Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Background: Identifying the most important genes in a cancer gene network is a crucial step in understanding the disease's functional characteristics and finding an effective drug. Method: In this study, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally. The novel approach involved incorporating gene expression data and protein to protein interaction network to create a customized pruned and weighted gene network. This was then readily provided to the influence maximization procedure. The weighted gene network was also processed through a widely accepted framework that identified essential proteins to benchmark the proposed method. Results: The proposed method's results had matched with the majority of the output from the benchmarked framework. The key takeaway from the experiment was that the influential genes identified by the proposed method, which did not match favorably with the widely accepted framework, were found to be very important by previous in-vivo studies on breast cancer. Interpretation &amp; conclusion: The new findings generated from the proposed method give us a favorable reason to infer that influence maximization added a more diversified approach to define and identify important genes and could be incorporated with other popular computational techniques for more relevant results. © 2021 Elsevier Ltd

World University root node

Department of Integrative Biology

School of Bio Sciences and Technology

Vellore Campus

Department of Information Security

School of Computer Science and Engineering

Computers in Biology and Medicine

Effective utilisation of influence maximization technique for the identification of significant nodes in breast cancer gene networks

Plastic pollution in the current scenario requires a sustainable and eco-friendly treatment process. Single-use plastics accumulate more than recyclable plastic wastes. Low-Density Polyethylene (LDPE) is one among the plastic family with inert characteristics. The traditional method, such as landfilling, develops pollution resistant micro-organisms. It is involved in the exploitation of the native microbes to the fullest. The soil of the Kodungaiyur, agriculture site, and Otteri dumpyard were used, which resulted in nearly 22.97 ± 2.7115%, 15.91667 ± 2.73775%, and 10.74 ± 0.502925% of LDPE degradation in 30 days without nutrient supplements. The enrichment of the column by organic nutrients increased the degradation of LDPE. The column enrichment was confirmed by the sulfur oxidizing bacteria (SOB) Escherichia coli and Pseudomonas stutzeri, which produced 195 mg/mL of sulfate ions. The FTIR of the LDPE degradation showed the polymer's oxygenation, while the electron microscopic images revealed cracks. In addition, an attempt was made to fit the experimental time-series data into suitable mathematical models to look at prediction and elementary forecasting. Three mathematical models, namely the customized moving averages model (CMAM), simple liinear regression model (SLRM), and a modified linear regression model (MLRM) with a lag, were able to represent the real experimental data complementarily. © 2021

Journal of Hazardous Materials

Integrated approach in LDPE degradation – An application using Winogradsky column, computational modeling, and pathway prediction

Sjögren-Larsson syndrome (SLS) is an autoimmune disorder inherited in an autosomal recessive pattern. To date, 80 missense mutations have been identified in association with the Aldehyde Dehydrogenase 3 Family Member A2 (ALDH3A2) gene causing SLS. Disruption of the function of ALDH3A2 leads to excessive accumulation of fat in the cells, which interferes with the normal function of protective membranes or materials that are necessary for the body to function normally. We retrieved 54 missense mutations in the ALDH3A2 from the OMIM, UniProt, dbSNP, and HGMD databases that are known to cause SLS. These mutations were examined with various in silico stability tools, which predicted that the mutations p.S308N and p.R423H that are located at the protein-protein interaction domains are the most destabilizing. Furthermore, to determine the atomistic-level differences within the protein-protein interactions owing to mutations, we performed macromolecular simulation (MMS) using GROMACS to validate the motion patterns and dynamic behavior of the biological system. We found that both mutations (p.S380N and p.R423H) had significant effects on the protein-protein interaction and disrupted the dimeric interactions. The computational pipeline provided in this study helps to elucidate the potential structural and functional differences between the ALDH3A2 native and mutant homodimeric proteins, and will pave the way for drug discovery against specific targets in the SLS patients.

VIT Unitversity

Department of Biotechnology

Advances in Protein Chemistry and Structural Biology Inflammatory Disorders - Part B

Comprehensive in silico screening and molecular dynamics studies of missense mutations in Sjogren-Larsson syndrome associated with the ALDH3A2 gene

Proper classification of fingerprints still poses difficult issues in large-scale databases due to ambiguity in intraclass and interclass structures, discontinuity in low-quality images, and ridges. To address these challenges, we propose a feature named local diagonal and directional extrema pattern (LDDEP) as a descriptor for classification of fingerprints. The proposed method utilizes first-order derivatives to find values and indices of local diagonal and directional extremas. The local extrema values are then compared with the central pixel intensity value to find the correlation with the neighbors. Eventually, the descriptor is generated with the help of the indices and local extrema values. Furthermore, the proposed descriptor is fed into K-nearest neighbor and support vector machine (SVM) for classifying the fingerprint images into four and five groups, respectively. The LDDEP descriptor is compared with the existing methods on two databases, namely National Institute of Standards Technology Special Database 4 (NIST SD 4) and Fingerprint Verification Competition (FVC). Our experiments have shown that, on the 4000 image NIST SD 4 test dataset, the proposed descriptor achieved a classification accuracy of 95.15% for five classes and 96.85% for four classes for half of the dataset, and an accuracy of 95.5% for five classes and 96.63% for four classes for the entire test dataset using SVM classifier. Similarly, FVC databases for the LDDEP descriptor gave classification accuracy of 98.2% using SVM classifier. The proposed method gave higher accuracies compared to the existing methods. © 2019 SPIE and IS T.

