The pandemic has taken the world by storm. Almost entire went into lockdown to save people from deadly COVID-19. Scientists around have come up with several vaccines for virus. Amongthem, Pfizer, Moderna, and AstraZeneca become quite famous. General however been expressing their feelings about safety effectiveness of on social media like Twitter. In this study, such tweets are being extracted Twitter using a API authentication token. raw stored processed NLP. data is then classified...

Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL also facilitated recent research on upscaling privatizing personalized recommendation services, using on-device data learn recommender models locally. These are then aggregated globally obtain a more performant model while maintaining privacy. Typically, systems (FRSs) do not take into account the lack resources availability at end-devices. In...

In this contemporary era, the uses of machine learning techniques are increasing rapidly in field medical science for detecting various diseases such as liver disease (LD). Around globe, a large number people die because deadly disease. By diagnosing primary stage, early treatment can be helpful to cure patient. research paper, method is proposed diagnose LD using supervised classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear...

Modeling heterogeneity by extraction and exploitation of high-order information from heterogeneous networks (HINs) has been attracting immense research attention in recent times. Such network embedding (HNE) methods effectively harness the small-scale HINs. However, real world, size HINs grow exponentially with continuous introduction new nodes different types links, making it a billion-scale network. Learning node embeddings on such creates performance bottleneck for existing HNE that are...

Learning vector representations (i.e., embeddings) of nodes for graph-structured information network has attracted vast interest from both industry and academia. Most real-world networks exhibit a complex heterogeneous format, enclosing high-order relationships rich semantic among nodes. However, existing embedding (HNE) frameworks are commonly designed in centralized fashion, i.e., all the data storage learning process take place on single machine. Hence, those HNE methods show severe...

One of the biggest issues for architects, planners, and landowners is house combustion. Singular sensors have been used in case a fire long time, but they cannot quantify volume to warn emergency service units. To resolve this problem, research aims develop an intelligent smart warning system that detects fires utilizing connected alerts property owners, services. The current model divided into three modules: Smoke Detection Module (SDM), which responsible detecting smoke prevent unwanted...

Modeling heterogeneity by extraction and exploitation of high-order information from heterogeneous networks (HINs) has been attracting immense research attention in recent times. Such network embedding (HNE) methods effectively harness the small-scale HINs. However, real world, size HINs grow exponentially with continuous introduction new nodes different types links, making it a billion-scale network. Learning node embeddings on such creates performance bottleneck for existing HNE that are...

Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL also facilitated recent research on upscaling privatizing personalized recommendation services, using on-device data learn recommender models locally. These are then aggregated globally obtain a more performant model, while maintaining privacy. Typically, systems (FRSs) do not consider the lack resources availability at end-devices. In...

This paper measures social media activity of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence data is investigated, overall and across disciplines. Second, correlation between bibliometric altmetric indices examined Spearman correlation. Third, a zero-truncated negative binomial model used to determine association various factors with increasing or decreasing citations. Lastly, effectiveness identify publications high citation impact...