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Foam concrete is one of the light weight concrete which contains cement, filler, water and foaming agent. Nowadays, this foam concrete has very useful characteristics for building construction. It requires no compaction and can easily fill the irregular cavities. In construction industry, various types of industrial waste materials was used to improve the strength and durability properties of the concrete. The endeavours to improve the strength properties such as cement, silica fume, fly ash, foaming Agent, Poly Vinyl Alcohol (PVA) fiber and carbon fiber were used. The combination of two fibers were added in the foam concrete with the different proportion of 0.3%, 0.4% and 0.5%. This paper describes the strength properties such as compressive strength, split tensile strength, water absorption characteristics and prediction of compressive strength of the fibre reinforced foam concrete using python. The targeted design density of foam concrete is 1650 kg/m3. All the specimens were tested and determined the strength at the age of 7 days, 14 days and 28 days.

Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and oneparametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results ofthese classifiers with training patterns of different sizes to study alcohol tests. In this paper, the Improved Version of thekNN (IVkNN) algorithm is presented which overcomes the limitation of the conventional kNN algorithm to classify winequality. The proposed method typically identifies the same number of nearest neighbours for each test example. Resultsindicate a higher Overall Accuracy (OA) that oscillates between 67% and 76%. Among the three classifiers, the leastsensitive to the training sample size was the kNN and produced the unrivalled OA, followed by sequential decision trees andlogistic regression. Based on the sample size, the proposed IVkNN model presented 80% accuracy and 0.375 root meansquare error (RMSE).

Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of these classifiers with training patterns of different sizes to study alcohol tests. In this paper, the Improved Version of the kNN (IVkNN) algorithm is presented which overcomes the limitation of the conventional kNN algorithm to classify wine quality. The proposed method typically identifies the same number of nearest neighbours for each test example. Results indicate a higher Overall Accuracy (OA) that oscillates between 67% and 76%. Among the three classifiers, the least sensitive to the training sample size was the kNN and produced the unrivalled OA, followed by sequential decision trees and logistic regression. Based on the sample size, the proposed IVkNN model presented 80% accuracy and 0.375 root mean square error (RMSE). [ABSTRACT FROM AUTHOR]