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1.
Sci Rep ; 13(1): 13804, 2023 08 23.
Article in English | MEDLINE | ID: mdl-37612354

ABSTRACT

Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs' measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods.


Subject(s)
Emotions , Recognition, Psychology , Humans , Brain , Benchmarking , Electroencephalography
2.
Article in English | WPRIM (Western Pacific) | ID: wpr-875794

ABSTRACT

@#Introduction: The twenty-first-century learning is adopting the student-centered learning techniques and the teachers are mainly facilitators to direct the process of learning and so social media and mobile applications became an important learning platform. Mobile learning (M-learning) is the practice of learning activities through a portable device such as cellular phone or a personal digital assistant. The aim of this research is to screen the medical students’ intention toward the adoption of M-learning and to determine factors affecting the intentions of the medical students to practice M-learning. Methods: A cross-sectional study among medical students was performed through a questionnaire based on the Theory of Reasoned Action and the Technology Acceptance Model. The study included 129 students in different stages of the medical study. Results: Results showed that the factors affecting the students’ inten¬tion to practice M-learning include the students’ attitude, perceived usefulness, perceived ease of use, and availability of resources. In the current sample 82.7% of students are already using M-Learning; 41.7% are using it for assessment, 22.8% are using it for learning and 35.5% are using it for both. Conclusion: It was concluded that most medical students have higher intention to adopt M-learning and they are mostly using it for assessment purposes rather than in learning.

3.
Int J Equity Health ; 18(1): 55, 2019 04 11.
Article in English | MEDLINE | ID: mdl-30971254

ABSTRACT

BACKGROUND: A consensus is developing on interventions to improve newborn survival, but little is known about how to reduce socioeconomic inequalities in newborn mortality in low- and middle-income countries. Participatory learning and action (PLA) through women's groups can improve newborn survival and home care practices equitably across socioeconomic strata, as shown in cluster randomised controlled trials. We conducted a qualitative study to understand the mechanisms that led to the equitable impact of the PLA approach across socioeconomic strata in four trial sites in India, Nepal, Bangladesh, and Malawi. METHODS: We conducted 42 focus group discussions (FGDs) with women who had attended groups and women who had not attended, in poor and better-off communities. We also interviewed six better-off women and nine poor women who had delivered babies during the trials and had demonstrated recommended behaviours. We conducted 12 key informant interviews and five FGDs with women's group facilitators and fieldworkers. RESULTS: Women's groups addressed a knowledge deficit in poor and better-off women. Women were engaged through visual learning and participatory tools, and learned from the facilitator and each other. Facilitators enabled inclusion of all socioeconomic strata, ensuring that strategies were low-cost and that discussions and advice were relevant. Groups provided a social support network that addressed some financial barriers to care and gave women the confidence to promote behaviour change. Information was disseminated through home visits and other strategies. The social process of learning and action, which led to increased knowledge, confidence to act, and acceptability of recommended practices, was key to ensuring behaviour change across social strata. These equitable effects were enabled by the accessibility, relevance, and engaging format of the intervention. CONCLUSIONS: Participatory learning and action led to increased knowledge, confidence to act, and acceptability of recommended practices. The equitable behavioural effects were facilitated by the accessibility, relevance, and engaging format of the intervention across socioeconomic groups, and by reaching-out to parts of the population usually not accessed. A PLA approach improved health behaviours across socioeconomic strata in rural communities, around issues for which there was a knowledge deficit and where simple changes could be made at home.


Subject(s)
Health Equity , Health Promotion , Infant Health/statistics & numerical data , Maternal Health/statistics & numerical data , Rural Population/statistics & numerical data , Africa , Asia , Female , Focus Groups , Health Impact Assessment , Humans , Infant, Newborn , Pregnancy , Qualitative Research , Socioeconomic Factors
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