A comparison of fuzzy approaches to e-commerce review rating prediction

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor

Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption

A computational intelligence approach to efficiently predicting review ratings in e-commerce

Sentiment analysis, also called opinion mining, is currently one of the most studied research fields which aims to analyse people's opinions. E-commerce websites allow users to share opinions about a product/service by providing textual reviews along with numerical ratings. These opinions greatly influence future consumer purchasing decisions. This paper introduces an innovative computational intelligence framework for efficiently predicting customer review ratings. The framework has been designed to deal with the dimensionality and noise which is typically apparent in large datasets containing customer reviews. The proposed framework integrates the techniques of Singular Value Decomposition (SVD) and dimensionality reduction, Fuzzy C-Means (FCM) and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The performance of the proposed approach returned high accuracy and the results revealed that when large datasets are concerned, only a fraction of the data is needed for creating a system to predict the review ratings of textual reviews. Results from the experiments suggest that the proposed approach yields better prediction performance than other state-of-the-art rating predictors which are based on the conventional Artificial Neural Network, Fuzzy C-Means, and Support Vector Machine approaches. In addition, the proposed framework can be utilised for other classification and prediction tasks, and its neuro-fuzzy predictor module can be replaced by other classifiers

Yukawa sector in non-supersymmetric renormalizable SO(10)

We discuss the ordinary, non-supersymmetric SO(10) as a theory of fermion masses and mixings. We construct two minimal versions of the Yukawa sector based on $\bar{126}_H$ and either $10_H$ or $120_H$. The latter case is of particular interest since it connects the absolute neutrino mass scale with the size of the atmospheric mixing angle $\theta_A$. It also relates the smallness of $V_{cb}$ with the largeness of $\theta_A$. These results are based on the analytic study of the second and third generations. Furthermore, we discuss the structure of the light Higgs and the role of the Peccei-Quinn symmetry for dark matter and the predictivity of the theory.Comment: 8 pages. Reference added, one formula correcte

Fuzzy logic as-a-service for Ambient Intelligence Environments

Fuzzy Logic Systems (FLSs) are normally associated with dedicated hardware/software systems. However, the distributed and pervasive architecture of many modern hardware/software systems is driving increasing interest in pervasive, distributed FLSs. Achieving this vision will require the design of FLS implementations which support client-server models and more specifically, cloud-computing and service-oriented solutions. Here, FLSs become a globally accessible service that enables openness, device independence, load balancing, resource sharing and ultimately cost effectiveness. In this paper, the recently standardised fuzzy mark-up language (IEEE-1855) and proposed extensions are used for designing Web Services for FLS computations. The novelty of this approach is in integrating different FLS components (input collection, processing and output) into a single web service platform which uses a well specified language for communication over the Web via HTTP request/responses. The utility of this approach is shown in the context of implementing FLSs in Ambient Intelligent Environments

Otx2 is an intrinsic determinant of the embryonic stem cell state and is required for transition to a stable epiblast stem cell condition

Mouse embryonic stem cells (ESCs) represent the naïve ground state of the preimplantation epiblast and epiblast stem cells (EpiSCs) represent the primed state of the postimplantation epiblast. Studies have revealed that the ESC state is maintained by a dynamic mechanism characterized by cell-to-cell spontaneous and reversible differences in sensitivity to self-renewal and susceptibility to differentiation. This metastable condition ensures indefinite self-renewal and, at the same time, predisposes ESCs for differentiation to EpiSCs. Despite considerable advances, the molecular mechanism controlling the ESC state and pluripotency transition from ESCs to EpiSCs have not been fully elucidated. Here we show that Otx2, a transcription factor essential for brain development, plays a crucial role in ESCs and EpiSCs. Otx2 is required to maintain the ESC metastable state by antagonizing ground state pluripotency and promoting commitment to differentiation. Furthermore, Otx2 is required for ESC transition into EpiSCs and, subsequently, to stabilize the EpiSC state by suppressing, in pluripotent cells, the mesendoderm-to-neural fate switch in cooperation with BMP4 and Fgf2. However, according to its central role in neural development and differentiation, Otx2 is crucially required for the specification of ESC-derived neural precursors fated to generate telencephalic and mesencephalic neurons. We propose that Otx2 is a novel intrinsic determinant controlling the functional integrity of ESCs and EpiSCs

Analytical and pharmacological aspects of therapeutic drug monitoring of mTOR inhibitors

