Dimensionality reduction, and function approximation of poly (lactic-co-glycolic acid) micro-and nanoparticle dissolution rate

Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles’ dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques

Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile

Predicting the dissolution rate of proteins plays a significant role in pharmaceutical/medical applications. The rate of dissolution of Poly Lactic-co-Glycolic Acid (PLGA) micro- and nanoparticles is influenced by several factors. Considering all factors leads to a dataset with three hundred features, making the prediction difficult and inaccurate. Our present study consists of three phases. Firstly, dimensionality reduction techniques are applied in order to simplify the task and eliminate irrelevant and redundant attributes. Subsequently, a heterogeneous pool of several classical regression algorithms is created and evaluated. Regression algorithms in the pool are independently trained to identify the problem at hand. Finally, we test several ensemble methods in order to elevate the accuracy of the prediction. The Evolutionary Weighted Ensemble method proposed in this paper offered the lowest RMSE and significantly outperformed competing classical algorithms and other ensemble techniques

A substitution of the general partial differential equation with extended polynomial networks

General partial differential equations, which can describe any complex functions, may be solved by means of the dimensional similarity analysis to model polynomial data relations of discrete data observations. Designed new differential polynomial networks define and substitute for a selective form of the general partial differential equation using fraction derivative units to model an unknown system or pattern. Convergent series of relative derivative substitution terms, produced in all network layers describe partial derivative changes of some combinations of input variables to generalize elementary polynomial data relations. The general differential equation is decomposed into polynomial network backward structure, which defines simple and composite sum derivative terms in respect of previous layers variables. The proposed method enables to form more complex and varied derivative selective series models than standard soft computing techniques allow. The sigmoidal function, commonly employed as an activation function in artificial neurons, may improve the polynomial and substituting derivative term abilities to approximate complicated periodic multi-variable or time-series functions in a system model

Distributed aggregation of heterogeneous Web-based Fine Art Information: enabling multi-source accessibility and curation

The sources of information on the Web relating to Fine Art and in particular to Fine Artists are numerous, heterogeneous and distributed. Data relating to the biographies of an artist, images of their artworks, location of the artworks and exhibition reviews invariably reside in distinct and seemingly unrelated, or at least unlinked, sources. While communication and exchange exists, there is a great deal of independence between major repositories, such as museum, often owing to their ownership or heritage. This increases the individuality in the repository’s own processes and dissemination. It is currently necessary to browse through numerous different websites to obtain information about any one artist, and at this time there is little aggregation of Fine Art Information. This is in contrast to the domain of books and music, where the aggregation and re-grouping of information (usually by author or artist/band name) has become the norm. A Museum API (Application Programming Interface), however, is a tool that can facilitate a similar information service for the domain of Fine Art, by allowing the retrieval and aggregation of Web-based Fine Art Information, whilst at the same time increasing public access to the content of a museum’s collection. In this paper, we present the case for a pragmatic solution to the problems of heterogeneity and distribution of Fine Art Data and this is the first step towards the comprehensive re-presentation of Fine Art Information in a more ‘artist-centric’ way, via accessible Web applications. This paper examines the domain of Fine Art Information on the Web, putting forward the case for more Web services such as generic Museum APIs, highlighting this via a prototype Web application known as the ArtBridge. The generic Museum API is the standardisation mechanism to enable interfacing with specific Museum APIs

Consumers' Acceptance and Use of Information and Communications Technology: A UTAUT and Flow Based Theoretical Model

The world has changed a lot in the past years. The rapid advances in technology and the changing of the communication channels have changed the way people work and, for many, where do they work from. The Internet and mobile technology, the two most dynamic technological forces in modern information and communications technology (ICT) are converging into one ubiquitous mobile Internet service, which will change our way of both doing business and dealing with our daily routine activities. As the use of ICT expands globally, there is need for further research into cultural aspects and implications of ICT. The acceptance of Information Technology (IT) has become a fundamental part of the research plan for most organizations (Igbaria 1993). In IT research, numerous theories are used to understand users’ adoption of new technologies. Various models were developed including the Technology Acceptance Model, Theory of Reasoned Action, Theory of Planned Behavior, and recently, the Unified Theory of Acceptance and Use of Technology. Each of these models has sought to identify the factors which influence a citizen’s intention or actual use of information technology. Drawing on the UTAUT model and Flow Theory, this research composes a new hybrid theoretical framework to identify the factors affecting the acceptance and use of Mobile Internet -as an ICT application- in a consumer context. The proposed model incorporates eight constructs: Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influences, Perceived Value, Perceived Playfulness, Attention Focus, and Behavioral intention. Data collected online from 238 respondents in Saudi Arabia were tested against the research model, using the structural equation modeling approach. The proposed model was mostly supported by the empirical data. The findings of this study provide several crucial implications for ICT and, in particular, mobile Internet service practitioners and researcher

International Conference on Computational Aspects of Social Networks, CASoN 2009, Fontainebleau, France, 24-27 June 2009

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Two New Methods for Network Analysis: Ant Colony Optimization and Reduction by Forgetting

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Proceedings of the Second International Afro-European Conference for Industrial Advancement, AECIA 2015, Villejuif (Paris-sud), France, 9-11 September 2015

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