ABSTRACT
As India has successfully developed a vaccine to fight against the COVID-19 pandemic, the government has started its immunization program to vaccinate the population. Initially, with the limited availability in vaccines, a prioritized roadmap was required to suggest public health strategies and target priority groups on the basis of population demographics, health survey information, city/ region density, cold storage facilities, vaccine availability, and epidemiologic settings. In this paper, a machine learning-based predictive model is presented to help the government make informed decisions/insights around epidemiological and vaccine supply circumstances by predicting India's more critical segments that need to be catered to with vaccine deliveries as quickly as possible. Public data were scraped to create the dataset;exploratory data analysis was performed on the dataset to extract important features on which clustering and ranking algorithms were performed to figure out the importance and urgency of vaccine deliveries in each region. Copyright © 2022 IGI Global.
ABSTRACT
Artificial intelligence (AI)/digital employees, or metaphorical software robots (bots), are the foundation of the business process automation technology known as robotic process automation (RPA). The term "software robotics" has been used sometimes (not to be confused with robot software). Using internal application programming interfaces (APIs) or specialized scripting languages, a software developer creates a set of steps to automate a job and interface to the back-end system in conventional workflow automation technologies. RPA systems, on the other hand, create the action list by seeing the user carry out the job in the graphical user interface (GUI) of the programme, and then carry out the automation by repeating those actions directly in the GUI. In products that may not normally have APIs for this purpose, this can lessen the barrier to the usage of automation. Nowadays, monitoring every day covid status is not possible for an individual. So, the plan is to send an updated covid status through email automatically to the end users by using Robotic Process Automation (RPA). The main goal of this paper is to send corona information to the end users who are in need of covid details. For this, the end user wants to provide their email id and country name which they want to know about. The rest of the RPA process will be done by the bot using data scraping. Then email automation will be done to send email automatically. It is easy to check the required particular data from the cluster of data. It is easy to read and understand for all end users. Copyright © 2022 Ubiquity Press. All rights reserved.
ABSTRACT
Social network users receive a large amount of social data every day. These data may contain malicious unwanted social spams, even though each social network has its social spam filtering mechanism. Moreover, spammers may send spam to multiple social networks concurrently, and the spam on the same topic from different social networks has similarities. Therefore, it is crucial to building a universal spam detection system across different social networks that can effectively fend off spam continuously. In this paper, we designed and implemented a tool Spam-Fender to facilitate spam detection across social networks. In order to utilize the raw social data obtained from multiple social networks, we utilized a semi-supervised learning method to convert unlabelled data into usable data for training the model. Moreover, we developed an incremental learning method to enable the model to learn new data continuously. Performance evaluations demonstrate that our proposed system can effectively detect social spam with satisfactory accuracy levels. In addition, we conducted a case study on the COVID-19 dataset to evaluate our system.
ABSTRACT
In the recent years, especially after covid-19 became a thing, people started finding information about Retail On- Demand Services online rather than relying on local contacts. This makes it a lot easier for people to find and book services online, rather than going out and booking them in person. So, a Django-based Web application with a real time database deployed on cloud that provides details of some service providers (Carpenters, Electricians etc.) in region of Kanuru (Vijayawada, India) has been designed. Data are collected from multiple sources using a scraping code that Beautiful Soup framework is used and stored in PostgreSQL database that is later deployed on Cloud platforms. This helps people from this region to access information quickly and help them book these On-Demand Services Faster. © 2022 IEEE.