Yogesh Meena

Malaviya National Institute of Technology Jaipur · Department of Computer Engineering

The struggle of organisations from all fields to find practical answers for identifying online-based fake news is a prevalent problem right now. The news is published on news Websites, which act as official sources. Social media has drawn the attention of individuals from all over the world who use it to disseminate fake news because of its accessi...

Catastrophic events such as pandemics, terrorist attacks, and natural calamities impact our society drastically. The forecast of such significant events to reduce the potential damage is an imminent research challenge. However, most of the existing computational methods are limited to only addressing the forecasting needs of specific domains. A str...

The majority of users were available on the Internet and created a number of social networking accounts during India’s COVID-19-caused lockdown, which lasted from March to June 2020. A massive amount of information is currently being disseminated on the Internet via various social networking accounts. Some false or fake information in the form of “...

The purpose of this study is to present an exhaustive analysis on research paper recommender systems which have become very popular and gained a lot of research attention. Though the major focus is on developing new recommendation algorithms, other research dimensions are left untouched. Renown recommendation classes include content-based approache...

The pandemic outbreak of severe acute respiratory syndrome caused by the Coronavirus 2 disease in 2019, also known as SARS-COV-2 and COVID-19, has claimed over 5.6 million lives till now. The highly infectious nature of the Covid-19 virus has resulted into multiple massive upsurges in counts of new infections termed as ‘waves.’ These waves consist...

With the increase of unstructured text on social media platforms from user opinions, deep neural network techniques have significantly contributed to the aspect extraction subtask of Aspect-Based Sentiment Analysis (ABSA). In a multi-sentence review, sentences are contextually interdependent, and static word embedding generates similar representati...

span>Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED sys...

In data mining, association rule mining algorithms executing over massive datasets, generate vast amounts of rules. These rules pose a gruesome strain of knowledge post-processing on the user. This compels the user to dig through the rules to find relevant knowledge for decision making. For simplification of this practice, we propose the concept of...

The spread of false news on an online social media platform has been a major concern in recent years. Many sources, such as news stations, websites, and even newspaper websites, post news pieces on social media. Meanwhile, most of the new material on social media is suspect and, in some circumstances, deliberately misleading. Fake news is a term us...

Terrorism is a globally prevalent dreaded form of crime against humanity in modern civil society. The nature of surprise, casualties caused, and the panic involved in terrorist activities compels improvisation of efforts to counter them. These counter-terrorism efforts require precise and reliable techniques to analyze the patterns existing in data...

In recent years, deep learning has yielded success in many research fields including machine translation, natural language processing, computer vision, and social network filtering. The area of deep learning in the recommender system is flourishing. Previous research has relied on incorporating metadata information in various application domains us...

Generative models in some way reflect probability distributions over multiple variables. For latent variable generative models, the density function is intractable and these models thus make some independent assumptions to minimize the number of factors and, in turn, the number of parameters in the model. Autoregressive models such as Neural Autore...

Existing recommender systems rely on user and item representations in a fixed continuous low-dimensional latent space. To predict ratings, they use only an implicit feedback matrix, whereas user and item side information is ignored. Furthermore, they use the same arbitrary priors for the user and item latent vectors, reducing the ability of the mod...

Social networking sites have a wealth of user-generated unstructured text for fine-grained sentiment analysis regarding the changing dynamics in the marketplace. In aspect-level sentiment analysis, aspect term extraction (ATE) task identifies the targets of user opinions in the sentence. In the last few years, deep learning approaches significantly...

Cancer bears a poisoning threat to human society. Melanoma, the skin cancer originates from skin layers and penetrates deep into subcutaneous layers. There exists an extensive research in melanoma diagnosis using dermatoscopic images captured through dermatoscope. While designing a diagnostic model for general handheld imaging systems is an emergin...

Research Paper Recommender Systems are developed to deal with the increasing amount of published information over web and provide recommendations for research articles based on the user preferences. Researchers invest their huge time in literature search to carry out the research work. To provide ease in building literature and finding useful resea...

Automatic text summarization (ATS) is the process of generating a summary by condensing text document by a computer machine. In this paper, we explored voting-based extractive approaches for text summarization. The main issue with most of the feature-based ATS methods is to find optimal feature weights for sentence scoring to optimize the quality o...

Due to increasing amount of text data available in WWW, it becomes time consuming for information system users to explore every text source in detail. Automatic text summarization (ATS) is the process of generating summary by condensing text document automatically by a computer machine that can save users precious time. Major issue with most of the...

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers...

Due to the exponential growth of documents on internet, users want all the relevant data at one place without any hassle. This led to the growth of Automatic Text Summarization. For this purpose a number of methods have been proposed by researchers but no method is able to work on all domains of text documents. Some methods which work for News doma...

Due to the exponential growth of documents on internet, users want all the relevant data at one place without any hassle. This led to the growth of Automatic Text Summarization. For extractive text summarization in which representative sentences from the document itself are selected as summary, various statistical, knowledge based and discourse bas...

Due to increasing amount of text data available on internet it becomes difficult for users to get the desired information quickly. In order to reduce this access time a summary could be utilized generated using Automatic Text Summarization. In general it could be extractive or abstractive. For extractive text summarization in which representative s...

The goal of text summarization is to reduce the size of the text while preserving its important information and overall meaning. With the availability of internet, data is growing leaps and bounds and it is practically impossible summarizing all this data manually. Automatic summarization can be classified as extractive and abstractive summarizatio...

Automatic text summarization is a major area of research in the domain of information systems. Most of the methods requires domain knowledge in order to produce a coherent and meaningful summary. In Extractive text summarization, sentences are scored on some features. A large number of feature based scoring methods have been proposed for extractive...

In today's era of World Wide Web, on-line information is increasing exponentially day by day. So there is a need to condense corpus of documents into useful information automatically. Automatic Text summarization plays an important role to extract salient feature from corpus of documents, which helps user to get useful information in short time and...

This Paper represents technique for user authentication using keystroke dynamics. In this paper we have included Inter key time, Key hold time as well as some other keystroke features to verify the user. As the user types a combination of string, its key hold time and inter key time is noted, which is then compared with trusted user values using Eu...

Data Mining helps to uncover the already unknown and non-redundant knowledge in large databases, which can be used for decision making purpose. Association rule mining is one of the key research area in the field of Data Mining. Association rule mining can be considered as unsupervised learning model, it discovers the interesting relationship among...

Advent of Internet and all legal transactions through it has made computer systems vulnerable. Malicious code writers launch illicit programs to the compromised systems to gain access to the resources, and confidential/intellectual information of the users. The primary reason for systems becoming vulnerable for attack is because of the ignorance of...