Shalini Gupta

Synthesis, Structural, and Physicochemical Study of First- and Second-Generation Melamine-Based Dendrimers

Abstract First-tier (G1) 2,4,6-tridiethymalonate-triazine (2,4,6-TDEMTA) and second-tier (G2) 2,4,6-hexadiethylmalonate-triazine (2,4,6-HDEMTA) dendrimers were prepared with melamine (1,3,5-triazine) as core (G0) and sodium malonate ester for bifurcation of chains. The trichlorotriazine (TCT) and sodium diethyl malonate (SDEM) aqueous solutions were mixed, and a creamy white precipitate of G1 was obtained. The G1 was hydrolyzed in alkaline medium to prepare 2,4,6-tridisodiummalonate-triazine (2,4,6-TDSMTA) by replacing −C2H5 groups of COOC2H5 by Na, which was further neutralized by dilute HCl to obtain 2,4,6-trimalonicacid-triazine (2,4,6-TMATA). The TMATA was treated with PCl5(s) to form –COCl, which was treated with SDEM to form the G2.

Construction of exact dynamical invariants in coupled oscillator problems

With a view to obtaining further insight into the theoretical understanding of the problem of coupled harmonic oscillators we carry out the construction of exact dynamical invariants for momentum- and time-dependent (TD) Hamiltonian systems in two dimensions. In particular, we investigate the systems H1 = 1 [α1p 2 + α2p 2 + β1x 2 + β2x 2 2 +2 β3x1x2 +2 α3p1p2] H2 = 1 α(p 2 1 + p 2 2 ) + 1 2 β(x 2 1 + x 2 2 ) + f( p1x2 − p2x1) where the parameters αi ,β i, i = 1, 2, 3, α, β, f may be TD. While the Lie algebraic method is employed for the TD forms of H1 and H2, the rationalization method, modified here for the momentum-dependent case, is used for the timeindependent versions of H1 and H2. The role and scope of the invariants so constructed is pointed out.

Scalable online product recommendation engine based on implicit feature extraction domain

This article presents a scalable and optimized recommender system for e-commerce web sites to maintain a better customer relationship management and survive among its competitors. The proposed system analyses the clickstream data obtained from an ecommerce site and predicts the preference level of the customer for the products clicked but not purchased using efficient classifiers such as decision trees, artificial neural networks and extended trees. Collaborative filtering technique is used to recommend products in which similarity measures are used along with efficient rough set leader clustering algorithm which helps in making accurate and fast recommendations. To determine the effectiveness of the proposed approach, an experimental evaluation has been done which clearly depicts the better performance of the system as compared to conventional approaches.

Spontaneity of reactions of triazine dendric molecules in aqueous medium illustrated with excess viscosities, volumes and free energies

The far-reaching applications of the dendric units – 2,4,6 trichloro-triazine (2,4,6 TCT), triaceto-triazine (2,4,6 TAT), trihydroxy-triazine (2,4,6 THT) and tridiethylmalonate-triazine (2,4,6 TDEMTA) for reactions in an aqueous medium have been assessed in the present study. Some of their fundamental properties, such as density and viscosity, have been measured to calculate the excess volume (V E), viscosity (η E) and molar free activation energy (Δ G E) at 298.15 K for 6.25–10.0 × 10−4 mol kg−1. The positive values of Δ G E for such units suggest their weaker response with water. THT around 3.0 × 10−7 mole fraction shows maximum Δ G E values. The lowest values of Δ G E are recorded for TCT.

A Business Intelligent Framework to Evaluate Prediction Accuracy for E-Commerce Recommenders

It is important for on-line retailers to better understand the interest of users for creating personalized recommendations to survive in the competitive market. Implicit details of user that is extracted from click stream data plays a vital role in making recommendations. These indicators reflect users’ items of interest. The browsing behavior, frequency of item visits, time taken to read details of an item are few measures that predict users’ interest for a particular item. After identifying these strong attributes, users are clustered on the basis of context clicks such as promotional and discounted offers and interest of the individual user is predicted for the particular context in user-context preference matrix. After clustering analysis is performed, neighborhood formation process is conducted using collaborative filtering on the basis of item category such as regular or branded items which depicts users’ interest in that particular category. Using these matrices, computational burden and processing time to generate recommendations are greatly reduced. To determine the effectiveness of proposed work, an experimental evaluation has been done which clearly depicts the better performance of the system as compared to conventional approaches.

A Propound Hybrid Approach for Personalized Online Product Recommendations

ABSTRACT The main aim of e-commerce websites is to turn their visitors into customers. For this purpose, recommender system is used as a tool that helps in turning clicks into purchases. Obtaining explicit ratings often faces problems such as authenticity of the ratings given by customers and queries that leads to low accuracy of the recommendations. Implicit ratings play a vital role in providing refined ranking of products. Preference level of the customers are predicted based on collaborative filtering (CF) approach using implicit details and mining click stream paths of like-minded users. Extracting the similarity among products using sequential patterns improves the accuracy of ranking. Integrating these two approaches improves the recommendation quality. Based on the results of experiment carried out to compare the performance of CF, sequential path of products viewed and integration of the two, we conclude that integration of mentioned approaches is superior to the existing ones.