Computing techniques ppt

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  • What are soft computing techniques?

    Soft computing is defined as a group of computational techniques based on artificial intelligence (human like decision) and natural selection that provides quick and cost effective solution to very complex problems for which analytical (hard computing) formulations do not exist..

  • What are the advantages of soft computing techniques?

    Advantages of Soft Computing:
    Approximate solutions: Soft computing techniques can provide approximate solutions to complex problems that are difficult or impossible to solve exactly.
    Non-linear problems: Soft computing techniques such as fuzzy logic and neural networks can handle non-linear problems effectively..

  • What is cloud computing with example PPT?

    Cloud computing is an umbrella term used to refer to Internet based development and services A number of characteristics define cloud data, applications services and infrastructure: Remotely hosted: Services or data are hosted on remote infrastructure.
    Ubiquitous: Services or data are available from anywhere..

  • What is computerized technology?

    Refers to inventions related to or associated with computers and devices with a central processing unit, such as the hardware and software of computers, the Internet and storage devices..

  • What is computing in soft computing?

    Soft computing is a set of algorithms, including neural networks, fuzzy logic, and evolutionary algorithms.
    These algorithms are tolerant of imprecision, uncertainty, partial truth and approximation.
    It is contrasted with hard computing: algorithms which find provably correct and optimal solutions to problems..

  • What is hard computing techniques?

    Hard Computing: Hard computing uses traditional mathematical methods to solve problems, such as algorithms and mathematical models.
    It is based on deterministic and precise calculations and is ideal for solving problems that have well-defined mathematical solutions..

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  • What is the main goal of soft computing?

    The main goal of Soft Computing is to develop intelligent machines to provide solutions to real world problems, which are not modeled, or too difficult to model mathematically..

  • Which are soft computing techniques?

    Soft computing is based on techniques such as fuzzy logic, genetic algorithms, artificial neural networks, machine learning, and expert systems.
    Although soft computing theory and techniques were first introduced in 1980s, it has now become a major research and study area in automatic control engineering..

  • Which topic is best for PPT presentation in computer science?

    You can select interesting topics on a computer science subject through this part.

    Artificial Intelligence and Machine Learning. Cybersecurity and Networking. Operating Systems and Computer Architecture. Human-Computer Interaction (HCI) Cloud Computing and Distributed Systems. The Future of Computer Science..

  • Why do we study soft computing?

    Soft computing provides an approach to problem-solving using means other than computers.
    With the human mind as a role model, soft computing is tolerant of partial truths, uncertainty, imprecision and approximation, unlike traditional computing models..

  • Hard computing is best for solving the mathematical problems which don't solve the problems of the real world.
    Soft computing is better used in solving real- world problems as it is stochastic in nature i.e., it is a randomly defined process that can be analyzed statistically but not with precision.
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  • Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data.
    The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms.
  • Some of the important branches of soft computing (SC) are artificial neural networks (ANNs), fuzzy logic (FL), genetic computing (GC) and probabilistic reasoning (PR).
    The soft computing methods are robust and low cost.
Dec 24, 2020Present introductory level to soft computing. Briefly present about neural network, fuzzy logic and evolutionary optimization.
Amorphous computing refers to computational systems that use very large numbers of identical, parallel processors each having limited computational ability and local interactions.
The term Amorphous Computing was coined at MIT in 1996 in a paper entitled external text>Amorphous Computing Manifesto by Abelson, Knight, Sussman, et al.

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