How to Apply Machine Learning (ML) in an Android App

• By Michelle Fernandes
• 29-12-2023
• Mobile App Development

Machine learning is the branch of artificial intelligence that makes future predictions based on trained data models. The process of machine learning comprises gathering data, inputting data, choosing a model, and training the data to evaluate patterns.

There is a wide range of machine learning applications that include AI-powered chatbots, speech recognition, image recognition, fraud detection, virtual personal assistants, fraud detection, and much more. The scope of machine learning is very vast, many well-known firms like Amazon, Netflix, Google, Facebook, Twitter, IBM, and HubSpot are making huge investments in machine learning algorithms to offer their customers an exceptional personalized experience.

Machine learning is revolutionizing the mobile app industry with its advanced algorithms that result in increased performance & efficiency, quick decision-making, advanced personalization, improved security, and user authentication.

Applications of Machine Learning in Mobile Apps

Let’s explore some of the applications of machine learning that are extensively used in mobile apps.

1. AI Voice Assistants

The most significant benefit that machine learning (ML) has brought to the mobile app market is the Voice Assistant. Voice-activated assistants, such as Amazon Alexa, Microsoft Cortana, Apple Siri, and Google Assistant, have revolutionized the way we communicate and are now more popular than ever. Voice-based searches and inquiries can be made by activating virtual assistants on mobile phones. Users can ask questions and instruct virtual assistants for tasks like 'remind me to visit the post office tomorrow' or 'what’s my schedule today.'

Netflix provides recommendations and suggestions to its users through an embedded machine-learning recommendation algorithm. This algorithm analyzes user behavior to suggest shows and movies based on their preferences.

Cortana is a powerful virtual assistant that enables users to perform various activities, including creating daily reminders, checking schedules, instantly locating information, and collaborating with voice assistance.

Similarly, Amazon Alexa has set the bar high by connecting to a wide range of gadgets and appliances. This virtual assistant collects and utilizes data to provide results tailored to users’ preferences.

2. AI-Powered Chatbots

AI-powered chatbots in Android apps are revolutionizing user interactions, delivering dynamic, responsive, and personalized experiences. These intelligent chatbots go beyond traditional rule-based systems by employing sophisticated machine learning algorithms.

They adeptly understand user queries, providing real-time assistance while adapting to individual preferences. Analyzing data patterns enables them to deliver context-aware responses, significantly enhancing user engagement. Whether answering queries or guiding users through tasks, these chatbots streamline communication, making the app experience seamless and user-friendly.

The evolving nature of these chatbots ensures continuous improvement, learning from user interactions to provide increasingly efficient and tailored support. The integration of AI-powered chatbots in Android apps represents a significant leap toward intuitive and user-centric mobile interactions. Users benefit from not only immediate assistance but also a personalized journey within the app, creating a more satisfying and interactive environment.

3. Fraud Detection System

Incorporating a fraud detection system into Android apps fortifies security measures, safeguarding users against malicious activities. This system, powered by advanced algorithms and machine learning, constantly analyzes user behavior, transactions, and patterns to identify potentially fraudulent activities in real-time.

Through anomaly detection and predictive modeling, it offers a robust defense against unauthorized access, phishing attempts, and financial fraud. The Android app's fraud detection system operates seamlessly in the background, ensuring a secure environment for users by promptly identifying and mitigating any suspicious or unauthorized activities, thereby instilling trust and confidence in the app's integrity and reliability.

Banks integrate fraud detection systems to identify and prevent fraudulent and suspicious activities, enhancing the security of online banking experiences. Customers consistently favor banks that prioritize secure transactions.

Notably, PayPal employs a machine learning system that analyzes millions of transactions, distinguishing between ethical and unethical activities among buyers and sellers. This exemplifies the proactive use of ML to maintain a secure financial environment, reassuring users and establishing trust in the system's ability to discern and address potential threats effectively.

4. Android Instant Apps

Android Instant Apps revolutionize user experiences by leveraging Machine Learning (ML) for intuitive functionality. ML algorithms enhance predictive user behavior analysis, allowing instant apps to adapt to individual preferences seamlessly.

