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This paper focusses on the nonlinear fractional Kadomtsev-Petviashvili (FKP) equation in space-time, employing the conformable fractional derivative (CFD) approach. The main objective of this paper is to examine the application of the (G'/G)-expansion method in order to find analytical solutions to the FKP equation. The (G'/G)-expansion method is a powerful tool for constructing traveling wave solutions of nonlinear evolution equations. However, its application to the FKP equation remains relatively unexplored. By employing traveling wave transformation, the FKP equation was transformed into an ordinary differential equation (ODE) to acquire exact wave solutions. A range of exact analytical solutions for the FKP equation is obtained. Graphical illustrations are included to elucidate the physical characteristics of the acquired solutions. To demonstrate the impact of the fractional operator on results, the acquired solutions are exhibited for different values of the fractional order α, with a comparison to their corresponding exact solutions when taking the conventional scenario where α equals 1. The results indicate that the (G'/G)-expansion method serves as an efficient method and dependable in solving the nonlinear FKP equation. [ABSTRACT FROM AUTHOR]

Myocardial Infarction (MI) is a condition often leading to death. It arises from inadequate blood flow to the heart, therefore, the classification of MI complications contributing to lethal outcomes is essential to save lives. Machine learning algorithms provide solutions to support the categorization of the MI complication attributes and predict lethal results. This paper compares various machine learning algorithms to classify myocardial infarction complications and to predict fatal consequences. The considered algorithms are Multilayer Perceptron (MLP), Naive Bayes (NB), and Decision Tree (DT). The main objective of this paper is to compare these algorithms in two scenarios: initially using the full dataset once and then using the dataset again, after implementing the WEKA attribute selection algorithm. To accomplish this goal, data from the Krasnoyarsk Interdistrict Clinical Hospital were employed. Results in general revealed that the MLP classifier demonstrated optimal performance regarding the full MI data, whereas the DT classifier emerged as more favorable when the dataset sample size was diminished through an attribute selection algorithm. [ABSTRACT FROM AUTHOR]