Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Cluster analysis & Wireless sensor network. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

An image segmentation method using logarithmic kbest gravitational search algorithm based superpixel clustering

Abstract: Image segmentation partitions an image into coherent and non-overlapping regions. Due to variations of visual patterns in images, it is a challenging problem. This paper introduces a new superpixel-based clustering method to efficiently perform the image segmentation. In the proposed method, initially superpixels from an image are obtained. The superpixels are further clustered into the required number of regions by a newly proposed variant of gravitational search algorithm namely; logarithmic kbest gravitational search algorithm. Experiments are conducted on the Berkeley Segmentation Dataset and Benchmark (BSDS500). It is affirmed from both visual and numerical analyses that the proposed method is efficacious and accurate in segmenting an image than the other considered segmentation methods.

Influence of Na substitution on structural, magnetic, optical and photocatalytic properties of bismuth ferrite nanoparticles

Abstract: We have reported the influence of Na substitution on structural, magnetic, optical and photocatalytic properties of sol–gel route synthesized Bi1−xNaxFeO3 (x = 0.0, 0.02, 0.04, 0.06 and 0.08) nanoparticles. Rietveld refinement of the XRD data and electron microscopy techniques revealed the phase purity and nanocrystalline nature of Bi1−xNaxFeO3 samples. The substitution of Na1+ leads to the structural distortion in BiFeO3 nanoparticles which is apparent from the XRD Rietveld refinement and Raman spectroscopy studies. The ferromagnetic ordering parameters increase with increasing Na content in BiFeO3 nanoparticles and the highest magnetization 0.91 emu/g at 30kOe is observed for x = 0.08 sample. The observed enhanced ferromagnetic behaviour of Bi1−xNaxFeO3 nanoparticles are also endorsed by the ESR analysis. The energy bandgap of BiFeO3 nanoparticles is altered by aliovalent Na ions substitution from 2.16 to 2.03 eV. Compared to pristine BiFeO3, the Na-substituted BiFeO3 nanoparticles revealed the much-advanced photocatalytic activity of methylene blue (MB) and crystal violet (CV) under UV–visible light irradiation. In the irradiation time of 100 min, Bi0.96Na0.04FeO3 nanoparticles showed ~ 95% MB and ~ 84% CV degradation. The outstanding changes in the magnetic and photocatalytic properties of Bi1−xNaxFeO3 nanoparticles are due to the differences in the ionic radii, aliovalency between the Bi3+ and Na1+ ions and oxygen vacancies.

User tweets based genre prediction and movie recommendation using LSI and SVD

Abstract: The emerging popularity and raise in users' posts on social media gave birth to numerous research challenges. Out of all challenges users' centric context information based recommendation is one prime research area to recommend jobs, events and movies. Here in this research work we focus on movie context aware recommendation and for this purpose, we analyze users' posted movie tweets to understand their intentions for the same. Therefore, the objective of this research work is to predict genre of movies based on user's posted movie tweets and recommending movies to users' according to predicted genre. For this purpose, we pre-processed twitter extracted movie tweets using tokenization, porter stemming, stop word removal and use Word-Net dictionary for synonym matching. Further, we apply Latent Semantic Indexing technique which in turn involves Singular Value Decomposition on this pre-processed data and predicts genre on the basis of IMDb movie genre categorization. The predicted genre conveys the movie interest of the user and to recommend movie on the basis of predicted genre which is measured through euclidean distance. We have extracted IMdb given movie data and further predicted genre using our proposed technique. To validate this we divided our dataset using pareto principle and matched with IMDb given genre data set and achieved approximate 70% accuracy using our approach.

More Precise FPGA Power Estimation and Validation Tool (FPEV_Tool) for Low Power Applications

Abstract: This paper provides a significant approach for designing the more accurate power estimation and validation models over the existing power estimation models given in the literature It is well established that one of the existing power estimation models is not able to accurately estimate the power of the designs incorporated with low power techniques like clock enable In this paper, an improvement over the existing power estimation model has been suggested termed as FPEV_Tool This tool is accurately estimating the power of both types of digital circuits ie designs with clock enable and without clock enable specifically, with an average error of approximately 3% and peak error of 17%, respectively The accuracy of the proposed tool is validated using Xpower Analyzer available for power analysis in Xilinx ISE and existing model given in the literature by Deng et al This tool helps researchers to validate and compare their results with the results of existing models and commercial tools available in the market This tool also provides a new move toward the power estimation and validation to the researchers those are working in the field of low power digital circuit design