Asian Journal of Research in Computer Science

Tumors are caused by uncontrolled growth of abnormal cells. Magnetic Resonance Imaging (MRI) is modality that is widely used to produce highly detailed brain images. In addition, a surgical biopsy of the suspected tissue (tumor) is required to obtain more information about the type of tumor. Biopsy takes 10 to 15 days for laboratory testing. Based on a study conducted by Brady in 2016, errors in radiology practice are common, with an estimated daily error rate of 3-5%. Therefore, using the application of artificial intelligence, is expected to simplify and improve the accuracy of doctor's diagnose.

Online medical forums allow users to research medical treatments or conditions and gain support from other users dealing with similar issues. These forums have become increasingly popular over the past decade, helping connect medical patients and professionals from various backgrounds and creating a supportive online community. This paper evaluates the adaptation of online medical forums in Nigeria, to analyse the opinions of Nigerian citizens in using the medical system. In this research, a Tweepy API (python library/ module that contains the required object and functions for managing the Twitter data), textblob (python library for processing textual data), and matplotlib modules (for creating statistical charts) were used to extract related tweets from the Twitter. The project involves steps like creating a Twitter developer account, which gives the privilege to create a Twitter application and has keys for accessing online resources. The analysis begins by searching for the data, storing it, filtering it and then returning the sentiment analysis to review the positive, neutral, and negative tweets. The output of this project returns a table and scatter graph that displays the Subjectivity and polarity of the opinions of Nigerians on the adaptation of online medical forums. Similarly, a bar chart is obtained that shows the positive tweets, the negative tweets and the neutral regarding online medical forums.

Abstract: Several studies have empirically explored biometric voting using the IoT to transfer votes to the central system. There aren't many bibliometric studies that categorize the output in this area, though. By keeping an eye on the papers posted on the Scopus platform, this study’s goal is to give research bibliometric analysis on biometric voting utilizing IoT to transfer votes to a central system, classifying trends, the state of the art, and other indications. 267 different materials made up the sample. Using the VOSviewer program, the data was processed and the outcomes graphically represented. According to the study, which examined publications’ simultaneous occurrence by year, trends of keyword, co-citations, coupling bibliographic, and co-authorship analysis, institutions, and countries, the body of knowledge on biometric voting that uses the Internet of Things to transfer votes to a central system is expanding quickly. More than 530 citations were found in just eight works. However, there are other industrious writers. The most significant of the 267 sources used in the review were published in 26.066 percent of the papers. China is the world's leader in this field. This study offers knowledge about the current state of the art and indicates research opportunities and gaps in IoT-based biometric voting.