This paper presents an empirical analysis of using deep learning features with an Extreme Learning Machine (ELM) for automatic modulation classification in wireless communication. The study utilizes advanced pre-deep learning models like VGG 16, Resnet 50, and Inception V3 to extract features, which are then fed to an ELM for classification. The ELM’s performance is enhanced by the moth flame optimization method (MFOP-ELM). To measure the efficacy of the proposed model, it is tested on two standard datasets - RADIOML2016.10A and RADIOML 2018.01A. The paper also compares the proposed model with other classifiers such as SVM, K-NN, and BPNN. Results show that the proposed model using Inception V3 features with the MFOP-ELM classifier outperforms other state-of-the-art models.

One of the most common women’s tumours infest the breast. Various benign disorders like cysts in a woman’s breast occur due to hormonal changes and are at the risk of becoming malignant. Several thermal models are reported to differentiate between normal and malignant tissues of the breast. But no thermal model is reported in the study of the effect of benign disorders on the literature to distinguish between benign and malignant disorders in women’s breasts. An attempt has been made in this paper to study the thermal disturbances caused by cysts and malignant tumours in the fat tissues of a woman’s breast. The model is developed for a two-dimensional steady-state case using penne’s bioheat equation and incorporating parameters like thermal conductivity, blood mass flow rate, and self-controlled metabolic heat generation. The appropriate adiabatic boundary conditions have been framed for various environmental conditions. The finite element method has been employed to obtain the solution. The results have been obtained for different spherical-shaped cysts and different depths of tissues in a hemispherical-shaped woman’s breast. Furthermore, the comparison of thermal profiles for cysts and malignant tumours in a woman’s breast has been performed. As a result, a contrast in the thermal behaviour of cyst and malignant tumour in a woman’s breast is observed, which can be helpful to distinguish between the malignant tumour and cyst in a woman’s breast to prevent false-positive test for malignant tumour. Accordingly, this study found that various factors could affect cancer classification and prediction. Therefore, in this study, Breast cancer data classification has been done using three classification techniques which are Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (R.F.); to improve the performance of the model, trained the model with selected features according to the analysis done.