter också fram behovet av fortsatt forskning för att adressera säkerhetsutmaningar relaterade till AI-lösningar. / Firewalls are a critical component of network security, controlling and filtering network traffic to protect against unauthorized access and cyber threats. Effective management of firewall rules is essential to ensure that a network operates smoothly and securely. In large enterprise networks like Scania, managing these rules can become complex and resourceintensive, leading to duplicate and overlapping rules that degrade system performance and security.This thesis investigates the application of generative AI (GAI) and machine learning to manage and optimize firewall rules, focusing on the identification and handling of duplicate and overlapping rules. The problem addresses the growing challenges of maintaining effective firewall rules in large enterprise networks like Scania. By implementing and evaluating a prototype based on XGBoost, this work explores the potential of AI techniques to improve the management and security of network traffic. The results demonstrate that AI can play a critical role in automating the processes for detecting and correcting faulty rules, contributing to increased network security and optimized resource usage. The study confirms that the use of AI in firewall management offers significant benefits but also highlights the need for further research to address security challenges related to AI solutions.

В работе решается актуальная бизнес-задача проектирования алгоритма прогнозирования сырья для производственного косметического предприятия на базе машинного обучения, модель экстремального градиентного бустинга (XGBoost), продемонстрировавшая высокую точность и стабильность прогнозов. Собраны наборы данных, проведен анализ и исследованы методы прогнозирования спроса и сырья, включая модели прогнозирования ARIMA, SARIMA, Хольта-Винтерса, Prophet и различные модели машинного обучения. Использовались метрики: MAE, MSE и MAPE, R2. Статистические модели и модели на основе нейронных сетей, такие как LSTM, показали менее стабильные результаты, чем машинное обучение. Разработан комплексный алгоритм прогнозирования сырья, включающий этапы прогнозирования спроса и расчета потребности в сырье. Прототип алгоритма реализован с использованием Streamlit. Предложены рекомендации по внедрению алгоритма, включая интеграцию с существующими системами и расчет экономической эффективности. / The work solves the current business problem of designing a raw material forecasting algorithm for a cosmetics manufacturing enterprise based on machine learning, the extreme gradient boosting model (XGBoost), which has demonstrated high accuracy and stability of forecasts. Collected data sets, analyzed and researched demand and raw material forecasting methods including ARIMA, SARIMA, Holt-Winters, Prophet and various machine learning models. Metrics used: MAE, MSE and MAPE, R2. Statistical and neural network models such as LSTM have shown less consistent results than machine learning. A comprehensive algorithm for forecasting raw materials has been developed, including the stages of forecasting demand and calculating the need for raw materials. The algorithm prototype is implemented using Streamlit. Recommendations are offered for the implementation of the algorithm, including integration with existing systems and calculation of economic efficiency.

The paper shows how crowdfunding campaigns aimed at African startups depend on the factors of their success and the actions of sponsors. Crowdfunding has emerged as an important financial solution to solve the problems that arise when using conventional financing methods in Africa, such as, for example, high-interest loans. Based on the study of regional, temporal and technological factors, this study suggests practical ways to improve crowdfunding mechanisms using machine learning models such as logistic regression, random forest, support vector machines, XGBoost. / В работе показано, как краудфандинговые кампании, ориентированные на африканские стартапы, зависят от факторов их успеха и действий спонсоров. Краудфандинг появился как важное финансовое решение, позволяющее решать проблемы, возникающие при использовании обычных способов финансирования в Африке, таких как, например, займы под высокие проценты. На основе изучения региональных, временных и технологических факторов это исследование предлагает практические способы улучшения краудфандинговых механизмов с применением моделей машинного обучения, таких как логистическая регрессия, случайный лес, методы опорных векторов, XGBoost.

Orientador: Anna Diva Plasencia Lotufo / Abstract: Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and, as a result, they face an ever-increasing number of interventions, most of the time on constant resource. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters on constant resource is therefore to predict their workload, i.e., their number of interventions per hour, based on explanatory variables conditioning human activity. The present work aims to develop three models that are compared to determine if they can predict the firefighters' response load in a reasonable way. The tools chosen are the most representative from their respective categories in Machine Learning, such as XGBoost having as core a decision tree, a classic method such as Multi-Layer Perceptron and a more advanced algorithm like Long Short-Term Memory both with neurons as a base. The entire process is detailed, from data collection to obtaining the predictions. The results obtained prove a reasonable quality prediction that can be improved by data science techniques such as feature selection and adjustment of hyperparameters. / Resumo: Muitos fatores ambientais, econômicos e sociais estão levando as brigadas de incêndio a serem cada vez mais solicitadas e, como consequência, enfrentam um número cada vez maior de intervenções, na maioria das vezes com recursos constantes. Por outro lado, essas intervenções estão diretamente relacionadas à atividade humana, o que é previsível: os afogamentos em piscina ocorrem no verão, enquanto os acidentes de tráfego, devido a tempestades de gelo, ocorrem no inverno. Uma solução para melhorar a resposta dos bombeiros com recursos constantes é prever sua carga de trabalho, isto é, seu número de intervenções por hora, com base em variáveis explicativas que condicionam a atividade humana. O presente trabalho visa desenvolver três modelos que são comparados para determinar se eles podem prever a carga de respostas dos bombeiros de uma maneira razoável. As ferramentas escolhidas são as mais representativas de suas respectivas categorias em Machine Learning, como o XGBoost que tem como núcleo uma árvore de decisão, um método clássico como o Multi-Layer Perceptron e um algoritmo mais avançado como Long Short-Term Memory ambos com neurônios como base. Todo o processo é detalhado, desde a coleta de dados até a obtenção de previsões. Os resultados obtidos demonstram uma previsão de qualidade razoável que pode ser melhorada por técnicas de ciência de dados, como seleção de características e ajuste de hiperparâmetros. / Mestre

The new Health 4.0 concept brings the idea of combining modern technologies from field of science and technology with research in healthcare and medicine. This work realizes a system of secured actigraph data transfer and preprocessing based on the concept of Health 4.0. The system is successfully designed, implemented, tested and secured. With the help of a non-invasive method of monitoring the movement and temperature of the subject using the GENEActiv actigraph allows the system to securely transfer, process and evaluate the subject's sleep data using the machine learning algorithm XGBoost. The proposed system is in accordance with the valid law of the Czech Republic and meets legal requirements.

Background: A problematic area in today’s large scale distributed systems is the exponential amount of growing log data. Finding anomalies by observing and monitoring this data with manual human inspection methods becomes progressively more challenging, complex and time consuming. This is vital for making these systems available around-the-clock. Aim: The main objective of this study is to determine which are the most suitable Machine Learning (ML) algorithms and if they can live up to needs and requirements regarding optimization and efficiency in the log data monitoring area. Including what specific steps of the overall problem can be improved by using these algorithms for anomaly detection and classification on different real provided data logs. Approach: Initial pre-study is conducted, logs are collected and then preprocessed with log parsing tool Drain and regular expressions. The approach consisted of a combination of K-Means + XGBoost and respectively Principal Component Analysis (PCA) + K-Means + XGBoost. These was trained, tested and with different metrics individually evaluated against two datasets, one being a Server data log and on a HTTP Access log. Results: The results showed that both approaches performed very well on both datasets. Able to with high accuracy, precision and low calculation time classify, detect and make predictions on log data events. It was further shown that when applied without dimensionality reduction, PCA, results of the prediction model is slightly better, by a few percent. As for the prediction time, there was marginally small to no difference for when comparing the prediction time with and without PCA. Conclusions: Overall there are very small differences when comparing the results for with and without PCA. But in essence, it is better to do not use PCA and instead apply the original data on the ML models. The models performance is generally very dependent on the data being applied, it the initial preprocessing steps, size and it is structure, especially affecting the calculation time the most.

In the context of providing credit online to customers in retail shops, the provider must perform risk assessments quickly and often based on scarce historical data. This can be achieved by automating the process with Machine Learning algorithms. Gradient Boosting Tree algorithms have demonstrated to be capable in a wide range of application scenarios. However, they are yet to be implemented for predicting the profitability of new customers based solely on the customers’ first purchases. This study aims to evaluate the predictive performance of the XGBoost, LightGBM, and CatBoost algorithms in this context. The Recall and Precision metrics were used as the basis for assessing the models’ performance. The experiment implemented for this study shows that the model displays similar capabilities while also being biased towards the majority class.

During the broadcast of a golf tournament, a way to show the audience what a player's swing looks like would be to draw a trace following the movement of the club head. A computer vision model can be trained to identify the position of the club head in an image, but due to the high speed at which professional players swing their clubs coupled with the low frame rate of a typical broadcast camera, the club head is not discernible whatsoever in most frames. This means that the computer vision model is only able to deliver a few sparse detections of the club head. This thesis project aims to develop a machine learning model that can predict the complete motion of the club head, in the form of a swing trace, based on the sparse club head detections. Slow motion videos of golf swings are collected, and the club head's position is annotated manually in each frame. From these annotations, relevant data to describe the club head's motion, such as position and time parameters, is extracted and used to train the machine learning models. The dataset contains 256 annotated swings of professional and competent amateur golfers. The two models that are implemented in this project are XGBoost and a feed forward neural network. The input given to the models only contains information in specific parts of the swing to mimic the pattern of the sparse detections. Both models learned the underlying physics of the golf swing, and the quality of the predicted traces depends heavily on the amount of information provided in the input. In order to produce good predictions with only the amount of input information that can be expected from the computer vision model, a lot more training data is required. The traces predicted by the neural network are significantly smoother and thus look more realistic than the predictions made by the XGBoost model.

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Quick and accurate detection of flame inside a gas turbine is very crucial to mitigaterisks in power generation. Failure of flame detection increases downtime and maintenancecosts and on rare occasions it may cause explosions due to buildup of incombustible fuel inside the combustion chamber.The aim of this thesis is to investigate the applicability ofmachine learning methods to detect the presence of flame within a gas turbine. Traditionally,this is done using an optical flame detection which converts the infrared radiation toa differential reading, which is further converted as a digital signal to the control systemand gives the flame status (1 for flame ON and 0 for flame OFF). The primary purpose ofthis alternative flame detection method is to reduce the instrument cost per gas turbine. Amachine learning model is trained with the data collected over several runs of the turbineengine and would estimate if there is an occurrence of the flame, to decide if the machineshould be ON or OFF. To reduce the instrumentation cost, the presented flame predictionmethod based on deep learning methods is employed, which takes standard data such as dynamic pressure and temperature values as input. These variables are observed to have a high correlation with the flame status. The pressure is measured using a piezocryst sensorand the temperature is measured using a thermocouple. A Study is performed by trainingon several machine learning models and coming up with which model among them have worked the best on this data.The Logistic is used as a baseline and is compared with othermodels such as KNN,SVM,Naïve Bayes,RandomForest and XGBoost is trained with thedata collected over several runs of the turbine and tested on to predict flame status insidethe gas turbine.It was observed that KNN and Random Forest performed exceptionallywell as compared to the baseline model. It is recorded that the minimum time for estimation of the flame status by the machine is 0.6 seconds and if the model implementedcan give a high accuracy with the same time then the proposed method can be an effective alternate flame detection method.