# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 6 Frameworks for Mobile Devices  
Current tools on the market that allow for the implementation of machine learning models on mobile, which, as we have seen, allow the functionalities of applications to be expanded to other areas. In addition to the tools already mentioned, other tools provide functionalities to generate machine learning models for mobile devices, as shown in Table 5.  
* TensorFlow Lite: Is a computational intelligence platform for local inference designed primarily for low-resource computing hardware such as mobile devices and embedded and edge systems. It enables on-device artificial intelligence by supporting programmers in running their models on relevant hardware and IoT devices [69]. This tool provides various methods of optimisation, compression and conversion of an ML model into a tflite format. This platform ensures data security through local device training without the need for an Internet connection [70]. * OpenCV: OpenCV is an open-source computer library developed in C and C++ on Linux, Windows and MacOS X with support for Python, Ruby Matlab and other languages [71]. It also supports mobile applications, which allows for the development of applications that require face recognition, object detection, image processing and manipulation, etc. * The ML Kit by Google: Is a free mobile development SDK for Google's machine learning model in Android and iOS applications. It has features in its computer vision and natural language processing APIs. All ML Kit APIs run on the device, enabling real-time use cases. This also means that the functionality is available offline [72]. * Core ML: Is a machine learning development kit for Apple devices. It offers easy integration of machine learning models into applications. This library allows the user to transform models generated by other libraries using the ML Core utility, in addition to allowing users to preview the model directly from Xcode and download it using the ML Core Ready utility. It also allows the user to transform models from other types of libraries using Core ML Converters or downloaded ready-made Core ML models and preview the model easily or directly in Xcode. Furthermore, the kit allows the user to create computer vision, natural language, speech and audio models [73]. * Google Cloud AI: Tools use Google technologies to help developers solve common AI problems. Google AI continuously updates products and implemented algorithms in order to achieve the best inference results for developers. Google AI features include speech-to-text conversion, natural language processing and optical character recognition, among others [74]. * CAFFE2: Caffe2 provides an easy way to provide proof of concept and take advantage of the contributions of new models and algorithms provided by the scientific community. GPUs can be use in the cloud to train large volumes of data and scale trained models to mobile devices using Caffe2's cross-platform libraries [75]. * DialogFlow: Is a natural language understanding platform with which users can design a conversational user interface and embed it in a mobile or web application. It analyzes different file types of input such as text input or audio input, such as a voice recording, and can respond to users in different ways such as through text or an artificial voice [76]. * Microsoft Cognitive Services: Is an artificial intelligence (AI) service that bases its operation on sending data to a central server that is in charge of carrying out the training and returning the trained model to the source device. This service helps developers add cognitive intelligence to applications without prior AI knowledge or skills. Azure Cognitive Services enables developers to add functionality to their applications such as the ability to see, hear, speak and analyze [77]. * The Firebase ML Kit: Is a set of tools and services that focused on offering the developer powerful machine learning so that can be included in apps using an Android or iOS system.