Christian Tchito Tchapga

TL;DR: A survey of classification algorithms for biomedical images can be found in this paper, where the authors describe how these algorithms can be applied to a big data architecture by using the Spark framework and propose the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature.

TL;DR: A linear augmentation control scheme is used to control a periodic attractor in the windows of multistability to a final chaotic attractor which survives to the variation of initial seed useful for image encryption.

TL;DR: Nonlinear dynamical tools including bifurcation diagrams, standard Lyapunov exponents, phase portraits, and cross section of initial conditions are exploited to reveal the selection scenarios of the survived attractor in the multistability control process of Chua's system.

TL;DR: In this article, support vector machine (SVM) was used as the best performant for the COVID-19 diagnosis, achieving an accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% with the dataset from Kaggle (https://www.kaggle.org/record/3886927#.YIluB5AzbMV).