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Full-Text Articles in Physical Sciences and Mathematics
Design An Automatic Appointment System To Improve Patient Access To Primary Health Care, Hongxiang Hu, Ping Yu, Jun Yan
Design An Automatic Appointment System To Improve Patient Access To Primary Health Care, Hongxiang Hu, Ping Yu, Jun Yan
Dr Jun Yan
Advanced Access model has been introduced in general practice in the United States to improve patient access to primary health care services for more than ten years. It has brought in the benefits of eliminating service provider’s waiting lists, improving patients’ timely access to services and reducing no-show rate. However, to implement this model, practices need to collect relevant information, develop contingency plans and set up practice strategies to balance the provision of care and patient’s demand. These tasks are not always easy to achieve. Understanding the requirements and constraints for effective management of patient booking is essential for developing …
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Dr Jun Yan
Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts …
Modelling And Simulation Of An Adaptive Neuro-Fuzzy Inference System (Anfis) For Mobile Learning, Ahmed Al-Hmouz, Jun Shen, Rami Al-Hmouz, Jun Yan
Modelling And Simulation Of An Adaptive Neuro-Fuzzy Inference System (Anfis) For Mobile Learning, Ahmed Al-Hmouz, Jun Shen, Rami Al-Hmouz, Jun Yan
Dr Jun Yan
With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) for delivering adapted learning content to mobile learners. The ANFIS model was designed using trial and error based on various experiments. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the adaptation of learning content according to learners' needs. Study results show that …