Quantum-Inspired Soft Computing in Environmental Science

Quantum-Inspired Soft Computing in Environmental Science

Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)
Release Date: January, 2016|Copyright: © 2016 |Runtime: 1 hr 9 mins
EISBN13: 9781522502357|DOI: 10.4018/978-1-5225-0235-7
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Soft computing, encompassing several intelligence tools and techniques in the form of neural networks, fuzzy logic, and evolutionary computation, is efficient in handling uncertainties and imprecision prevalent in real-world settings. One of the more recent applications of soft computing falls within the realm of environmental science as a way to model and predict hazards to the environment in an age of increasing pollution.

Quantum-Inspired Soft Computing in Environmental Science explores the use of a hybrid soft-computing paradigm for the prediction of the adsorption capacity of an environmentally-friendly and low-cost adsorbent. Presenting a viable alternative to conventional time-consuming batch study experiments within environmental analysis, this video is beneficial to environmental scientists, researchers, and graduate students specializing in environmental prediction, modeling, and analysis.

Topics Covered

  • Adsorbent Collection
  • Multilayer Perception (MLP)
  • Network Architecture
  • Neural Networks
  • Quantum Backpropagation Multilayer Perception (QBMLP)
  • Quantum Computing
  • Quantum Measurement

Table of Contents

4:42 mins
Section 1:Introduction to the Problem at Hand
Lesson 1:Prediction of Iron Adsorption Capacity of Calcareous Soil from Aqueous Solution
13:56 mins
Section 2:Multilayer Perceptron
Lesson 2:Multilayer Perceptron in Quantum Computing
14:30 mins
Section 3:Quantum Computing Fundamentals
Lesson 3:History and Definition of Quantum Computing Components
9:07 mins
Section 4:Quantum Backpropagation Multilayer Perceptron (QBMLP)
Lesson 4:Quantum Neuron Model in Network Architecture
10:12 mins
Section 5:Materials and Methods
Lesson 5:Practical Projections and Results of Quantum Computing
9:46 mins
Conclusion and Dedication
7:34 mins