Abstract
Expert systems, a form of artificial intelligence (AI), are typically designed to solve many real-world problems by reasoning through knowledge, which is primarily represented as IF–THEN rules, with the information acquired from humans or domain experts. However, to assume such rules for personalized decision-making in an intelligent, context-aware mobile application is a challenging issue. The reason is that different mobile users may behave differently in various day-to-day situations, i.e., not identical, and thus the rules for personalized services must be reflected according to their symmetrical or asymmetrical behavioral activities. Therefore, our key focus is to solve this issue through adding personalized decision-making intelligence to develop powerful mobile applications to assist the end-users. To achieve our goal, in this paper, we explore on “Mobile Expert System”, where we take into account machine-learning rules as knowledge-base rather than traditional handcrafted static rules. Thus, the concept of a mobile expert system enables the computing and decision-making processes more actionable and intelligent than traditional ones in the domain of mobile analytics and applications. Our experiment section shows that the context-aware machine learning rules discovered from users’ mobile phone data can contribute in building a mobile expert system to solve a particular problem, through making personalized decisions in various context-aware test cases.
Highlights
The smartphone sector has risen tremendously in the mobile phone application industry as a result of recent developments in science and technology [1,2]
Various machine learning classification techniques [11], as well as deep learning techniques [9] within the area of artificial intelligence (AI) are popular, we explore on expert system modeling using rule-based machine learning method to achieve our goal
A form of artificial intelligence (AI), are typically designed to solve many real-world problems by reasoning through knowledge, which is primarily represented as IF– rules, with the information acquired from humans or domain experts
Summary
The smartphone sector has risen tremendously in the mobile phone application industry as a result of recent developments in science and technology [1,2] Because of their many capabilities, which include data storage and processing, these devices are often regarded as one of the most important Internet-of-Things (IoT) devices [3]. A static execution environment is usually intended for a desktop computer program, whether it is used in the workplace or at home, or in other static locations This static prerequisite does not apply to mobile networks or systems in general. In [30], Sarker et al presented a machine learning-based user behavior model based on contextual smartphone data that was based on contextual smartphone data In addition to these methods, Pejovic developed a model to manage mobile interruptions [7]. Classification-based prediction models may not always provide high predictive accuracy in user behavior modeling in a variety of scenarios, since there may be an over-fitting problem owing to a lack of generalization in the classification-based predictions [19,32]
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