A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

A fuzzy logic model for evaluating the standard performance of a prototype online voting system

This paper described the major challenges associated with existing methods of voting; hence a prototype online voting system was developed and proposed for credible election in Edo state with a mind set to trash out the various problems identified with the existing system. In order to determine if the prototype online voting system developed is of standard performance a fuzzy clustering means (FCM) was designed to evaluate and ascertain its performance based on certain criteria gathered using questionnaire designed. The FCM model was simulated and tested for evaluation taking into consideration stakeholders of election that were drawn from twelve (12) local government areas, out of the Eighteen (18) local government areas of Edo state. Opinions of stakeholders of the election concerning the wished-for model were arbitrarily sampled and analyzed for the use of assessment in particular when compared to the present system of selection. In addition, other factors that can promise an open and just election were also discussed and place into consideration throughout the implementation of the developed prototype online voting system. The result from the evaluation revealed that the seven (7) local government areas which formed about (58.33%) of the beyond least standard cluster and the five (5) local government areas, which also formed about (41.66%) of the regular standard cluster of the entire population of (12) local government areas were both above the average acceptable benchmark for elections, which is a key indicator that the developed prototype online voting software meets more than the standard for a credible election process and it is therefore proficient as a verdict announcer for a transparent electoral process when fully implemented and deployed for usage