Copper complex of isatin Schiff base encapsulated in zeolite as active heterogeneous catalyst: an efficient protocol for the acetylation reaction

Copper (II) complex of 3-phenylimino-1,3-dihydro-indol-2-one encapsulated in the super cages of zeolite-Y has been synthesized by flexible ligand method and characterized by various physicochemical measurements. The catalytic activity of cationic exchanged zeolite, copper complex of ligand and complex encapsulated inside the zeolite was investigated for the decomposition of H2O2 and for the acetylation of p-cresol. All catalysts show good to excellent yield. The results showed that conversion of p-cresol varies in the order homogeneous complex \NaY-Zeolite\Cu-Y-Zeolite\heterogeneous comple

CBTS: Correlation based transformation strategy for privacy preserving data mining

Mining useful knowledge from corpus of data has become an important application in many fields. Data mining algorithms like clustering, classification work on this data and provide crisp information for analysis. As these data are available through various channels into public domain, privacy for the owners of the data is increasing need. Though privacy can be provided by hiding sensitive data, it will affect the data mining algorithms in knowledge extraction, so an effective mechanism is required to provide privacy to the data and at the same time without affecting the data mining algorithms. Privacy concern is a primary hindrance for quality data analysis. Data mining algorithms on the contrary focus on the mathematical nature than on the private nature of the information. Therefore instead of removing or encrypting sensitive data, we propose transformation strategies that retain the statistical, semantic and heuristic nature of the data while masking the sensitive information. The proposed Correlation Based Transformation Strategy (CBTS) combines Correlation Analysis in tandem with data transformation techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Non Negative Matrix Factorization (NNMF) provides the intended level of privacy preservation and enables data analysis. The outcome of CBTS is evaluated on standard datasets against popular data mining techniques with significant success and Information Entropy is also accounted

Catalyic Activity of Ruthenium(III) and Palladium(II) Complexes of 2-Methylbenzimidazole (Mebzlh) Encapsulated in Zeolite-Y and ZSM-5 for Liquid Phase Hydroxylation of Phenol

Ruthenium(III) and palladium(II) complexes of 2-methylbenzimidazole (Mebzlh) ligand encapsulated in the super cages of zeolite-Y and ZSM-5 have been synthesized and characterized by various physico-chemical measurements. A suitable reaction condition has been optimized for [Ru(Mebzlh)]-Y by considering the effect of various parameters such as different solvents, concentration of substrate, reaction time and amount of oxidant etc., for the maximum conversion of phenol to a mixture of catechol and hydroquinone. The results obtained showed that selectivity for the catechol formation is ca. 85%, even though the conversion of phenol varies in the order [Ru(Mebzlh)]-Y (35%) > [Pd(Mebzlh)]-Y (15 %) > [Ru(Mebzlh)]- ZSM-5(10 %) > [Pd(Mebzlh)]- ZSM-5 (2 %) after 6 h of reaction time

Generic CBTS: Correlation based Transformation Strategy for Privacy Preserving Data Mining

Mining useful knowledge from corpus of data has become an important application in many fields. Data Mining algorithms like Clustering, Classification work on this data and provide crisp information for analysis. As these data are available through various channels into public domain, privacy for the owners of the data is increasing need. Though privacy can be provided by hiding sensitive data, it will affect the Data Mining algorithms in knowledge extraction, so an effective mechanism is required to provide privacy to the data and at the same time without affecting the Data Mining results. Privacy concern is a primary hindrance for quality data analysis. Data mining algorithms on the contrary focus on the mathematical nature than on the private nature of the information. Therefore instead of removing or encrypting sensitive data, we propose transformation strategies that retain the statistical, semantic and heuristic nature of the data while masking the sensitive information. The proposed Correlation Based Transformation Strategy (CBTS) combines Correlation Analysis in tandem with data transformation techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Non Negative Matrix Factorization (NNMF) provides the intended level of privacy preservation and enables data analysis. The proposed technique will work for numerical, ordinal and nominal data. The outcome of CBTS is evaluated on standard datasets against popular data mining techniques with significant success and Information Entropy is also accounted

Real-Time Customer Satisfaction Analysis using Facial Expressions and Head Pose Estimation

Background/Purpose: Quantification of consumer interest is an interesting, innovative, and promising trend in marketing research. For example, an approach for a salesperson is to observe consumer behaviour during the shopping phase and then recall his interest.  However, the salesperson needs unique skills because every person may interpret their behaviour in a different manner.  The purpose of this research is to track client interest based on head pose positioning and facial expression recognition. Objective: We are going to develop a quantifiable system for measuring customer interest.  This system recognizes the important facial expression and then processes current client photos and does not save them for later processing.  Design/Methodology/Approach: The work describes a deep learning-based system for observing customer actions, focusing on interest identification. The suggested approach determines client attention by estimating head posture. The system monitors facial expressions and reports customer interest. The Viola and Jones algorithms are utilized to trim the facial image. Findings/Results: The proposed method identifies frontal face postures, then segments facial mechanisms that are critical for facial expression identification and creating an iconized face image. Finally, the obtained values of the resulting image are merged with the original one to analyze facial emotions. Conclusion:  This method combines local part-based features with holistic facial information. The obtained results demonstrate the potential to use the proposed architecture as it is efficient and works in real-time. Paper Type: Conceptual Research

Real-Time Customer Satisfaction Analysis using Facial Expressions and Head Pose Estimation

Background/Purpose: Quantification of consumer interest is an interesting, innovative, and promising trend in marketing research. For example, an approach for a salesperson is to observe consumer behaviour during the shopping phase and then recall his interest. However, the salesperson needs unique skills because every person may interpret their behaviour in a different manner. The purpose of this research is to track client interest based on head pose positioning and facial expression recognition. Objective: We are going to develop a quantifiable system for measuring customer interest. This system recognizes the important facial expression and then processes current client photos and does not save them for later processing. Design/Methodology/Approach: The work describes a deep learning-based system for observing customer actions, focusing on interest identification. The suggested approach determines client attention by estimating head posture. The system monitors facial expressions and reports customer interest. The Viola and Jones algorithms are utilized to trim the facial image. Findings/Results: The proposed method identifies frontal face postures, then segments facial mechanisms that are critical for facial expression identification and creating an iconized face image. Finally, the obtained values of the resulting image are merged with the original one to analyze facial emotions. Conclusion: This method combines local part-based features with holistic facial information. The obtained results demonstrate the potential to use the proposed architecture as it is efficient and works in real-time. Paper Type: Conceptual Research.</jats:p