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.

Multiferroic and optical properties of Pr-substituted bismuth ferrite ceramics

Abstract: Pr-doped BiFeO3 ceramics with x ≤ 0.20 were synthesized by a solid-state reaction method. X-ray diffraction patterns of these samples have shown single-phase rhombohedral structure with R3c space group symmetry. Remanent magnetization has been found to increase with Pr concentration. Magnetization versus temperature plots for x = 0.0 and 0.20 exhibit a broad magnetic anomaly at around 375 and 393 °C, respectively, indicating the Neel temperature “TN”. Dielectric constant (ϵr) versus T plots exhibit dielectric anomalies at around 375 °C in these materials, which may correspond to the transition temperature TN. The photoluminescence spectra of these ceramics exhibit band-edge emission in the blue region and defect-related emission in the green region. The FTIR spectra show two broad vibrational modes at 430 and 560 cm−1.

Analysis and Classification of Crime Tweets

Abstract: Nowadays social Networking and micro-blogging sites like Twitter are very popular and millions of users are registered on these websites. The users present on these website use these websites as a platform to express their thoughts and opinions. Our analysis of content posted on Twitter shows that users often post crime related information on Twitter. Among these crime related tweets some tweets are the crime messages that need police attention. Detection of such tweets can be beneficial in utilizing pattroling resources. The analysis of the data present on these websites can have an enormous impact. In this paper,the work is done on analyzing Twitter data to identify crime tweet that need police attention. Text mining based approach is used for classification of 369 tweets into crime and not-crime class. Classifiers such as Naive Bayesian, Random Forest, J48 and ZeroR are used. Among all of these four classifiers, Random forest classifier give the best accuracy of 98.1%.

A New Fuzzy Cluster Validity Index for Hyperellipsoid or Hyperspherical Shape Close Clusters With Distant Centroids

Abstract: Determining the correct number of clusters is essential for efficient clustering and cluster validity indices are widely used for the same. Generally, the effectiveness of a cluster validity index relies on two factors: first, separation, defined by the distance between a pair of cluster centroids or a pair of data points belonging to different clusters and second, compactness, which is determined in terms of the distance between a data point and a centroid or between a pair of data points belonging to the same cluster. However, the existing cluster validity indices for centroid-based clustering are unreliable when the clusters are too close, but corresponding centroids are distant. To mitigate this, a new cluster validity index, Saraswat-and-Mittal index, has been proposed in this article for hyperellipsoid or hyperspherical shape close clusters with distant centroids, generated by fuzzy c-means. The proposed index computes compactness in terms of the distance between data points and corresponding centroids, whereas the distance between data points of disjoint clusters defines separation. These parameters benefit the proposed index in the analysis of close clusters with distinct centroids efficiently. The performance of the proposed index is validated against ten state-of-the-art cluster validity indices on artificial, UCI, and image datasets, clustered by the fuzzy c-means.