Introduction:  multiple sclerosis is a chronic autoimmune disorder causing the degeneration of the myelin sheath, affecting nerve signal transmission. Symptoms include muscle weakness, visual disturbances, balance impairments, and incoordination. Early diagnosis is crucial for effective disease management and preventing irreversible neurological damage. This research was designed to explore diagnostic methods and introduces machine learning for automated data analysis and faster diagnosis. Materials & Methods: This study reviewed diagnostic methods for multiple sclerosis (MS), including electroencephalography (EEG), electromyography (EMG), clinical data, cerebrospinal fluid analysis, magnetic resonance imaging (MRI), and optical coherence tomography (OCT). Artificial intelligence (AI)-based approaches were also introduced to enable automated data analysis and expedite disease diagnosis. A novel platform-based method was proposed as an exclusive approach for automated detection through the integration of established diagnostic techniques. Results: Findings indicated that magnetic resonance imaging (MRI) demonstrates high accuracy in the diagnosis of multiple sclerosis. Based on the average performance of artificial intelligence-based methods across the primary diagnostic modalities, accuracies of 90%, 75%, 80%, 90%, and 95% were achieved for MRI, optical coherence tomography (OCT), electroencephalography (EEG), electromyography (EMG), and cerebrospinal fluid analysis, respectively. The proposed platform integrates these modalities to enhance both the speed and accuracy of disease detection. Conclusion: The utilization of advanced diagnostic techniques, coupled with the integration of multiple methodologies, markedly improves the early detection and therapeutic intervention of multiple sclerosis, thereby reducing the associated complications of the disease.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Purpose: To report a patient with MEWDS.Patient and findings: A 65-year-old man presented with sudden decrease of vision in his right eye. On ophthalmoscopy of the involved eye, numerous small discrete white lesions were detected superficially and deep in the retina around the disc and macula. On visual field examination, enlargement of the blind spot was detected. Fluorescein angiography was unremarkable. On electroretinography, the a-wave was decreased minimally. The patient was followed and after 2 months, significant improvement of visual acuity and visual field was observed.Conclusion: MEWDS is a rare intraocular inflammatory disease of unknown etiology and in contrast to our patient, more frequent in females. The natural course is generally benign with spontaneous disappearance of the lesions and improvement of vision in 6 to 8 weeks.

The purpose of this study was to determine the effectiveness of educational method of intelligence education based on Gardner multiple intelligence theory on emotional intelligence, creativity of female students. The research method was semi experimental with pretest design and control group. The statistical population consisted of all 5th and 6th grade students in Isfahan city in the academic year of 1391-97. The sampling method was multi-stage cluster sampling. Therefore, 30 students of a school were selected and assigned to experimental and control groups (each group was 15). The research data were collected using bar-on emotional intelligence and Abdi’ s creativity and analyzed using single-variable and multivariate covariance analysis. The results of the study showed that the intelligence approach based on Gardner multiple intelligence theory improved emotional intelligence (interpersonal relationships, p