Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur over the edges of social networks. Many prior works have carried out empirical studies and diffusion models to understand how information propagates in online social networks; however they suffer from problems. In this paper, we propose an information diffusion model inspired by information propagation among people. Our proposed Social Behavioural Information Diffusion Model, abbreviated as SBIDM, considers the effect of mainstream media like TV and radio, as well as interaction with the neighbours. The advantages of our approach are four-fold. First, it models information diffusion in social networks inspired by social life, which considers the effect of aggregate social behaviour to diffuse information; second, it allows partial knowledge to be held in each individual; third, it considers the effects of social media in propagating information as well as the effects of interacting with neighbours; and last but not least, it is applicable to different types of data including synthetic and well-known real social networks like Facebook, Amazon, Epinions and DBLP. To explore the advantages of our approach, many experiments with different settings and specifications were conducted. The obtained results are very promising.

No abstract

No abstract

No abstract

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.