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: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Substitution driven structural and magnetic transformation in Ca-doped BiFeO3 nanoparticles

Abstract: Bi1−xCaxFeO3; (x = 0–0.20) nanoparticles were synthesized by tartaric acid based sol–gel route. X-ray diffraction and electron microscopy studies reveal the phase purity and nanocrystalline nature (45–90 nm) of Bi1−xCaxFeO3 samples. The Ca ion substitution driven structural transition from rhombohedral (space group R3c) to orthorhombic (Pnma) symmetry leads to enhancement in saturation magnetization due to the distorted cycloid spin structure (as also suggested by Mossbauer studies) and uncompensated surface spins which is accorded with electron paramagnetic resonance (EPR) studies as well. The ferromagnetic ordering contribution increases up to x = 0.15 samples with maximum saturation magnetization of 0.09 emu g−1 for x = 0.15 sample. The presence of high content orthorhombic phase for x = 0.20 sample results in the sharp decrease in the ferromagnetic component due to appearance of collinear antiferromagnetic ordering in agreement with EPR results. X-ray photoelectron spectroscopy confirmed the dominancy of Fe3+ oxidation states along with the shifting of the binding energy of Bi 4f orbital indicating the substitution of Ca2+ at Bi-site. Systematic change of Mossbauer parameters of nanoparticulate samples with Ca concentration are obtained by Mossbauer spectroscopy. The results of both one- and two-sextet fittings of the Mossbauer spectra provide evidence for destruction of the spin cycloid in Ca-doped BiFeO3 nanoparticles.

Spam review detection using spiral cuckoo search clustering method

Abstract: Nowadays, online reviews play an important role in customer’s decision. Starting from buying a shirt from an e-commerce site to dining in a restaurant, online reviews has become a basis of selection. However, peoples are always in a hustle and bustle since they don’t have time to pay attention to the intrinsic details of products and services, thus the dependency on online reviews have been hiked. Due to reliance on online reviews, some people and organizations pompously generate spam reviews in order to promote or demote the reputation of a person/product/organization. Thus, it is impossible to identify whether a review is a spam or a ham by the naked eye and it is also impractical to classify all the reviews manually. Therefore, a spiral cuckoo search based clustering method has been introduced to discover spam reviews. The proposed method uses the strength of cuckoo search and Fermat spiral to resolve the convergence issue of cuckoo search method. The efficiency of the proposed method has been tested on four spam datasets and one Twitter spammer dataset. To validate the efficacy of proposed clustering method it is compared with six metaheuristics clustering methods namely; particle swarm optimization, differential evolution, genetic algorithm, cuckoo search, K-means, and improved cuckoo search. The experimental results and statistical analysis validate that the proposed method outruns the existing methods.