Soft biometric information, such as gender, iris, and voice, can be helpful in various applications, such as security, authentication, and validation. Iris is secure biometrics with low forgery and error rates due to its highly certain features are being used in the last few decades. Iris recognition could be used both independently and in part for secure recognition and authentication systems. Existing iris‐based gender classification techniques have low accuracy rates as well as high computational complexity. Accordingly, this paper presents an authentication approach through gender classification from iris images using support vector machine (SVM) that has an excellent response to sustained changes using the Zernike, Legendre invariant moments, and Gradient‐oriented histogram. In this study, invariant moments are used as feature extraction from iris images. After extracting these descriptors' attributes, the attributes are categorized through keycode fusion. SVM is employed for gender classification using a fused feature vector. The proposed approach is evaluated on the CVBL data set and results are compared in state of the art based on local binary patterns and Gabor filters. The proposed approach came out with 98% gender classification rate with low computational complexity that could be used as an authentication measure.

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The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre‐processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k‐nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.

Background and Aim: Multiple sclerosis (MS) is a chronic progressive inflammatory disease of the central nervous system that patients suffer from its complications and experience physical and emotional side effects in their lifetime. The purpose of this study was to compare the effectiveness of acceptance and commitment therapy (ACT) and cognitive behavior therapy (CBT) on fatigue in MS patients. Materials and Methods: This was a clinical trial with pre and post-test design. Among the patients with MS referring to the neurology department of Baqiyatallah Hospital, 30 patients were selected by convenient sampling method and randomly divided into two experimental and one control groups. Fatigue severity scale (FSS) was used to assess fatigue in the MS patients in the pre-treatment (pre-test) and post-treatment (post-test) stages. Results:The results showed that ACT and CBT only with a little difference had similar effects on the improvement and decrease of severity of fatigue so that, we found a significant decrease in the scores of severity of fatigue in the post-test compared to the scores in the pretest. Conclusion:The results indicated that ACT and CBT had the same effect on reducing the severity of fatigue in the MS patients.