Soft computing what is

This is realm of NLP Natural language processing. This field of computer science has, for a long time, tried to understand and teach computers to properly process natural language data. For decades most NLP systems were based on sets of hand-written rules and transformational grammar. The problem continued to grow more complex, despite programmers having a clear understanding of what the results should be.

Funny enough, it was the relinquishing of these understandings to unsupervised and semi-supervised machine learning algorithms that have created the model which powers the useful tools in your everyday life, like Alexa, OK Google, and Siri. Language processing powered by statistical natural language processing SNLP was the breakthrough. Increasing complexity with human-written rulesets was hard computing. Writing rules for exceptions, variance and more meant that data was the enemy.

Soft computing and increasing the Machine Learning statistical model with all these data was a natural fit, and a way to change the weakness into strength. Resizing images is a hard computing problem.

You can feather, constrain proportions, cover the full area, stretch, or even repeat images. Each with their corresponding reactionary canvas and algorithms for such. The most impressive hard computing algorithm for resizing is probably the seam carving method. This is commonly called content-aware image resizing, and plenty of code can be found which illustrates these examples in action.

With such impressive hard computing algorithms, you might be surprised to know that soft computing has found its way into this field as well. This new domain gives you a more human solution. Keep the most interesting parts. Using cloudinary. This can sometimes be called automatic gravity selection as part of their responsive images capability , where soft computing attempts to identify the most interesting part of an image, and choose the resize intelligently.

Empowered with skills like this, we can confidently update our website content, with assurance that we can resize content dynamically for mobile, tablets, and more.

We can even blur faces, like in Google maps. Hard computing or traditional computing is fast, efficient, and reliable with deterministic outcomes. But as a human, interfacing with each of these can feel inhuman. Soft computing bridges that gap and gives you an interaction level intelligence, which can offload some of the menial work, that previously only a human could possibly do. Adding items to your grocery list, calendar, and more with the ease of soft computing, gives us a new layer.

Providing a soft and forgiving interface for driving this hard computing filled world is the advent of a new and exciting computing age. Learn more about our Computer Science degree programs , where you can learn the latest programming languages, and experiment with both hard and soft computing techniques. To start preparing for your programming courses visit our Prepare for University section. Neural networks were developed in the s, which helped soft computing to solve real-world problems, which a computer cannot do itself.

We all know that a human brain can easily describe real-world conditions, but a computer cannot. An artificial neural network ANN emulates a network of neurons that makes a human brain means a machine that can think like a human mind.

Thereby the computer or a machine can learn things so that they can take decisions like the human brain. Artificial Neural Networks ANN are mutually connected with brain cells and created using regular computing programming.

It is like as the human neural system. Genetic algorithm is almost based on nature and take all inspirations from it. There is no genetic algorithm that is based on search-based algorithms, which find its roots in natural selection and the concept of genetics. Hard computing uses existing mathematical algorithms to solve certain problems. It provides a precise and exact solution of the problem. Any numerical problem is an example of hard computing.

On the other hand, soft computing is a different approach than hard computing. In soft computing, we compute solutions to the existing complex problems. The result calculated or provided by soft computing are also not precise. They are imprecise and fuzzy in nature. JavaTpoint offers too many high quality services. Mail us on [email protected] , to get more information about given services. Please mail your requirement at [email protected] Duration: 1 week to 2 week.

Problem 1 Are string1 and string2 same? Solution No, the solution is simply No. It does not require any algorithm to analyze this. Problem 2 How much string1 and string2 are same? Solution Through conventional programming, either the answer is Yes or No. Next Topic Types of Angles. Reinforcement Learning. R Programming. The following are the characteristics of soft computing.

There are three types of soft computing techniques which include the following. It is a connectionist modeling and parallel distributed network. A neural network that processes a single element is known as a unit. The components of the unit are, input, weight, processing element, output. It is similar to our human neural system. The main advantage is that they solve the problems in parallel, artificial neural networks use electrical signals to communicate.

But the main disadvantage is that they are not fault-tolerant that is if anyone of artificial neurons gets damaged it will not function anymore. An example of a handwritten character, where a character is written in Hindi by many people, they may write the same character but in a different form.

As shown below, whichever way they write we can understand the character, because one already knows how the character looks like. This concept can be compared to our neural network system.

Why do we need soft-computing when we already have hard computing models in order? So, we can safely say that soft computing is handy when it comes to solving tricky situations. It helps in getting a workable solution for ambiguous cases. And, with the complexity of situations rising every day, it can be a safe bet to assume that soft computing is going to be used as a part of mainstream computing.

It is for a matter of the fact that soft computing can rise above its challenges and will be revolutionizing the way computing functions. A tech fanatic and an author at HiTechNectar, Kelsey covers a wide array of topics including the latest IT trends, events and more.

Cloud computing, marketing, data analytics and IoT are some of the subjects that she likes to write about. We send you the latest trends and best practice tips for online customer engagement:.

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