High speed facilities are considered the backbone of any successful transportation system; Interstates, freeways, and expressways carry the majority of daily trips on the transportation network. Although these types of roads are relatively considered the safest among other types of roads, they still experience many crashes, many of which are severe, which not only affect human lives but also can have tremendous economical and social impacts. These facts signify the necessity of enhancing the safety of these high speed facilities to ensure better and efficient operation. Safety problems could be assessed through several approaches that can help in mitigating the crash risk on long and short term basis. Therefore, the main focus of the research in this dissertation is to provide a framework of risk assessment to promote safety and enhance mobility on freeways and expressways. Multi-level Safety Performance Functions (SPFs) were developed at the aggregate level using historical crash data and the corresponding exposure and risk factors to identify and rank sites with promise (hot-spots). Additionally, SPFs were developed at the disaggregate level utilizing real-time weather data collected from meteorological stations located at the freeway section as well as traffic flow parameters collected from different detection systems such as Automatic Vehicle Identification (AVI) and Remote Traffic Microwave Sensors (RTMS). These disaggregate SPFs can identify real-time risks due to turbulent traffic conditions and their interactions with other risk factors. In this study, two main datasets were obtained from two different regions. Those datasets comprise historical crash data, roadway geometrical characteristics, aggregate weather and traffic parameters as well as real-time weather and traffic data. iii At the aggregate level, Bayesian hierarchical models with spatial and random effects were compared to Poisson models to examine the safety effects of roadway geometrics on crash occurrence along freeway sections that feature mountainous terrain and adverse weather. At the disaggregate level; a main framework of a proactive safety management system using traffic data collected from AVI and RTMS, real-time weather and geometrical characteristics was provided. Different statistical techniques were implemented. These techniques ranged from classical frequentist classification approaches to explain the relationship between an event (crash) occurring at a given time and a set of risk factors in real time to other more advanced models. Bayesian statistics with updating approach to update beliefs about the behavior of the parameter with prior knowledge in order to achieve more reliable estimation was implemented. Also a relatively recent and promising Machine Learning technique (Stochastic Gradient Boosting) was utilized to calibrate several models utilizing different datasets collected from mixed detection systems as well as real-time meteorological stations. The results from this study suggest that both levels of analyses are important, the aggregate level helps in providing good understanding of different safety problems, and developing policies and countermeasures to reduce the number of crashes in total. At the disaggregate level, real-time safety functions help toward more proactive traffic management system that will not only enhance the performance of the high speed facilities and the whole traffic network but also provide safer mobility for people and goods. In general, the proposed multi-level analyses are useful in providing roadway authorities with detailed information on where countermeasures must be implemented and when resources should be devoted. The study also proves that traffic data collected from different detection systems could be a useful asset that should be utilized iv appropriately not only to alleviate traffic congestion but also to mitigate increased safety risks. The overall proposed framework can maximize the benefit of the existing archived data for freeway authorities as well as for road users.

Organizations gather enormous amounts of data and analyze these data to extract insights that can be useful for them and help them to make better decisions. Predictive data analytics is a crucial subfield within data analytics that make accurate predictions. Predictive data analytics extracts insights from data by using machine learning algorithms. This thesis presents the supervised learning algorithm to perform predicative data analytics in Embedded Test System at the Nordic Engineering Partner company. Predictive Maintenance is a concept that is often used in manufacturing industries which refers to predicting asset failures before they occur. The machine learning algorithms used in this thesis are support vector machines, multi-layer perceptrons, random forests, and gradient boosting. Both binary and multi-class classifier have been provided to fit the models, and cross-validation, sampling techniques, and a confusion matrix have been provided to accurately measure their performance. In addition to accuracy, recall, precision, f1, kappa, mcc, and roc auc measurements are used as well. The prediction models that are fitted achieve high accuracy.

The objective of this thesis is to investigate the possibility of using machine learn- ing at Statistics Sweden within the Foreign Trade in Services (FTS) statistic, to predict the likelihood of a unit to conduct foreign trade in services. The FTS survey is a sample survey, for which there is no natural frame to sample from. Therefore, prior to sampling a frame is manually constructed each year, starting with a register of all Swedish companies and agencies and in a rule- based manner narrowing it down to contain only what is classified as units likely to trade in services during the year to come. An automatic procedure that would enable reliable predictions is requested. To this end, three different machine learning methods have been analyzed, two rule- based methods (random forest and extreme gradient boosting) and one distance- based method (k nearest neighbors). The models arising from these methods are trained and tested on historically sampled units, for which it is known whether they did trade or not. The results indicate that the two rule-based methods perform well in classifying likely traders. The random forest model is better at finding traders, while the extreme gradient boosting model is better at finding non-traders. The results also indicate interesting patterns when studying different metrics for the models. The results also indicate that when training the rule-based models, the year in which the training data was sampled needs to be taken into account. This entails that cross-validation with random folds should not be used, but rather grouped cross-validation based on year. By including a feature that mirror the state of the economy, the model can adapt its rules to this, meaning that the rules learned on training data can be extended to years beyond training data. Based on the observed results, the final recommendation is to further develop and investigate the performance of the random forest model.

