What is Soft Computing?

Soft computing, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximations. In effect, the role model for soft computing is the human mind.

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. The techniques of soft computing are nowadays being used successfully in many domestic, commercial, and industrial applications. With the advent of the low-cost and very high-performance digital processors and the reduction of the cost of memory chips, it is clear that the techniques and application areas of soft computing will continue to expand.

Here are some examples where soft computing is applied:

• Image processing

  • Image processing refers to the manipulation of digital images in order to extract more information then is actually visible on the original image.

  • Application of image processing: image colorization, movie post production, compression, medical image processing.

• Computer Vision

  • Computer vision (CV) is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos.
  • Today, the most popular tools for computer vision are Convolutional neural networks (CNN)
  • Some of the problems that computer vision can solve are image classification, activity recognition, object detection, image captioning.

• Natural language processing

  • Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken.

• Autonomous driving

  • An autonomous car is a vehicle that can guide itself without human conduction.

  • Here are 5 levels of autonomous driving: no automation, driver assistance, partial assistance, conditional automation, high automation, fully automation