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 Computational Intelligence

The main aim of this paper is to comprehend, gain insight of the current trends in application of big data in healthcare, and to identify the various potential healthcare horizons. A brief analysis was done on 'big data analytics in healthcare' focusing on collection of data, the tools employed, the aspects of health that were addressed, the type of machine learning algorithms and which statistics commissioned to compare the performance of these algorithms. The focus was mainly on prediction of the diseases, emergency department visits or a disease outbreak, using 'HADOOP' and 'WEKA' tool, by obtaining data from University of California machine learning repository, hospitals and government agencies. Support vector machine, artificial neural networks, naive Bayes and decision tree were commonly used algorithms whose efficacy was compared statistically using 'accuracy'. In my perspective, apart from prediction of disease other domains of health are to be addressed. Copyright © The Authors(s) 2019.

World University root node

School of Computer Science and Engineering

Vellore Campus

International Journal of Data Mining, Modelling and Management

An insight into application of big data analytics in healthcare

Due to technological advancement and deployment of handheld devices, mobile computing devices have gained the user attention to save resource usage, service access, and application use. In practice, it can be deployed widely to experience massive data storage and less computation cost. However, wireless channel access is vulnerable and insecure to prevent various potential attacks such as replay, redirection, etc. As a consequence, a seamless secure anonymous authentication scheme (S-SAAS) is proposed to create a secure session in cloud-based mobile edge computing. Since IoT, cloud computing, big-data and mobile technologies are converged to permit the group-based communication systems, it is highly demanded to strengthen the authentication process in order to reduce the security risk and vulnerabilities. Moreover, the experimental analysis proves that the proposed S-SAAS scheme consumes less signaling congestion and routing control overhead to achieve better service connection rate. © 2020

Computers and Electrical Engineering

Seamless secure anonymous authentication for cloud-based mobile edge computing

Internet of Things (IoT) leverages the sensor inter-connectivity that offers a wide range of real-time monitoring opportunities for smart environmental systems. As a consequence, it is witnessed that IoT plays an important role in the next generation of smart communication networks. In the future, the evolution of smart environment becomes a pillar of 2020 society for the various innovative services. In order to deal with the challenges, such as supporting urban development and improving the quality of people's life, an interdisciplinary approach is preferred. It is evident that the infrastructure of smart information and communication technology (ICT) claims to be a de facto standard to realize the smart vision of emerging technologies. Thus, an IoT-Based Smart CAN (IoT-BSFCAN) framework is proposed to monitor the smart environment continuously through smart computing devices over cloud-enabled networks. The objective of this framework is to minimize computation cost along with communication fairness, while it uses different kinds of user applications. Moreover, the illustrative result proves that the proposed IoT-BSFCAN framework yields better successful execution results than the other alternative solutions. © 2020 Elsevier B.V.

Future Generation Computer Systems

IoT-BSFCAN: A smart context-aware system in IoT-Cloud using mobile-fogging

Tourism became most popular in these decades, which the intensity of the user needs is enlarged significantly. Usually the popular travel websites give wide-ranging information such as cost details of the hotel, transportation prices, etc. But this information is not enough for the customers to get satisfied with travels in this model world. They expect more challenging information for their journey, and it should be presented on the websites, which helps to make creatively and provides more special travel plans for the users. Many more existing schemes are there on the web to improve the user for tourism. But these schemes give same and repeated information, and in many cases, the websites provide a smaller amount of secured and confidential user data and present many numbers of Ads, MNC tight-up companies oriented news for making money. In this proposed system, using the concept of text mining to recommend the best tourist spot based on budget, climate, crowd, etc. and also the data of the user are to be maintained very secure. In addition, many research activities are going on to provide more effective tourist spot for the tourists. The implementation tools are MongoDB, Angular Js, and the Spring framework is used in the development of the system. In this research designing the platform for tourists in Tamil-Nadu to include additional information about the tourist locations and also access the clear view on user review that is available in our platform helps fasten the tourist’s decision-making process. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

International Journal of Advanced Trends in Computer Science and Engineering

Tourist spot proposal system using text mining

This paper aims to implement the concept of preserving the Confidentiality and Integrity of DICOM Images Using Cryptographic Algorithms. This work uses two types of cryptographic algorithms namely AES (Advanced Encryption Standard) and Blake Hash Function. Using these algorithms, we implement and check the confidentiality and integrity of DICOM images. Our proposed system also ensures that the images are transferred from one hospital database to another in a secure manner and the system will be able to tackle any changes while the image is being transferred. The initial concern of the system lies in providing confidentiality in the communication of the images which can be done using AES algorithm. The integrity can be provided using Blake Hash Function. The comparative analysis of AES with other algorithms such as Triple DES, IDEA, Blowfish and SEED with respect to their execution time is also done. In this work, DICOM files are taken as input and then kept in the encrypted format for storage purpose and also they can be sent over the network in the encrypted format. © 2019 Elsevier Ltd. All rights reserved.

Materials Today: Proceedings

Secure Transmission of DICOM Images by comparing different cryptographic algorithms

Context: Locating products or services on-line that meet users want is more and more troublesome thanks to the massive pool of decisions to think about before inbound at the required one. A user might pay a substantial quantity of your time exploring varied on-line resources to find things that work his needs. Moreover, users might not forever categorical their preferences in an exceedingly manner that simply matches them to things that might meet them. Objective: Searching out things on-line has been done principally through information queries that come back an inventory of the foremost appropriate things. Recommendation systems technology may be applied to ease the task of locating the desired things on-line. Method: This study proposes a recommender system allows that permits that allow users to hold out a preference-based cooperative filtering search on rental properties and enables them to refine those preferences based on shallow learning. The room also is visualized using AR and Vuforia. Results: This recommendation approach has been shown to supply additional correct search results. The system was developed as an Internet application victimization Handlebars for front-end and Nodes- ExpressJs for Back-end. Conclusion: The system performs better than existing algorithms and predicts better in memory-based approach. © 2020 The Authors. Published by Elsevier B.V.

