Image classification is one of the most important tasks in the digital era. In terms of cultural heritage, it is important to develop classification methods that obtain good accuracy, but also are less computationally intensive, as image classification usually uses very large sets of data. This study aims to train and test four classification algorithms: (i) the multilayer perceptron, (ii) averaged one dependence estimators, (iii) forest by penalizing attributes, and (iv) the k-nearest neighbor rough sets and analogy based reasoning, and compares these with the results obtained from the Convolutional Neural Network (CNN). Three types of features were extracted from the images: (i) the edge histogram, (ii) the color layout, and (iii) the JPEG coefficients. The algorithms were tested before and after applying the attribute selection, and the results indicated that the best classification performance was obtained for the multilayer perceptron in both cases.

One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.

This paper explores the usability of the Dice CAPTCHA via analysis of the time spent to solve the CAPTCHA, and number of tries for solving the CAPTCHA. The experiment was conducted on a set of 197 subjects who use the Internet, and are discriminated by age, daily Internet usage in hours, Internet experience in years, and type of device where a solution to the CAPTCHA is found. Each user was asked to find a solution to the Dice CAPTCHA on a tablet or laptop, and the time to successfully find a solution to the CAPTCHA for a given number of attempts was registered. Analysis was performed on the collected data via association rule mining and artificial neural network. It revealed that the time to find a solution in a given number of attempts of the CAPTCHA depended on different combinations of values of user’s features, as well as the most meaningful features influencing the solution time. In addition, this dependence was explored through prediction of the CAPTCHA solution time from the user’s features via artificial neural network. The obtained results are very helpful to analyze the combination of features having an influence on the CAPTCHA solution, and consequently, to find the CAPTCHA mostly complying to the postulate of “ideal” test.