Stacked Autoencoder and Meta-Learning based Heterogeneous Domain Adaptation for Human Activity Recognition

The field of human activity recognition (HAR) using machine learning approaches has gained a lot of interest in the research community due to its empowerment of automation and autonomous systems in industries and homes with respect to the given context and due to the increasing number of smart wearable devices. However, it is challenging to achieve a considerable accuracy for recognizing actions with diverse set of wearable devices due to their variance in feature spaces, sampling rate, units, sensor modalities and so forth. Furthermore, collecting annotated data has always been a serious issue in the machine learning community. Domain adaptation is a field that helps to cope with the issue by training on the source domain and labeling the samples in the target domain, however, due to the aforementioned variances (heterogeneity) in wearable sensor data, the action recognition accuracy remains on the lower side. Existing studies try to make the target domain feature space compliant with the source domain to improve the results, but it assumes that the system has a prior knowledge of the feature space of the target domain, which does not reflect real-world implication. In this regard, we propose stacked autoencoder and meta-learning based heterogeneous domain adaptation (SAM- HDD) network. The stacked autoencoder part is trained on the source domain feature space to extract the latent representation and train the employed classifiers, accordingly. The classification probabilities from the classifiers are trained with meta-learner to further improve the recognition performance. The data from tar- get domain undergoes the encoding layers of the trained stacked autoencoders to extract the latent representations, followed by the classification of label from the trained classifiers and meta- learner. The results show that the proposed approach is efficient in terms of accuracy score and achieves best results among the existing works, respectivel

E-waste flows, resource recovery and improvement of legal framework in Pakistan

International audienc

Environment and air pollution: health services bequeath to grotesque menace

The objective of the study is to establish the link between air pollution, fossil fuel energy consumption, industrialization, alternative and nuclear energy, combustible renewable and wastes, urbanization, and resulting impact on health services in Malaysia. The study employed two-stage least square regression technique on the time series data from 1975 to 2012 to possibly minimize the problem of endogeniety in the health services model. The results in general show that air pollution and environmental indicators act as a strong contributor to influence Malaysian health services. Urbanization and nuclear energy consumption both significantly increases the life expectancy in Malaysia, while fertility rate decreases along with the increasing urbanization in a country. Fossil fuel energy consumption and industrialization both have an indirect relationship with the infant mortality rate, whereas, carbon dioxide emissions have a direct relationship with the sanitation facility in a country. The results conclude that balancing the air pollution, environment, and health services needs strong policy vistas on the end of the government officials

Preparedness and impact of COVID 19 infection at tertiary care neurology centers in Pakistan

Objective: We aimed to assess the response and impact of covid 19 pandemic at tertiary care centers in Pakistan especially pertaining to neurological care, facilities and training.Methods: A pre-tested survey form was sent to 40 neurology tertiary care centers in all the provinces in the country in the first week of July 2020. 33 filled forms were received, out of which 18 were public (government) and 15 were private hospitals.Results: Estimated 1300 HCW (faculty, medical officers, trainees and nurses) work at these 33 participating centers. There were 17 deaths among HCW (1.3%) at ten centers. Sufficient personal protective equipment (PPE) were provided to 158 HCW (12%). 129 (10%)HCW tested positive for COVID 19 at 31 centers including trainees/medical officers (39), consultants (29) and nursing and other staff (61). Due to low neurology admissions, 23/33 hospitals (70%) posted neurology trainees in COVID 19 units to contribute to covid care. Less than 50% hospitals did covid screening PCR before admission to neurology wards. Only 10% hospitals provide training and regular update to HCW. Neurology tele-health services were started for clinically stable patients at 15 (45%) centers. Only 60% neurology training programs were able to start online training. Ongoing research studies and trials focusing neurological manifestations of COVID-19 were done at 10 (30%) centers. Modification of facilities for COVID patients showed that 24(72%) hospitals strictly reduced the number of attendants accompanying patients. Only 10 (30%) centers had neurophysiological tests being conducted on COVID-19 patients. Mental health support services to HCW were provided at 12 (36%) centers.Conclusions: Among HCW 10% tested positive for covid and 1.3% died. Mental health support services offered for HCW were available in 36% institutions. Neurology training was substantially affected due to low admissions, limited ward rounds and limited availability of online training

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Illustration of coarse-graining procedure using mean of data points in a non-overlapping window equal in length to the time scale.

<p>Illustration of coarse-graining procedure using mean of data points in a non-overlapping window equal in length to the time scale.</p