These apps, without requiring installation, harness ML for personalized content recommendations, predictive input suggestions, and streamlined user interfaces. Through continuous learning, instant apps optimize themselves based on user interactions, providing a dynamic and responsive environment.

This integration of ML in Android Instant Apps not only ensures efficiency in resource utilization but also delivers a tailored and engaging experience, aligning with user expectations in the ever-evolving landscape of mobile technology.

5. Fitness-Based Mobile Apps

Fitness-based Android mobile apps enriched with machine learning redefine health and wellness experiences. These apps employ ML algorithms to analyze user data, offering personalized workout plans, nutrition recommendations, and real-time performance insights.
ML enables precise activity tracking, predicting optimal exercise routines based on individual progress and goals. Adaptive algorithms adjust workouts to user capabilities, ensuring continuous improvement.

Additionally, ML-driven features enhance sleep tracking, stress management, and overall well-being assessments. These fitness apps, empowered by ML, not only provide tailored fitness solutions but also foster a holistic approach to health, aligning seamlessly with users' unique lifestyles and aspirations.

6. Beacon Technology

An Android app incorporating beacon technology and machine learning marks a transformative leap in location-based interactions. Beacons transmit signals, and ML algorithms within the app interpret these signals to deliver context-aware and personalized user experiences.

By analyzing user preferences and historical data, the app adapts content delivery, offering tailored recommendations and notifications. ML enhances the accuracy of location-based services, optimizing user engagement.

This synergy between beacon technology and ML ensures a dynamic, responsive app environment. Whether for retail, navigation, or personalized offers, the fusion of beacon technology and ML in Android apps opens avenues for innovative, location-centric interactions that elevate user engagement and satisfaction.

7. Internet of Things (IoT)

The integration of the Internet of Things (IoT) with Android machine learning apps creates a powerful synergy, enhancing functionality and user experiences. IoT devices, such as sensors and smart appliances, generate vast amounts of real-time data. Android machine learning apps leverage this data to make informed decisions and predictions, optimizing performance. For instance, in a smart home setting, an Android app with machine learning capabilities can analyze IoT sensor data to predict user preferences, automate routines, and enhance energy efficiency.

Moreover, IoT-connected devices often act as data sources for training and updating machine learning models within Android apps. This continuous feedback loop ensures that the models stay relevant and accurate over time. The combination of IoT and machine learning in Android apps extends beyond smart homes, reaching industries like healthcare, logistics, and agriculture, where data from IoT devices enriches decision-making processes and augments the app's overall intelligence.

MACHINE LEARNING TOOLS FOR ANDROID APPS

As we’ve discovered the mobile apps that use machine learning, let’s take a look at the AI and ML tools and libraries that are used in the development of Android apps.

1. OpenCV

Many developers prefer OpenCV as it is one of the most popular open-source machine vision libraries that store thousands of ML algorithms. Android app developers use this library for image analysis, image processing, and object recognition. It’s developed for many programming languages including Java, Python, MATLAB, and Ruby. There are almost 2500 optimized machine learning and computer vision algorithms in OpenCV for creating advanced ML mobile apps including:

• Face recognition
• Image segmentation
• 3D models of objects
• Video tracking app
• Camera movement recognition
• Gesture recognition
• Eye movement tracking

The developer needs to select one of the languages and include this library in the implementation process to create a highly functional ML mobile app.

2. TensorFlow Lite

For creating seamless iOS and Android apps, TensorFlow Lite is your way-to-go framework that is powered by Google. It enables mobile developers to deploy machine learning models on various devices, such as mobile, desktop, web, and other edge devices.

It’s an open-source cross-platform that is lightweight with low latency. It allows you to discover ready-to-use turn-key APIs for your mobile applications. It is a way to unlock and enhance mobile experiences by processing text, audio, and video in real-time. TensorFlow Lite keeps the data on-device without the need to send user data to the cloud. The best part is that doesn’t require a network connection as it works offline.

