Anuja Arora

Bio: Anuja Arora is an academic researcher from Jaypee Institute of Information Technology. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 14, co-authored 69 publications receiving 659 citations.

Linguistic feature based learning model for fake news detection and classification

Abstract: Social media is used as a dominant source of news distribution among users. The world's preeminent decisions such as politics are acclaimed by social media to influence users for enclosing users' decisions in their favor. However, the adoption of social media is much needed for awareness but the authenticity of content is an unknown factor in the current scenario. Therefore, this research work finds it imperative to propose a solution to fake news detection and classification. In the case of fake news, content is the prime entity that captures the human mind towards trust for specific news. Therefore, a linguistic model is proposed to find out the properties of content that will generate language-driven features. This linguistic model extracts syntactic, grammatical, sentimental, and readability features of particular news. Language driven model requires an approach to handle time-consuming and handcrafted features problems in order to deal with the curse of dimensionality problem. Therefore, the neural-based sequential learning model is used to achieve superior results for fake news detection. The results are drawn to validate the importance of the linguistic model extracted features and finally combined linguistic feature-driven model is able to achieve the average accuracy of 86% for fake news detection and classification. The sequential neural model results are compared with machine learning based models and LSTM based word embedding based fake news detection model as well. Comparative results show that features based sequential model is able to achieve comparable evaluation performance in discernable less time.

Text and image based spam email classification using KNN, Naïve Bayes and Reverse DBSCAN algorithm

Abstract: Internet has changed the way of communication, which has become more and more concentrated on emails. Emails, text messages and online messenger chatting have become part and parcel of our lives. Out of all these communications, emails are more prone to exploitation. Thus, various email providers employ algorithms to filter emails based on spam and ham. In this research paper, our prime aim is to detect text as well as image based spam emails. To achieve the objective we applied three algorithms namely: KNN algorithm, Naive Bayes algorithm and reverse DBSCAN algorithm. Pre-processing of email text before executing the algorithms is used to make them predict better. This paper uses Enron corpus's dataset of spam and ham emails. In this research paper, we provide comparison performance of all three algorithms based on four measuring factors namely: precision, sensitivity, specificity and accuracy. We are able to attain good accuracy by all the three algorithms. The results have shown comparison of all three algorithms applied on same data set.

Deep Learning Convolutional Neural Network for Apple Leaves Disease Detection

Abstract: Apple trees are perhaps one of the most popular plants to grow in large plantations and in-home gardens. At the same time, Apple plants are among the plants that are the most prone to diseases. Disease identification at an early stage and its prevention before spreading into other parts of the plant is a challenge even for the expert’s eye. Therefore, an adequate system is required to detect plant disease in the initial stage. This paper displays the prowess of Convolutional Neural Networks to automatically detect and address the issue. Images of Apple leaves, covering various diseases as well as healthy samples, from the Plant Village dataset are used to validate results. Image filtering, image compression, and image generation techniques are used to gain a large train-set of images and tune the system perfectly. The trained model achieves high accuracy scores in all the classes with a net accuracy of 98.54% on the entire dataset, sampled and generated from 2561-labelled images.

A bug Mining tool to identify and analyze security bugs using Naive Bayes and TF-IDF

Abstract: Bug report contains a vital role during software development, However bug reports belongs to different categories such as performance, usability, security etc. This paper focuses on security bug and presents a bug mining system for the identification of security and non-security bugs using the term frequency-inverse document frequency (TF-IDF) weights and naive bayes. We performed experiments on bug report repositories of bug tracking systems such as bugzilla and debugger. In the proposed approach we apply text mining methodology and TF-IDF on the existing historic bug report database based on the bug s description to predict the nature of the bug and to train a statistical model for manually mislabeled bug reports present in the database. The tool helps in deciding the priorities of the incoming bugs depending on the category of the bugs i.e. whether it is a security bug report or a non-security bug report, using naive bayes. Our evaluation shows that our tool using TF-IDF is giving better results than the naive bayes method.

The DBLP Computer Science bibliography: Evolution, research issues, perspectives

Abstract: Publications are essential for scientific communication. Access to publications is provided by conventional libraries, digital libraries operated by learned societies or commercial publishers, and a huge number of web sites maintained by the scientists themselves or their institutions. Comprehensive meta-indices for this increasing number of information sources are missing for most areas of science. The DBLP Computer Science Bibliography of the University of Trier has grown from a very specialized small collection of bibliographic information to a major part of the infrastructure used by thousands of computer scientists. This short paper first reports the history of DBLP and sketches the very simple software behind the service. The most time-consuming task for the maintainers of DBLP may be viewed as a special instance of the authority control problem; how to normalize different spellings of person names. The third section of the paper discusses some details of this problem which might be an interesting research issue for the information retrieval community.

Impacts of Luxury Fashion Brand’s Social Media Marketing on Customer Relationship and Purchase Intention

Abstract: Luxury brands have always been fashion industry leaders, with admirable aesthetic value and innovative yet traditional business management. The brands...