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Institution

VIT University

EducationVellore, Tamil Nadu, India
About: VIT University is a(n) education organization based out in Vellore, Tamil Nadu, India. It is known for research contribution in the topic(s): Cloud computing & Wireless sensor network. The organization has 19943 authors who have published 24419 publication(s) receiving 261879 citation(s). The organization is also known as: Vellore Engineering College.
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Journal ArticleDOI
Abstract: The field of biomaterials has become a vital area, as these materials can enhance the quality and longevity of human life and the science and technology associated with this field has now led to multi-million dollar business. The paper focuses its attention mainly on titanium-based alloys, even though there exists biomaterials made up of ceramics, polymers and composite materials. The paper discusses the biomechanical compatibility of many metallic materials and it brings out the overall superiority of Ti based alloys, even though it is costlier. As it is well known that a good biomaterial should possess the fundamental properties such as better mechanical and biological compatibility and enhanced wear and corrosion resistance in biological environment, the paper discusses the influence of alloy chemistry, thermomechanical processing and surface condition on these properties. In addition, this paper also discusses in detail the various surface modification techniques to achieve superior biocompatibility, higher wear and corrosion resistance. Overall, an attempt has been made to bring out the current scenario of Ti based materials for biomedical applications.

3,382 citations


Journal ArticleDOI
Nilanjana Das1, Preethy Chandran1Institutions (1)
TL;DR: An updated overview of petroleum hydrocarbon degradation by microorganisms under different ecosystems is presented and it is shown that many indigenous microorganisms in water and soil are capable of degrading hydrocarbon contaminants.
Abstract: One of the major environmental problems today is hydrocarbon contamination resulting from the activities related to the petrochemical industry. Accidental releases of petroleum products are of particular concern in the environment. Hydrocarbon components have been known to belong to the family of carcinogens and neurotoxic organic pollutants. Currently accepted disposal methods of incineration or burial insecure landfills can become prohibitively expensive when amounts of contaminants are large. Mechanical and chemical methods generally used to remove hydrocarbons from contaminated sites have limited effectiveness and can be expensive. Bioremediation is the promising technology for the treatment of these contaminated sites since it is cost-effective and will lead to complete mineralization. Bioremediation functions basically on biodegradation, which may refer to complete mineralization of organic contaminants into carbon dioxide, water, inorganic compounds, and cell protein or transformation of complex organic contaminants to other simpler organic compounds by biological agents like microorganisms. Many indigenous microorganisms in water and soil are capable of degrading hydrocarbon contaminants. This paper presents an updated overview of petroleum hydrocarbon degradation by microorganisms under different ecosystems.

1,256 citations


Journal ArticleDOI
TL;DR: It is demonstrated that the bactericidal efficacy of ZnO nanoparticles increases with decreasing particle size, and it is proposed that both the abrasiveness and the surface oxygen species of ZNO nanoparticle promote the biocidal properties of ZngN nanoparticles.
Abstract: In this study, we investigate the antibacterial activity of ZnO nanoparticles with various particle sizes. ZnO was prepared by the base hydrolysis of zinc acetate in a 2-propanol medium and also by a precipitation method using Zn(NO3)2 and NaOH. The products were characterized by x-ray diffraction (XRD) analysis, transmission electron microscopy (TEM) and photoluminescence (PL) spectroscopy. Bacteriological tests such as minimum inhibitory concentration (MIC) and disk diffusion were performed in Luria-Bertani and nutrient agar media on solid agar plates and in liquid broth systems using different concentrations of ZnO by a standard microbial method for the first time. Our bacteriological study showed the enhanced biocidal activity of ZnO nanoparticles compared with bulk ZnO in repeated experiments. This demonstrated that the bactericidal efficacy of ZnO nanoparticles increases with decreasing particle size. It is proposed that both the abrasiveness and the surface oxygen species of ZnO nanoparticles promote the biocidal properties of ZnO nanoparticles.

1,112 citations


Book ChapterDOI
Lincy Meera Mathews1, Seetha Hari2Institutions (2)
01 Jan 2018
TL;DR: This chapter aims to highlight the existence of imbalance in all real world data and the need to focus on the inherent characteristics present in imbalanced data that can degrade the performance of classifiers.
Abstract: Pattern Identification on various domains have become one of the most researched fields. Accuracy of all traditional and standard classifiers is highly proportional to the completeness or quality of the training data. Completeness is bound by various parameters such as noise, highly representative samples of the real world population, availability of training data, dimensionality etc. Another very pressing and domineering issue identified in real world data sets is that the data is well-dominated by typical occurring examples but with only a few rare or unusual occurrences. This distribution among classes make the real world data inherently imbalanced in many domains like medicine, finance, marketing, web, fault detection, anomaly detection etc. This chapter aims to highlight the existence of imbalance in all real world data and the need to focus on the inherent characteristics present in imbalanced data that can degrade the performance of classifiers. It provides an overview of the existing effective methods and solutions implemented towards the significant problems of imbalanced data for improvement in the performance of standard classifiers. Efficient metrics for evaluating the performance of imbalanced learning models followed by future directions for research is been highlighted.

1,109 citations


Journal ArticleDOI
L. Suganthi1, Anand A. Samuel2Institutions (2)
TL;DR: In this paper an attempt is made to review the various energy demand forecasting models to accurately predict the future energy needs.
Abstract: Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

853 citations


Authors

Showing all 19943 results

NameH-indexPapersCitations
Jeffrey G. Andrews11056263334
Muhammad Imran94305351728
Jürgen Eckert92136842119
Sourav Ghosh7328750764
Costas A. Varotsos643298358
Ming Tien6316915736
James D. Kubicki5821610151
Arun Kumar Sangaiah5739810691
Oluwole Daniel Makinde5657613757
Vivek K Goyal5527911342
Chandrasekharan Rajendran521929404
Amitava Mukherjee5035010915
L. John Kennedy481686401
Teh Hui Kao47999103
Gunasekaran Manogaran471155570
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2022166
20213,907
20203,162
20192,837
20182,658
20172,812