Comparison of two methods on vector space model for trust in social commerce

The study of dealing with searching information in documents within web pages is Information retrieval (IR). The user needs to describe information with comments or reviews that consists of a number of words. Discovering weight of an inquiry term is helpful to decide the significance of a question. Estimation of term significance is a basic piece of most information retrieval approaches and it is commonly chosen through term frequency-inverse document frequency (TF-IDF). Also, improved TF-IDF method used to retrieve information in web documents. This paper presents comparison of TF-IDF method and improved TF-IDF method for information retrieval. Cosine similarity method calculated on both methods. The results of cosine similarity method on both methods compared on the desired threshold value. The relevance documents of TF-IDF method are more extracted than improved TF-IDF method

Trust Prediction Framework for Social Networking

In today's hyper-connected society,understanding the mechanisms of trust is crucial.Trust issues are critical to solving problems. Trustphenomenon has been extensively explored by avariety of disciplines across the social sciences,including economics, social psychology, and politicalscience. Trust is a concept with many facets anddimensions. In this paper, Prediction TrustFramework based on the evaluation of trust valueand Improved PageRank Algorithm will propose forevaluation trust in OSNs (Online Social Networks).This evaluation framework designs to integratetheoretical concepts from the trust literature, socialnetwork and helps to other different trust-relatedproblems in OSNs

INHALEd nebulised unfractionated HEParin for the treatment of hospitalised patients with COVID‐19 (INHALE‐HEP): Protocol and Statistical Analysis Plan for an investigator‐initiated international meta‐trial of randomised studies

Aims: inhaled nebulised unfractionated heparin (UFH) has a strong scientific and biological rationale that warrants urgent investigation of its therapeutic potential in patients with COVID‐19. UFH has antiviral effects and prevents the SARS‐CoV‐2 virus' entry into mammalian cells. In addition, UFH has significant anti‐inflammatory and anticoagulant properties, which limit progression of lung injury and vascular pulmonary thrombosis.Methods: the INHALEd nebulised unfractionated HEParin for the treatment of hospitalised patients with COVID‐19 (INHALE‐HEP) metatrial is a prospective individual patient data analysis of on‐going randomised controlled trials and early phase studies. Individual studies are being conducted in multiple countries. Participating studies randomise adult patients admitted to the hospital with confirmed SARS‐CoV‐2 infection, who do not require immediate mechanical ventilation, to inhaled nebulised UFH or standard care. All studies collect a minimum core dataset. The primary outcome for the metatrial is intubation (or death, for patients who died before intubation) at day 28. The secondary outcomes are oxygenation, clinical worsening and mortality, assessed in time‐to‐event analyses. Individual studies may have additional outcomes.Analysis: we use a Bayesian approach to monitoring, followed by analysing individual patient data, outcomes and adverse events. All analyses will follow the intention‐to‐treat principle, considering all participants in the treatment group to which they were assigned, except for cases lost to follow‐up or withdrawn.Trial registration, ethics and dissemination: the metatrial is registered at ClinicalTrials.gov ID NCT04635241. Each contributing study is individually registered and has received approval of the relevant ethics committee or institutional review board. Results of this study will be shared with the World Health Organisation, published in scientific journals and presented at scientific meetings