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Atmospheric Radar Signal Processing is one field of Signal

Introduction: Tobacco use among school children is becoming a serious problem in developing countries. The early age of initiation underscores the urgent need to intervene and protect this vulnerable group from falling prey to this addiction. Tobacco use is a leading cause of preventable deaths world over, more so in developing countries. The tobacco situation in India is unique because of a vast spectrum of tobacco products available for smoking as well as smokeless use. Aims and Objectives: To assess the awareness of risk factors and consequences of tobacco use among adolescent and young adults. Materials and Method: Materials used are - Questionnaire pertaining to the awareness of smoking. Methodology: Assessment of awareness and knowledge of tobacco smoking was done by means of answering a pretested validated questionnaire with annonimity. Results: According to the study 78.23% said that they were aware of effects of smoking on health.39.83% and 14.78% thought it would lead to lung disease and heart disease respectively. 7.8% of the smokers were aware that smoking leads to cancer Conclusion: According to the study, 78.23% of the study sample were aware of harmful effects of tobacco use on health.

Skin cancer is very important notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasible challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized by dividing all the RGB values by 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, by using the kernel filter matrix. MaxPool reduce the computational cost by down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less number of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.