SVM Applications in Real World
As we have seen in the previous article, Support Vector Machine is a really powerful Supervised Machine Learning Algorithm. The algorithm is quite flexible and provides us with effective results. Though SVMs are capable of handling both regression and classification problems, we mainly use SVM for classification problems. In this article, we are going to have a brief introduction to some of the most widely spread applications of SVM. Let’s start!!!
SVM Applications
1. Face Detection
As the name suggests, the problem deals with identifying facial features of the image that we give in as the input. In this type of problem, the algorithm tends to classify the objects in the image as facial and non-facial. For the training of SVM to deal with this type of problem, we train the model with n x n pixels. After processing, we classify this data as facial (storing +1) and non-facial (storing -1).
The algorithm processes each pixel of the input image and classifies it to be facial and non-facial. We can even highlight the faces seen in the images by fabricating a box-like boundary around the face. With this, users can segregate the face from the rest of the image.
2. Text and Hypertext Classification
We can classify both types of data, namely, inductive and transductive using Support Vector Machine classification for text and hypertext. We train the model using labeled data that we already segregate as new articles, e-mails, blogs, web pages, and so on. As soon as we pass our input text to the model, the trained model tends to classify the given text into one of the predefined classes. Some of the examples of text classification are:
Classifying news into various categories like “Sports”, “Business”, and “Entertainment”.
Classifying web pages into categories like personal pages, blogs, business sites, and so on.
While training the data, we instruct the model to reserve some threshold values which the model can use later to classify the data. As soon as we pass the input text to the model, it calculates the score related to the document on a certain criterion. After this, the model compares this score to the predefined threshold values which help it in classifying the given text. As a default case, if the score of a particular document does not surpass any threshold, then the model considers it to be a general document.
This technique helps the model to provide accurate and reliable results by calculating the scores related to the input documents.
3. Classification of Images
When we use image classifying algorithms along with SVM, we can get accurate results for the classification of the input images. We train the model in such a way that it extracts useful features from the image and stores it for classification. The distinct features help the model in providing many accurate classifying results. SVM algorithms along with image processing algorithms provide highly accurate results as compared to conventional query-based reinforcement schemes.
4. Stenographic Detection in Digital Images
SVM models are capable of judging the input images to verify if the images are pure or if they are adulterated with filters. This helps the security-based organizations to look up any hidden messages and other information. We can easily reveal the encrypted information in the image with the help of SVM.
When we deal with high-resolution images, the images contain a higher level of pixelation that provides a convenient environment for image encryption. However, with Support Vector Models, we can easily decipher these encrypted messages. SVM provides reliable and accurate results even with varying datasets and is able to analyze this encrypted information accurately.
5. Protein Fold and Remote Homology Detection
For developers in today’s world, Protein remote homology proves to be a primary problem in the field of computational biology. The most reliable and widely used solution to cater to this problem is the Support Vector Machine. In recent years, we have seen a massive surge in the usage of SVM for the detection of protein remote homology.
SVM is capable of identifying a wide range of biological sequences. The results of these algorithms depend mostly on the architecture of these protein sequences. This further helps us to classify genes and thus diagnose ailments with more precision.
6. Handwriting Recognition
SVM proves to be a really good tool for recognizing handwritings from the given input dataset. This helps security agencies in authorizing users and providing better security to the users. It is also helpful in data entry and signature validation for accounting issues.
7. Generalized Predictive Control
We make use of SVM-induced GPCs in order to control the chaotic dynamics with usable parameters. These algorithms provide effective results for controlling the systems. We measure the chaotic dynamics of the system with respect to the local stabilization of the target element.
8. Inverse Geosounding Problem
Geo Sounding problem deals with the determination of the layered structure of the planet. While determining the structure of the planet, we have to deal with enormous amounts of electromagnetic data. In such cases, the SVM model proves beneficial for the linear function to handle the data.
We develop these models with linear programming techniques. The smaller the size of the problem, the smaller will the dimension of the data that we have to encounter.
9. Data Classification
SVM is beneficial in a lot of mathematics-based problems. We make use of various smoothing methods for solving numerous math problems. There is also a small variation, we don’t use the normal SVM method for this. We will use the SSVM, more commonly known as Smooth SVM.
SSVM makes classification a lot easier as it consists of strong convexity. In addition to this, the use of kernel function for this is arbitrary. For solving SSVM’s unconstrained optimization problem, we make use of the Newton-Armijo algorithm for this.
SSVM operates faster on larger datasets than the normal SVM. We can also use it for the generation of the checkerboard-shaped nonlinear dividing surface.
