Design and Simulation of Worldwide Interoperability for Microwave Access Computer Network for 3×3 Km Universal Sample of Building Campus

The aim of this study to design a wireless computer network of a particular network as a large-scale company or university to improve mobility and to let the teachers and students of the university, for example, stay interacted and connected at any time in any campus location or site. Therefore, This study needed to cover the overall area of this campus with efficient wireless coverage that exceeds the university boundaries to maintain wireless signal strength. To do that, the researchers thought that it is very significant to design a Worldwide Interoperability for Microwave Access (WiMax) computer network with the most powerful and advanced hardware component capabilities to full fit teachers’ and students’ requirements of fast net browsing and files’ download. After designing the university campus of computer network, simulation has done by OPNET 14 Modular to determine the WiMax network design parameters. The purpose of the current research is to find if the design of the campus network is efficient or not and also to determine the performance of theimplemented network

A model for skin cancer using combination of ensemble learning and deep learning

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively