Hai V. Pham

Smart care spaces: needs for intelligent at-home care

Pressures on the availability of healthcare spaces, the high costs of institutional care, and the desires of those being cared for, cause a current move toward care either at home or within low-supervision environments. This brings about an important question: how can smart care spaces be created that intelligently link the home care environment to the needs of the cared-for? To a significant degree this involves development of sensored spaces connected to intelligent computer-systems. However, that intelligence requires an understanding of how sensors can provide more than just environmental variables, instead making systems aware of symptoms, comfort and potential needs for intervention. Therefore, this paper discusses the current need for development of smart care spaces, provides an introduction to some of the cost-effective sensors available, and reviews links between sensor data and medical conditions. It will conclude that there is a growing need for smart care spaces that allow effective monitorin...

Personalization and rule strategies in data-intensive intelligent context-aware systems

The concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

E-Education 3.0: Challenges and Opportunities for the Future of iCampuses

In our exciting world of pervasive computing and always-available mobile internet, meeting the educational needs of students has seen a growing trend toward collaborative electronic and mobile learning systems that build on the vision of Web 2.0. However, other trends relevant to modern students must not be ignored, including data freedom, brokerage and interconnectivity. Such factors are associated with the Internet of Things and the vision for Web 3.0, and so include the needs for greater consideration of data context and educational personalization so important to the future of campus-based, distance and vocational study. Therefore, future education can be expected to require a deeper technological connection between students and learning environments, in a manner requiring significant use of sensors, mobile devices, cloud computing and rich-media visualization. This paper considers the challenges associated with adopting such a futuristic concept as a means of enriching learning materials and environments within a university context. It will be concluded that much of the technology required to embrace the vision of Web 3.0 in education already exists, but that further research in key areas is required for the concept to achieve its full potential.

Hybrid Kansei-SOM model using risk management and company assessment for stock trading

Risk management and stock assessment are key methods for stock trading decisions. In this paper, we present a new stock trading method using Kansei evaluation integrated with a Self-Organizing Map model for improvement of a stock trading system. The proposed approach aims to aggregate multiple expert decisions, achieve the greatest investment returns, and reduce losses by dealing with complex situations in dynamic market environments, such as downward, upward, steady market trends, and other uncertain conditions. Kansei evaluation and fuzzy evaluation models are applied to quantify trader sensibilities about stock trading, market conditions, and stock market factors with uncertain risks. In Kansei evaluation, group psychology and sensibility of traders are quantified that represent in fuzzy weights. Kansei and stock-market data sets are visualized by SOM, together with aggregate expert preferences in order to find potential companies, matching with trading strategies at the right time and eliminating risky stocks. The proposed approach has been tested and performed well in daily stock trading on the HOSE, HNX (Vietnam), NYSE and NASDAQ (US) stock markets. The experiments through case studies show that the new approach, applying Kansei evaluation enhances the capability of investment returns and reduce losses. The experimental results also show that the proposed approach performs better than other current methods to deal with various market conditions.

Towards Integrating Emotion into Intelligent Context

Context-aware systems have traditionally employed a limited range of contextual data. While research is addressing an increasingly broad range of contextual data, the level of intelligence generated in context-aware systems is restricted by the failure to effectively implement emotional response. This paper considers emotion as it relates to context and the application of computational intelligence in context-aware systems. Following an introduction, personalization and the computational landscape is considered and context is introduced. Computational intelligence and the relationship to the Semantic Web is discussed with consideration of the nature of knowledge and a brief overview of knowledge engineering. Cognitive conceptual models and semiotics are introduced with a comparative analysis and approaches to implementation. Ongoing research with illustrative ‘next generation’ intelligent context-aware systems incorporating emotional responses are briefly considered. The paper concludes with a discussion where the challenges and opportunities are addressed; there are closing observations, consideration of future directions for research, and identification of open research questions.

Context matching with reasoning and decision support using hedge algebra with Kansei evaluation

There have been far reaching Societal and Geo-Political developments in healthcare domains locally, nationally, and globally. Healthcare systems are essentially patient centric and decision driven with the clinician focus being on the identification of the best treatment options for patients in uncertain environments. Decision-support systems must focus on knowledge-based decisions using both tacit and explicit knowledge. Decisions are generally made using a qualitative approach in which linguistic (semantic) terms are used to express parameters and preferences to determine the optimal decision from a range of alternative decisions. The study presented in this paper proposes an approach which implements context-matching using hedge algebra integrated with Kansei evaluation. The proposed approach is designed to enable quantification of qualitative factors for linguistic variables while accommodating decision-makers preferences and sensibilities (constraint satisfaction) in decision-making. Experimental results demonstrate that our proposed approach achieves a significant improvement in the performance accuracy. In this paper our proposed approach uses the healthcare domain as a use-case however we argue that the posited approach will potentially generalize to other domains and systems where knowledge-based decision support is a principal requirement.

