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All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Global Pharma Technology
Shruti Jain
Jaypee University of Information Technology
Medicine & Pharmacology

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A review of various image fusion types and transforms Ayodeji Olalekan Salau; Shruti Jain; Joy Nnenna Eneh
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1515-1522

Abstract

Utilizing multiple views of an image is an important approach in digital photography, video editing, and medical image fusion applications. Image fusion (ImF) methods are used to improve an image's quality and remove noise from the image signal, resulting in a higher signal-to-noise ratio. A complete assessment of the literature on the different transform kinds, techniques, and rules utilized in ImF is presented in this paper. To assess the outcomes, a white flower image was fused using discrete wavelet transform (DWT) and discrete cosine transform (DCT) techniques. For validation of results, the red, green, blue (RGB) and intensity hue saturation (IHS) values of individual and fused images were evaluated. The results obtained from the fused images with the spatial IHS transform method give a remarkable performance. Furthermore, the results of the performance evaluation using DWT and DCT fusion techniques show that the same peak signal to noise ratio (PSNR) of 114.04 was achieved for both PSNR 1 and PSNR 2 for DCT, and different results were obtained for DWT. For signal to noise ratio (SNR), SNR 1 and SNR 2 achieved slightly similar values of 114.00 and 114.01 for DCT, while a SNR of 113.28 and 112.26 was achieved for SNR 1 and SNR 2 respectively.
IMPLEMENTATION OF MARKER PROTEINS USING STANDARDISED EFFECT Shruti Jain
Journal of Global Pharma Technology Volume 09 Issue 05
Publisher : Journal of Global Pharma Technology

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Abstract

In this paper we have discussed the null hypothesized mean,  standardized effect, true population mean, group sample size,  population S.D., type I error rate (Alpha),  critical value ,and power of different marker proteins (AkT, EGFR, ERK, JNK, MK2, IRS and FKHR) theoretically and validate it with Minitab and Statistica Software. Different plots have been plotted between power and sample size, power and standardized effect, power and type 1 error rate. Later we have plotted a correlation graph for different marker proteins which shows mean, SD, max, min, histogram plot, and scatter plot for each marker protein.
Computational Analysis of MK2 Protein for HT Carcinoma Cells using Pre-Processing and Characterization Techniques for Cell Death/ Survival Shruti Jain
Journal of Global Pharma Technology Volume 10 Issue 11 (2018) November 2018
Publisher : Journal of Global Pharma Technology

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Abstract

This paper presents the computational analysis using pre-processing and different characterization of MK2 proteins for cell death/ survival for HT carcinoma cells. Initially data is collected which than pre processed by completing the missing data, removing various outliers, checking whether the data is normal or non- normal. Later various characterisation steps were applied which includes: classification, regression, clustering, association rules etc. Different data variables use different approaches of pre processing and characterisation. In this paper we have collected the different data of MK2 proteins where we have cleaned the data by using various clustering approaches, than regression analysis, correlation matrix and covariance matrixes are calculated. In last we have classified the data by using different machine learning approaches (k-NN, SVM and Neural network). For k-NN we have compared the results of different techniques like chebyshev, city block and Euclidean. For SVM we have used linear, polynomial, RBF and sigmoid function for both types (Type 1 and Type 2) and for neural network we have calculated training, test and validation perfection using different hidden, and output activation function. This paper shows best 10 network using MLP and RBF approach. 
Co-Authors Ayodeji Olalekan Salau Joy Nnenna Eneh