scholarly journals Mobile cross-platform app development tools

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Anton Nedyak ◽  
Oleg Rudzeyt ◽  
Artem Zainetdinov ◽  
Petr Ragulin
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
High Performance ◽  
Application Development ◽  
Development Environment ◽  
Development Tools ◽  
Cross Platform ◽  
Platform Development ◽  
Online Stores ◽  
Application Developers

This article discusses the existing popular tools for mobile cross-platform application development. It is contrasted with the so – called native application development-when applications are created using the tools provided by the development companies of the respective platforms. Google provides a tool like Android Studio for its Android mobile operating system. The main programming languages used to create applications for this platform are Java and Kotlin. Apple, in turn, offers developers such an integrated development environment as XCode, in which development is carried out using the Swift programming language. The authors reviewed some of the most popular tools for cross-platform development, such as React Native, Xamarin and Flutter. React Native is a product developed by the company Facebook. It inherits the main technological aspects from another framework from Facebook-React. Programming here is conducted in the JavaScript language. React Native is suitable for web developers who have worked with React before and now want to try their hand at developing mobile applications. Xamarin is a framework supported by Microsoft. It provides tools for creating cross-platform applications for the iOS and Android mobile operating systems. This framework is bundled with Microsoft Visual Studio as a downloadable component. What is typical for a Microsoft product, here the C# programming language is used for development. This is a significant advantage of the Xamarin framework: during the creation of a mobile application, developers can use all the important and convenient features of this programming language. Flutter is a tool developed by Google to create apps for Android and iOS using a single code base. Unlike other popular solutions, Flutter is not a framework: it is a set of software development tools that contains everything you need to create cross-platform applications. It includes a visualization engine, ready-made widgets, and tools for working with the command line. The main programming language here is Dart. In the course of studying these tools, the authors made the following conclusions: tools for cross-platform development are an excellent solution if you need to create an application that does not require high performance: displaying information received from the server, simple local information processors, such as applications for social networks, online stores, organizer applications. These tools are not suitable for creating applications that require computing resources.

scholarly journals Development of cross-platform applications

2021 ◽  
pp. 102-105
Author(s):  
Alexander Bezverkhy ◽  
Alexander Kutsenko
Keyword(s):  
Programming Languages ◽  
Development Tools ◽  
Cross Platform ◽  
Platform Development

The work is devoted to the study of cross-platform development of applications, elements of architecture, tools, programming languages and frameworks. During the work, the existing development tools were considered, one framework was studied in detail, which is currently the only one of its kind today. Developed recommendations for creating applications.

scholarly journals Comparative Analysis of Native and Cross-Platform iOS Application Development

2021 ◽  
Vol 15 (1) ◽  
pp. 61-64
Author(s):  
Márk Kovács ◽  
Zsolt Csaba Johanyák
Keyword(s):  
Comparative Analysis ◽  
Operating Systems ◽  
Mobile Applications ◽  
Mobile Application ◽  
Application Development ◽  
Development Environment ◽  
Cross Platform ◽  
Platform Development ◽  
Ios Application ◽  
And Performance

Abstract Nowadays, mobile applications are developed for more and more areas, providing great help for our everyday lives. When designing a mobile application, the first important decision to make is to choose the targeted platform. Is it only phone or tablet as well? Should the app run on Android or iOS, or should it be available on both mobile operating systems? In the latter case, besides the native development environments, it is worth considering a cross-platform development environment to write the software. This study investigates both the development and performance aspects of some possibilities for iOS application development, namely, native iOS development in Xcode, Xamarin.iOS, and Xamarin.Forms frameworks.

scholarly journals The Development of A Mobile Application Prototype Tour Taman Mini Indonesia Indah

I-STATEMENT ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 01-08
Author(s):  
Anne Sukmayani ◽  
Erza Sofian ◽  
Abdul Barir Hakim
Keyword(s):  
Programming Languages ◽  
Information Exchange ◽  
Mobile Application ◽  
Location Based Services ◽  
Life Cycle Approach ◽  
Application Development ◽  
Development Environment ◽  
Tourist Attractions ◽  
Open Platform ◽  
Development Life Cycle

The development of information technology has a direct impact on the improvement of the mobile phone industry, resulting in increased production and use of smartphones as a medium of information exchange. This development also creates an evolution in the world of mobile services. Android is one of the operating systems on mobile phones that provides an open platform for developers to build applications on various mobile devices. This research aims to build an Android-based mobile application that provides information on tourist attractions in Taman Mini Indonesia Indah. in real time and apply location-based services to the application. This TMII travel guide mobile application was created using Android Studio as an Integrated Development Environment (IDE), Google Maps API, and SQLite and MySql. The programming languages used are java, xml, sql, and php. The research method used is the SDLC (Software Development Life Cycle) approach with the Rapid Application Development (RAD) model.

