In 2020, there were more than 1.2 million new skin cancer diagnoses, and melanoma was the most recurrent type of cancer. On the other hand, melanoma is the least common but most serious form of skin cancer affecting both men and women. This work aims to assemble classification models to detect a case of melanoma with high accuracy based on a Convolutional Neural Networks system. The methodology considers training 21 models for image classification, with the best assembly performance of EfficientNet and VGG-19 architectures, the data augmentation technique was used to the images to improve its performance. The results show 92.85% of accuracy, 71.50% of sensitivity, and 94.89% of specificity, with an improvement of 0.06% in accuracy and specificity. The assembly of the classification models achieved higher accuracy in melanoma skin cancer image classification.

Teacher job satisfaction is an important aspect of academic performance, student retention, and teacher retention. We propose to determine the predictive model of job satisfaction of basic education teachers using machine learning techniques. The original data set consisted of 15,087 instances and 942 attributes from the national survey of teachers from public and private educational institutions of regular basic education (ENDO-2018) carried out by the Ministry of Education of Peru. We used the ANOVA F-test filter and the Chi-Square filter as feature selection techniques. In the modeling phase, the logistic regression algorithms, Gradient Boosting, Random Forest, XGBoost and Decision Trees-CART were used. Among the algorithms evaluated, XGBoost and Random Forest stand out, obtaining similar results in 4 of the 8 metrics evaluated, these are: balanced accuracy of 74%, sensitivity of 74%, F1-Score of 0.48 and negative predictive value of 0.94. However, in terms of the area under the ROC curve, XGBoost scores 0.83, while Random Forest scores 0.82. These algorithms also obtain the highest true-positive values (479 instances) and lowest false-negative values (168 instances) in the confusion matrix. Economic income, satisfaction with life, self-esteem, teaching activity, relationship with the director, perception of living conditions, family relationships; health problems related to depression and satisfaction with the relationship with colleagues turned out to be the most important predictors of job satisfaction in basic education teachers.

Suicide is one of the most distinguished causes of death on the news worldwide. There are several factors and variables that can lead a person to commit this act, for example, stress, self-esteem, depression, among others. The causes and profiles of suicide cases are not revealed in detail by the competent institutions. We propose a simulation with a systematically generated dataset; such data reflect the adolescent population with suicidal tendency in Peru. We will evaluate three algorithms of supervised machine learning as a result of the algorithm C4.5 which is based on the trees to classify in a better way the suicidal tendency of adolescents. We finally propose a desktop tool that determines the suicidal tendency level of the adolescent.

Skin cancer is one of the most common types of cancer in humans, it covers about one third of all neoplasms. Within skin cancer we find basal cell carcinoma (BCC), this being the most frequent type of cancer worldwide. Solutions with convolutional neural networks generally use the Softmax layer (classic model) to perform a BCC classification, however, in other similar fields such as image classification of microscopic bacteria they have replaced this Softmax layer with a support vector machine (SVM) achieving a better result. Given this, we propose a hybrid model of convolutional neural network and a support vector machine (CNN+SVM) to classify the BCC. Our model is composed of 4 convolution blocks with 32, 64 and 128 filters to carry out the extraction of characteristics and then pass it to the classifier, to which the L1-SVM loss function is implemented. The average results obtained for the CNN+SVM hybrid model were measured with the precision, accuracy, recall and F1-score metrics, obtaining 96.200%, 96.200%, 96.205% and 96.200% respectively compared to the classical model for the metrics of precision, accuracy, recall and F1-score where 95.661%, 95.673%, 95.661%, 95.660% respectively were obtained. The results show that the hybrid model achieves better results than the classic model to classify the BCC.

Heart disease is one of the biggest problems in the world that will continue to be research. We have made an overview of the research related to heart disease. We verify that algorithms have been used for the classification of cardiac diseases: Apriori, decision tree, naive Bayesian. Neural network, SVM, ANN, KN and others. In this research, we have designed three architectures of neural networks to evaluate which of them adapts and predicts better the presence of heart diseases, we have determined that the architecture that best adapts is a neural network Backpropagation with quadratic error 0.01788 with a 99.26% accuracy. In addition, we have designed a web application tool to detect heart disease, this tool has been designed with the steps of software engineering.

In the field of computer science, many efforts have been made with respect to music recommendation in order to offer the user songs much more in line with his current context or tastes and thus also reduce the large number of musical pieces found on the web. However, there are few studies that take into account the user’s feelings for this task. In this paper we present a model and recommendation system that emphasizes sentiment analysis to make music recommendations using natural language processing, this is achieved by using different artificial intelligence tools such as Word2Vec to vectorize words and neural networks to recognize the sentimental information of the texts. In the results, we show that this approach improves the recommendation results obtained by 80% for the accuracy metrics.