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

Department of Software and Systems Engineering

BACKGROUND:The present investigation looks at the most likely possibilities of usage of a naturally occurring photosynthetic pigment, Pheophytin a, from the seagrass, Syringodium isoetifolium, for plausible use as human TSPO ligand. METHODS:Pheophytin a isolated in our laboratory previously was administered to A549 cell lines in vitro to examine its effects on cell migrations, DNA, cell cycle, Mitochondrial Membrane Potential and gene expressions. In silico tools were used to predict the nature of the compound and target binding. RESULTS:Pheophytin a hadIC50 values of 22.9 ± 5.8 µM for cancerous A549 cell lines, whilst not targeting non-cancerous vero cells [IC50: 183.6 ± 1.92 µM]. Pheophytin a hindered cellular migration, fragmented DNA, arrested cell cycle precisely at S phase, reduced ∆ψmit and directed mRNA expressions toward apoptosis. In silico tools indicate that the compound binds to TSPO with high effectiveness to collapse ∆ψmit(which is proved using wet lab experiments) to promote mitophagy. CONCLUSION:Hence Pheophytin a could be seen as a possible TSPO ligand for targeting metastatic alveolar cancers like A549 via intrinsic apoptotic pathway. GENERAL SIGNIFICANCE:Given the inherent non-toxic nature of the compound and easy extractability from almost all autotrophic eukaryotes, one could be confident to testing in animal models.

World University root node

School of Information Technology and Engineering

Vellore Campus

Department of Integrative Biology

School of Bio Sciences and Technology

Phytomedicine

A natural anticancer pigment,Pheophytin a,from a seagrass acts as a high affinity human mitochondrial translocator protein (TSPO) ligand, in silico, to reduce mitochondrial membrane Potential (∆ψ ) in adenocarcinomic A549 cells.

Department of Digital Communications

Cognitive Systems Research

A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks

In the field of computational biology, to gauge the meaningful and accurate feature for protein function predications, either the profile-based protein data or sequence-based data has been used. As we know that the prediction of enzyme class from an unknown protein is most interacted research in the current era. In this context, machine learning and statistical classification technique has been used. In this article, we have use six different machine learning and statistical classification technique such as CRT, QUEST, CHAID, C5.0, ANN and SVM for classification of 4314 number of human protein sequence data. These data are extracted form UniprotKB databank with the help of PROFEAT server. The extracted data are categorized in seven different classes. To manipulate the high dimensional protein sequence data with some missing value, the SPSS has been used for classification and estimation of the performance of classification technique. The experimental results highlight that the class C4, C5, C6 and C7 data are imbalanced that affect the overall performance of classification technique. This article provides an extensive comparative analysis of different classification technique on sequence-based protein data. The experimental analysis highlights that the SVM and C5.0 classification technique gives better result than others and can be used for protein classification and predictions.

International Journal of Recent Technology and Engineering 2

Human Protein Sequence Classification using Machine Learning and Statistical Classification Techniques

The mobile computing and cloud computing together form into Mobile Cloud Computing (MCC). The mobile devices have some limitations like computation power, battery life, etc., These limitations can be overcome by integrating with the cloud technology. Even in this case, delay will be introduced due to WAN properties during the offloading process of the tasks from mobile environment to cloud environment. This is reduced with the use of cloudlet environment. In this paper, fault tolerant dynamic task offloading with cloudlets in mobile cloud computing is proposed. The technical process of resisting against diseases by the human is incorporated in MCC to fight against the faulty machines in the system. The proposed system is simulated using openstack++ and evaluated in terms of task completion time. The performance of the proposed system is compared with the algorithm where rescheduling is not carried out and DR approach [28] and is proved to be performing better.

VIT Unitversity

International Journal of Innovative Technology and Exploring Engineering Special Issue

Fault Tolerant Dynamic Task Offloading with Cloudlets in Mobile Cloud Computing

Scientific collaboration plays a vital role in generating novel ideas and innovative research progress among the researchers. Similarly, news diffusion also has an important role among the research communities. Though the collaboration networks have made an impact in scientific activities and attracted the attention of scientific communities, no work so far analyzed the cause which can determine the future research publications. The objective of this paper is to study influence of news trends on scientific collaboration and research publications. For this purpose, we have collected the top technological news trends and applied the LDA model to identify top research keywords from the articles. The results show that the news trends play a significant role on scientific collaborations and innovative research progress. It is found that the researchers identify their research gap, make future collaborations and does interdisciplinary research. Our results highlight the important role of diffusion of news, which influence the young researchers to generate novel ideas and tend to collaborate more with different scientific communities. © 2018 John Wiley &amp; Sons, Ltd.

