Artificial intelligence is making machines capable of learning and interacting like humans to assist with tasks. AI applications that can be integrated into Android apps include automated reasoning, image labeling, face detection, text recognition, and curating personalized content. Google has shifted priorities to "AI First" and released new toolkits to promote AI development on Android. Key frameworks for deploying AI and machine learning in Android include TensorFlow, PyTorch, Google Cloud ML, Firebase ML Kit, and OpenCV. A TensorFlow model can be trained on Android by collecting and preprocessing data, creating labeled image folders, retraining the model, optimizing for devices, and embedding the .tflite file.

1. Guided By- Dr. Gopal Krishna Sharma
(Dept. of computer Science)
DSVV,Haridwar
By- Anikesh Kumar
(BCA 6th Sem)

4. Content
• How Is AI Making a Difference?
• AI Applications That Can be Integrated into Android Apps
• Impact of Artificial Intelligence in Android Devices
• How to deploy AI and ML in Android App
• ML Frameworks and APIs
• Training a TensorFlow Model on Android

5. How Is AI Making a Difference?
Artificial Intelligence is making machines capable of learning and interacting in a manner similar
to that of human beings.
The AI machines and applications are meant to provide benefits to humans by assisting in
getting basic tasks done to deliver desired results.
Another reason for the productivity of AI-based apps and machines is that they are not
subjected to emotional challenges that are usually faced by humans and can eventually lead to
errors in tasks.
For instance, AI apps are not biased towards a particular situation, so there won't be any flaw in
the judgment made with AI technology.

6. AI Applications That Can be Integrated into Android Apps
1. Automated Reasoning
• The first powerful function of AI in Android app development is automated reasoning.
• In automated reasoning, it concerned with applying reasoning in the form of logic to
computing systems. If given a set of assumptions and a goal, an automated reasoning system
should be able to make logical inferences towards that goal automatically
• It requires the app developer to use the system for logical reasoning to resolve obstacles like
puzzles and theorems. Due to this feature, AI enabled system or apps excel in stock trading
and chess.
• Another excellent example of the automated reasoning in Android apps is Uber.
• The Android app discovers the best routes by checking the traffic conditions through
automated reasoning and come up with the shortest route.

7. 2. Image Labeling
In the process of image labeling, developers have the option to
use an Image Labeler app that can interactively label the ground
data in a collection of images, or they can label rectangular ROIs
(Region of Interest) for the purpose of object detection, pixel
semantic segmentation, and even image classification of scenes.
3. Face Detection
Face detection can be defined as the computer technology that
is now being used in a variety of applications that identifies
human faces in digital images.
It can also be used to detect faces in real time for the
surveillance purpose or tracking of person/objects.
Today, it is widely used in Android as well as iOS smartphone
cameras to identify multiple appearances in the frame.

8. 4. Text Recognition
The process of detecting the text in images, as well as video formats and then recognizing the text obtained from
the media files, is known as ‘Text Recognition’.
After the text is detected, AI determines what the actual text means by breaking it down into blocks and segments,
so that the true form of a text can be revealed.
The app developers can use this feature of text recognition as a stand-alone application, or it can further be
combined with different mobile apps as an additional feature. For example, there are many gaming apps that use
this feature in combination with different tasks.
5. To Improve the App’s Productivity
Interestingly, AI can be effectively used to increase Android apps’ overall productivity. Microsoft Office 365 and
Google's G Suite are the two prominent apps that engage AI in their operations. For instance, users to these apps
get auto-generated email responses for the messages they receive.
Microsoft has further included AI innovation to its other software like Delve and Office Graph.
With the assistance of AI, Microsoft Delve can quickly go through a pile of data and scan for the vital information.
And the Office Graph get hold of the required information like the documents from the held communication.

9. 6. Curating Personalized Content
• It’s the most widely used feature of AI in Android apps. Most of the apps don’t get enough attention from their
target audience because the app fails to connect with the user. It’s not the content that you create, it’s the cord
that you strike with the user.
• But by bringing in AI into the apps, the developer can observe the interests of the user and embed it into the
learning algorithm.
• Any app that is based on sell-up business can strategically pitch content to the user and make use of this
wonderful AI functionality.

10. Impact of Artificial Intelligence in Android Devices
Google made the official announcement of shifting its primary priorities in the Google I/O Conference 2017. So,
instead of 'Mobile First,' Google has now opted for 'A.I. First,' and along with this, an entirely new series of
programs and toolkits were released as well.
The motive behind releasing these new AI-based toolkits and programs was to promote the technology on the
basic level where Android developers from all across the globe can smoothly create artificial intelligence apps
for the Android platform.
With the new Machine Learning (ML) Kit, the Android app developers have now access to more innovative
tools to know about the trending technology and implement its practices in the real world. Also, the base APIs
provided in the ML Kit enables some of the top-notch mobile application development services to be
integrated functionalities that can help us in our day to day activities.
For instance, China has the world's largest monitoring system with 170 million CCTV cameras installed across
the country. And it took Chinese authorities a total of just seven minutes to locate and capture John Sudworth,
a BBC reporter, with its powerful facial recognition technology and the huge network of CCTV cameras.

