This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

Accurate detection of water quality changes is a crucial task of water companies. Water supply companies must provide safe drinking water. Nowadays in different areas, we find sensible sensors which monitor data during the time. Normally the data registered by the sensors contain a meaning, such as there can be any event. Sometimes the data are ill-understood and stating if there is an event which is difficult. This work represents the description of several approaches to identifying changes or anomalies occurring on water quality time series data. This work also discusses and proposes a solution to some challenges when dealing with time series data. The following models are applied to water quality data: logistic regression, linear discriminant analysis, support vector machines (SVM), artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN) and long short-term memory (LSTM). The performance evaluation is conducted using F-score metric. A simulation study is conducted to check the performance of each algorithm using F-score. Solving imbalanced data is basically intentionally biasing the data to get interesting results instead of accurate results. The results show that all algorithms are vulnerable although SVM, ANN and logistic regressions tend to be a little less vulnerable, while DNN, RNN and LSTM are very vulnerable.

Bagging is a popular method used to increase the accuracy of classification, by training a set of classifiers on slightly different datasets and aggregating their output by voting. Usually, the majority voting is used for this purpose, or the plurality voting, when the problem has multiple class values. In this study, we analyze the influence of several voting methods on the performance of two classification algorithms used for datasets with different levels of difficulty. The results reveal that the single transferable vote can be a good alternative to plurality voting, although it has the drawback of a higher computational cost related to the calculation of preference ordering.