RESUMEN
CsPbI3 perovskite quantum dots (CPQDs) have received great attention due to their potential in large-scale applications. Increasing the efficiency of CPQDs solar cells is an important issue that is addressed in this paper. Here, we have simulated a 14.61% colloidal CPQD solar cell with the least fitting parameter that shows the accuracy of the following results. The absorber layer properties are varied and different power conversion efficiency (PCE) is achieved for the new device. The results show that colloidal CsPbI3 material properties have a significant effect on the PCE of the device. Finally, the optimized parameters for the absorber layer are listed and the optimum efficiency of 29.88% was achieved for this case. Our results are interesting that help the researchers to work on CsPbI3 materials for the achievement of highly efficient, stable, large-scale, and flexible CPQDs solar cells.
RESUMEN
Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-organizing map (SOM) and complex-valued neural network (CVNN), for reliable detection of breast cancer. The dataset used in this paper consists of 822 patients with five features (patient's breast mass shape, margin, density, patient's age, and Breast Imaging Reporting and Data System assessment). The proposed model was used for the first time and can be categorized in two stages. In the first stage, considering the input features, SOM technique was used to cluster the patients with the most similarity. Then, in the second stage, for each cluster, the patient's features were applied to complex-valued neural network and dealt with to classify breast cancer severity (benign or malign). The obtained results corresponding to each patient were compared to the medical diagnosis results using receiver operating characteristic analyses and confusion matrix. In the testing phase, health and disease detection ratios were 94 and 95%, respectively. Accordingly, the superiority of the proposed model was proved and can be used for reliable and robust detection of breast cancer.