MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

Cryptanalysis is used to analyze the strength of a cryptographic algorithm. Various cryptanalytic attacks against AES algorithm target reduced-round variants. These attacks are theoretical and are generally considered infeasible due to the demand for a large number of computations. To meet this high-computational requirement, an FPGA-based High-Performance Computing (HPC) platform is presented in this paper. FPGAs are advantageous for implementing cryptanalytic attacks, as the modular arithmetic is implemented more efficiently in FPGAs as compared to GPUs. The proposed HPC platform consists of four Spartan6 FPGAs connected in a mesh topology. A brute force cryptanalytic attack on the AES algorithm with a 128-bit key is implemented on the proposed HPC platform. Four-AES key search engines are designed in each FPGA. Thus, 16-AES key search engines are instantiated in parallel to perform AES cryptanalysis using different keys in parallel. To allocate distinct the key space to the 16 AES key search engines, an efficient key generator is also proposed in this work. The proposed architecture achieves a computational complexity of 2124 for an attack against 10-rounds AES algorithm. © 2020, Springer Nature Singapore Pte Ltd.

School of Electronics & Communication Engineering

World University root node

Computing in Engineering and Technology. Advances in Intelligent Systems and Computing

FPGA-Based High-Performance Computing Platform for Cryptanalysis of AES Algorithm

School of Computer Engineering & Technology

2020 5th International Conference on Computer and Communication Systems (ICCCS)

Predictive Analysis of Heterogeneous Data – Techniques Tools

School of Petroleum Engineering

EGM 2007 International Workshop, cp-

Study of a Confined Hydrothermal Systemover the Geothermal Province of Bakreswar, Eastern India

In the domain of psychological practice, experts follow different methodologies for the diagnosis of psychological disorders and might change their line of treatment based on their observations from previous sessions. In such a scenario, a standardized clinical decision support system based on big data and machine learning techniques can immensely help professionals in the process of diagnosis as well as improve patient care. The technology proposed in this paper, attempts to understand psychological case studies by identifying the psychological disorder they represent along with the severity of that particular case, with the help of a Multinomial Naive Bayes model for disorder identification and a regular expression based severity processing algorithm. A knowledge base is created based on the knowledge of human experts of psychology. Psychological disorders however need not possess distinct symptoms to easily differentiate between them. Some are very closely connected with a variety of overlapping symptoms between them. Our work, in this paper, focuses on analyzing the performance of such psychological disorders represented in the form of case studies in a decision support system, with an aim of understanding this gray area of psychology. © 2020, Springer Nature Switzerland AG.

Advances in Intelligent Systems and Computing

Performance Analysis of Psychological Disorders for a Clinical Decision Support System

International Journal of research and analytical reviews

E-recruitment system

School of Mechanical Engineering

Test Engineering and Management

Lean Manufacturing Application for Process Improvement in Pump Manufacturing Industry

International Journal of Future Generation Communication and Networking

Literature Survey -- Automated IDPS for Database Intrusion Attacks from Insiders

Cadmium Zinc Telluride Imager (CZTI) is one of five payloads of AstroSat, a multi-wavelength satellite, which has observed multiple stellar phenomena using coded mask imaging. This mask convolves with the observation before being detected. Thus, some preprocessing of this detected data is required before it can be analysed. Deconvolution is often implemented in such situations where we try to recover the original source by removing the effect of convolution on the observed data. Deconvolution is often seen in filtering or for removal of interfering phenomena such as point spread functions. This paper proposes a new method for deconvolution of two multi-dimensional discrete signals, resulting in the recovery of the original source intended for capture. The discrete signals are represented as matrices and then deconvolved in the Z-domain. This has been verified using two and three dimensional matrices and has been thus been extended for m-dimensional signals.

Iterative Deconvolution for Multidimensional Signals

The efficiency of any content-based image retrieval system depends on the extracted feature vectors of individual images stored in the database. Generation of a compact feature vector with good discriminative power is a real challenge in the image retrieval system. This paper presents the experimentation carried out to generate compact feature vectors for a colour image retrieval system based on image content. It has two stages of operation. In first stage the energy compaction property of image transforms is used whereas in the second stage, the statistical tree approach is used for feature vector generation. Performance of image retrieval is tested using image feature database as per various performance evaluation parameters such as precision recall crossover point (PRCP) along with newly proposed conflicting string of images (CSI). With different colour spaces, image transforms and statistical measures, proposed approach achieves the reduction in the feature vector size with better discriminative power.

International Journal of Computational Science and Engineering

Statistical tree-based feature vector for content-based image retrieval

Big data analytics gain significant interest over the traditional data-processing methodologies that engage in extracting the hidden patterns and correlations from the massive data, termed as big data. With the aim of relieving the computational complexity the clustering method plays a significant role. With the knowledge of the clustering algorithms, the big data arriving from the distributed sources is processed using the MapReduce framework (MRF). The MRF possesses two functions, namely, map function and reduce function, such that the map function is based on the proposed Fractional Sparse Fuzzy C-Means (FrSparse FCM) algorithm and reduce function is based on particle swarm optimisation-based whale optimisation algorithm (P-Whale). Initially, the optimal centroids are computed using the proposed algorithm in the mapper phase that is optimally tuned in the reducer phase, and it is clear that the proposed FrSparse FCM-based MRF ensures the parallel processing of the big data. Experimentation is performed using the Skin data set and the localisation data set taken from the UCI machine learning repository, and the analysis is progressed using the metrics, such as accuracy and DB Index. The analysis proves that the proposed method acquired a maximum accuracy of 90.6012% and a minimum DB Index of 5.33.

Fulltext

IET Image processing jorunal , https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1049%2Fiet-ipr.2019.0899

Map Reduce Framework based big data clustering using Fractional integrated Sparse Fuzzy C Means algorithm