Journal of Electronic Imaging

Fingerprint image classification using local diagonal and directional extrema patterns

Rapid increase in human population and their encroachment into forest areas have posed several threats to human life and their livelihood. This has subsequently led to a demand for surveillance system that detect the motion of objects in particular areas where threat is possible. This application also detects the pattern of the object i.e. the time at which he comes and goes to their place. For detecting, the presence of particular wildlife in a particular area and their sleep pattern and various things about their identity can be known by the use of this application. The application detects the motion of the object that come in the visual region of the camera and plot the time for which the object remain in its visual sight. © 2018 IEEE.

Department of Software Systems

Department of Database Systems

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI)

Web Camera Based Motion Detector Application

Bin packing is a NP-hard combinatorial problem which provides a certain placement of non-overlapping objects in a container in such a way that one or more objectives are optimised. This packing arrangement has many variants in dimension, namely single dimensional, two dimensional or multi-dimensional. The problem objectives can be as simple as minimizing the number of bins to a more complex one like considering a trade-off between the number of bins. Problems can also be categorized into optimization of a single objective or multi objective. Bi objective optimization is considered as a multi objective one in most of the studies, presented in this review. Solution to these problems can be categorised in many ways. In addition to the limited number of exact methods, the extensive literature on bin packing categorises the heuristic solution approaches into three categories, namely heuristics and approximation algorithms, meta heuristics and hyper heuristics. This article mainly focuses on surveying the effectiveness and applicability of these three categories of heuristic approaches on 2D and 3D packing with both single and multi-objective optimization. © 2016, International Journal of Pharmacy and Technology. All rights reserved.

International Journal of Pharmacy and Technology

Bin packing problems: Comparative analysis of heuristic techniques for different dimensions

Medical images are available in different modalities, each with its own usage. For example, preferred use for CT scan is in imaging bone injuries, cardiothoracic imaging and cancer diagnosis. MRI is generally used for soft tissue imaging and brain tumour detection. Medical images from different modalities often yield pathological information and correspondingly physiological information as well. This has made the study of multimodal medical image fusion very attractive. There are many occasions which require the integration of such comprehensive information for clinical diagnosis. The system proposed contains a design for a multi-modality medical image fusion system using a Sugeno Fuzzy Logic (SFL) based fusion method. The efficiency of the SFL based fusion method is established by comparing it with the existing methods, such as Principal Component Analysis (PCA), Laplacian Pyramid Approach(LPA), Discrete Wavelet Transform (DWT), Redundancy Discrete Wavelet Transform (RDWT) and Dual-Tree Complex Wavelet Transform (DTCWT) using quantitative metrics such as Entropy (EN), Signal to Noise Ratio (SNR) and Mutual Information (MI). The experimental results reveal that SFL based fusion method provides better quality of information in terms of Entropy, shows less noise ratio in terms of SNR and the higher value of MI indicate that more information from the original images are transferred to the fused image. © 2016, International Journal of Pharmacy and Technology. All rights reserved.

A sugeno fuzzy logic based CT and MRI image fusion technique with quantitative analysis

Image fusion combines more than one image from various environments into a single image. This can be useful for subsequent processing of the image, especially in medical imaging where it can help in disease diagnosis. This paper uses the block based Intuitionistic Fuzzy Sets(IFS) to fuse the multimodality medical images. IFSs can effectively handle the inherent uncertainties of digital images. Initially, in this model, entropy is used to deduce the optimal parameter value for defining the membership and non-membership function. This, in turn generates the Intuitionistic Fuzzy Images (IFI) from the original image. Finally, the IFIs are partitioned into image blocks and then recombined by the generated membership function. This paper compares the proposed method with popular ones like Principal Component Analysis (PCA), simple averaging (AVG), Laplacian Pyramid Approach(LPA), Discrete Wavelet Transform (DWT) and MPA (Morphological Pyramid Approach) on various performance measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Universal Image Quality Index (UIQI), Mean and Standard Deviation (STD). The experimental results show better image visualization generated through the proposed method compared to the other methods, in overall. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.

IIOAB Journal

Multimodality medical image fusion using block based intuitionistic fuzzy sets

Computer Science departmental Web pages of universities usually portray the gist of their academic activities, with special emphasis on fields of research, faculty strength with their respective specialties and research labs. By text mining the departmental Web pages and individual faculty Web pages, we can get a better idea of the actual strengths and weaknesses of the specific department. This strength or weakness is quantified by performing frequency analysis of technical words of specific subcategory in the Web pages. Consequently, by analyzing the ratio of a category of technical words in the departmental Web page to the faculty Web pages, we can get an idea if the departmental claims are matched by the faculty Web pages. For this particular objective, frequency analysis came out to be a better technique than clustering.