Predicting Intelligence Using Hybrid Artificial Neural Networks in Context-Aware Tunneling Systems under Risk and Uncertain Geological Environment

In pervasive computing environments the availability of real-time computation models is expected to predict a performance of Tunnel Boring Machine (TBM). Context awareness allows an entity adapt to uncertain environment, offering a number of intelligent prediction methods for tunneling. This study presents a proposal of a Context-Aware Tunneling System using Hybrid Artificial Neural Networks for prediction of TBM performance and risk response in uncertain geological environments. The proposed approach is essential to predict the TBM performance, together warning disaster risks in terms of the performance and risk response for the planning projects of tunneling. In addition, the proposed approach aims to predict TBM performance and utilization through a network in complex underground conditions such as rock mass, geology, lithography, and disaster in tunnel projects. The proposed approach has tested in experiments using data series from tunnel projects in Japan and Asian countries. To validate the significance of the findings and show added valuable parameters of the proposed approach, the results are compared with conventional statistical methods in terms of TBM performance evaluation. In order to evaluate the effectiveness of this approach, experimental results show that the proposed approach performs better than other current methods under uncertain geological environments.

Human Reasoning Awareness Quantified by Self-Organizing Map Using Collaborative Decision Making for Multiple Investment Models

Collaborative Decision Making (CDM) is one of the concepts of human reasoning awareness, which refers to expert knowledge of the group and its preferences in a dynamic market environment. In this paper, we present a new approach, which is a framework for collaborative decision making, together with expert feelings about market dynamics to deal with multiple models of stock investment portfolios. The framework aims to aggregate collective expert preferences, including of group expert psychology and sensibility, assists a dynamic trading support system and achieve the greatest investment returns. Kansei evaluation uses to quantify trader sensibilities about trading decisions, market conditions with uncertain risks. Collective group psychology and preference of traders are quantified that represent in membership weights. The framework is used to quantify Kansei, quantitative and qualitative data sets, which are visualized by Self-Organizing Map (SOM) in order to select the best alternatives with dynamic solutions for investment. To confirm the models performance, the proposed approach has been tested and performed well in stock trading for stock investment portfolios. The experiments through case studies show that the new approach, applying Kansei evaluation enhances the capability of investment returns and reduce losses to deal with various financial investment models.

On Context-Aware Evidence-Based Data Driven Development of Diagnostic Scales for Depression

There is a growing interest in health information technology using evidence-based approaches in clinical decision-support systems, the goal for such systems is ‘precision medicine’ using ‘interventional informatics’. However, the impact has been less than positive and it has been argued that interventional informatics using data-driven interventions is required to achieve evidence-based clinical decision-support. In this paper we discuss context-Aware, evidence-based, data driven development of diagnostic scales created using multi-disciplinary collaborative development. The goal is the development of novel dynamic scales for decision-support in healthcare provision and, while clinicians may derive benefit from such systems, there are potentially greater benefits for all stakeholders in medical triage systems using both face-to-face and remote consultations. While our focus lies in depression, the proposed approach will generalise to a diverse range of domains, systems, and technologies.

Machine Cognition and the Integration of Emotional Response in the Monitoring of Mental Disorders

Computer science relies heavily on computational modeling and while the value of such an approach is generally recognized the methodological account of computational explanation is not up-to-date. In this paper we explore machine cognition with the creation of effective cognitive modeling and consider the elemental components that combine to create an effective cognitive model. The creation of such a model will enable the processing of information in intelligent context-aware systems while integrating emotion (more accurately stated as emotive response). We present a brief review of related research addressing cognitive science and machine cognition in which we identify the concept of self. Modeling is introduced with an overview of conceptual models and semiotics followed by consideration of implementation using a proposed approach based on fuzzy sets. We introduce depression as a use-case to illustrate the proposed approach and a general discussion where future directions for research and open research questions are considered. The paper closes with concluding observations. We posit that creating an effective cognitive model offers the potential to integrate emotive response and thereby improve context-aware systems in a broad and diverse range of domains and systems along with improvements in the levels of computational intelligence.