An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors.

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

Users' intention to continue using mHealth services: A DEMATEL approach during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) has changed the way we use and perceive online services. This study examined the influence of service quality factors during COVID-19 on individuals' intention to continue use mHealth services. A decision-making trial and evaluation laboratory (DEMATEL) approach was used to identify and analyse the relationships between service quality and individuals' intention to continue use mHealth during the COVID-19 pandemic. Individuals' direct, indirect, and interdependent behaviours in relation to service quality and continues use of mHealth were studied. A total of 126 respondents were involved in this study. The results identified several associations between service quality factors and individuals' continuous use of mHealth. The most important factor found to influence users' decision to continuously use mHealth was assurance, followed by hedonic benefits, efficiency, reliability, and content quality. The relevant cause-and-effect relationships were identified and the direction for quality improvement was discussed. The outcomes from this study can support healthcare policy makers to swiftly and widely respond to COVID-19 challenges. The findings provide fundamental insights for healthcare organisations to promote continuous use of mHealth among people by prioritising service improvements.

Actual use of social media for engagement to enhance students' learning.

Learners can interact and connect with one another in new ways thanks to social media. This study employs two models to investigate the factors that contribute to students' involvement in order to improve their learning: constructivism and the technology acceptance model (TAM). Therefore, the objective of this research is to create a model of real use of social media for engagement by conducting an empirical examination into students' adoption of actual use of social media for education. A survey was distributed to 410 university students in order to achieve this goal. A quantitative research approach and partial least squares structural equation modelling were used to acquire the results (PLS-SEM). The outcomes of our empirical examination suggest that determining discriminant validity has become a widely accepted prerequisite for analysing latent factor connections. The studies also demonstrated that using social media to engage students and improve their learning in higher education is extremely beneficial. The findings revealed real use of social media for interaction via interactions variables and TAM model acceptance. Students' pleasure with learning was also favourably associated to their actual usage of social media and involvement, according to the findings. As a conclusion, the result of R-Square's perceived usefulness was 0.611%, students' engagement was 0.561%, actual use of social media was 0.582%, students' satisfaction was 0.611%, and students' learning was 0.627%. This study's findings and ramifications are presented.

ALL classification using neural ensemble and memetic deep feature optimization.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

Digital Innovations for Occupational Safety: Empowering Workers in Hazardous Environments.

BACKGROUND: The quest to increase safety awareness, make job sites safer, and promote decent work for all has led to the utilization of digital technologies in hazardous occupations. This study investigated the use of digital innovations for safety and health management in hazardous industries. The key challenges and recommendations associated with such use were also explored. METHOD: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a total of 48 studies were reviewed to provide a framework for future pathways for the effective implementation of these innovations. FINDINGS: The results revealed four main categories of digital safety systems: wearable-based systems, augmented/virtual reality-based systems, artificial intelligence-based systems, and navigation-based systems. A wide range of technological, behavioral, and organizational challenges were identified in relation to the key themes. CONCLUSION: Outcomes from this review can inform policymakers and industrial decision-makers about the application of digital innovations for best safety practices in various hazardous work conditions.

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.