Trending Machine Learning Applications in 2022
Machine learning has found its way into almost every aspect of our life. In this article, we will discuss some of the trending machine learning applications in different industries. In the article, we will discuss the application of machine learning in industries like healthcare, finance, retail, marketing, manufacturing, transport, security, and customer service.
Machine Learning Applications in Marketing
Marketing is what connects businesses to customers. With the advent of social media, consumers can now post reviews and complaints online on different platforms. Similarly, brands can also communicate with their customers using different digital tools. Machine learning also finds its applications in these processes.
Social Media Analytics
Companies can now analyze complaints and reviews using techniques like sentiment analysis to identify the sentiment of the consumers towards their products and services. This helps them assess and pivot their marketing campaigns so that consumers’ concerns can be addressed. The companies also perform aspect-based opinion mining to identify what the consumers think about each aspect of the business. This helps them improve the areas where they lack quality.
Deep Fake Videos
With improvements in machine learning and deep learning tools, we are now able to create deep fake videos that seem almost real. Nowadays, Many brands use deep fake videos to show a personality at a younger age. This helps create a nostalgic experience for the consumers.
Have you ever wondered why you see ads for only those things in which you are interested when you visit different websites? This is made possible by using your browsing data and machine learning algorithms. The digital advertising companies collect your data, feed it into their machine learning models and predict what sort of products you are interested in. Then, they show you the ads for the specific products.
By adopting machine learning in digital marketing, companies have been able to reduce customer acquisition costs as only potential customers are shown ads for the products. With an increase in digitization and the introduction of digital constructs like metaverse, the application of machine learning in digital marketing is going to increase for sure.
Machine Learning Applications in Healthcare
The healthcare industry plays a vital role in a country’s development. In recent years, the healthcare industry has leaped ahead of the other industries in utilizing machine learning algorithms. Nowadays, you can find numerous machine learning applications in healthcare when you visit a healthcare facility. Let us discuss some of the machine learning applications in healthcare.
Identification and Diagnosis of Diseases
With a sea of patient data available, healthcare professionals have started using machine learning algorithms to leverage the available data. Expert systems are designed using expert knowledge and available data can easily identify diseases by utilizing the MRIs and diagnostic reports. These expert systems are also capable of suggesting correct diagnoses for the identified diseases.
An example of machine learning applications in healthcare is IBM Watson Genomics. It integrates cognitive computing with genome-based tumor sequencing to facilitate the faster diagnosis of diseases.
With the increasing usage of smartwatches, health bands, and other devices that use IoT technologies, machine learning has found many applications in telehealth. Telehealth devices can easily identify any abnormality in pulse rate, heartbeat, breathing pattern, etc. Sensing danger, the devices can trigger an alarm or send messages to doctors or the kins of the person wearing the device. In recent years, smartwatches have saved many lives by prompting users about abnormalities before it was too late.
You can find other machine learning applications in telehealth in tools like insulin injection devices. These devices are trained using past data and machine learning algorithms to calculate the requirement of insulin based on the blood components. In the future, as digitization increases, telehealth services will find more and more machine learning applications.
By pairing past healthcare data with patient data and machine learning algorithms, healthcare organizations can offer personalized treatments with better customer satisfaction. Predictive algorithms, when fed with a person’s data, can predict the diseases the person is prone to. It can also suggest measures to control and avoid diseases. This will help the patients leverage the healthcare facilities to the best.
Suggested Reading: Best coding apps for android in 2022.
Machine Learning Applications in Finance
Finance is the backbone of the world order. Nowadays, almost every financial institution is using or planning to use the available financial data for predictive analytics. Due to this, machine learning finds its applications in many of the tasks performed by financial institutions.
With the digitization of financial services and the increase in digital transactions, the number of financial frauds has also increased. To combat this problem, credit card companies and banks have started using customer data for fraud detection.
Credit Risk Calculation
What was the last time you applied for a loan or a credit and your bank declined it? How do you think the banks decide to provide credit services? Banks have a credit history and transaction data of lakhs of customers. By applying machine learning and analytical techniques, banks predict whether or not a person with certain transactional data is fit for getting credit.
