scholarly journals Virtual Personal Assistant for Desktop Automation using Selenium

2021 ◽  
Vol 9 (VI) ◽  
pp. 3261-3264
Author(s):  
Tummala Sri Ranga Sai Krishna
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Smart Phones ◽  
The Internet ◽  
Query Result ◽  
Complete Automation ◽  
Personal Assistant ◽  
Personal Assistants ◽  
The Individual ◽  
Simple Definition

In recent years, Virtual Personal Assistants(VPA) have worked with utmost efficacy sorting out queries and specific tasks posted by the individual users on the website by AI and Natural Language Processing . VPA developers develop functions to either scrape the query result from the Internet. The result data include copious formats from a simple definition in Wikipedia to complex calculations or recommendations. However, VPA’s designed for desktops do not work as extensively as the VPA’s featuring in the smart phones . They do not provide a complete automation of desktop websites due to continuous and frequent development. The current desktop personal assistant’s can show you the top results of the query ‘Biryani’, but cannot order on behalf of you. In this study, we propose a Virtual Personal Assistant ARCHER for desktop automation using Selenium by using the specifications of the behavior data of websites.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

Detecting Linguistic Markers of Violent Extremism in Online Environments

2016 ◽  
pp. 374-390 ◽  
Author(s):  
Fredrik Johansson ◽  
Lisa Kaati ◽  
Magnus Sahlgren
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Terrorist Attacks ◽  
The Internet ◽  
Written Text ◽  
Linguistic Markers ◽  
Geographical Regions ◽  
Violent Extremism ◽  
Online Environments

The ability to disseminate information instantaneously over vast geographical regions makes the Internet a key facilitator in the radicalisation process and preparations for terrorist attacks. This can be both an asset and a challenge for security agencies. One of the main challenges for security agencies is the sheer amount of information available on the Internet. It is impossible for human analysts to read through everything that is written online. In this chapter we will discuss the possibility of detecting violent extremism by identifying signs of warning behaviours in written text – what we call linguistic markers – using computers, or more specifically, natural language processing.

scholarly journals Applying NLP techniques to malware detection in a practical environment

2021 ◽  
Author(s):  
Mamoru Mimura ◽  
Ryo Ito
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Dynamic Analysis ◽  
Language Processing ◽  
Malware Detection ◽  
Experimental Results ◽  
The Internet ◽  
Multiple Sources ◽  
Filtering Method

AbstractExecutable files still remain popular to compromise the endpoint computers. These executable files are often obfuscated to avoid anti-virus programs. To examine all suspicious files from the Internet, dynamic analysis requires too much time. Therefore, a fast filtering method is required. With the recent development of natural language processing (NLP) techniques, printable strings became more effective to detect malware. The combination of the printable strings and NLP techniques can be used as a filtering method. In this paper, we apply NLP techniques to malware detection. This paper reveals that printable strings with NLP techniques are effective for detecting malware in a practical environment. Our dataset consists of more than 500,000 samples obtained from multiple sources. Our experimental results demonstrate that our method is effective to not only subspecies of the existing malware, but also new malware. Our method is effective against packed malware and anti-debugging techniques.

scholarly journals Fake News Detection with Machine Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 124-127
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Online News ◽  
The Internet ◽  
Daily Routine ◽  
Fake News ◽  
Criminal Records ◽  
The Public ◽  
Processing Techniques

As the internet is becoming part of our daily routine there is sudden growth and popularity of online news reading. This news can become a major issue to the public and government bodies (especially politically) if its fake hence authentication is necessary. It is essential to flag the fake news before it goes viral and misleads the society. In this paper, various Natural Language Processing techniques along with the number of classifiers are used to identify news content for its credibility.Further this technique can be used for various applications like plagiarismcheck , checking for criminal records.

scholarly journals Avaliação de Modelos de Redes Neurais Recorrentes para Anonimização de Textos em Português

2019 ◽  
Author(s):  
Antônio Franco ◽  
Leonardo Oliveira
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Automatic Generation ◽  
The Other ◽  
The Internet ◽  
Writing Style ◽  
Trade Off