10. Cancer Diagnosis and Prognosis
Detection of cancerous cells is amongst the top growing research fields to date. Developers and researchers are trying to deploy various Machine Learning models to tackle this problem. One of the trending topics in this field is the usage of image classification for detecting cancer cells.
SVM is a really powerful tool to help developers fabricate a model for cancer diagnosis. It is capable of producing numerous models for the diagnosis as well as prognosis of cancerous tissues. It tends to analyze the microscopic images of these cells to distinguish the cancerous cells from the normal cells.
11. Speech Recognition
Speech Recognition deals with the segregation of individual words from the continual speech that we feed as input to the model. These models study the features of the extracted words for their classification and usage. Speech recognition has the potential to develop an interface for deaf individuals to communicate conveniently. For this, we have to deal with the acoustic data.
There are many functions available like LPC, LPCC, and MFCC to help us collect the acoustic data. Acoustic is useful for training SVM models. The results that we acquire from such models are generally precise.
12. Texture Classification
In this application of SVM, we make use of the images of certain textures and extract their features. We then use that data to classify whether the surface is smooth or not. This SVM application proves to be of great use. If we use a sensitive camera to take pictures for higher resolutions and use that data in our model, we would be able to train a really powerful model.
Also, if we capture images of surfaces, we could classify the surfaces as smooth or gritty. SVM, in this case, classifies the surface as smooth or gritty.
13. Facial Expression Classification
There are many ways in which we can use this classification. We can use it in numerous life-care systems, along with the detection of normal happy or sad look classification. We can use it for generating filters. If we modify certain facial expressions, that would add the specific filter as per the expression. The range of expressions that we can modify lies between happy and sad.
14. Security-Based Application
As an astonishing fact, there are many applications where we can use SVMs for basic encryption along with complex analysis of different materials to observe and even break the encryptions and various other security measures.
We can also use SVMs to detect the encryption schemas uploaded to the images, in order to hide them. We can also use images to hide the encryption patterns in secretive transmissions. When the resolution of images is higher, the more difficult it becomes to detect those patterns due to the increased number of pixels and crack the schema. This makes the SVMs useful when it comes to analyzing and getting small and observing changes and modifications in the images.
15. Text-Based Classification
We can make use of the support vector machines to classify the handwriting of two different individuals. We can even train SVMs better when it comes to applications such as detection of the curves and straights that we use in typical handwriting. Also, we can use SVMs in pure computer-based texts.
As an example, a typical text-based classification task is email spam classification. In this type of classification, we need to verify an email that is spam from the email which is not spam. It is one of the most widely appreciated applications in the email delivery systems provided by popular platforms like Gmail. SVMs can be accurately trained so that they can correctly classify the spam from the pool of emails.
Some of the SVMs trained precisely on structured data can achieve as high as 97 percent precision for this purpose.
16. Computational Biology
When it comes to the medical discipline, AI has always attempted to give a reliable solution. Conventionally, the primary applications of SVM in the medical field were based on cancer recognition, which was an image-based classification application. However, the algorithm took its lead in this field when it was deployed in the protein analysis tasks.
We all are aware that human-based proteins are very delicate structures and are much more prone to huge noise along with errors while using the algorithms for recognition. Another field in which we can deploy SVM models is remote homology which uses SVMs to the fullest. This is the field in which the analysis is dependent on how the protein sequences are modeled.
As a matter of fact, the use of SVMs does spread over to the diagnosis of various diseases, based on either image data or text data. However, as they were perceived earlier, we are not going to commit to the same process again. It is crucial to mention that we use them extensively in many fine-crafted classification applications, that are necessary for the medicinal discipline.
17. Seismic Liquefaction Potential
In such problems, we have to encounter two main types, namely, Standard Penetration Test (SPT) and Cone Penetration Test (CPT). The major concern of both of the tests is the occurrence and nonoccurrence of liquefaction. SVM is a really helpful algorithm for such problems due to its ability to provide highly accurate results.
In these problems, SVM produced the results with an accuracy of 96-97%. SVM is really helpful in these cases for the modeling of complex parameters like soil parameters and liquefaction potentials.
Conclusion
And with that, we have reached the end of this article that talked about the applications of SVM. These were some of the widely used applications of SVM. Support Vector Machines continues to dominate the field of Supervised Machine Learning algorithms. Its power to produce highly accurate and reliable results is what makes it preferable for classification problems.
Apart from classification, we also came across various other applications of SVM. From the above-mentioned applications, we can surely conclude that SVM models will continue to reign in classification problems in the future as well.