fuzzy method

Multiple sclerosis(MS) is a disease that affects the central nervous system, during which the myelin present on the nerve fibers that have a protective role is destroyed, and therefore the conduction of electric current is disturbed and the symptoms of MS disease appear. TIn this disease, the white blood cells that play a defensive role in the body attack the myelin, which is a protection for nerve fibers, as a foreign agent, and each time these blood cells attack the nerve fibers of one of the organs of the patient's body. which is unclear, that organ will have problems. The best way to diagnose MS is to examine brain MRI images. Therefore, the existence of a fast and accurate method to evaluate changes in brain atrophy or the creation and increase of lesions (plaques) caused by this disease is a key component in diagnosing and evaluating the progress of the disease and the effectiveness of its treatment courses. Manual detection of changes in lesions (plaques) and brain atrophy caused by this disease usually requires a trained specialist and is very slow and difficult, and the results are somewhat subjective. Therefore, the existence of an automatic system for extracting and checking these changes is essential. Although many automatic methods have been proposed, the segmentation results are not accurate enough. As a result, there is a great need to develop a strong, fast and accurate method to diagnose MS and brain lesions caused by it. In this article, by combining two fuzzy methods and the controlled watershed algorithm, we propose a fast method with high accuracy to diagnose MS from brain MR images.

Sentiment Analysis, which is a new subfield of the processing of natural language and text mining, categorizes the texts based on the sentiment expressed in them. Sentiment plays a significant role in decision-making. So sentiment analysis technology has a broad scope for scientific applications. On the other hand, a huge amount of information in the world today is in the form of text. Therefore, text mining techniques are important. Exploring comments or analyzing sentiment as a branch of text mining, means finding the author's perspective on a specific subject. The Internet allows users to easily express their opinions and get informed about the opinions of others. The high volume and the lack of proper structure for the text of the comments provided on the web, make it difficult to use hidden knowledge within them. Therefore, it is important to provide methods that can prepare and provide this knowledge in a summarized and structured way. In this research, it has been tried to provide a fuzzy method for analyzing the following comments on news sites according to the text of the report. In this regard, it has been tried to investigate the relationship with the author's commentary and opinion in light of the subject of the text using the grammatical features of texts such as noun and verb, as well as sentimental load analysis of sentences. Subsequently, the method is evaluated by implementing it on the dataset collected from news and comments. The proposed method has 87% diagnosis accuracy.