A Review on Information Accessing Systems Based on Fuzzy Linguistic Modelling

This paper presents a survey of some fuzzy linguistic information access systems. The review shows information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools, which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us to represent and manage the subjectivity, vagueness and imprecision that is intrinsic and characteristic of the processes of information searching, and, in such a way, the developed systems allow users the access to quality information in a flexible and user-adapted way.European Union (EU) TIN2007-61079 PET2007-0460Ministry of Public Works 90/07Excellence Andalusian Project TIC529

An adaptive neuro-fuzzy propagation model for LoRaWAN

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine

Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C)

Integrating Ontologies and Fuzzy Logic to Represent User-Trustworthiness in Recommender Systems

Information Technology and Quantitative Management (ITQM 2015)Recommender systems can be used to assist users in the process of accessing to relevant information. In the literature we can find sundry approaches for generating personalized recommendations and all of them make use of different users’ and/or items’ features. Building accurate profiles plays an essential role in this context, so that the system's success depend to a large extent on the ability of the learned profiles to represent the user's preferences. An ontology works very well to characterize the users profiles. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modelling, this way in the recommendation generation process we do not take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.Projects UJA2013/08/41TIN2013-40658-PTIC5299TIC-5991TIN2012-36951 co-financed by FEDER and TIC610

Can fuzzy Multi-Criteria Decision Making improve Strategic planning by balanced scorecard?

Strategic management is momentous for organizational success and competitive advantage in an increasingly turbulent business environment. Balanced scorecard (BSC) is a framework for evaluating strategic management performance which translates strategy into action via various sets of performance measurement indicators. The main objective of this research is to develop a new fuzzy group Multi-Criteria Decision Making (MCDM) model for strategic plans selection process in the BSC. For this to happen, the current study has implemented linguistic extension of MCDM model for robust selection of strategic plans. The new linguistic reasoning for group decision making is able to aggregate subjective evaluation of the decision makers and hence create an opportunity to perform more robust strategic plans, despite of the vagueness and uncertainty of strategic plans selection process. A numerical example demonstrates possibilities for the improvement of BSC through applying the proposed model

An alternative approach to firms’ evaluation: expert systems and fuzzy logic

Discounted Cash Flow techniques are the generally accepted methods for valuing firms. Such methods do not provide explicit acknowledgment of the value determinants and overlook their interrelations. This paper proposes a different method of firm valuation based on fuzzy logic and expert systems. It does represent a conceptual transposition of Discounted Cash Flow techniques but, unlike the latter, it takes explicit account of quantitative and qualitative variables and their mutual integration. Financial, strategic and business aspects are considered by focusing on twenty-nine value drivers that are combined together via “if-then” rules. The output of the system is a real number in the interval [0,1], which represents the value-creation power of the firm. To corroborate the model a sensitivity analysis is conducted. The system may be used for rating and ranking firms as well as for assessing the impact of managers’ decisions on value creation and as a tool of corporate governance.Firms’ evaluation, fuzzy logic, expert system, rating, acquisition, sensitivity analysis