In another scenario, has your bank offered you a credit card even without asking for it? If yes, their machine learning applications must have found your financial data fit for credit. There are various other ways in which the banks employ machine learning models for credit risk calculation and personalized banking services that will yield maximum profits.
Nowadays, stock markets are dominated by software applications. For placing buy-sell orders in fractions of seconds, traders now use pre-programmed trading bots. The trading bots are trained using expert knowledge and past data. The bots, depending on the price of the stocks, decide the timing and volume to buy the stocks. Algorithmic trading has gained popularity in recent years due to its high success rate. In the future, it is going to dominate the stock market for sure.
Machine Learning Applications in Retail
Have you ever wondered how a certain product comes into suggestion when you buy a certain product on any e-commerce site? How does the website know that buying certain items will lead to buying certain other products for sure? This is made possible by machine learning applications that are working on the backend.
The unsupervised machine learning algorithms are trained using past transaction data to find the association between different products. The machine learning applications then find the probability of buying a certain product when a related product is bought. Based on the findings, the products are shown in the suggestions. It helps the businesses increase sales and hence profits.
In the retail industry, you have to predict the demand and supply of the products accordingly. Otherwise, one of your stores might keep the products on the shelves while another store will be out of stock. This problem can easily be solved using machine learning applications.
The machine learning applications, when fed with past data, can perform time-series forecasting to predict the demand. One such application of historical data and forecasting is the use of a supercomputer by Asian paints to predict the demand for paints.
The supercomputer at Asian paints, which has been fed with data from the 1980s till now, can accurately predict the color and quantity of paint required at a particular time at a particular store. Their products are always available in the required quantities at each store. Due to this, Asian paints are currently one of the market leaders in the paint industry in India.
Machine Learning Applications in Manufacturing
Manufacturing industries have also started using machine learning applications for various purposes such as product development, quality control, logistics, inventory management, etc. The use of robots is one of the prime examples of machine learning applications in the manufacturing industry.
The cost of failure is very high in the manufacturing industries. When the machines fail, they cause huge economic losses as well as loss of human resources. Nowadays, machine learning applications are being used to predict equipment failures. This helps the companies schedule timely maintenance and reduce unnecessary losses.
In recent years, various machine learning applications have predicted possible failures with up to 92 percent accuracy. This helps businesses to plan their maintenance schedules more effectively, improving asset reliability and product quality. With proper maintenance, the overall equipment performance can also be increased leading to better throughput and efficiency.
Logistics and Inventory Management
The manufacturing industry requires a wide variety of raw materials in its manufacturing process. To facilitate the needs, and predict the logistic requirements, machine learning applications can be used to reduce costs and boost efficiency. Machine learning can also help streamline the resource management process which can help in better management.
With the help of computer vision applications, machine learning applications can be trained with data to differentiate between good and bad products. With proper training data representing the images of each type of defect, machine learning can find great applications in visual quality control in many industries.
Machine Learning Applications in Transport Industry
The transport industry also leverages machine learning to increase profits and efficiency.
The airline companies use dynamic pricing for ticket bookings. According to the demand, location of the customer, and time they adjust the prices in a manner that helps them make maximum profit. All this is made possible by machine learning applications.
Another use of machine learning techniques in the transport industry is in route finding. When you courier a package, the machine learning software decides the route by which your product will be shipped.
The algorithms work towards optimizing the transportation time and cost. It helps the courier companies make a good profit. On the other hand, the customer is also able to get their shipment in the shortest time.
In this article, we have discussed various trending machine learning applications in 2022. I have omitted some obvious applications of machine learning algorithms like spam filtering and chatbots, which are prevalent in the software industry.
The application of machine learning in day-to-day tasks is going to increase for sure. If you find that any use case of machine learning should have made it to our list and it hasn’t, do mention it in the comments.
I hope you enjoyed reading this article.
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