Currently, there are several approaches to provide anonymity on the Internet. However, one can still identify anonymous users through their writing style. With the advances in neural network and natural language processing research, the success of a classifier when accurately identify the author of a text is growing. On the other hand, new approaches that use recurrent neural networks for automatic generation of obfuscated texts have also arisen to fight anonymity adversaries. In this work, we evaluate two approaches that use neural networks to generate obfuscated texts. In our experiments, we compared the efficiency of both techniques when removing the stylistic attributes of a text and preserving its original semantics. Our results show a trade-off between the obfuscation level and the text semantics.

scholarly journals Online Assignment Plagiarism Detector

2021 ◽  
pp. 528-535
Author(s):  
Nikhil Paymode ◽  
Rahul Yadav ◽  
Sudarshan Vichare ◽  
Suvarna Bhoir
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Technique ◽  
The Internet ◽  
Plagiarism Detection ◽  
Efficient Manner ◽  
Detection Approach ◽  
Different Sources ◽  
Student Program

Plagiarism is a big intricacy for companies, Schools, Colleges, and those who published their document on the web. In-Schools and Colleges maximum students write their assignments and experiments by copying other documents. Using this system teachers and examiners can detect the documents and sheets either it is written by a respective student or it is copied from someone else. For checking plagiarism the system takes two or more documents as a input and after using string matching algorithms, NLP ( natural language processing) technique, as well as an NLTK toolkit (natural language toolkit), produces output. In the output, the system returns some score which is an interval of 0 to 1. Where 1 and 0 refer to exactly similar and nothing is similar (Unique) respectively. If a score between 0 to 1 then it shows only some part of the document is similar. The main objective of the system is to find the more accurate plagiarism content in the documents with similar meanings and concepts that are correctly identified in an efficient manner. It is very easy to copy the data from different sources which includes the internet, papers, books over the internet, newspapers, etc. there is a need of detecting plagiarism to increase and improve the learning of students. To solve this problem, a student program plagiarism detection approach is proposed based on Natural Language Processing.

scholarly journals Virtual Voice Assistant

2021 ◽  
Vol 9 (VI) ◽  
pp. 4097-4101
Author(s):  
Ch. V. Tejaswi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Speech Synthesis ◽  
Voice Recognition ◽  
Computer Software ◽  
Human Beings ◽  
Desktop Application ◽  
Personal Assistant ◽  
The Voice

This is desktop application which can assist people with basic tasks using natural language. Virtual Voice Assistants can go online and search for an answer to a user’s question. Actions can be triggered using text or voice. Voice is the key. A virtual voice assistant is a personal assistant which uses natural language processing (NLP) , voice recognition and speech synthesis to provide a service through a particular application. Natural Language Processing in short is called as NLP. It is basically a branch of artificial intelligence which mainly deals with the interaction between personal computers and human beings using the natural language. The main objective of NLP is to read, convert, understand, and make use of the human languages in a manner that is valuable. Voice recognition is a hardware device or computer software program with the potential to decode the voice of human beings. Voice recognition is usually used to operate a gadget, execute commands, or write without making use of any mouse, keyboard, or press any buttons. Artificial production of human speech is called as Speech Synthesis. A system used for this purpose is called a speech computer or speech synthesizer and can be implemented in many products of software’s and hardware’s.

scholarly journals Detection of Email Spam using Natural Language Processing Based Random Forest Approach

2021 ◽  
Author(s):  
Alanazi Rayan ◽  
Ahmed I. Taloba
Keyword(s):  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
The Internet ◽  
Natural Languages ◽  
Efficient Detection ◽  
Random Node ◽  
A Company ◽  
Email Spam

Abstract An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanismthrough feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.

scholarly journals An Innovative Recommender System for E-Commerce Websites using Natural Language Processing

2020 ◽  
Vol 8 (6) ◽  
pp. 4085-4089
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Language Processing ◽  
Recommender System ◽  
Opinion Mining ◽  
The Internet

A Recommender System has become the go-to application for the internet generation these days. Mono-variate, bi-variate and multi-variate Recommender Systems are available to consumers of various products and services for the last 10 years or so only. In this paper, opinion mining dependent sentiment analysis using NLP tools will be used to recommend products to their purchasers on e-commerce websites. The application can be developed on the Python platform can be commercially used and will be precisely used to people who have to spend money without traditionally touching or feeling the item

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