Objective: The main cognitive impairments in multiple sclerosis (MS) affect the working memory, processing speed, and performances that are in close interaction with one another. Cognitive problems in MS are influenced to a lesser degree by disease recovery medications or treatments,but cognitive rehabilitation is considered one of the promising methods for cure. There is evidence regarding the effectiveness of cognitive rehabilitation for MS patients in various stages of the disease. Since the impairment in working memory is one of the main MS deficits, a particular training that affects this cognitive domain can be of a great value. This study aims to determine the effectiveness of memory rehabilitation on the working memory performance of MS patients.
Method: Sixty MS patients with cognitive impairment and similar in terms of demographic characteristics, duration of disease, neurological problems, and mental health were randomly assigned to three groups: namely, experimental, placebo, and control. Patients' cognitive evaluation incorporated baseline assessments immediately post-intervention and 5 weeks post-intervention. The experimental group received a cognitive rehabilitation program in one-hour sessions on a weekly basis for 8 weeks. The placebo group received relaxation techniques on a weekly basis; the control group received no intervention.
Results: The results of this study showed that the cognitive rehabilitation program had a positive effect on the working memory performance of patients with MS in the experimental group. These results were achieved in immediate evaluation (post-test) and follow-up 5 weeks after intervention. There was no significant difference in working memory performance between the placebo group and the control group.
Conclusions: According to the study, there is evidence for the effectiveness of a memory rehabilitation program for the working memory of patients with MS. Cognitive rehabilitation can improve working memory disorders and have a positive effect on the working memory performance of these patients.
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http://dx.doi.org/10.1080/13803395.2017.1356269 | DOI Listing |
Sensors (Basel)
December 2024
Department of Information and Electronic Engineering, International Hellenic University, 57001 Thessaloniki, Greece.
Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation.
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December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
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December 2024
Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.
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December 2024
Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, C/Camino de Vera, s/n, 46022 Valencia, Spain.
A Mixed Reality (MR) application using an optical see-through headset was developed to assess short-term spatial memory. A study with 29 participants was conducted. Data from this study were compared to two previous studies using mobile Augmented Reality (AR) and Virtual Reality (VR) with headsets.
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December 2024
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.
Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification.
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