Fulltext

Procedia Computer Science

Smart Rent Portal using Recommendation System Visualized by Augmented Reality

Unmanned Aerial Vehicle (UAV) has extensively been practiced in the military and civilian surveillance systems that access sensitive data over the cellular networks. However, channel insecurity and battery limitation may not protect the aerial coverage area. Thus, sensitive information gathered through aerial vehicles causes security threats. To manage the security issues, Smart Internet of Drone (S-IoD) have been evolved to use Intelligent Personal Assistant (IPA) as a software agent while monitoring and observing areas of interest. The current state-of-the-art technologies provide ubiquitous communication to enable several Internet of Things (IoT) paradigms. It achieves a feature of a decision support system that allows the smart interaction and communication between real-time entities. IPA offers smart interaction with other smart real-time entities to gain the user's knowledge and awareness. This paper presents an S-IoD framework for a UAV environment that independently collects sensible information. In order to reduce the computation cost of the authentication protocol, a lightweight privacy-preserving scheme (L-PPS) is introduced. The proposed L-PPS is constructive to provide the robustness between the IoT devices with a valid authentication period. To demonstrate the security and performance efficiencies, the formal verification was performed using a verification tool, Scyther, and a random oracle model. In addition, the proposed L-PPS introduces a secret token and dynamic user authentication to speed up the authentication process between the communication entities. Importantly, the authentication session of L-PPS does not use any complex cryptographic operations, whereby it has less computation and communication costs to meet the standard constraints of surveillance systems. Moreover, the obtained simulation analysis proves that the proposed L-PPS achieves better quality metrics than other authentication schemes in the literature. © 2020 Elsevier B.V.

Computer Communications

A smart lightweight privacy preservation scheme for IoT-based UAV communication systems

Medical imaging systems contribute much towards effective decision-making by the physicians, which is highly essential in the day-to-day life of humans. In this study, Taylor-Krill herd (KH)-based support vector machine (SVM) is proposed for medical image denoising. The Taylor-KH-based SVM is the integration of Taylor series in KH optimisation algorithm, which is used for tuning the optimal weights of the SVM classifier. The efficiency of KH is due to two global and two local optimisers, and the adaptive operators ensure the adaptive nature of KH. Above all, KH never uses the derivative information as it employs the stochastic search and thereby, reduces the complexity of the algorithm. The proposed method tunes the hyperplane parameters of SVM optimally so that the optimal identification of the noisy pixels in the image is ensured and replaced with adaptive weights. The proposed method is analysed based on the metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and the comparative analysis is done with existing methods for showing the effectiveness of the proposed method. The simulation result shows that the proposed method acquired a PSNR of 30.36 dB and SSIM of 0.89, respectively. © The Institution of Engineering and Technology 2019

IET Image Processing

Integrating Taylor-Krill herd-based SVM to fuzzy-based adaptive filter for medical image denoising

Metaheuristic algorithms are a family of algorithms that help solve NP-hard problems by providing near-optimal solutions in a reasonable amount of time. Galactic Swarm Optimization (GSO) is the state-of-the-art metaheuristic algorithm that takes inspiration from the motion of stars and galaxies under the influence of gravity. In this paper, a new scalable algorithm is proposed to help overcome the inherent sequential nature of GSO and helps the modified version of the GSO algorithm to utilize the full computing capacity of the hardware efficiently. The modified algorithm includes new features to tackle the problem of training an Artificial Neural Network. The proposed algorithm is compared with Stochastic Gradient Descent based on performance and accuracy. The algorithm's performance was evaluated based on per-CPU utilization on multiple platforms. Experimental results have shown that PGSO outperforms GSO and other competitors like PSO in a variety of challenging settings. © 2020 - IOS Press and the authors. All rights reserved.

Department of Information Security

Department of Analytics

Journal of Intelligent and Fuzzy Systems

A new parallel galactic swarm optimization algorithm for training artificial neural networks

Water is a strategic resource for both socio-economic development and human life. The current study has been carried out for spatio-temporal change detection of surface water bodies during winter period using hybrid modelling approach. The study area has fallen in the northern part of Bangladesh and is locally called Chalan Beel with 5 million of in habitants, a prominent intensive crop production, surface and groundwater irrigation, high evapotranspiration, and water scarcity. For the detection of water body changes, satellite images of 1999 and 2011 were used, and the following image fusion techniques were applied: (a) Gram-Schmidt (GS), (b) modified intensity hue saturation (IHS), (c) high-pass filter (HPF), and (d) wavelet. Landsat 7/ETM + panchromatic (PAN) band of 15 m × 15 m resolution in 1999 and Landsat 5/TM multispectral (MS) bands of 30 m × 30 m resolution in 2011 were allied each other to generate high-resolution image that contains information of two different years. The fused images were classified to extract the water bodies using four classification methods: (a) artificial neural network (ANN), (b) support vector machine (SVM) and (c) maximum likelihood (ML). To analyze the quality of the fused images, statistical calculation (quantitatively) and Laplacian edge detection (qualitatively) were used. To validate the fused image classification results, the multispectral images from 1999 and 2011 were again individually classified using principal component analysis (PCA), normalized difference water index (NDWI), and image differencing (ID) processes and compared with the previous classification. Surprisingly, the results showed that two-third of the areas dried up in 10 years. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.