The process of creating a mobile app on TensorFlow Lite is pretty simple, pick up a model, convert it to a compressed flat buffer, and then deploy the compressed file into a mobile device. Barcode scanning, image labeling, selfie segmentation, pose detection, text recognition, object detection, etc. are visions of TensorFlow Lite.

3. ML Kit SDK

ML Kit is an open-source, easy-to-use tool that brings Google's machine-learning capabilities to mobile developers. With solutions that are tailored to run on devices, you can make your iOS and Android apps more entertaining, personalized, and useful. It’s one of the easiest and fastest SDK kits for building ML-powered mobile apps. The processing in ML Kit occurs on-device, which aids in unlocking real-time use cases such as text, camera input, and image processing. It’s built with Google expertise that is truly optimized for mobiles.

ML kit has easy-to-use APIs, best-in-class machine learning models, and advanced processing pipelines. Some of the models are available locally and some on the cloud, which enables your app to run machine learning tasks through the device's internet connection. The ML Kit APIs allow developers to label images and detect faces, barcodes, landmarks, and objects.

4. PyTorch Mobile

PyTorch Mobile is an extension of the PyTorch deep learning framework designed for mobile devices. It empowers developers to deploy machine learning models on Android and iOS platforms seamlessly. With PyTorch Mobile, you can optimize and export trained models to run efficiently on mobile devices, enabling real-time inference for applications like image recognition or natural language processing.

Its lightweight nature and compatibility with popular mobile frameworks make it user-friendly. Integrating PyTorch Mobile into Android apps involves converting PyTorch models to the TorchScript format and leveraging the PyTorch Android API for deployment. Additionally, developers often use PyTorch Mobile to create apps for image recognition, natural language processing, and other AI-driven functionalities on mobile devices. You might find examples in areas like healthcare for medical image analysis, finance for fraud detection, or even in entertainment for augmented reality experiences.

It's always a good idea to check the latest sources, forums, or PyTorch community updates for specific examples and case studies of apps built on PyTorch Mobile as the field is continually evolving. This facilitates the creation of powerful and responsive machine learning-driven Android applications, enhancing user experiences with on-device AI capabilities.

5. Scikit-Learn

Scikit-Learn, a powerful machine learning library in Python, simplifies the development and deployment of machine learning models. While it's not directly designed for mobile applications, developers can leverage its capabilities to create machine learning models on desktop environments first. Once the model is trained and optimized using Scikit-Learn, it can be exported, and its predictions integrated into Android apps.

For Android development, this typically involves using tools like TensorFlow Lite or ONNX to convert the Scikit-Learn model into a format compatible with mobile devices. By incorporating these converted models, developers can bring machine learning functionalities, such as classification or regression, to Android apps. While Scikit-Learn may not be native to mobile, its role in model training and subsequent integration showcases its versatility in extending machine learning capabilities beyond traditional computing environments.

6. Caffe2

Caffe2, a deep learning framework developed by Facebook, provides a robust platform for creating and deploying machine learning models. While its successor, PyTorch, has gained prominence, Caffe2 remains relevant, especially for mobile applications. To integrate Caffe2 into Android apps, developers typically follow a two-step process. First, they train and optimize their models using Caffe2 on desktop environments.

Once the model is ready, it can be converted to a mobile-friendly format, such as ONNX or TensorFlow, using tools like ONNX Runtime or Caffe2's own mobile exporter. The converted model can then be seamlessly integrated into Android apps, enabling on-device machine-learning functionalities.

This approach allows developers to harness the power of Caffe2 for model training while still leveraging the efficiency of on-device inference for mobile applications, ensuring a smooth and responsive user experience.

Conclusion

The field of artificial intelligence and machine learning is constantly growing and evolving in the mobile app industry. A mobile app built with advanced machine learning algorithms works exceptionally well by automating the decision-making process. The AI-powered app works smartly, quickly, and efficiently to give an exceptional user experience. Developers can easily integrate the above-mentioned machine learning frameworks in their mobile applications for building flawless ML-powered next-generation Android apps.