A tunable mid-infrared (MIR) laser (quantum cascade laser, QCL) was used for the detection of TNT and RDX in soil samples at a concentration range from 0 to˜20% w/w. This type of sensing is complicated due to the complexity of the matrix, i.e., the diversity of compounds contained in soil. Thus, the high explosives (HE) detection in soil by QCL was assisted with an Artificial Intelligence (AI) system. AI managed to predict these HE in seven kinds of soils using minimum information Machine Learning (ML). The models were generated only from neat HE and soil spectra, without necessity using experimental spectra of the mixes. AI used these neat spectra to simulate the spectra of HEs/soil mixes. The simulated data was used to train the ML models and then were tested with real spectra of HEs/soils mixes. The method was designated as "Self-Simulated Learning Artificial Intelligence" (SSLAI). This methodology has advantages for applications in field scenarios where the matrices are unknown because SSLAI models do not need to be trained with real samples a priori. Models would only have to be fed with spectra for the neat components to train itself. The methodology was tested with mixes of seven soils and two explosives. Test samples were classified into three concentrations ranges: high concentration test (Test H > 10% w/w), medium concentration test (10% w/w > Test M > 3% w/w), and low concentration test (Test L < 3% w/w). The results show that it is possible to correctly predict these two HE/soil mixes from the simulated data. Specifically, for TNT and RDX, SSLAI achieved a high precision in the prediction for the high and medium concentration tests (Test H and Test M). However, for both samples with concentrations below 3% w/w (Test L), the number of false positives increased, and the precision was reduced.

Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database.

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