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.

&nbsp;

&nbsp;

VIT University

Recently, microRNAs (miRNAs) have been shown to play significant roles in the progression of major human diseases. Identifying associations between human diseases and miRNAs using computational tools have attracted considerable attention. Experimental identification and validation of disease–miRNA associations are rare and time consuming. We propose a computational method for predicting associations to understand pathogenicity and uncover prognosis markers. Existing methods employed approaches based on network and machine learning to predict miRNA disease. However, these approaches do not consider the cluster information between miRNAs. Performance results of these methods’ predictions are not satisfactory due to more number of false positives and false negatives. The proposed methodology comprises of two phases: first, we use miRNA cluster information in addition to miRNA-functional similarity and disease semantic similarity. MiRNAs and diseases are placed in the same cluster by transforming the miRNA–disease association data matrix into a low-rank model using Principal Component Analysis (PCA). Second, the Adamic/Adar index was applied that computes the closeness of miRNAs and diseases based on shared neighbors, improving the prediction results. The problem of overestimation is resolved by incorporating similarity information about miRNA and disease. Case studies on Leukemia, Carcinoma, Glioma, Pancreatic Neoplasms, and Melanoma exhibit satisfactory performance with Area Under Curve (AUC) values ranging from 0.736 to 0.834. miRNAs predicted using the proposed method are ascertained to be matching the known associations found in the database of HMDD V3.2, dbDEMC V2.0, and PhenomiR V2.0.&nbsp;

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Department of Biotechnology

School of Bio Sciences and Technology

Network Modeling Analysis in Health Informatics and Bioinformatics

<a href="https://link.springer.com/article/10.1007/s13721-021-00292-9">An improved unsupervised learning approach for potential human microRNA–disease association inference using cluster knowledge</a>

The paper aims to find an approach for predicting the visibility percentage of the foggy images based on the factors like image brightness, luminance, intensity, and variance. The scope of using an image for predicting weather and providing information about the other details i.e. predicting if weather is foggy has increased. The idea is to utilize the data images to firstly classify them as being foggy or not. Secondly to detect the image visibility based on the image of a particular location. Thus the paper presents the implementation of convolutional neural network for the task of detecting and classifying the images into fog and non-fog. After getting the classification output, the brightness, luminance, variance and intensity of the images is calculated to find the visibility percent of the fog images. © Springer Nature Switzerland AG 2020.

VIT Unitversity

Advances in Intelligent Systems and Computing Intelligent Computing, Information and Control Systems

Fog Image Classification and Visibility Detection Using CNN

Cloud computing technology is emerging rapidly due to increasing demand of service required by the different organizations, institutions, and individuals. Nonetheless, the data in the cloud environment is still not completely secure from the outside and inside intrusions and attacks. In this paper, an improvised approach for securing the data in cloud computing environment using parallel and multistage security mechanism (PMSSM) is proposed to provide simultaneous security checks and procedural multistage security using authentication techniques, intrusion detection and encryption. By taking this approach into consideration, the chances of identifying the attack can be increased as the parallelism in the verification and multistage security will enhance the probability of identifying the intrusion or the attack being performed by the attacker. Also, considering the dynamic nature of the intrusion, the parallel and multistage security can help in avoiding such scenario. The discussion of the proposed mechanism suggested that this model can be used to provide enhanced cloud data security to the cloud users. © 2020, Springer Nature Singapore Pte Ltd.

Department of Information Security

Advances in Intelligent Systems and Computing Soft Computing for Problem Solving

Securing the Data in Cloud Environment Using Parallel and Multistage Security Mechanism

Since these sudden bursts of Technological advancements have taken over and are making forays into the corporate worlds research and development, it is important for the marketing investment sectors to embrace and adapt with the changes, and thus to move forward, need to implement concepts such as machine learning to keep up with the pace.Thus in this paper, we will discuss why it is necessary to do so, and what the desired output has been on some sample applications of machine learning. © BEIESP.

World University root node

International Journal of Innovative Technology and Exploring Engineering

Necessity of machine learning and data visualization principles in marketing investment management

In the cloud, users can easily share and modify the data as a group using the data storage and sharing services. To make certain shared information, integrity can be checked publicly; users in the organization need to compute signatures on the maximum of the blocks in shared data. Various blocks in shared data are regularly signed by different customers as a result of modifications done by various users. For security reasons, as soon as a person is revoked from the organization, the blocks which have been formally signed by using this revoked user should be re-signed by a pre-existing user. The best technique, permitting a pre-existing user to download the corresponding part of shared records and re-sign it at some stage in user revocation, is inefficient because of the huge size of shared data in the cloud. In this, we propose a secure and cost-effective user revocation mechanism for data analysis in the cloud. With the help of proxy re-signatures at revocation, we permit the cloud to re-sign blocks for existing customers, to ensure that existing users do not require to save and re-sign blocks again. In addition, a public verifier is used to check the integrity of public data without retrieving the whole data from the cloud, in spite of the fact that some amount of shared data has been re-signed by using the cloud. Additionally, this mechanism can support group auditing by validating many auditing tasks simultaneously. © IAEME Publication

International Journal of Mechanical Engineering and Technology

Secure and cost effective user revocation mechanism for data analysis in the cloud

In the current evergrowing traffic, security is one of the biggest concerns during travel. From airbags and ABS to EBD and automatic braking, work is on to make road travel more secure by the day. This project describes a design and implementation of an effective alert system to monitor an automobile's condition during travel. It is designed to send out an alert in case the vehicle has met with an accident. Our design of the system uses the in-built vibration sensor in the Airbag Electronic Control Unit to detect the abrupt vibrations from the occurrence of an accident. This detection and messaging system is composed of a GPS receiver, a microcontroller and a GSM modem. This enables the monitoring of the vehicle and in the event of an accident; it can immediately be intimated to the emergency services. © 2017 IEEE.