Fuzzy optimization to improve mobile wellness applications for young-elderly

Mobile applications and specifically wellness applications are used increasingly by different age-segments of the general population. This is facilitated by the large amount of data collected through various built-in sensors in the smartphone or other mobile devises, e.g. smart watches. Young-elderly cohort (60-75 year old individual) is probably one of the most potential user groups that would benefit from using mobile health and wellness applications, if their needs and preferences are precisely addressed. General knowledge is limited on understanding to what extent mobile wellness applications can and should provide precise recommendations which improve the users’ health and physical conditions. To address this problem, the current study identifies the potential benefits of utilizing fuzzy optimization tools to design recommendation systems that can take into consideration the (i) imprecision in the data and (ii) the imprecision by which one can estimate the effect of a recommendation on the user of the system. The proposed approach, depending on the context of use, identifies a set of actions to be taken by the users in order to optimize the physical or mental condition from various perspectives. The model is illustrated through the example of walking speed optimization which is an important issue for the young-elderly

Possibilistic Clustering for Crisis Prediction: Systemic Risk States and Membership Degrees

Research on understanding and predicting systemic financial \ risk has been of increasing importance in the recent \ years. A common approach is to build predictive models \ based on macro-financial vulnerability indicators to \ identify systemic risk at an early stage. In this article, we \ outline an approach for identifying different systemic risk \ states through possibilistic fuzzy clustering. Instead of directly \ using a supervised classification method, we aim at \ identifying coherent groups of vulnerability with macrofinancial \ indicators for pre-crisis data, and determine the \ level of risk for a new observation based on its similarity \ to the identified groups. The approach allows for differentiating \ among different possible pre-crisis states, and \ using this information for estimating the possibility of systemic \ risk. In this work, we compare different fuzzy clustering \ methods, as well as conduct an empirical exercise \ for European systemic banking crises

Factors influencing the adoption of mobile services consumers' preferences using analytic hierarchy process

The rapid and widespread development of innovations in mobile services is changing societies and improving lives around the world. Due to lagging adoption, many of these new innovations have yet failed to generate revenue that was expected by mobile network operators, application and content developers. There are several factors which are affecting the service adoption by consumers. This paper aims to provide practitioners and academics, an insight on what consumers' preferences are by using an Analytic Hierarchy Approach (AHP). The objective of this paper is to identify factors influencing the adoption of the mobile services. In this study we have considered Payment Mode, Functionality, Added Value and PQCP (perceived quality, cost and performance) as the main service adoption factors. The survey results indicate that Functionality is the most important influencing factor for the respondents, followed by Added Value, PQCP and Payment Mode.Adoption,AHP,Mobile Value Services,Consumer's Preferences

Credit Risk Evaluation in Peer-to-peer Lending With Linguistic Data Transformation and Supervised Learning

The widespread availability of various peer-to-peer lending solutions is rapidly changing the landscape of ï¬nancial services. Beside the natural advantages over traditional services,a relevant problem in the domain is to correctly assess the risk associated with borrowers. In contrast to traditional ï¬nancial services industries, in peer-to-peer lending the unsecured nature of loans as well as the relative novelty of the platforms make the assessment of risk a difï¬cult problem. In this article we propose to use traditional machine learning methods enhanced with fuzzy set theory based transformation of data to improve the quality of identifying loans with high likelihood of default. We assess the proposed approach on a real-life dataset from one of the largest peer-to-peer platforms in Europe. The results demonstrate that (i) traditional classiï¬cation algorithms show good performance in classifying borrowers, and (ii) their performance can be improved using linguistic data transformatio

Digital Coaching - An Exploratory Study on Potential Motivators

The objective of our study is to explore the importance of different sets of functionalities in a digital coaching system. Digital coaching is defined as systems providing the user with actionable advice and feedback to reach fitness goals. From previous research we identify five sets of functionalities likely to be important in the digital coaching context: mental support, exercise programs, goal setting, feedback and social functionality. We employ Fuzzy-set Qualitative Comparative Analysis to understand users’ opinions of digital coaching. Our results highlight the importance of exercise programs and goal setting functionality, whereas feedback and social functionality are surprisingly not so important. Some gender-related differences emerge

Knowledge Mobilisation for Knowledge Whenever and Wherever Needed

Knowledge mobilisation is a transition from the prevailing knowledge management technology to some innovative methods for knowledge representation, formation and development and for knowledge retrieval and distribution. Knowledge mobilisation also carries the connotation on “knowledge on mobile phones” and this is actually one of the platforms that will be used. Fuzzy ontology replaces classical ontology for knowledge representation. We will show that fuzzy ontology is useful to represent real world knowledge and to give us answers which are sufficiently good for real world situations for which we need sufficiently good knowledge. We demonstrate the knowledge mobilisation approach by showing how amateurs can become wine connoisseurs with support from the technology

Factors Influencing Entrepreneurship Educators’ Pedagogical Choices—A Configurational Approach

Entrepreneurship education is critical for developing the entrepreneurial skills of tomorrow’s entrepreneurs and leaders. In this paper, we aim to identify factors influencing entrepreneurship educators’ pedagogical choices, in particular, factors influencing their preferences to become either a teacher-centric or a student-centric educator. Our analysis includes job satisfaction, self-efficacy, and department support as the antecedent variables influencing the outcome. The data are collected from 289 global entrepreneurship educators, and fuzzy-set comparative qualitative analysis (fsQCA) was used to obtain multiple configurations of conditions leading to either a teacher-centric or student-centric model. The fsQCA analysis reveals that for teacher-centric educators, job satisfaction and more than 10 years of teaching experience are the most important factors, whereas for student-centric educators, teaching experience is not important factor, but self-efficacy and entrepreneurship teaching training are influential factors. In the article we discuss the important theoretical and practical contributions resulting from the analysis

Using multi-granular fuzzy linguistic modelling methods for supervised classification learning purposes

Classification learning is a very complex process whose success and failure ratio depends on a high amount of elements. One of them is the representation mean used for the data that is employed in the process. Granularity of the data used for classification learning purposes can affect dramatically the success and failure ratio of the obtained classification. In this paper, multi-granular fuzzy linguistic modelling methods are applied over the classification learning data in order to modify their granularity and increase the classification success ratio. Thanks to multi-granular fuzzy linguistic modelling methods, it is possible to automatically modify the data granularity in order to determine which data representation is the one that provides the better classification results in the learning process

Improving supervised learning classification methods using multi-granular linguistic modelling and fuzzy entropy

Obtaining good classification results using supervised learning methods is critical if we want to obtain a high level of precision in the classification processes. The training data used for the learning process plays a very important role in achieving this objective. Therefore, it is important to represent the data in a way that best expresses its meaning. For this purpose, we propose to apply linguistic modeling methods in order to obtain a linguistic representation. With the help of multi-granular linguistic modelling, data can be transformed and expressed using different (unbalanced) linguistic label sets. Expressing the data using linguistic expressions instead of numbers increases the readability, reduces the complexity of the problem and data recovering methods allow us to manually control the level of precision. In this paper, several datasets are transformed and utilized for classification tasks using several supervised learning algorithms. For each combination of datasets and algorithms, the data has been expressed using several linguistic label sets that have different granularity values. After carrying out the testing processes, we can conclude that, in some cases, reducing data complexity leads to better classification results. Therefore, it is found that linguistic representation of the training data with just the necessary and sufficient precision can improve the reliability of the classification process