We can’t deny the fact that our personal and professional life relies on the internet. Today we are all dependent on technology. Almost a decade ago, we used to rely on all manual ways to fulfill our objectives and never imagined that in this era, we could even think of Machine Learning (ML) applications. Let's take a closer look at real-world examples of it.
Image Recognition
In the real world, image recognition is a well-known and widely used example of Machine Learning (ML). It can identify an object as a digital image based on the pixel intensity in black and white or color images. Image recognition applications include labeling an X-ray as cancerous or not, assigning a name to a photographed face, and recognizing handwriting by segmenting a single letter into smaller images. ML is also frequently used for image facial recognition. The system can identify commonalities and match them to faces by using a database of people. This term is frequently used in law enforcement.
Speech Recognition and Virtual Personal Assistant
Speech can be converted to text using ML. Live audio and recorded speech can be converted to text files using certain software tools. Speech can also be split based on the intensity of time-frequency bands. Speech recognition applications in the real world include voice search, voice dialing, and appliance control, among others.
Stock Market Trading and Analytics
ML is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning’s long short-term memory neural network is used for the prediction of stock market trends.
Autonomous Vehicles
One of the most exciting applications of ML is self-driving cars. ML plays a significant role in self-driving cars. It is using unsupervised learning method to train the car models to detect people and objects while driving.
Email Spam and Malware Filtering
When we receive a new email, it is immediately categorized as important, routine, or spam. ML is the technology that allows us to receive essential messages in our inbox with the important symbol and spam emails in our spam box. Content filters, header filters, general blacklist filters, rules-based filters, and permission filters are some of the spam filters utilized by mail websites. Some ML algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier are used for email spam filtering and malware detection.
Online Fraud Detection
By detecting fraud transactions, ML makes our online transactions safer and more secure. When we conduct an online transaction, there are several methods for a fraudulent transaction to occur, including the use of fake accounts, fake identification, and the theft of funds in the middle of a transaction. To detect this, the Feed Forward Neural Network assists us by determining whether the transaction is genuine or fraudulent. The output of each valid transaction is translated into some hash values, which are then used as the input for the next round. There is a certain pattern for each genuine transaction that changes for the fraud transaction, thus it detects it and makes our online transactions safer.
Automatic Language Translation
It is not a problem if we visit a new place and do not know the language; ML can help us with this as well by converting the text into the language we speak. This feature is provided by Google Neural Machine Translation (GNMT), which is a Neural Machine Learning that translates text into our familiar language and is known as automatic translation. The technology underlying automatic translation is a sequence of the learning algorithm, which is used in conjunction with image recognition to translate text from one language to another.
Medical Diagnosis
ML is also used in medical science to diagnose diseases. As a result, medical technology is rapidly evolving and is capable of creating 3D models that can predict the precise location of lesions in the brain. It facilitates the detection of brain tumors and other brain-related diseases. ML can aid in disease diagnosis. Many doctors use chatbots with speech recognition to identify patterns in symptoms. Examples of real-world medical diagnoses in oncology and pathology use ML to recognize cancerous tissue and analyze bodily fluids, assisting in the formulation of a diagnosis or recommending a treatment option. In the case of rare diseases, the combination of facial recognition software and ML allows for the scanning of patient photos and the identification of phenotypes that correlate with rare genetic mutations.
Statistical Arbitrage
Arbitrage is a finance-related automated trading strategy used to manage a large volume of securities. A trading algorithm is used to analyze a set of securities using economic variables and correlations. The real-world examples of statistical arbitrage include algorithmic trading, which analyses the microstructure of a market and analyses large data sets to identify real-time arbitrage opportunities. ML improves the arbitrage strategy by optimizing it.
Predictive Analytics
ML can classify available data into groups, which are then defined by rules set by analysts. When the classification is complete, the analysts can calculate the probability of a fault. Real-world examples of predictive analytics are predicting whether a transaction is fraudulent or legitimate and improving prediction systems to calculate the possibility of faults. Predictive analytics is one of the most promising examples of ML. It’s applicable for everything — from product development to real estate pricing.
Traffic Prediction
If we want to visit a new place, we use the maps that show us the correct path with the shortest route and predict the traffic conditions such as whether traffic is clear, slow-moving, or heavily congested. Everyone who is using the map apps is helping them make it better. It takes information from the user and sends it back to its database to improve its performance.
Data Extraction
Structured information can be extracted from unstructured data using ML. Customers provide organizations with massive amounts of data. ML algorithm automates the process of annotating datasets for predictive analytics tools like generating a model to predict vocal cord disorder, developing methods to prevent, diagnose, and treat disorders, and assisting physicians in quickly diagnosing and treating problems. These procedures are typically time-consuming. ML, on the other hand, can track and extract information from billions of data samples.
ML is a breakthrough in artificial intelligence. In its early applications, ML has already enhanced our daily lives and the future.
Photo: Phonlamai Photo/Shutterstock
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