Search

The current sea monitoring equipments are being used for a variety of purposes around the world. Currently used vehicles have some drawbacks. The first is the high fuel cost. The Vehicle engines cost more fuel as they have to release more power and environment and pollution. As well as not being able to stay under the sea for long days, there will often be a need for vehicles to come to the surface to refuel. The second is the vibrations and noise of these vehicles. The vibrations caused by these can be detrimental to the biodiversity of the ocean. Also, the noise makes it easier for enemies to identify our vehicles. Similarly when these vehicles go under water, water waves form on the surface. With this in mind, radar can detect what a vehicle under the sea looks like. In this paper, an artificial intelligence based microbiological model was proposed to monitor the sea level. With this biological model can greatly reduce fuels. It can get more capacity than normal vehicles. As fuel consumption decreases, so it does environmental pollution and since it operates quietly and without high vibrations, there is no threat to the biodiversity of the ocean.

The deadliest forms of skin cancer, melanomas have a large fatality rate. In the United States of America, 196,060 new cases of melanoma are anticipated in 2020. In the past, many automated methods for diagnosing skin lesions have been proposed, but they have not yet proven to be very accurate. Based on skin cells’ exposure to sunlight, aberrant skin cell development frequently results in skin cancer. Ultraviolet radiation, viruses, bacteria, chemicals, and fungi are the main contributors to skin conditions. The creation of a precise computer-aided system for diagnosing breast cancer is of tremendous clinical importance. An improved machine learning framework has been developed in this research to detect skin lesions or skin cancer. Hence it is important to segment and classify the skin lesion. The research utilizes the fruit fly optimization algorithm and machine learning framework to segment and classifies skin disease and cancer. This platform's central idea is to use the fruit fly optimization algorithm (FOA) to improve two crucial SVM variables and create an FOA-based SVM (FOA-SVM) for the diagnosis of skin cancer. The integrative approach not only improves accuracy but also provides important data for more accurate classification.