The selection of machine learning algorithm used to solve a problem is an important choice. This paper outlines research measuring three performance metrics for eight different algorithms on a prediction task involving under- graduate admissions data. The algorithms that were tested are k-nearest neighbours, decision trees, random forests, gradient tree boosting, logistic regression, naive bayes, support vector machines, and artificial neural net- works. These algorithms were compared in terms of accuracy, training time, and execution time. / Thesis / Master of Applied Science (MASc)

Para diagnosticar un cáncer se realiza, entre otras pruebas, algún test de imagen, como puede ser una radiografía, ecografía o resonancia magnética. Mediante estos tests pueden detectarse zonas con alta sospecha tumoral, cuyo diagnóstico debe confirmase finalmente mediante la realización de una biopsia. Este tipo de imágenes, sin embargo, no son fáciles de interpretar, lo que provoca que el profesional encargado de analizarlas, a pesar de su experiencia, no sea capaz de detectar en ellas un porcentaje importante de tumores (falsos negativos). Una posibilidad para mejorar el diagnóstico y disminuir el número de falsos negativos consiste en utilizar sistemas de diagnóstico asistido por ordenador o computer-aided diagnosis (CAD). Un sistema de CAD analiza la imagen médica y trata de detectar zonas sospechosas de contener alguna anomalía. Estas zonas son marcadas sobre la propia imagen con un doble objetivo: llamar la atención del profesional encargado de analizarla hacia la zona sospechosa y aportar una segunda opinión respecto al diagnóstico. En esta tesis se presentan y evaluan diversas técnicas de visión por computador y reconocimiento de formas orientadas a la detección de tumores en imágenes médicas, con el objetivo de diseñar sistemas de CAD que permitan un mejor diagnóstico. El trabajo se ha centrado en el diagnóstico de cáncer de próstata a partir de imágenes de ecografía, y en el diagnóstico de cáncer de mama a partir de imágenes de radiografía. Se han evaluado diversos métodos de extracción de características basados en la intensidad, frecuencia, texturas o en gradientes. En la etapa de clasificación se ha utilizado un clasificador no paramétrico basado en distancias (k-vecinos más cercanos) y otro paramétrico basado en modelos de Markov. A lo largo del trabajo se evidencian las distintas problemáticas que surgen en este tipode tareas y se proponen soluciones a cada una de ellas. El diagnóstico de cáncer de próstata asistido por ordenador es una tarea extrema / Llobet Azpitarte, R. (2006). Aportaciones al diagnóstico de cáncer asistido por ordenador [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1862

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.

This master thesis describes the design of a track-and-hold (T&H) circuit with 10bit resolution, 80MS/s and 30MHz bandwidth. It is designed in a 0.18µm CMOS process with a supply voltage of 1.8 Volt. The circuit is supposed to work together with a 10bit pipelined analog to digital converter. A switched capacitor topology is used for the T&H circuit and the amplifier is a folded cascode OTA with regulated cascode. The switches used are of transmission gate type. The thesis presents the design decisions, design phase and the theory needed to understand the design decisions and the considerations in the design phase. The results are based on circuit level SPICE simulations in Cadence with foundry provided BSIM3 transistor models. They show that the circuit has 10bit resolution and 7.6mW power consumption, for the worst-case frequency of 30MHz. The requirements on the dynamic performance are all fulfilled, most of them with large margins.

<p>This master thesis describes the design of a track-and-hold (T&H) circuit with 10bit resolution, 80MS/s and 30MHz bandwidth. It is designed in a 0.18µm CMOS process with a supply voltage of 1.8 Volt. The circuit is supposed to work together with a 10bit pipelined analog to digital converter. </p><p>A switched capacitor topology is used for the T&H circuit and the amplifier is a folded cascode OTA with regulated cascode. The switches used are of transmission gate type. </p><p>The thesis presents the design decisions, design phase and the theory needed to understand the design decisions and the considerations in the design phase. </p><p>The results are based on circuit level SPICE simulations in Cadence with foundry provided BSIM3 transistor models. They show that the circuit has 10bit resolution and 7.6mW power consumption, for the worst-case frequency of 30MHz. The requirements on the dynamic performance are all fulfilled, most of them with large margins.</p>

This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.

Táto diplomová práca je zameraná na preukazovanie vplyvov rôznych architektúr spätnej recirkulácie spalín na preplňované benzínové motory. Simulácie boli vytvorené v termodynamickom simulačnom programme GT-Power. Práca začína porovnávaním vplyvov spätnej recirkulácie na ustálené stavy motora, najmä z hľadiska spotreby, ktoré sú následne implementované do tranzientných modelov, simulujúc emisné testovacie cykly. Na konci práce sa venujem vplyvom spätnej recirkulácie na funkciu oxidačno-redukčného katalyzátora a na funkciu turbodúchadla.