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Institution

Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.


Papers
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Journal ArticleDOI
TL;DR: The cell attachment on HAp surfaces, cytotoxicity evaluation and MTT assay, which are carried out in RAW macrophage-like cell line media demonstrate good biocompatibility, and the histological analysis supports the bioaffinity of processed HAp biomaterials in Wistar rat model.
Abstract: The chemically treated Labeo rohita scale is used for synthesizing hydroxyapatite (HAp) biomaterials. Thermogravimetric and differential thermal analyses of fish scale materials reveal the different phase changes with temperature and find out the suitable calcination temperatures. The composition and structures of wet ball-milled calcined HAp powders are characterized by Fourier transform infrared spectroscopy, X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray analysis (EDX). The EDX as well as chemical analysis of fish scale-derived apatite materials confirms that the Ca/P ratio is 1.71. The compressive stress, hardness and porosity have been evaluated on sintered HAp biomaterials. The cell attachment on HAp surfaces, cytotoxicity evaluation and MTT assay, which are carried out in RAW macrophage-like cell line media demonstrate good biocompatibility. The histological analysis also supports the bioaffinity of processed HAp biomaterials in Wistar rat model for investigating the contact reaction and stability at the artificial or natural prosthesis interface.

41 citations

Journal ArticleDOI
TL;DR: A novel, principled approach to resolve the remained problems of substitution technique of audio Steganography and maintained randomness in message bit insertion into audio data for hiding the data from hackers and multi-objective GA is used to reduce distortion.
Abstract: In the current internet community, secure data transfer is limited due to its attack made on data communication. So more robust methods are chosen so that they ensure secured data transfer. One of the solutions which came to the rescue is the audio Steganography. "A GA Based Audio Steganography with enhanced security" is one propose system which is based on audio Steganography and cryptography, ensures secure data transfer between the source and destination. Here we present a novel, principled approach to resolve the remained problems of substitution technique of audio Steganography. We use most powerful encryption algorithm (RSA) to encrypt message in the first level of security, which is very complex to break. In the second level, we use a more powerful GA based LSB (Least Significant Bit) Algorithm to encode the encrypted message into audio data. Here encrypted message bits are embedded into random and higher LSB layers, resulting in increased robustness against noise addition. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well. On the other hand, to reduce the distortion, GA operators are used. The basic idea behind this paper is maintained randomness in message bit insertion into audio data for hiding the data from hackers and multi-objective GA is used to reduce distortion.

39 citations

Journal ArticleDOI
TL;DR: The results thus obtained have been found to be satisfactory and the sample has been designated as charcoal-immobilized papain (CIP) and used for further studies of mercury removal.

39 citations

Proceedings ArticleDOI
23 Jan 2014
TL;DR: This paper presents a Recommender System based on data clustering techniques to deal with the scalability problem associated with the recommendation task, and implements voting algorithms to recommend items to the user depending on the cluster into which it belongs.
Abstract: Recommender Systems (RS) are widely used for providing automatic personalized suggestions for information, products and services. Collaborative Filtering (CF) is one of the most popular recommendation techniques. However, with the rapid growth of the Web in terms of users and items, majority of the RS using CF technique suffer from problems like data sparsity and scalability. In this paper, we present a Recommender System based on data clustering techniques to deal with the scalability problem associated with the recommendation task. We use different voting systems as algorithms to combine opinions from multiple users for recommending items of interest to the new user. The proposed work use DBSCAN clustering algorithm for clustering the users, and then implement voting algorithms to recommend items to the user depending on the cluster into which it belongs. The idea is to partition the users of the RS using clustering algorithm and apply the Recommendation Algorithm separately to each partition. Our system recommends item to a user in a specific cluster only using the rating statistics of the other users of that cluster. This helps us to reduce the running time of the algorithm as we avoid computations over the entire data. Our objective is to improve the running time as well as maintain an acceptable recommendation quality. We have tested the algorithm on the Netflix prize dataset.

38 citations

Journal ArticleDOI
TL;DR: In this paper, the optimization of five process parameters such as pH, agitation, temperature, inoculum percentage and incubation time were optimized by Taguchi robust design method for obtaining enhanced biomass and phenol degradation by the isolated Alcaligenes faecalis JF339228 from Durgapur steel industry (DSP), India.
Abstract: The optimization of five process parameters such as pH, agitation, temperature, inoculum percentage and incubation time were optimized by Taguchi robust design method for obtaining enhanced biomass and phenol degradation by the isolated Alcaligenes faecalis JF339228 from Durgapur steel industry (DSP), India. About 18 experiments were conducted with a different combination of factors and the results obtained in terms of growth of specific bacterial strain and phenol degradation rates were processed in the Qualitek-4 software to study the main effect of individual factors. The main effect, interaction effects and optimal levels of the process factors were determined using signal-to-noise (S/N) ratio. The effect of factors has been studied for bacterial growth and phenol degradation by A. faecalis JF339228. Optimization of the said parameters has been evaluated by Taguchi method and analysed by analysis of variance. Predicted results showed enhanced process performance such as biomass (131.78%) and ...

38 citations


Authors

Showing all 581 results

NameH-indexPapersCitations
Debnath Bhattacharyya395786867
Samiran Mitra381985108
Dipankar Chakravorty353695288
S. Saha Ray342173888
Tai-hoon Kim335264974
Anindya Sen291093472
Ujjal Debnath293353828
Anirban Mukhopadhyay291693200
Avijit Ghosh281212639
Mrinal K. Ghosh26642243
Biswanath Bhunia23751466
Jayati Datta23551520
Nabarun Bhattacharyya231361960
Pinaki Bhattacharya191141193
Dwaipayan Sen18711086
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20227
2021110
202087
201992
201883
2017103