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Natural fibre composites are ideal material substitutes for combating the issues of pollution and non-biodegradability. Several industries, the automobile industry, in particular, have shown tremendous inclination towards the usage of natural fibre composites in their deliverables. Besides providing a wide array of useful properties, natural fibre composites have offered flexibility in terms of choosing various combinations of fibres and resins. Owing to this, this paper aims to collect data and categorize the natural fibre composites based on the types of treatments and properties they exhibit. Characterization was done by taking into consideration chemical and enzymatic treatments as well as tests such as the tensile, flexural, compressive, impact, shear and hardness. Based on the values obtained from the characterization, the paper suggests suitable and feasible natural fibre composites as biocompatible replacements to conventionally used materials in the automotive industry.

Diabetes mellitus is a serious health issue in healthcare industry, which is a type of uncontrolled level of sugar. It is a chronic disease happened to the person who are having low insulin production and increase level of blood glucose because glucose is not properly utilized by body. In the medical field, predicting the correct diabetes is an important area that is under research to define a good predictive system to help the doctors to diagnose the disease. In the predictive system, feature selection plays on vital role to select the relevant feature for classification. There are several algorithms were applied on classification of diabetes data. In this proposed work, the features are transformed into high dimensional space before selection. So that the transformation of the features will give the better selection of attributes. With this effort, the proposed work implements the Kernel Principal Component Analysis for dimensionality reduction. KPCA will reduce the features space better than PCA. Once the features are transformed, the proposed work uses Genetic Algorithm to select the relevant and optimal features from the dataset. Then at the last Support Vector Machine is used as a classifier to classify the diabetes mellitus data. The proposed research on applying feature reduction before feature selection will reduce the irrelevant features that will improve the accuracy of the classification based on the selected relevant features. This proposed algorithm on diabetes mellitus data will compare with the existing algorithms to prove the effectives of the algorithm.