ABSTRACT
Since the discovery of corona virus (nCOV-19), and its subsequent progression into a global pandemic, an enormous hurdle faced by hospitals and their healthcare staff has been to streamline, and look after the huge flow of cases. It has become increasingly difficult to consult a Covid specialist when the first wave occurred in rural and areas not connected as well to modern amenities. Thus, it has become obvious that an interactive Chatbot with efficient execution can help patients living in such areas by educating on the appropriate preventive measures, news on virus strains, reducing the psychological damage caused by the fear of the virus and mental effects of solitary isolation. This study displays and discusses the schematics of an artificial intelligence (AI) chatbot for the purpose of evaluation, diagnosis and recommending immediate preventive as well as safety measures for patients who have been exposed to nCOV-19, and doubles as a virtual assistant to aid in measuring the severity of the infection via symptom analysis and connects with the authorised medical facilities when it progresses to a serious stage. © 2022 IEEE.
ABSTRACT
As businesses extend their online offerings, demand for UI-UX and digital services is increasing. Both in-house teams and agencies will be under pressure to offer services on time, with the total number of internet users worldwide increasing by 301 million since April 2019 (We are Social). After the covid-19 it's demand in e-commerce market is also increasing exponentially. In this paper User-centred design (UCD) method is used to process the whole prototyping. User flow is also discussed based on 3 levels(launch screen, create and account, home screen). The test result for the sample prototyping were the app's design elements with 4 points being the most common response on the scale of 5, the main menu page and its experience with 5 points, and the overall UI-UX of the app with 5 points. © 2022 IEEE.
ABSTRACT
The Internet as a whole is a large network of interconnected computer networks and their supporting infrastructure which is divided into 3 parts. The web is a list of websites that can be accessed using search engines like Google, Firefox, and others, this is called as Surface Web. The Internet's layers stretch well beyond the surface material that many people can quickly reach in their everyday searches. The Deep Web material, which cannot be indexed by regular search engines like Google, is a subset of the internet. The Dark Web, which extends to the deepest reaches of the Deep Web, contains data that has been purposefully hidden. Tor may be used to access the dark web. Tor employs a network of volunteer devices to route users' web traffic via a succession of other users' computers, making it impossible to track it back to the source. We will analyze and include results about the Dark Web's presence in various spheres of society in this paper. Further we take dive into about the Tor metrics how the relay list is revised after users are determined based on client requests for directories (using TOR metrics). Other way we can estimate the number of users in anonymous networks. This analysis discusses the purposes for which it is frequently used, with a focus on cybercrime, as well as how law enforcement plays the adversary position. The analysis discusses these secret Dark Web markets, what services they provide, and the events that take place there such as cybercrime, illegal money transfers, sensitive communication etc. Before knowing anything about Dark Web, how a rookie can make mistake of letting any threat or malware into his system. This problem can be tackled by knowing whether to use Windows, or any other OS, or any other service like VPN to enter Dark world. The paper also goes into the agenda of how much of illegal community is involved from India in these markets and what impact does COVID-19 had on Dark Web markets. Our analysis is carried out by searching scholarly journal databases for current literature. By acting as a reference guide and presenting a research agenda, it contributes to the field of the dark web in an efficient way. This paper is totally built for study purposes and precautionary measures for accessing Dark Web. © 2022 IEEE.
ABSTRACT
Today during ‘Covid-20’, people are more inclined towards online shopping. In general practice, analysis of browsing history and customer’s micro behaviour against online shopping habits have been used for future suggestions. Due to this, the predictions made were suffereing from over-similarity problem and the user was unable to find any novelty in the recommended items. Observing these issues, e-shopping quality can be enhanced by adding a factor other than similarity. The current research suggests and advertise those products which belongs to a person’s region. For this research work the data has been collected on the basis of area-wise, like, country-based seggregation. Here the considered dataset belongs to country, ‘India’, its culture, its handicraft and its citizens. Datasets and their combinations based on multiple attributes are input for the proposed predictive system. In this paper, existing data is also considered for collecting customers demographic details which is further mapped with the area-wise dataset. Also, a framework has been proposed which uses database and user query as input for its predictive system in order to generate default suggestions for the user other than the submitted query also. © 2021, Institute of Advanced Engineering and Science. All rights reserved.