I samarbete med STIGA Sports AB undersöker den här rapporten stommar hos bordtennisracketar och sambandet mellan den subjektiva upplevelsen av snabbhet och deras egenskaper. Undersökningen fokuserar på de fysiska egenskaperna, tjocklek och vikt, skillnaden i produktion, form, handtagssort, limsort och lack, samt uppmätta frekvenssvar. Kategoriseringen av racketarna utfördes i samarbete med bordtennisspelare med ett så kallat knackningstest, en subjektiv men beprövad metod för att på ett effektivt sätt bedöma racketstommars snabbhet. Studien utgick från en maskininlärningsmodell av typen Random Forest för att rangordna egenskapernas betydelse med hjälp av Importance-funktionerna MDA, Permutation Feature Importance, och MDI, Random Forest Feature Importance. Datamängden bestod av 100 racketstommar av samma modell med varierandeegenskaper. Studiens resultat visar på betydelsen av mätdatans omfattning för att kunna dra allmängiltiga slutsatser. Detta till trots visar resultaten på trender vad gäller sambandet mellan den subjektiva upplevelsen och frekvenspikar, form samt densitetsfaktorn (vikt/tjocklek). Tydligast koppling återfanns mellan snabbhet och frekvenspiken motsvarande torsionmoden.

Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.

In recent years with the advances in Machine Learning and Artificial Intelligence, the demand for ever smarter automation solutions could seem insatiable. One such demand was identified by Fortnox AB, but undoubtedly shared by many other industries dealing with contractual services, who were looking for an intelligent solution capable of predicting the expiration date of a contractual period. As there was no clear evidence suggesting that Machine Learning models were capable of learning the patterns necessary to predict a contract's expiration, it was deemed desirable to determine subject feasibility while also investigating whether it would perform better than a commonplace rule-based solution, something that Fortnox had already investigated in the past. To do this, two different solutions capable of predicting a contractual expiration were implemented. The first one was a rule-based solution that was used as a measuring device, and the second was a Machine Learning-based solution that featured Tree Decision classifier as well as Neural Network models. The results suggest that Machine Learning models are indeed capable of learning and recognizing patterns relevant to the problem, and with an average accuracy generally being on the high end. Unfortunately, due to a lack of available data to use for testing and training, the results were too inconclusive to make a reliable assessment of overall accuracy beyond the learning capability. The conclusion of the study is that Machine Learning-based solutions show promising results, but with the caveat that the results should likely be seen as indicative of overall viability rather than representative of actual performance.

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.