When asked about How to use Machine Learning in Mobile App, someone replied that it makes machines learn to simulate human behavior. Another one said that it makes machines accomplish tasks way faster than humans. It might have sounded a bit competitive to you, but this is what the race of utilizing the best of machine learning means to technology leaders.
Every technology becomes successful when it is brought to the lives of the common people. Those who utilize the technology in their day-to-day tasks become the supreme carrier of its recognition.
Mobile apps have emerged as the most favorable medium to facilitate users with the features of machine learning. With defined algorithms, mobile apps are extracting data to engage in useful predictive analysis and execution of tasks for the users.
There are plenty of use cases of machine learning in mobile app development which we will bring to the spotlight through this blog post.
Voice Assistants
The market of voice assistant applications has witnessed dramatic growth in recent years. It has become a crucial part of the internet’s search methods. Leading search engines and mobile apps have incorporated features that are compatible with speech recognition, voice search, and voice assistance.
Business giants like Microsoft, Google, Apple, Amazon, and Samsung have introduced their voice assistants. There has always been close competition between all these technology leaders.
Let’s have a look at the most popular voice assistant apps;
- Microsoft’s Cortana
- Google Assistant
- Apple’s Siri
- Amazon’s Alexa
- Samsung’s Bixby
Pattern Recognition
It is a machine learning technique that allows a mobile application to identify the distinct data pattern from different entities of data. It indulges in a humanoid approach of realizing the type of data.
A mobile app development company can implement pattern recognition in many ways i.e. image recognition, image segmentation, and object detection. This technique has played a crucial role in the booming user experience of modern-age mobile apps.
Let’s have a look at some examples that would help you comprehend different use cases of machine learning through pattern recognition in mobile apps;
- Image recognition and object identification to detect different elements existed in a picture clicked on a mobile camera.
- Social media networking sites detecting and abandoning offensive, graphic or vulgar content.
- A photo sharing and storage mobile app arranges the pictures in an impressive order or story with additional information.
- Mobile app Nuru utilizes Google’s TensorFlow to detect visualized symptoms of different crop diseases.
- A mobile security system utilizes face recognition to identify the user. It distinguishes between data elements through different elements i.e. distance between the eyes, nostril width, nose length, chin width, etc.
- Mobile apps using speech recognition for authentication and receiving instructions from users.
Risk Assessment
ML development services are being utilized in the risk assessment of Software applications. The primary goal of such incorporations is to indulge in improved and secured data operations.
To make applications infallible and incur simplification of complex algorithms, technology leaders embrace the usage of machine learning in identifying system loopholes and vulnerabilities.
Alongside this, machine learning solutions are assisting organizations to counter malicious attacks on their resources from cyber thieves and hackers.
There are three main vulnerable areas where machine learning can help in risk assessment.
- Data input can be a doorway for many risks and vulnerabilities. For any data mismatch or irregularities, there is a need for risk assessment in this part of the process.
- While designing algorithms for applications, there can be flaws in modeling techniques, code, and logic.
- There can be process-related issues while making output decisions. Risk assessment tools ensure that there is no misinterpretation of output and underlying standard along with it.
Predictive Analysis
Execution of a smart predictive analysis is interconnected to smooth data mining as it indulges in secure and flawless storage, maintenance, and analysis of data. However, machine learning development services providers scope the utilization of data extracted through mining for predicting different perspectives.
Let’s have a look at how different mobile apps can utilize predictive analytics;
- A video streaming app like Netflix predicts movie suggestions you may like.
- A navigation app predicts lists of possibly empty parking slots in the area through data extracted from different locations using advanced features.
- A direct messaging app detects your message’s expression and predicts words or emoji you may want to add.
- An eCommerce can predict and sort products the users might be interested in purchasing.
- Sports apps can project a win-lose probability for teams playing in a match of any particular game.
- Weather forecasting apps send alerts and updates to remind you to carry an umbrella, etc.
Healthcare
From the identification of cancerous tumors using Google’s ML algorithms to the detection of skin cancer using Stanford’s deep learning algorithm, machine learning has coped peer-to-peer with the healthcare industry in recent years.
Now that we are more shifted to mobile devices, there was a need to transform healthcare through mobile apps. Best utilization of data had a crucial role to play here.
There are several significant use cases of machine learning in healthcare;
- Fitness apps helping people to provide them with data on different healthcare insights on their bodies.
- Medicine apps providing a list of medicines scanning doctor’s consultation receipts. Further, reminds when the stock of medicines is available or may stock out.
- Clinical apps for doctors to provide the data on patients with maximum risk.
- Mobile apps are being used to create reminders for patients that have dues or need financial assistance.
Maps and Navigation
Navigation apps have been among the most popular use cases of machine learning. From sharing satellite images that create the real-time route for app users. It adds additional information to the images and makes them more authoritative and useful.
Also, it extracts data from different places and centralizes it to share a complete map route with traffic conditions. Restaurants, offices, or public organizations can add their location through the application.
There are a few important examples of how ML keep navigation effortless;
- Predicting traffic by analyzing insights of traffic conditions in the past
- Updating data time whenever there is a change in real-time location
- Send reminder on traffic conditions, merging lanes, or diverted routes
Fintech
Fintech itself is a technological transformation in the financial processes. Technology like machine learning had a role to play to back its fundamental needs.
From utilizing data in finance to predict different insights of investment, market situation, and most favorable conditions, machine learning has become the backbone of rising possibilities in the socio-economic background of the industry.
The most crucial advantages of implementing ML to Fintech include;
- Increased monitoring leading to less process-related risks.
- Investment predictions helping small businesses to keep themselves prepared.
- Enhance automation at the core of the process to maximize productivity.
Conclusion
Machine learning has led the transformation in mobile app development services with its advanced algorithms and technological flexibility. Every industry utilizing machine learning also needs a medium to implement it so they utilize mobile devices and computers. With such an approach, the role of mobile app development companies becomes more vital.
They require to evolve new trends to identify new possibilities of implementing machine learning solutions to as many processes as it is possible. Also, It is equally important to adopt strong security standards with development because when technology upgrades, it upgrades for everyone.