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Sub-grade soils of lateritic origin encountered in the construction of highway embankments in various regions of India, often comprise intrusions of soft lithomargic soils that result in large settlements during constructions, and differential settlements at later stages. This necessitates the use of appropriate soil improvement techniques to improve the load-carrying capacity of pavements. This work deals with accelerated consolidation of un-reinforced and coir-reinforced lateritic lithomargic soil blends, provided with three vertical sand drains. The load-settlement characteristics were studied for various preloads ranging from 50 kg (0.0013 N/mm 2 ) to 500 kg (0.013 N/mm 2 ) on soil specimens prepared in circular ferrocement moulds. It was observed that at lower preloads up to 200 kg, across the blends, the relative increase in consolidation (R ct ) for randomly reinforced soil with vertical drains was significantly higher than that of un-reinforced soil without vertical drains, with an average value of 124.8%. Also, the R ct for un-reinforced soil with vertical drains was quite higher than that of un-reinforced soil without vertical drains, with an average value of 103.9%. In the case of higher preloads, the R ct values for randomly reinforced soil with vertical drains were moderate with an average value of 30.88%, while the same for un-reinforced soil with vertical drains was about 20.4%. The aspect-ratio of coir fibers used was 1:275.

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The researcher explained the implementation process of finding the scholarship for the students by using machine learning supervised learning algorithm i.e. Naïve Bayes algorithm. Addition to this it includes a small description of naïve bayes classifier which used to be used through the authors. It explains the significance of training facts set and trying out information set in Machine mastering techniques. Machine learning nowadays becomes plenty used technique in the field of IT industry. It is a very effective instrument and technique for many quite a number fields such as education, IT and even in enterprise industry. In this paper, the researcher attempt to find computerized end result reputation of scholarships of college students by way of using naïve bayes classifier algorithm primarily based on the scholar educational performance, conversation skills, greedy power, IHS, income, time management, regularity etc. A scholarship offers a strength and self assurance to a student. It also boosts the performance of students indirectly. Usually scholarships are furnished by governments or authorities organizations. It is very essential for students to recognize their personal potentiality early in their educational profession so that they faster its growth, receiving attention from an employer or corporation helps college students take this step. Students can apply for scholarships primarily based on the eligibility criteria (such as caste category, annual income, etc). The scholarship will be issued based on merit, student performance and career specific. Different schemes of scholarships are provided for the students based on distinct eligibility criteria. By the use of a naïve bayes classifier, the researcher acquired a end result with accuracy of 96.7% and error of 3.3%. The repute of scholarship students was once displayed in the form of yes or no.