Computer Implementation of Genetic Programming

2018 ◽  
pp. 132-182
Keyword(s):  
Genetic Programming ◽  
Programming Languages ◽  
Programming Language ◽  
Object Oriented Programming ◽  
Computer Implementation ◽  
Classification Problems ◽  
Development Environment ◽  
Terminal Set ◽  
C Programming ◽  
Multiclass Classification Problems

This chapter presents the computer implementation of the tree-based genetic programming in C# programming language. Since C# is a common object-oriented programming language, with little modification the source code presented in the chapter can be easily transformed into Java or C++ programming languages. The chapter covers all aspects of the implementation: node, chromosome, population, function set, and terminal set class implementations. The chapter is carefully structured, so at the end of the chapter fully working GP computer program will be implemented which can solve regression and multiclass classification problems. The reader should not worry about specific operating system, or development environment, since all code implementations are based on cross-OS and open source integrated development environment visual studio code which can run on Windows, Mac, or Linux.

Programming Language Support for Implementing Machine Learning Algorithms

2020 ◽  
pp. 402-421
Author(s):  
Anitha Elavarasi S. ◽  
Jayanthi J.
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Programming Language ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Mathematical Optimization ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
Machine Learning Applications ◽  
And Control

Machine learning provides the system to automatically learn without human intervention and improve their performance with the help of previous experience. It can access the data and use it for learning by itself. Even though many algorithms are developed to solve machine learning issues, it is difficult to handle all kinds of inputs data in-order to arrive at accurate decisions. The domain knowledge of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory plays a major role in developing machine learning based algorithms. The key consideration in selecting a suitable programming language for implementing machine learning algorithm includes performance, concurrence, application development, learning curve. This chapter deals with few of the top programming languages used for developing machine learning applications. They are Python, R, and Java. Top three programming languages preferred by data scientist are (1) Python more than 57%, (2) R more than 31%, and (3) Java used by 17% of the data scientists.

scholarly journals A Pattern Recognition Model of Python Programming using Artificial Neural Network via NeMo

2020 ◽  
Vol 8 (5) ◽  
pp. 109-113
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Programming Languages ◽  
Programming Language ◽  
High Performance ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Simulator ◽  
Source Codes ◽  
Python Programming

Background/Objectives: In the field of software development, the diversity of programming languages increases dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability to detect the pattern of a programming language characteristic by using NeMo(High-performance spiking neural network simulator) of neural network and testing the ability of this toolkit to provide detailed analyzable results. Methods/Statistical analysis: the method of achieving these objectives is by using a back propagation neural network via NeMo based on pattern recognition methodology. Findings: The results show that the NeMo neural network of pattern recognition can identify and recognize the pattern of python programming language with high accuracy. It also shows the ability of the NeMo toolkit to represent the analyzable results through a percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of NeMo simulator to provide beneficial platform for studying and analyzing the complexity of the backpropagation neural network model.

Dynamic Wireless Application Development II

2005 ◽  
pp. 193-227
Author(s):  
Lei-da Chen ◽  
Gordon W. Skelton
Keyword(s):  
Programming Languages ◽  
User Interfaces ◽  
Web Application ◽  
Web Applications ◽  
Ease Of Use ◽  
Application Development ◽  
Development Environment ◽  
Wireless Applications ◽  
Server Side ◽  
Wireless Application

In the previous chapter, we created an m-business application using ColdFusion. Besides ColdFusion, many other development tools can be used to develop m-business applications. Visual Studio .NET, an integrated development environment by Microsoft, has become an increasingly popular corporate applicationdevelopment tool due to its ease of use and support for a wide range of programming languages. Besides traditional Windows and Web applications, Visual Studio .NET also allows developers to build mobile and wireless applications with relative ease. The focus of this chapter is to discuss the tools and techniques for developing wireless applications using Visual Studio .NET. Wireless applications are developed using the ASP .NET Mobile Web Application template. The template provides developers with WYSIWYG tools for creating user interfaces for various mobile devices. These tools work seamlessly with ASP.NET, which uses a form-based approach to build server-side applications for processing user requests and interacting with databases. In this chapter, we will develop a business-to-consumter wireless application using Visual Studio .NET.

Model-Driven Prototyping Support for Pervasive Healthcare Applications

2010 ◽  
pp. 251-281
Author(s):  
Werner Kurschl ◽  
Stefan Mitsch ◽  
Johannes Schoenboeck
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Application Development ◽  
Healthcare Applications ◽  
Development Environment ◽  
Pervasive Healthcare ◽  
Model Driven

Pervasive healthcare applications aim at improving habitability by assisting individuals in living autonomously. To achieve this goal, data on an individual’s behavior and his or her environment (often collected with wireless sensors) is interpreted by machine learning algorithms; their decision finally leads to the initiation of appropriate actions, e.g., turning on the light. Developers of pervasive healthcare applications therefore face complexity stemming, amongst others, from different types of environmental and vital parameters, heterogeneous sensor platforms, unreliable network connections, as well as from different programming languages. Moreover, developing such applications often includes extensive prototyping work to collect large amounts of training data to optimize the machine learning algorithms. In this chapter the authors present a model-driven prototyping approach for the development of pervasive healthcare applications to leverage the complexity incurred in developing prototypes and applications. They support the approach with a development environment that simplifies application development with graphical editors, code generators, and pre-defined components.

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