Computational machine learning, especially selfenhancing algorithms, prove remarkable effectiveness in applications, including cardiovascular medicine. This review summarizes and cross-compares the current machine learning algorithms applied to electrocardiogram interpretation. In practice, continuous real-time monitoring of electrocardiograms is still difficult to realize. Furthermore, automated ECG interpretation by implementing specific artificial intelligence algorithms is even more challenging. By collecting large datasets from one individual, computational approaches can assure an efficient personalized treatment strategy, such as a correct prediction on patient-specific disease progression, therapeutic success rate and limitations of certain interventions, thus reducing the hospitalization costs and physicians' workload. Clearly such aims can be achieved by a perfect symbiosis of a multidisciplinary team involving clinicians, researchers and computer scientists. Summarizing, continuous cross-examination between machine intelligence and human intelligence is a combination of precision, rationale and highthroughput scientific engine integrated into a challenging framework of big data science.

The financial market has undergone a major revolution in recent decades with the advance and spread of multiobjective optimization metaheuristics, which can be more successfully applied to various aspects of financial decisions. Portfolio optimization is one of them. The purpose of this paper is to adapt, implement in Matlab, assess and compare the performance of 15 metaheuristics belonging to four different classes (NSGA, MOPSO, MOEA/D and SPEA classes) when applying to the Markowitz's Portfolio Optimization Problem. For comparing the performance of these algorithms, we use several specific metrics quantifying the convergence and/or the diversity of the approximate Pareto front against the true Pareto front. The optimal portfolios in the sense of Pareto are selected from a universe of 20 assets listed on the Bucharest Stock Exchange.