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Institution

Vardhaman College of Engineering

About: Vardhaman College of Engineering is a based out in . It is known for research contribution in the topics: Dielectric & Ceramic. The organization has 593 authors who have published 565 publications receiving 2660 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors have focused mainly on recent advances in MoS2 nanostructured materials for the applications in the broad area of energy and environment, where special attention has been paid to their applications in dye-sensitized solar cells, supercapacitor, Li-ion battery, hydrogen evolution reaction, photocatalysis for degradation of organic pollutants, chemical/bio sensors and gas sensors.

213 citations

Journal ArticleDOI
TL;DR: A review of various polymer composites consisting of ZnO nanoparticles (NPs) as reinforcements, exhibiting excellent properties for applications such as the dielectric, sensing, piezoelectrics, electromagnetic shielding, thermal conductivity and energy storage.

170 citations

Proceedings ArticleDOI
29 Mar 2018
TL;DR: Various machine learning algorithms used for developing efficient decision support for healthcare applications are reviewed to help in reducing the research gap for building efficient decisionSupport system for medical applications.
Abstract: Machine Learning is modern and highly sophisticated technological applications became a huge trend in the industry. Machine Learning is Omni present and is widely used in various applications. It is playing a vital role in many fields like finance, Medical science and in security. Machine learning is used to discover patterns from medical data sources and provide excellent capabilities to predict diseases. In this paper, we review various machine learning algorithms used for developing efficient decision support for healthcare applications. This paper helps in reducing the research gap for building efficient decision support system for medical applications.

169 citations

Journal ArticleDOI
TL;DR: In this article, the effects of structural and electrical properties of biopolymer based electrolytes on the solar energy conversion efficiencies of DSSCs and their compatibility with lithium or other salts in battery applications are summarized.
Abstract: Photovoltaic technologies represent one of the leading research areas of solar energy which is one of the most powerful renewable alternatives of fossil fuels. In a common photovoltaic application the batteries play a key role in storage of energy generated by solar panels. Although it will take time for dye sensitized solar cells (DSSCs) and batteries based on biopolymer electrolytes to take their places in the market, laboratory studies prove that they have a lot to offer. Most efficient DSSCs and batteries available in market are based on liquid electrolytes. The advantages of liquid electrolytes are having high conductivity and good electrode-electrolyte interface whereas, disadvantages like corrosion and evaporation limit their future sustainability. Biopolymer electrolytes are proposed as novel alternatives which may overcome the problems stated above. In this review, we focus on fabrication, working principle as well as up to date status of DSSCs and batteries using biopolymer electrolytes. The effects of structural and electrical properties of biopolymer based electrolytes on the solar energy conversion efficiencies of DSSCs and their compatibility with lithium or other salts in battery applications are summarized. Biopolymer electrolyte based DSSCs are categorized on the basis of types of additives and recent outcomes of author's laboratory studies on biopolymer electrolyte based DSSCs and batteries are also presented.

104 citations

Journal ArticleDOI
TL;DR: This paper has increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant, and applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection.
Abstract: In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.

83 citations


Authors
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
202215
2021125
202084
201966
201874