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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=hybrid+system">
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/534/Knowledge-Based-Systems-and-Other-AI-Applications-for-Tableting"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/419/Generalization-of-a-Prototype-Intelligent-Hybrid-System-for-Hard-Gelatin-Capsule-Formulation-Development"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/417/A-hybrid-intelligent-system-for-formulation-of-BCS-Class-II-drugs-in-hard-gelatin-capsules"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/418/A-Prototype-Intelligent-Hybrid-System-for-Hard-Gelatin-Capsule-Formulation-Development"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning">
  <title><![CDATA[A Hybrid Quantum Enabled RBM Advantage: Convolutional Autoencoders for Quantum Image Compression and Generative Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning</link>
  <description><![CDATA[Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our hybrid autoencoder algorithm using two datasets, the MNIST dataset and MNIST Fashion dataset. We evaluate the qua...]]></description>
  <dc:date>2020-01-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/534/Knowledge-Based-Systems-and-Other-AI-Applications-for-Tableting">
  <title><![CDATA[Knowledge-Based Systems and Other AI Applications for Tableting]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/534/Knowledge-Based-Systems-and-Other-AI-Applications-for-Tableting</link>
  <description><![CDATA[The pharmaceutical industry is under continual pressure to speed up the drug development process, reduce costs, and improve process design. At the same time, FDA’s new Process Analytical Technology initiatives encourage the building of product quality and the development of meaningful product and process specifications that are ultimately linked to clinical performance. Together, these two issues present significant challenges to formulation and process scientists because of the complex, ty...]]></description>
  <dc:date>2008-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/419/Generalization-of-a-Prototype-Intelligent-Hybrid-System-for-Hard-Gelatin-Capsule-Formulation-Development">
  <title><![CDATA[Generalization of a Prototype Intelligent Hybrid System for Hard Gelatin Capsule Formulation Development]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/419/Generalization-of-a-Prototype-Intelligent-Hybrid-System-for-Hard-Gelatin-Capsule-Formulation-Development</link>
  <description><![CDATA[The aim of this project was to expand a previously developed prototype expert network for use in the analysis of multiple biopharmaceutics classification system (BCS) class II drugs. The model drugs used were carbamazepine, chlorpropamide, diazepam, ibuprofen, ketoprofen, naproxen, and piroxicam. Recommended formulations were manufactured and tested for dissolution performance. A comprehensive training data set for the model drugs was developed and used to retrain the artificial neural networ...]]></description>
  <dc:date>2005-10-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/417/A-hybrid-intelligent-system-for-formulation-of-BCS-Class-II-drugs-in-hard-gelatin-capsules">
  <title><![CDATA[A hybrid intelligent system for formulation of BCS Class II drugs in hard gelatin capsules]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/417/A-hybrid-intelligent-system-for-formulation-of-BCS-Class-II-drugs-in-hard-gelatin-capsules</link>
  <description><![CDATA[In this paper, we describe a hybrid intelligent system for formulation of BCS Class II drugs in hard gelatin capsules. Several significant challenges are involved in drug-formulation: the active ingredients and the fillers in the capsule must be chemically compatible according to bio-pharmaceutical principles; the formulation must be manufacturable; and it must meet the prescribed drug release requirement. Traditional trial and error approach to drug-formulation design is too costly and time ...]]></description>
  <dc:date>2002-11-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/418/A-Prototype-Intelligent-Hybrid-System-for-Hard-Gelatin-Capsule-Formulation-Development">
  <title><![CDATA[A Prototype Intelligent Hybrid System for Hard Gelatin Capsule Formulation Development]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/418/A-Prototype-Intelligent-Hybrid-System-for-Hard-Gelatin-Capsule-Formulation-Development</link>
  <description><![CDATA[Although hard gelatin capsules are
perceived to be a simple dosage form, the
design of formulations for the capsules can
present significant challenges.The authors
have created a prototype hybrid system by
linking a decision module (ES) with a
prediction module (ANN) capable of
yielding formulations of a model BCS
Class II drug.]]></description>
  <dc:date>2002-09-01</dc:date>
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