Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases

Abstract

Farming is essential to the long-term viability of any economy. It differs in each country, but it is essential for long-term economic success. Only a few of the agricultural industry’s issues include a lack of suitable irrigation systems, weeds, and plant monitoring concerns as a consequence of efficient management in distinct open and closed zones for crop and plant treatment. The objective of this work is to carry out a study on the use of artificial intelligence and computer vision methods for diagnosis of diseases in agro sectors in the context of agribusiness, demonstrating the feasibility of using these techniques as tools to support automation and obtain productivity gains in this sector. During the literary analysis, it was determined that technology could improve efficiency, hence decreasing these types of concerns. Given the consequences of a wrong diagnosis, diagnosis is work that requires a high level of precision. Fuzzy cognitive maps were shown to be the most efficient method of utilizing bibliographically reviewed preferences, which led to the consideration of neural networks as a second option because this technique is the most robust in terms of the qualifying criteria of the data stored in databases.

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

“…One of the significant advances has been in medical imaging [4]. AI algorithms can analyse medical images to identify anomalies that might be missed by human clinicians, improving the accuracy of diagnosis and treatment [4][5][6][7].…”

Retracted: Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”