Mammalian Target Of Rapamycin (mTOR) inhibitors represent a new class of immunosuppressant drugs extensively used for the prevention and the treatment of graft rejection in organ transplant recipients. Their current use is due to referred low nephrotoxic effects, particularly important in kidney transplanted and/or patients with renal failure. The most representative drugs of such class are Sirolimus (Siro) and Everolimus (Rad). Both drugs show a narrow therapeutic window, therefore, monitoring of whole-blood drug levels is recommended in order to optimize the therapy. Among the available assays, Liquid Chromatography coupled with UltraViolet or Electrospray Tandem Mass Spectrometry methods (LC/UV or LC/ESI-MSMS) are the most accurate and specific ones. A reliable alternative is represented by immunoassays, which offer the opportunity to minimize sample pre-treatment, thus reducing the time between drawing blood sample and measuring the drug concentration, an important aspect in high-throughput analyses. Despite this, a limitation in the use of immunoassays for therapeutic drug monitoring is the lower specifity compared with the chromatographic methods when analysing structurally-related drugs. New insights to optimize mTOR inhibitors regimens seem to be offered by the evaluation of CYP450 3A activity by using the probe drug approach. To such purpose, there are a number of major probe drugs used for in vivo studies including: midazolam, cortisol, lidocaine, nifedipine, dextromethorphan, erythromycin, dapsone and alfentanil. The aim of the present paper is to report the most recent knowledge concerning this issue, supplying a critical and comprehensive review for whom are involved both in the clinical and analytical areas

Fuzzy logic on quantum annealers

Quantum computation is going to revolutionize the world of computing by enabling the design of massive parallel algorithms that solve hard problems in an efficient way, thanks to the exploitation of quantum mechanics effects, such as superposition, entanglement and interference. These computational improvements could strongly influence the way how fuzzy systems are designed and used in contexts, such as big data, where computational efficiency represents a non-negligible constraint to be taken into account. In order to pave the way towards this innovative scenario, this paper introduces a novel representation of fuzzy sets and operators based on Quadratic Unconstrained Binary Optimization (QUBO) problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers

One-loop effective potential for SO(10) GUT theories in de Sitter space

Zeta-function regularization is applied to evaluate the one-loop effective potential for SO(10) grand-unified theories in de Sitter cosmologies. When the Higgs scalar field belongs to the 210-dimensional irreducible representation of SO(10), attention is focused on the mass matrix relevant for the SU(3)xSU(2)xU(1) symmetry-breaking direction, to agree with low-energy phenomenology of the particle-physics standard model. The analysis is restricted to those values of the tree-level-potential parameters for which the absolute minima of the classical potential have been evaluated. As shown in the recent literature, such minima turn out to be SO(6)xSO(4)- or SU(3)xSU(2)xSU(2)xU(1)-invariant. Electroweak phenomenology is more naturally derived, however, from the former minima. Hence the values of the parameters leading to the alternative set of minima have been discarded. Within this framework, flat-space limit and general form of the one-loop effective potential are studied in detail by using analytic and numerical methods. It turns out that, as far as the absolute-minimum direction is concerned, the flat-space limit of the one-loop calculation about a de Sitter background does not change the results previously obtained in the literature, where the tree-level potential in flat space-time was studied. Moreover, when curvature effects are no longer negligible in the one-loop potential, it is found that the early universe remains bound to reach only the SO(6)xSO(4) absolute minimum.Comment: 25 pages, plain Tex, plus Latex file of the tables appended at the end. Published in Classical and Quantum Gravity, Vol. 11, pp. 2031-2044, August 199

Fuzzy inference on quantum annealers

Quantum computers can potentially perform certain types of optimisation problems much more efficiently than classical computers, making them a promising tool for solving complex fuzzy logic problems. In two recent developments, based on solving Quadratic Unconstrained Binary Optimization (QUBO) problems on a type of quantum computers known as quantum annealers, we have introduced novel representations of a) fuzzy sets; b) implementations of some basic fuzzy logic operators (union, intersection, alpha-cut and maximum) and; c) the centroid defuzzification. In this paper, the previous works are further extended by presenting an implementation of Mamdani inference on the quantum annealer machines. We first present how the fuzzy rules can be formulated for such an implementation, then we present how to cascade different quantum-fuzzy operators in order to implement the quantum-fuzzy inference, and finally, a sample implementation of the inference on a real quantum computer is demonstrated. Having the main components of a rule-based fuzzy logic system implemented on quantum computers, this paper provides an integrated solution for implementing a whole fuzzy rule-based system on quantum computers