Intelligent Mobile Applications: A Systematic Mapping Study

Abstract

Smart mobiles as the most affordable and practical ubiquitous devices participate heavily in the enhancement of our daily life by the use of many convenient applications. However, the significant number of mobile users in addition to their heterogeneity (different profiles and contexts) obligates developers to enhance the quality of their apps by making them more intelligent and more flexible. This is realized mainly by analyzing mobile user’s data. Machine learning (ML) technology provides the methodology and techniques needed to extract knowledge from data to facilitate decision-making. Therefore, both developers and researchers affirm the benefits of combining ML techniques and mobile technology in several application fields as e-health, e-learning, e-commerce, and e-coaching. Thus, the purpose of this paper is to have an overview of the use of ML techniques in the design and development of mobile applications. Therefore, we performed a systematic mapping study of papers published on this subject in the period between 1 January 2007 and 31 December 2019. A total number of 71 papers were selected, studied, and analyzed according to the following criteria, year, sources and channel of publication, research type, and methods, kind of collected data, and finally adopted ML models, tasks, and techniques.

Usabilidade da Experiência do Usuário no M-Commerce: Uma Revisão Sistemática e Proposta de Agenda de Pesquisa

“…Autores Título do artigo Periódico Diante dos estudos identificados, percebe-se uma pluralidade de temáticas relacionadas aos campos principais de interesse deste estudo, a saber, user experience e m-commerce. Os artigos encontrados exploram desde questões relacionadas com experiência do usuário em mobile banking (Peevers et al, 2011;Otair, 2012;Qu, 2018;Zhang et al, 2018;Valaei et al, 2019), passando por tecnologia e novas mídias (Cortimiglia et al, 2011;Shirazi & Iqbal, 2017;Hinarejos et al, 2019;Rachad & Idri, 2020;Yoo, 2020), turismo (Dorcic et al, 2019;Chuang, 2020), fatores críticos de usabilidade e questões técnicas (Zhang et al, 2010;Zahra et al, 2013;Desruelle & Gielen, 2015;Padovani et al, 2017), percepção e satisfação dos consumidores (Bilgihan et al, 2016;Pellet et al, 2018;Zheng & Jin, 2019;Jha & Mahmoud, 2019), grupos específicos (Boateng et al, 2013;Missen et al, 2019) e inclusão (Liu et al, 2010). A presença de tamanha variedade entre os tópicos demonstra que o campo ainda está em amadurecimento, mas também que permite uma multiplicidade de abordagens que vai desde novas tecnologias à comportamento do consumidor e segmentação de mercado.…”

The Development and Adoption of Online Learning in Pre- and Post-COVID-19: Combination of Technological System Evolution Theory and Unified Theory of Acceptance and Use of Technology

“…Automatic classifications of facial features create digital interlocutors that optimize the interactions between humans and machines (Fuentes-Hurtado et al 2019). Machine-learning updates the quality and design of previous applications, making them more intelligent and flexible (Rachad and Idri 2020). Data mining algorithm contributes to evaluate e-learning courses about the validity of the ranking positions and their quantity and quality of contents (Kazanidis et al 2020).…”

Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

“…Many ML tasks such as classification, regression, clustering, and association can be combined or used individually to analyze data and extract new patterns. Recently, ML techniques can be used to ensure intelligent interactions with mobile users by adapting mobile apps interfaces and actions to mobile users' contexts, profiles, and interaction history [23].…”