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 Computer Applications

A data that is picture rich could be an internet based life the billions of pictures transferred by clients who are identified with data like proprietor, shopper, maker, explanations, and remarks. During this article by the information recovery in a picture rich data system and suggestion in such systems is tended to. We are introducing a methodology that is measures the likeness maintained the two associations based and content based. The connection likeness relies on the system that is social like labels, gatherings and human explanation over the photos. Content likeness that is based the image content properties concealing histogram and so forth. For estimating the similitude. The grade that is consolidated of measures is employed according to comprise the convivial assets or assists with arranging the snap shots within image prosperous information systems. © 2020 Innovare Academics Sciences Pvt. Ltd. All rights reserved.

World University root node

School of Information Technology and Engineering

Vellore Campus

Journal of Critical Reviews

Enhanced resemblance measures for integration in image-rich information networks

Presents the front cover or splash screen of the proceedings record.

Department of Software and Systems Engineering

Department of Information Technology

International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

[Front cover]

Data redundancy is a significant issue that wastes plenty of storage space in the cloud-fog storage integrated environments. Most of the current techniques, which mainly center around the static scenes, for example, the backup and archive systems, are not appropriate because of the dynamic nature of data in the cloud or integrated cloud environments. This problem can be effectively reduced and successfully managed by data deduplication techniques, eliminating duplicate data in cloud storage systems. Implementation of data deduplication (DD) over encrypted data is always a significant challenge in an integrated cloud-fog storage and computing environment to optimize the storage efficiently in a highly secured manner. This paper develops a new method using Convergent and Modified Elliptic Curve Cryptography (MECC) algorithms over the cloud and fog environment to construct secure deduplication systems. The proposed method focuses on the two most important goals of such systems. On one side, the redundancy of data needs to be reduced to its minimum, and on the other hand, a robust encryption approach must be developed to ensure the security of the data. The proposed technique is well suited for operations such as uploading new files by a user to the fog or cloud storage. The file is first encrypted using the Convergent Encryption (CE) technique and then re-encrypted using the Modified Elliptic Curve Cryptography (MECC) algorithm. The proposed method can recognize data redundancy at the block level, reducing the redundancy of data more effectively. Testing results show that the proposed approach can outperform a few state-of-the-art methods of computational efficiency and security levels. © 2020, The Author(s).

Fulltext

Journal of Cloud Computing

A secure data deduplication system for integrated cloud-edge networks

Decision-making has been one of the challenging tasks in solving real-world problems. Decision-making mimics human activity which in turn has noteworthy impacts. Several researches have proved that the quality of decisions has been improved by developing computer-based technologies that aid and extend the human capabilities. Decision support systems (DSS), which augment in making proper decisions, to an extent do sometimes lack intelligence. The recent advances in the field of artificial intelligence (AI) have made this possible in a wide range of multifarious applications. AI has the capability of mimicking human decision-making and has also shown that they are capable of assisting and improving human decision-making in real-time complex environments. Embedding intelligence into the DSS using the AI capabilities has been progressing, thus forming the intelligent support systems for decision-making (ISSDM). These support systems have been used in simplifying the decision-making process in several areas, namely, healthcare, cybersecurity, finance, marketing, and commerce. This chapter reviews the role of artificial intelligence tools such as artificial neural networks, fuzzy logic, bio-inspired algorithms, and intelligent agents in the decision-making process. © Springer Nature Switzerland AG 2020.

Department of Technology Management

School of Mechanical Engineering

Internet of Things for Industry 4.0 EAI/Springer Innovations in Communication and Computing

Decision Making Models Through AI for Internet of Things

Breast cancer is the maximum joint intrusive cancer in females which is the additional foremost reason of cancer decease in females just subsequently lung cancer. Initial diagnostics suggestively upsurges the likelihoods of accurate cure and survival which is monotonous and frequently primes to a divergence amid practitioners. Computer-aided diagnosis structures exposed prospective for refining the investigative accurateness. Breast cancer classification is developed from computer vision and image processing to deep convolution neural networks. From last 15 years, advances in image processing and computer vision techniques consent computer-aided diagnosis systems to help practitioners designate supplementary dynamic, unprejudiced and reliable in the medical diagnosis procedure. © 2020 SERSC.

