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Institution

Vignan University

EducationGuntur, Andhra Pradesh, India
About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.


Papers
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Journal ArticleDOI
TL;DR: An effective fluorescent chemosensor C1 is reported for selective recognition and detection of vanadyl ions inadium compounds that exhibit anti-tumor properties and insulin-like activity.
Abstract: Vanadium compounds exhibit anti-tumor properties and insulin-like activity, but an excess is toxic Development of a selective fluorescent chemosensor for the detection of vanadyl ions has not been reported so far For the first time, we have reported an effective fluorescent chemosensor C1 for selective recognition and detection of vanadyl ions C1 was evaluated as a turn “ON” fluorescent chemosensor for vanadyl ions (VO2+) based on a photoinduced electron transfer (PET) mechanism The association constant for the complex C1–VO2+ was found to be 419 × 104 M−1 C1 also behaves as a NOT molecular logic gate where H+ and VO2+ were given as inputs The proposed binding mode between C1 and vanadyl ions was explained using DFT calculations

10 citations

Journal ArticleDOI
TL;DR: In this article, the authors reviewed the characteristics of arsenic waste produced by arsenic treatment systems and the treatment and disposal methods of this waste and found that incorporating additives could increase the effectiveness of the solidification/stabilization (S/S) process depending on the type and dose of additives.
Abstract: As with other water treatment systems, arsenic treatment creates not only quality water but arsenic waste as well. Management of arsenic waste is now becoming a major public concern due to its harmful effects on the surrounding environment, including serious health problems such as skin cancers and various internal carcinomas. The main aim of this paper is to review: (i) the characteristics of arsenic waste produced by arsenic treatment systems; and (ii) the treatment and disposal methods of this waste. Arsenic waste type or its characteristics play an important role in choosing the best method of treatment and disposal. Currently, encapsulation of arsenic waste through solidification/stabilization (S/S) techniques is considered to be the most attractive solution and this method is the focus of this review. A number of studies have used cement by itself and in combination with additives such as lime, iron, silicates, or fly ash in the S/S process. Although there is a lack of systematic investigations and differing procedures for testing the effectiveness of the treatment methods, it was agreed that incorporating additives could increase the effectiveness of the S/S process depending on the type and dose of additives.

10 citations

Journal ArticleDOI
TL;DR: In this article, the fabrication of S-Glass epoxy composite hybrid composite materials from a combination of 7-mill glass fabric, ceramic paper as intermediate and carbon fabric is described.

10 citations

Journal ArticleDOI
TL;DR: In this article, the free-standing porous anodic alumina membranes (AAM) were prepared in oxalic acid by varying electrolyte concentration from 0.1 to 0.9m with a constant potential of 40V at 8°C.

10 citations

Journal ArticleDOI
TL;DR: The main objective of this paper is intrusion detection system for a cloud environment using combined PFCM-RNN and results clearly demonstrate the proposed technique outperformed conventional methods.
Abstract: The main objective of this paper is intrusion detection system for a cloud environment using combined PFCM-RNN. Traditional IDSs are not suitable for cloud environment as network-based IDSs (NIDS) cannot detect encrypted node communication, also host-based IDSs (HIDS) are not able to find the hidden attack trail. The traditional intrusion detection is largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. Accordingly, this proposed work consists of two modules namely clustering module and classification module. In clustering module, the input dataset is grouped into clusters with the use of possibilistic fuzzy C-means clustering (PFCM). In classification module, the centroid from the clusters is given to the recurrent neural network which is used to classify whether the data is intruded or not. For experimental evaluation, we use the benchmark database and the results clearly demonstrate the proposed technique outperformed conventional methods.

10 citations


Authors
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202322
202231
2021352
2020254
2019250
2018159