Concurrency and Computation: Practice and Experience

Analyzing the impact of news trends on research publications and scientific collaboration networks

Load balancing is an important aspect in cloud to share load among different virtual machines running on various physical nodes. The user response time which is an important performance metric is being highly influenced by the efficient load balancing algorithm for cloud data centers. Virtual machines which are part of the cloud data centers consist of various types of physical devices. The user response time is affected significantly by the capacity of physical devices that exist as part of the data centers. Several load balancing algorithms exist in the literature to allocate task effectively on various virtual machines running in data centers. We investigate the performance of round robin based load balancing algorithm with closest data center as service broker policy in cloud data centers. We have performed a simulation with data centers that consist of devices with different physical characteristics such as memory, storage, bandwidth, processor speed and scheduling policy using Round Robin load balancing algorithm with closest data centers as service broker policy. We present the merits of heterogeneous device characteristics in reducing the user response time and the data center request service time. We used Cloud Analyst, an open source simulation tool for cloud computing environment

Department of Information Technology

International Journal of Innovative Technology and Exploring Engineering Regular Issue

Analysis of Heterogeneous Device Characteristics in Round Robin Based Load Balancing Algorithm with Closest Data Center as Service Broker Policy in Cloud

Massive Open Online Courses (MOOC) has gained a huge popularity amongst the current generation students mainly because of its open nature and its ubiquity. MOOC made it possible for thousands of aspiring learners to learn from their favorite Universities. Though this online learning platform has its advantages, many studies have proved that these massive courses are suffering from tremendous rates in students’ dropouts. This study surveys the major causes of dropouts and would try to link the MOOC failures with the learners’ stress levels. The study also proposes a framework which could be used while designing MOOC courses and will help MOOC providers to personalize the content delivery according to the online learners’ stress levels.

Framework for Personalizing Mooc Courses based on Learners’ Stress Levels Towards Increased Course Completion Rates

In this fast growing data universe, data generation and data storage are moving into the next-generation process by generating petabytes and gigabytes in an hour. This leads to data accumulation where privacy and preservation are certainly misplaced. This data contains some sensitive and high privacy data which is to be hidden or removed using hashing or anonymization algorithms. In this article, the authors propose a hybrid k anonymity algorithm to handle large scale aircraft datasets with combined concepts of Big Data analytics and privacy preservation of storing the dataset with the help of MapReduce. This published anonymized data are moved by MapReduce to the Hive database for data storage. The authors propose a multi-dimensional hybrid k-anonymity technique to solve the privacy issue and compare the proposed system with other two anonymization methods such as BUG and TDS. Three experiments were performed for evaluating classifier error, calculating disruption value and p% hybrid anonymity and estimation of processing time.

International Journal of Web Portals

Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization

Image Quality Assessment (IQA) becomes intensely salient in several applications, namely, acquisition of images, watermarking, image compression, image transmission, enhancement of images and so on, due to the extensive use of digital images. In the past decades, considerable advancements have been developed in IQA using Region of Interest (ROI). However, ROI localization is a labour-intensive process that takes multiple passes of sliding-window in search of proper ROI. The efficiency of examination, reduction in the time taken for ROI localization by multiple passes and the quality of the image can be improved by the proposed method, Histogram-Equalized Hypercube Adaptive Linear Regression (HE-HALR) scheme. HE-HALR scheme first performs the pre-processing step for input images. In this step, the features used to describe the quality of images are analysed using Histogram-Equalization-based Contrast Masking (HE-CM) model. The HE-CM model performs ROI localization with the parallelization programming that identifies the contrast masking and luminance value in a parallel manner. With the resultant feature vectors, dimensional reduction is performed using machine learning technique, namely, hypercubical neighbourhood. Finally, IQA is performed with the dimensionality-reduced features using Adaptive Linear Regression. © 2019, Indian Academy of Sciences.

Fulltext

Sādhanā

Histogram-Equalized Hypercube Adaptive Linear Regression for Image Quality Assessment

In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).