International Journal of Advanced Science and Technology

Exploring breast cancer classification of histopathology images from computer vision and image processing algorithms to deep learning

This paper utilizes the cognitive radio (CR) spectrum to the fullest extent for extended applications requiring discretion. The CR technology provides various supports for cognitive radio networks (CRNs). The latter has CR nodes that sense free channels. Then, the CRN allocates the unused channels to secondary users (SUs) or unlicensed users. This allocation is termed the spectrum handoff. In this paper, by considering the identical channels in CR networks, a novel machine learning algorithm (the support vector machine—SVM) is employed. In addition, the queuing model of the preemptive resume priority M/M/1 is used. The proposed spectrum handoff algorithm selects the best possible CR network channel. The spectrum handoff algorithm uses the stated SVM algorithm scheme, which covers the transmitted and received power, the minimum service time, the data rate and the maximum vacancy time for the SU, to attain the maximum throughput. However, in multi-user greedy channel selection (GCS), only two parameters are considered. The proposed spectrum handoff algorithm based on the SVM scheme enhances the performance, and the SU throughput is improved to 68.7%. This approach is better than the GCS channel selection scheme. Additionally, this approach decreases the number of spectrum handoffs. As a result, the training accuracy of the SVM method is 97.6%, and it outperforms conventional methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Department of Smart Computing

Circuits, Systems, and Signal Processing

Machine Learning-Based Algorithm for Channel Selection Utilizing Preemptive Resume Priority in Cognitive Radio Networks Validated by NS-2

The emergence of the Internet of Things (IoT) has revolutionized our digital and virtual worlds of connected devices. IoT is a key enabler for a wide range of applications in today's world. For example, in smart healthcare systems, the sensor-embedded devices monitor various vital signs of the patients. These devices operate on small batteries, and their energy need to be utilized efficiently. The need for green IoT to preserve the energy of these devices has never been more critical than today. The existing smart healthcare approaches adopt a heuristic approach for energy conservation by minimizing the duty-cycling of the underlying devices. However, they face numerous challenges in terms of excessive overhead, idle listening, overhearing, and collision. To circumvent these challenges, we have proposed a cluster-based hierarchical approach for monitoring the patients in an energy-efficient manner, i.e., green communication. The proposed approach organizes the monitoring devices into clusters of equal sizes. Within each cluster, a cluster head is designated to gather data from its member devices and broadcast to a centralized base station. Our proposed approach models the energy consumption of each device in various states, i.e., idle, sleep, awake, and active, and also performs the transitions between these states. We adopted an analytical approach for modeling the role of each device and its energy consumption in various states. Extensive simulations were conducted to validate our analytical approach by comparing it against the existing schemes. The experimental results of our approach enhance the network lifetime with a reduced energy consumption during various states. Moreover, it delivers a better quality of data for decision making on the patient's vital signs. © 2020 IEEE.

IEEE Access

A Centralized Cluster-Based Hierarchical Approach for Green Communication in a Smart Healthcare System

Over the decades, the security in mobile commerce application via One Time Password (OTP) plays a vital role. With new developments in mobile wallets, the volume of transactions increase globally and the need for securing transactions is still a challenging aspect. A strong user authentication mechanism is required since many of the attacks take minimum effort to break the security. User Authentication is a critical requirement since many of the attacks take minimum effort to break the security. Hence a novel cryptographic scheme is proposed to bridge the gap between security and authentication mechanisms. Initially, the scheme captures the user image to analyze the face for its structure, texture and feature identification. Once the image components are matched from the available template database user authentication is successful. To avoid conflict during template matching the face components are further subjected to segment location, radius and center of eyeballs at subpixellic accuracy. Iris matching is done via spatial histogram. If the iris information is similar, the Transaction Server (TS) generates OTP and sends it to the user mobile in order to complete the transaction. The proposed model is proven to be highly efficient against False Acceptance Rate (FAR) and False Rejection Rate (FRR) measures and is validated over Q- Fire dataset. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Multimedia Tools and Applications

Securing one-time password generation using elliptic-curve cryptography with self-portrait photograph for mobile commerce application

The work implemented here is crime prediction system (CPS). We first created hypothetical datasets samples of major city areas and different crimes taking place and then we used the algorithms to analyze it. We used HTML and CSS along with PHP, while wamp as a Web server to this application. The objective of the proposed work is to analyze and predict the chance of a crime happening using apriori algorithm. In addition, we used decision tree as a searching algorithm and naïve Bayesian classifier to predict about the crime in particular geographical location at a particular point of time. The result of this can be used to raise people’s awareness regarding the dangerous locations and to help agencies to predict future crime in a specific location within a particular time. © 2020, Springer Nature Singapore Pte Ltd.

Department of Software Systems

School of Computer Science and Engineering

Lecture Notes in Networks and Systems

Crime Prediction System

The use of unmanned aerial vehicle (UAV) as a dynamic node in point to point communication of 5G and beyond 5G cellular networks is an emerging technology. It provides a reliable and efficient mode of wireless communication network in the sky. Existing architectures include single tier and multi-tier drone architecture. Major challenges in multi-tier architecture is in establishing an air traffic control system and in efficient resource sharing between the drones. To overcome the challenges associated with the multitier drone and cellular drone networks, a bidirectional multitier cognitive swarm drone network (BMCSDN) is proposed and investigated in terms of spectrum efficiency and allocation of optimized altitude and power for down link transmission. The proposed method uses the cognitive swarm drone (CSD) concept and provides enhancement of capacity and coverage with optimized transmission efficiency. The work also analyses the effect of interference when all CSDs transmit and receive with static and dynamic frequency allocations. © 2020 IEEE.