This special issue aims to highlight the application of revenue management practices to analysing consumption and investment behaviour. Today there are tremendous opportunities to explore the rationale behind consumption and investment behaviour in the discipline of revenue management.
Since the application of revenue management practices to predicting consumer behaviour and to explaining consumer investment behaviour in equity and real estate markets in an Asian context are recent research trends, this issue seeks to publish high-quality papers that help promote revenue management practices with a focus on Asian consumer and family behaviour. Papers that are the joint work of practitioners and academicians are particularly welcome.
Machine learning through computer systems, which propagates from network to network, is at the heart of computer intelligence. Machine learning is the key to simplifying the definition of a problem-solving platform. Basically, it is a mechanism for pattern search and building intelligence into a computer (e.g. machine) to be able to learn, implying that it will be able to do better in the future from its own experience.
This special issue aims to present machine learning research pertaining to the Internet of Things (IoT). Machines learning from IoT devices, networks and data, in particular to detect and unveil possible hidden structures and regularity patterns associated with their generation mechanism, is important. This issue will promote analysis and understanding of the nature of the machine learning data, which can be used to make predictions for future decisions and actions for computer processing. Its objective is to develop and publish efficient algorithms for designing models and analysis for machine learning prediction and to present research on how to analyse data for such applications in a way that meets demands for algorithms to be computationally efficient and at the same time robust in their performance.
Today, medical imaging becomes a crucial part of the medical management of diseases. Biomedical imaging has undergone rapid technological advancements over the last several decades and has seen the development of many new applications. New techniques have been gaining recognition in areas ranging from basic research to clinical applications, and from the cellular level to the whole-organ level. It is an interdisciplinary field that requires teamwork among biologists, medical physicists, computer scientists, biomedical engineers and clinicians of all specialities.
At the same time, issues like radiation during diagnostics seriously affect the human body. The higher the dose of radiation delivered at any one time, however, the greater the risk of long-term damage. If a patient receives repeated doses, harm can also occur from the cumulative effect of those multiple doses over time. Conversely, using insufficient radiation may increase the risk of misdiagnosis, delayed treatment, or, if the initial test is inadequate, repeat testing with the patient exposed to even more radiation.
The purpose of this special issue is to publish original, high-quality papers on innovative research and development in the analysis of medical imaging and issues in medical imaging.
Numerous technologies (e.g., clouding computing, big data, deep learning, Internet of Things, social media, multicore, and mobility) and their applications are changing our daily life. For these technologies, computer science constructs a solid foundation. It is also one of the most important driving forces behind these technologies. With the rapid growth of computer science related technologies, this change is continuously moving forward.
This special issues aims to explore future trends and applications in computer technologies.
The issue will carry revised and substantially extended versions of selected papers presented at The International Computer Symposium 2016, but we also strongly encourage researchers unable to participate in the conference to submit articles for this call.
Suitable topics include, but are not limited to, the following:
Algorithms, bioinformatics, and computation theory
Artificial intelligence and machine learning
Computer architecture, embedded systems, VLSI/EDA, and applications
Computer networks, web service technologies, and software defined networking
Cryptography and information security
Database, data mining, big data, and information retrieval
Image processing, computer graphics, and multimedia
Information literacy and social media
mobile computing and wireless communications
High-performance computing, parallel processing, and cloud computing
Cyber-physical system and Internet of Things
Wearable computing for smart services
Green systems and applications over next generation network
Submission of manuscripts: 28 February, 2017
Notification to authors: 1 June, 2017
Final versions due: 31 July,, 2017
The performance of vehicle noise, vibration and harshness (NVH) has become an important evaluation index of the product research and development for automobile enterprises, and also a core concern for consumers. Vehicle noise and vibration problems are unfriendly to the environment and would degrade ride and comfort of the occupant dramatically, moreover statistical results show that about one third of vehicle component faults are related to vehicle NVH problems.
The research about vehicle NVH always combines simulation method and test technology. The theories and methods of modelling include the lumped parameters, multi-body dynamics, finite element, mode synthesis, boundary element, statistical energy analysis method etc. By these methods, mechanisms of NVH problem and its suppressing measures can be analysed systematically, while test technologies have been developed and generally used to verify simulation results and the validity of those measures.
This special issue aims at providing a platform intended to present emerging ideas of accurate and rational modelling method, mechanism analysis, suppressing method and subjective-objective evaluation method of vehicle NVH problems.
Suitable topics include, but are not limited to, the following:
Air noise, electromagnetic noise
Mechanical noise (gear noise, tyre noise, brake noise, etc.)
Sound quality and its quantitatively objective evaluation
The advanced methods of test, data acquisition and processing
Suppressing measures of noise and vibration: vibration isolation or elimination components/optimisation/control/prediction