Mohammadreza Amiri

Mohammadreza amiri, GholamAli montazer, Ebrahim Mousavi

The E-learning environment offers greater flexibility compared to face-to-face interactions, allowing for adapting educational content to meet learners' individual needs and abilities through personalization and customization of e-content and the educational process. Despite the advantages of this approach, customizing the learning environment can reduce the costs of tutoring systems for similar learners by utilizing the same content and process for co-like learning groups. Various indicators for grouping learners exist, but many of them are conceptual, uncertain, and subject to change over time. In this article, we propose using the Felder-Silverman model, which is based on learning styles, to group similar learners. Additionally, we model the behaviors and actions of e-learners in a network environment using Fuzzy Set Theory (FST). After identifying the learning styles of the learners, co-like learning groups are formed, and each group receives adaptive content based on their preferences, needs, talents, and abilities. By comparing the results of the experimental and control groups, we determine the effectiveness of the proposed grouping method. In terms of "educational success," the weighted average score of the experimental group is 17.65 out of 20, while the control group achieves a score of 12.6 out of 20. Furthermore, the "educational satisfaction" of the experimental group is 67%, whereas the control group's satisfaction level is 37%.

Mohammadreza Amiri, Monireh Hosseini

Despite being among the most common psychological disorders, anxiety-related conditions are still primarily identified through subjective assessments, such as clinical interviews and self-evaluation questionnaires. These conventional methods often require significant time and may vary depending on the evaluator. However, the emergence of advanced artificial intelligence techniques has created new opportunities for detecting anxiety in a more consistent and automated manner. To address the limitations of traditional approaches, this study introduces a comprehensive model that integrates deep learning architectures with optimization strategies inspired by swarm intelligence. Using multimodal and wearable-sensor datasets, the framework analyzes physiological, emotional, and behavioral signals. Swarm intelligence techniques including genetic algorithms and particle swarm optimization are incorporated to refine the feature space and optimize hyperparameters. Meanwhile, deep learning components are tasked with deriving layered and discriminative representations from sequential, multi-source inputs. Our evaluation shows that the fusion of these two computational paradigms significantly enhances detection performance compared with using deep networks alone. The hybrid model achieves notable improvements in accuracy and demonstrates stronger generalization across various individuals. Overall, the results highlight the potential of combining metaheuristic optimization with deep learning to develop scalable, objective, and clinically meaningful solutions for assessing anxiety disorders