11. Key Takeaways:-
•AI is crucial to understand user behavior, as it can easily analyze huge volumes of data in Android applications.
•The email scanning and automatic smart reverts are the practical application of Android A.I. technology.
•Artificial Intelligence is indirectly improving our lifestyles with its integration into a majority of apps like fitness trackers.
•Regardless of the field or industry vertical, Artificial Intelligence and AI-based apps have made humans more efficient.
•Nowadays, the customer service industry is actively using AI applications for better engagement.

12. How to deploy AI and ML in Android App
Design for Machine Learning
Applying machine learning as a solution requires product managers, designers and developers to work
together to define product goals, design, build and iterate.
Google has produced two guides in this area:
• The People + AI Guidebook provides best practices to help your team make human-centered AI
product decisions.
• The The Material Design for Machine Learning spec contains a collection of design guidelines
and patterns for machine learning-powered features such as object detection and barcode
scanning.
There are several directions to choose from depending upon how much power, flexibility
developers want, how much specific their use case is.
We can choose from ready made, fully baked AI offering from Google Cloud or AWS platforms or
we can deploy our own custom models.

13. Build and Train a Model
Machine learning requires a model that's trained to perform a particular task, like making a prediction, or
classifying or recognizing some input.
You can select (and possibly customize) an existing model, or build a model
from scratch.
Model creation and training can be done on a development machine, or using cloud
infrastructure.
Explore pre-trained models-
Pre-trained models are available in ML Kit and Google Cloud.
Create your own models with TensorFlow-
For a deeper hands-on development experience, you can use these TensorFlow resources:
• Tensorflow Tutorials
• The TensorFlow for Poets codelab shows how to customize a pre-trained image labelling model using
transfer learning.

14. Creating ML model: -

15. Training and using ML model: -

16. ML Frameworks and APIs
1) TensorFlow/Keras model — Tensor Flow is an open source machine learning framework from Google.
https://www.tensorflow.org/

17. https://keras.io/

18. 2) PyTorch from Facebook — This library from Facebook is deep learning library based on Python. This is mainly used for
applications like Natural language processing.
https://pytorch.org/

19. 3) Google Cloud ML/Cloud Vision — Google Cloud vision framework is that part of ML offering from Google which
specifically deals with computer vision or ‘image analysis
https://cloud.google.com/vision/docs

20. Cloud vision API demo

21. 4) Firebase ML kit with TensorFlow Lite — Firebase ML kit is younger sibling of Cloud Vision API which is focused on
mobile developers.
https://firebase.google.com/docs/ml-kit/

22. 5) AWS suite— AWS also provides vast variety of ML features out of the box.
These features include image analysis (AWS Recognition), speech to text( AWS Transcribe), translation(AWS
Translate), chatbot(AWS Lex), text to speech(AWS Polly) and a lot of other features.
https://aws.amazon.com/

23. 6) OpenCV — this is by far the most famous computer vision and machine learning library which is really powerful, easy
to use and open source.
https://opencv.org/platforms/

24. 7) Kaggel— very useful spot for AI developers to get datasets/models for their specific use cases.
https://www.kaggle.com/

25. 8) TFLite—
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-ios/#3

26. Training a TensorFlow Model on Android
You can do this training by following below steps –
• Step 1: Collect training data
• Step 2: Transform the data into required images
• Step 3: Create folders of images and group them
• Step 4: Retrain the model with the fresh images
• Step 5: Optimize the model for accessible mobile devices
• Step 6: Embed .tflite file into the application
• Step 7: Run the application locally and perceive if it detects the
images
https://towardsdatascience.com/how-to-apply-machine-learning-ml-in-an-android-app-33e848c0dde6
https://medium.com/@elye.project/applying-tensorflow-in-android-in-4-steps-to-recognize-superhero-f224597eb055
*Click and know-

27. References
1. https://www.mobileappdaily.com/2018/11/30/artificial-intelligence-in-android-app-development
2. https://medium.com/@yannicksergeobam/plant-disease-classification-with-tensorflow-2-0-268fe7f72c2a
3. https://developer.android.com/ml Click and know more.
4. https://towardsdatascience.com/understanding-ai-and-ml-for-mobile-app-development-d07a3788d508
5. https://towardsdatascience.com/how-to-apply-machine-learning-ml-in-an-android-app-33e848c0dde6
6. https://www.tensorflow.org/
7. https://keras.io/
8. https://pytorch.org/
9. https://cloud.google.com/vision/docs
10. https://firebase.google.com/docs/ml-kit/
11. https://aws.amazon.com/
12. https://opencv.org/platforms/
13. https://www.kaggle.com/
14. https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2-ios/#3
15. https://towardsdatascience.com/how-to-apply-machine-learning-ml-in-an-android-app-33e848c0dde6