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

School of Computer Engineering & Technology

In almost every organization where user sensitive data is available, security and privacy of the data plays a vital role. As far as computer science is concerned, it is just a game of saving data in unrecognizable format and accessible to authorized person. User sensitive data mainly includes passwords which are required for the sessions but need to be handled and stored safely.As storage of these information is overhead in database, Tokens are generated which handles sessions and also self contains user details. One of such widely used stateless token is Json Web Token. This paper deals with the introduction, working and algorithms of Json web token. Also pros, cons, hacking possibilities, Proper usage and security measures of JWT are discussed.

World University root node

Engineering and Technology

Fulltext

International Journal of Recent Technology and Engineering (IJRTE)

Insights of JSON Web Token

International Journal for Research in Applied Science and Engineering Technology

Aesthetics in Digital Photography: A Survey

Aesthetic assessment of images has been getting a lot of attention for the past decade in the field of computer vision. Large amounts of social media and advertising data in the form of images is continuously analyzed to assign it an aesthetic quality value to improve businesses as well as for gaining more popularity across the web. Visual perception by humans cannot be fully replicated by a machine and continuously more work is being published on aesthetic classification of images. In this paper, we have presented a convolutional neural network model which automatically extracts high level features and distinguishes a set of images into pleasing and non-pleasing categories. Our dataset has been compiled from a variety of sources on the web to make it as diverse as possible. Compared to the traditional handcrafted methods and other machine learning models, our CNN model has provided a better classification accuracy of 68% on our dataset.

International Journal of Engineering Research & Technology (IJERT)

Image Aesthetics Assessment using DeepLearning (based on High Level ImageAttributes)

Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo–Grey wolf based Correlative Naive Bayes classifier and MapReduce Model (CGCNB-MRM). Accordingly, a novel classifier, named Cuckoo–Grey wolf based Correlative Naive Bayes classifier (CG-CNB), is designed by modifying CNB classifier with a newly developed optimization algorithm, Cuckoo–Grey Wolf based Optimization (CGWO). CGWO algorithm is designed by the effective integration of Cuckoo Search (CS) Algorithm into Grey Wolf Optimizer (GWO), to optimize the CNB model by the optimal selection of the model parameters. Finally, the proposed CGCNB-MRM approach performs the classification for each data samples based on the probability index table and the posterior probability of the data. Three metrics, such as accuracy, sensitivity, and specificity, are utilized for the performance evaluation of the proposed CGCNB-MRM approach, where it could achieve 80.7% accuracy with 84.5% sensitivity and 76.9% specificity and thus, prove its effectiveness in big data classification.

Data & Knowledge Engineering

Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification

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

Predictive Analysis of Heterogeneous Data – Techniques Tools

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

International Journal of Future Generation Communication and Networking

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

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.