Application of a Recurrent Neural Network to Prediction of Drug Dissolution Profiles

Goh, Wei Yee; Lim, Chee Peng; Peh, Kok Khiang; Subari, Khairanum

doi:10.1007/s005210200003

Cited by 37 publications

(11 citation statements)

References 15 publications

(13 reference statements)

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“…Peh et al [39,40] applied both multi-layer perceptrons and recurrent neural networks [41] to model successfully the release of theophylline from a matrix controlled release pellet formulation prepared using extrusion and spheronization. The pellets are either produced by using extrusion and spheronization or by layering onto sugar cores.…”

Section: Controlled Release Oral Formulationsmentioning

confidence: 99%

Artificial intelligence in pharmaceutical product formulation: Neural computing

Ibrić

Đurić

Parojčić

et al. 2009

CI&CEQ

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The properties of a formulation are determined not only by the ratios in which the ingredients are combined but also by the processing conditions. Although the relationships between the ingredient levels, processing conditions, and product performance may be known anecdotally, they can rarely be quantified. In the past, formulators tended to use statistical techniques to model their formulations, relying on response surfaces to provide a mechanism for optimazation. However, the optimization by such a method can be misleading, especially if the formulation is complex. More recently, advances in mathematics and computer science have led to the development of alternative modeling and data mining techniques which work with a wider range of data sources: neural networks (an attempt to mimic the processing of the human brain); genetic algorithms (an attempt to mimic the evolutionary process by which biological systems self-organize and adapt), and fuzzy logic (an attempt to mimic the ability of the human brain to draw conclusions and generate responses based on incomplete or imprecise information). In this review the current technology will be examined, as well as its application in pharmaceutical formulation and processing. The challenges, benefits and future possibilities of neural computing will be discussed.

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Section: Controlled Release Oral Formulationsmentioning

confidence: 99%

Artificial intelligence in pharmaceutical product formulation: Neural computing

Ibrić

Đurić

Parojčić

et al. 2009

CI&CEQ

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show abstract

“…We first employed common approaches in pharmaceutical formulation analysis, i.e., the multiple regression models and the MLP network, to model the drug dissolution process [12]. The Elman network, without boosting, was then applied to test on the first data set [13]. We can thus assess and compare the effectiveness of the boosted Elman networks together with the modified AdaBoost algorithm with other results from our previous studies.…”

Section: Experimental Studiesmentioning

confidence: 99%

Predicting drug dissolution profiles with an ensemble of boosted neural networks: a time series approach

Goh

Lim

Peh

2003

IEEE Trans. Neural Netw.

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Applicability of an ensemble of Elman networks with boosting to drug dissolution profile predictions is investigated. Modifications of AdaBoost that enables its use in regression tasks are explained. Two real data sets comprising in vitro dissolution profiles of matrix-controlled-release theophylline pellets are employed to assess the effectiveness of the proposed system. Statistical evaluation and comparison of the results are performed. This work positively demonstrates the potentials of the proposed system for predicting desired drug dissolution characteristics in pharmaceutical product formulation tasks.

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“…They are networks of highly interconnected neural computing elements that have the ability to respond to input stimuli and to learn to adapt to the environment (Goh et al 2002). ANNs operate as black-box models because no detailed information about the system is required.…”

Section: Anns-based Supervisory Control Strategiesmentioning

confidence: 99%

Supervisory and Optimal Control of Building HVAC Systems: A Review

Wang¹,

2008

HVAC&R Research

388

150

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HVAC systems are the major energy consumers in buildings. Operation and control of HVAC systems have significant impacts on the energy or cost efficiency of buildings besides their designs. Buildings nowadays are mostly equipped with comprehensive building automation systems (BASs) and building energy management control systems (EMCSs) that allow the possibility of enhancing and optimizing the operation and control of HVAC systems. Supervisory and optimal control, which addresses the energy or cost-efficient control of HVAC systems while providing the desired indoor comfort and healthy environment under the dynamic working conditions, is attracting more attention of the building professionals and the society and provides incentives to make more efforts in developing more extensive and robust control methods for HVAC systems. This paper provides a framework for categorizing the main supervisory and optimal control methods and optimization techniques developed and/or utilized in the HVAC field. The application characteristics of each control method and optimization technique are also identified and compared. A comprehensive overall review of the state of the art of the research and development, as well as application of supervisory and optimal control, in HVAC systems is also presented.

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Application of a Recurrent Neural Network to Prediction of Drug Dissolution Profiles

Cited by 37 publications

References 15 publications

Artificial intelligence in pharmaceutical product formulation: Neural computing

Artificial intelligence in pharmaceutical product formulation: Neural computing

Predicting drug dissolution profiles with an ensemble of boosted neural networks: a time series approach

Supervisory and Optimal Control of Building HVAC Systems: A Review

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