ZrxCa30-xP70 thermoluminescent bio glass, structure and elasticity.

Phosphate glasses of calcium oxide have been well proved materials for various bio bones and dental implants. However, still there is a lot of scope and demand to produce efficient elastic bio implants and resource. In view of this, ZrxCa30-xP70 phosphate materials are prepared by using melt quenching method. Bio, physical, thermoluminescence and elastic techniques are used to characterize the samples. Additionally, simulated body fluid was prepared and it is used especially for bio techniques. Further, the glasses are taken for different dose (~0, 10, 20 & 50 kGy) of gamma irradiation around half an hour. And again similar techniques are used to characterize the samples. All the findings from bio, physical, thermoluminescence and elastic characterization results are analysed and took for better comparison with previous studies to develop various bio bone (or) bio dental resource. Structural reports suggests that the ZrxCa30-xP70 materials were glassy before immersion in SBF solution and immersed (~720 h) samples are showing partial ceramic nature. The weight loss and pH reports suggests them for alternative bio resource as a bio bones and dental implants. Observed thermal stability, microhardness and elastic modulus evaluations of ZrxCa30-xP70 materials in required standards are also additional advantage. Furthermore, thermoluminiscence (TL) under different γ-irradiation doses is reported for glasses with and without immersing in a simulated body fluid. The glasses lose TL intensity when immersed in simulated body fluid for nearly 720 h. This is useful to modulate bio-behaviour in terms of hydroxyapatite layer growth on the glass surface.

Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease.

Enormous data growth in multiple domains has posed a great challenge for data processing and analysis techniques. In particular, the traditional record maintenance strategy has been replaced in the healthcare system. It is vital to develop a model that is able to handle the huge amount of e-healthcare data efficiently. In this paper, the challenging tasks of selecting critical features from the enormous set of available features and diagnosing heart disease are carried out. Feature selection is one of the most widely used pre-processing steps in classification problems. A modified differential evolution (DE) algorithm is used to perform feature selection for cardiovascular disease and optimization of selected features. Of the 10 available strategies for the traditional DE algorithm, the seventh strategy, which is represented by DE/rand/2/exp, is considered for comparative study. The performance analysis of the developed modified DE strategy is given in this paper. With the selected critical features, prediction of heart disease is carried out using fuzzy AHP and a feed-forward neural network. Various performance measures of integrating the modified differential evolution algorithm with fuzzy AHP and a feed-forward neural network in the prediction of heart disease are evaluated in this paper. The accuracy of the proposed hybrid model is 83%, which is higher than that of some other existing models. In addition, the prediction time of the proposed hybrid model is also evaluated and has shown promising results.