Alfredo Bolt

Joaquín Cordero, Alfredo Bolt, Mauricio Valle

Introduction: An important chain of supermarkets in the western zone of the capital of Chile, needs to obtain key information to make decisions, this information is available in the databases but needs to be processed due to the complexity and quantity of information which becomes difficult to visualiz,. Method: For this purpose, an algorithm was developed using artificial neural networks applying Kohonen's SOM method. To carry it out, certain key procedures must be followed to develop it, such as data mining that will be responsible for filtering and then use only the relevant data for market basket analysis. After filtering the information, the data must be prepared. After data preparation, we prepared the Python programming environment to adapt it to the sample data, then proceed to train the SOM with its parameters set after test results. Result: the result of the SOM obtains the relationship between the products that were most purchased by positioning them topologically close, to form promotions, packs and bundles for the retail manager to take into consideration, because these relationships were obtained as a result of the SOM training with the real transactions of the clients. Conclusion: Based on this, recommendations on frequent shopping baskets have been made to the supermarket chain that provided the data used in the research

Ignacio Muñoz, Alfredo Bolt

Introduction: Mobile apps, through artificial vision, are capable of recognizing vegetable species in real time. However, the existing species recognition apps do not take in consideration the wide variety of endemic and native (Chilean) species, which leads to wrong species predictions. This study introduces the development of a chilean species dataset and an optimized classification model implemented to a mobile app. Method: the data set was built by putting together pictures of several species captured on the field and by selecting some pictures available from other datasets available online. Convolutional neural networks were used in order to develop the images prediction models. The networks were trained by performing a sensitivity analysis, validating with k-fold cross validation and performing tests with different hyper-parameters, optimizers, convolutional layers, and learning rates in order to identify and choose the best models and then put them together in one classification model. Results: The final data set was compounded by 46 species, including native species, endemic and exotic from Chile, with 6120 training pictures and 655 testing pictures. The best models were implemented on a mobile app, obtaining a 95% correct prediction rate with respect to the set of tests. Conclusion: The app developed in this study is capable of classifying species with a high level of accuracy, depending on the state of the art of the artificial vision and it can also show relevant information related to the classified species.

Toon Jouck, Alfredo Bolt, Benoît Depaire et al.

Process mining offers techniques to exploit event data by providing insights and recommendations to improve business processes. The growing amount of algorithms for process discovery has raised the question of which algorithms perform best on a given event log. Current evaluation frameworks for empirically evaluating discovery techniques depend on the notation used (behavioral identical models may give different results) and cannot provide more general statements about populations of models. Therefore, this paper proposes a new integrated evaluation framework that uses a classification approach to make it modeling notation independent. Furthermore, it is founded on experimental design to ensure the generalization of results. It supports two main evaluation objectives: benchmarking process discovery algorithms and sensitivity analysis, i.e. studying the effect of model and log characteristics on a discovery algorithm's accuracy. The framework is designed as a scientific workflow which enables automated, extendable and shareable evaluation experiments. An extensive experiment including four discovery algorithms and six control-flow characteristics validates the relevance and flexibility of the framework. Ultimately, the paper aims to advance the state-of-the-art for evaluating process discovery techniques.