Understanding Unstructured Data Using Recognition Pattern Technology

Artificial Intelligence is not a new term anymore but with ever-expanding possibility due to a wide range of applications. AI use cases fall into one or more of seven common patterns.  According to analyst firm Cognilytica, the seven patterns are hyper-personalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Out of these, the most common is the recognition pattern. The main objective of this application is implementing machine learning and cognitive technology to analyze unstructured data. This data can occur in the form of images, text, videos, or any other format of quantified data. This helps us to understand data inputs in form pictures, sound, handwriting, facial expressions, and gestures.

While structured data can be interpreted by using a query, stack technology, and informatics systems, comprehending the more abundant unstructured data is beyond the ability of the human brain. This is where AI helps us to mine previously missed insights and increase the value of such data. Since the methods adopted for structured data analysis cannot be employed here, machine learning algorithms can recognize, identify, and match patterns that prevail in data. Here the programmers feed well-labeled training data to teach the system to classify input as per identifiable keywords and class sets. Then the algorithm of the now labeled data points is used to train a neural network to categorize data into those classes. Using multiple retraining the computer system is now qualified to figure out the type of information that is being sought out from the available data. Therefore, researchers, programmers make sure to use clear, qualitative, diverse, and already marked dataset to train system before using the same model for real-world application. These applications can be:

• Security and surveillance monitoring where machine learning recognition patterns are used to study simultaneous real-time video streams. This can be used to track vehicles to observe any suspicious behavior in public areas and identify fraudulent activities. In light of COVID-19, it is used to check if people are maintaining social distancing and checks if they are wearing masks or not.

• Radiology imaging in the medical industry where recognition patterns are used to identify fractures, blockages, aneurysms, potentially cancerous formations, and even being used to help diagnose potential cases of tuberculosis or Covid-19 infections.

• Cataloging in e-commerce and online retail websites, where product pictures are detected as per specified attributes, tags, and categories. This helps users to get results based on their entered keywords and check for the availability and delivery time of those products. This saves much time as earlier humans had to do the same process by themselves, whereas now AI does this with better efficiency and minimal human assistance.

• Identifying songs and voices. Apps like Shazam can recognize which song is played, display lyrics of the songs, and other details like the singer's name and platforms where one can listen to them. Some apps can figure out which eminent personality is speaking or what animal sound one is hearing. The later helps in preserving wildlife. E.g. Elephant Listening Project uses acoustic sensors to listen to the already endangered forest elephants in Africa. This is used to detect the presence and number of elephants in a given surrounding without disturbing them.

Even the Police department employs voice recognition software to track and identify criminals. In the manufacturing sector, sounds recognition patterns are used to help experts determine the presence of cracks and faults and sometimes counterfeit goods.

• Optical Character Recognition (OCR) where printed characters, handwriting, texts are mapped. The advantage of this is that not only it can recognize text in a wide range of printed or handwritten mode, but also it can recognize the type of data that is being recorded. One of the use cases of this is ATMs, where customers can insert their handwritten checks into the machine and make their deposits. OCR is also used in the evaluation of OMR answer sheets of entrance exams and carry out card transactions at payment counters or biometric card logins.

The recognition pattern feature of AI is used in almost every sector now. So, without any doubt, this will bring a digital change in how we function and engage with these AI-based revolutionary transformations.