Skin illness affects a large percentage of the world's population. The proposed study proposed a deep learning-based model for skin disease predication, In the traditional system it was time taking to predict the result and the accuracy is not accurate, Different machine learning methods can be used to classify skin disorders. In this study, we used machine learning algorithms to categories skin disease classes using ensemble approaches, and then used a feature selection method to compare the findings produced. Specialist can detect the disease type with the help of a web-based framework which is developed in Python Django frame- work. In the proposed study, we present a novel approach to detect the skin disease. Here we have used Support Vector Machine (SVM) Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) classifiers to identify the disease. Specialist need to upload the image and Deep learning algorithms will predict the disease and display the accuracy. The proposed model is easy to use, but it also provides a higher level of accuracy than previous methods. As a result of this model, we were able to achieve a 95% accuracy rate in the diagnosis of various skin conditions. The proposed system provides a state of art accuracy for early skin disease detection

Plastic products play a significant role in fulfilling daily necessities, but the non-decomposable nature of plastic leads to inescapable environmental damage. Recycling plastic material is the most appropriate solution to avoid pollution and short product lifespan. The present study shows the recycling effect on carbon nanofiber (CNF) reinforced polypropylene (PP) nanocomposite to attain the purpose of reuse and sustainability. 30 wt% CNF melt-blended with polymer and PP-nanocomposites were fabricated using the injection molding technique. PP-CNF nanocomposites were recycled, and mechanical, thermal, and morphological properties were investigated. Three-point bending and tensile testing showed a low decrement of ~1% and ~5% in bending and tensile strength after recycling 30 wt% PP-CNF nanocomposites. Scanning electron microscopy (SEM) images show that the alignment of CNF was disturbed after recycling due to the decrement in the aspect ratio of CNF. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) showed that the crystallinity of PP increases with recycling. The lowering of interfacial interaction between CNF and PP after recycling was studied by a stress-controlled rheometer. The decrement in mechanical properties of PP-CNF nanocomposite is not significant due to CNF reinforcement; hence, it can be reused for the same or other structural applications.