The purpose of this paper is to attempt to use two industrial wastes; waste foundry sands (WFS) and molasses (M) along with lime (L) to improve the strength…
The purpose of this paper is to attempt to use two industrial wastes; waste foundry sands (WFS) and molasses (M) along with lime (L) to improve the strength characteristics of clayey soil.
In the first part of the study, the optimum percentages of materials (WFS, molasses, lime) have been found out by conducting differential free swell (DFS) and consistency limit tests on clayey soil by adding various admixtures. The second and third part of the study investigates the compaction behaviour and unconfined compressive strength (UCS) of clayey soil on addition of optimum amount of various materials alone and in combination with each other. Finally, the micro-structural behaviour of addition of optimum percentages of lime, WFS and molasses using Scanning electron microscopic technique has been discussed.
The laboratory results revealed that the addition of optimum content of lime along with WFS and molasses reduced DFS and plasticity index and increased maximum dry density and UCS values. The microstructural behaviour showed that the presence of lime and molasses filled the voids present in the soil and the addition of WFS helped in providing compact structure, thus improving the strength characteristics.
The study will be helpful in designing low-cost pavement designs for rural roads.
The adverse effect of waste materials on environment may be solved by using them in improving the strength characteristics of clayey soils, thereby providing healthy environment to living beings.
The study will help to provide low-cost methods to improve strength characteristics of clayey soil along with the use of waste materials; the disposal of whose is a challenging task.
The purpose of this study is to alleviate the specified issues to a great extent. To promote patients’ health via early prediction of diseases, knowledge extraction using…
The purpose of this study is to alleviate the specified issues to a great extent. To promote patients’ health via early prediction of diseases, knowledge extraction using data mining approaches shows an integral part of e-health system. However, medical databases are highly imbalanced, voluminous, conflicting and complex in nature, and these can lead to erroneous diagnosis of diseases (i.e. detecting class-values of diseases). In literature, numerous standard disease decision support system (DDSS) have been proposed, but most of them are disease specific. Also, they usually suffer from several drawbacks like lack of understandability, incapability of operating rare cases, inefficiency in making quick and correct decision, etc.
Addressing the limitations of the existing systems, the present research introduces a two-step framework for designing a DDSS, in which the first step (data-level optimization) deals in identifying an optimal data-partition (Popt) for each disease data set and then the best training set for Popt in parallel manner. On the other hand, the second step explores a generic predictive model (integrating C4.5 and PRISM learners) over the discovered information for effective diagnosis of disease. The designed model is a generic one (i.e. not disease specific).
The empirical results (in terms of top three measures, namely, accuracy, true positive rate and false positive rate) obtained over 14 benchmark medical data sets (collected from https://archive.ics.uci.edu/ml) demonstrate that the hybrid model outperforms the base learners in almost all cases for initial diagnosis of the diseases. After all, the proposed DDSS may work as an e-doctor to detect diseases.
The model designed in this study is original, and the necessary parallelized methods are implemented in C on Cluster HPC machine (FUJITSU) with total 256 cores (under one Master node).
Innovation and entrepreneurship are regarded as the key drivers to steer the engine of economic development in any nation. As a result, to understand the context and…
Innovation and entrepreneurship are regarded as the key drivers to steer the engine of economic development in any nation. As a result, to understand the context and process of innovation and entrepreneurship there has been a steady rise in scientific literature and empirical studies. The purpose of this paper is to study the trends and progress of academic research on innovation and entrepreneurship in India by identifying the key articles, journals, authors and institutions.
Scientometric methods especially bibliometrics is used, for measuring the maturity of this research field in the country. The paper studies the research landscape in innovation and entrepreneurship in India by doing a bibliometric analysis using data from publications indexed in the Scopus database from the year 2000 to 2018. The study takes a multidisciplinary review of the literature in innovation and entrepreneurship research in India and could be used as a reference for future studies in this theme.
The study finds an increase in the scholarly studies in innovation and entrepreneurship in India in the last decade. It was also found that a large number of publications were joint-authored and collaborations between Indian and foreign universities is happening. The paper also highlights the authorship patterns, top journals and the most cited papers.
A major limitation of this study is that it has considered publications which are indexed in Scopus. This paper has contributed by highlighting the growth of studies in the field of innovation and entrepreneurship in the Indian context. The results can be used by future studies in this area as a starting point to highlight the nature of this research area.
The study attempts to present a trend analysis of published literature on innovation and entrepreneurship in India.