Developing a creative idea generation system for innovative software reliability research

Reliability is one of the important aspects of any software, which has been studied for decades with many techniques proposed and developed. In software engineering, software reliability cannot be ignored in any application. However, it is still hard to measure and difficult to ensure the reliability of software, which means that it is necessary to find new ways to study on the software reliability, especially for researchers. Naturally, idea generation has to be involved in the process of getting innovative ideas. Although a considerable number of applications and research studies have been made in the past years in order to increase the effectiveness of idea making process, it is lack of efforts working on creativity in the idea generation process particularly. Moreover, creative computing is suitable to be employed to enhance the innovation of research studies on the software reliability, as it emphasises being creative in the whole software lifecycle. Therefore, the objective of this research paper is to propose an idea generation process to inspire new studies on software reliability. This paper first represents an idea generation framework. Then, after critically reviewed the creativity features and attributes, three creativity elements are proposed with corresponding sub-dimensions to support on balancing new and value of the ideas. Formal algorithms are designed to calculate and incorporate the creativity elements and their sub-dimension. Furthermore, a prototype of the proposed idea generation system is developed that is named as Research Topics Generation System (RTGS), in which a set of new research topics on software reliability are generated to inspire further studies, whilst creative computing ontology is created as the basis of the idea generation

An Intelligence and Creative Computing Based Mental Health Care Framework

Artificial intelligence is being developed and applied in business, medical, tourism and other subjects. Health care can be related to artificial intelligence, serving more on protecting people’s health through supervising some indexes of people and providing advice for people’s normal life. As health care is required colleting the physical data, voice data and video data, wearable products are suitable. The user is connected with wearable products through minor cameras, sensors, and chips. In this research, an artificial intelligence-based health care framework is achieved for supervising and managing user’s health conditions and being applied with intelligent shoes. The aim of this research is to serve people on mental health care in normal life based on physical data analysis and mental data analysis, to protect people from being sub-health, to advise people on different aspects of being healthy. An applicable system can be generated with specific AI algorithms and machine learning algorithms, including body index data statistical model analysis, voice data analysis algorithm, and video data analysis. The framework can collect health related data and analyse it, generating health state reports and advice report for the user. The entire system is based on artificial intelligence theories and methods, and machine learning algorithms, completing health data processing. Abstraction method is combined with conventional nueral network and recurrent neural network are used for voice data analysis and video data analysis. Statistical model is created based on body index data categories. Three kinds of data can be processed for supervising the emotions of the user. This research will generate a mental health care framework and a mental health supervision and management system that can be set on the wearable products for the application