## 2 Summary of the Contributions
### Other Topics  
A medical care application of ML is considered in [31]. This work is on a sleep apnea which is a common sleep-related disorder that significantly affects the population. It is characterized by repeated breathing interruption during sleep. The authors propose a new probabilistic algorithm based on oronasal respiration signal for automated detection of apnea events during sleep. Unlike classical threshold-based classification models, they use a Gaussian mixture probability model for detecting sleep apnea based on the posterior probabilities of the respective events. The results show significant improvement in the ability to detect sleep apnea events compared to a rule-based classifier that uses the same classification features and also compared to the previously published studies. Paper [32] deals with a discrete optimization problem of product placement and of order picking routes in a warehouse. The authors propose a genetic algorithm that minimizes the sum of the order picking times. The product placement is optimized by another genetic algorithm. To improve and accelerate an optimization process, several ideasare proposed such as a multi-parent crossover, caching procedure, multiple restart and order grouping. A proposed solution decreases significantly the total order picking times. ML techniques have been actively applied to the meteorology and climatology fields in recent years. They are used for forecasting in different horizons, modelling climatic data, quality control and correction of observed weather data. Paper [33] deals with the topic of the climate change. It presents a framework for selecting general circulation models (GCMs) in homogeneous climatic zones and detecting future climate change trends. With the support of ML techniques, long records of climate data, from numerous gauging sites and web sources, were analyzed and used to determine historical and projected trends of climate change. In [34], to detect the weather phenomena such as precipitation and fog from the backscatter data obtained from the lidar ceilometer, three ML models were applied: random forest, SVM, and NN. The prediction results showed the potential for precipitation detection, but fog detection was found to be very difficult. The emission of carbon dioxide caused by various sectors, including construction and industrial processes, has emerged as a severe problem that dramatically affects global climate change. A portland cement production process accounts for a large part global anthropogenic CO2 emission. A fly ash-based geopolymer concrete (FAGP) offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide content. In [35], ML methods including artificial NN, deep NN and ResNet were employed to predict mechanical properties of FAGP concrete. The obtained results indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Paper [36] deals with a vibration test in the space structure testing. During the physical tests, the structure must not be overtested to avoid any risk of damage. In order to solve the issues associated with existing methods of live monitoring of the structure's response, the authors investigated the use of artificial NNs to predict the system's responses during the test. The conducted research accounts for a novel method for live prediction of stresses, allowing failure to be evaluated for different types of material via yield criteria. Software vulnerabilities are one of the main causes of cybersecurity problems, resulting in huge losses. Existing solutions to automated vulnerability detection are mostly based on features that are defined by human experts and directly lead to missed potential vulnerability. Deep learning is proposed in [37] as an effective method for automating the extraction of vulnerability characteristics.