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From the perspective of clinical decision-making in a Medical IoT-based healthcare system, achieving effective and efficient analysis of long-term health data for supporting wise clinical decision-making is an extremely important objective, but determining how to effectively deal with the multi-dimensionality and high volume of generated data obtained from Medical IoT-based healthcare systems is an issue of increasing importance in IoT healthcare data exploration and management. A novel classifier or predicator equipped with a good feature selection function contributes effectively to classification and prediction performance. This paper proposes a novel bagging C4.5 algorithm based on wrapper feature selection, for the purpose of supporting wise clinical decision-making in the medical and healthcare fields. In particular, the new proposed sampling method, S-C4.5-SMOTE, is not only able to overcome the problem of data distortion, but also improves overall system performance because its mechanism aims at effectively reducing the data size without distortion, by keeping datasets balanced and technically smooth. This achievement directly supports the Wrapper method of effective feature selection without the need to consider the problem of huge amounts of data; this is a novel innovation in this work.

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Speciation of copper in the fly ash solidification process has been studied by X-ray based spectroscopies inthe present work. Fourier transformed EXAFS (extended X-ray absorption fine structural) spectra of the solidified fly ashes showed that the bond distance of Cu-O (first shell) was 1.96 A with a coordination number (CN) of about 3.0. However, in the second shell of copper atoms, the bond distance of Cu-(O)-Cu was decreased by 0.12-0.22 A during solidification, which might cause the stabilization of the CuO species in the solidified fly ash matrix. By the least-squares fits of the XANES (X-ray absorption near edge structural) spectra, fractions of the main copper species in the solidified fly ashes such as CuCl2 (0.08-0.11), Cu2O (0.07-0.09), Cu(OH)2 (0.31-0.33), and CuO (0.49-0.52) were observed. Combined EXAFS and XANES observations suggested that chemical reactions such as hydroxylation of CuCl2 and oxidation of Cu2O and/or metallic Cu might involve in the solidification process, which also led to a significant reduction of the leachability of copper from the solidified fly ashes.

Multi-label classification has attracted increasing attention for use in various application scenarios, such as medical diagnosis and semantic annotation. A large number of algorithms have been proposed for multi-label classification where many are ensemble-based. However, these ensemble-based methods usually employ bagging schemes for ensemble construction, with comparatively few stacked ensembles for multilabel classification. Existing research on stacked ensemble schemes remains active, but several issues remain such as (1) little has been done to learn the weights of classifiers for combined classifier selection; (2) pairwise label correlations is not investigated sufficiently to improve classification performance. To address these issues, we propose a novel approach that simultaneously exploits label correlations and the process of learning classifier weights to improve the existing stacked ensemble schemes. First, we introduce a weighted stacked ensemble for multi-label classification and use sparsity for regularization to facilitate classifier selection and ensemble construction. Second, we consider pairwise label correlations for assigning high similar weights to improve the classification performance. Finally, we develop an optimization algorithm based on the accelerated proximal gradient and the block coordinate descent techniques to find the optimal solution efficiently. Extensive experiments on publicly available datasets and real Cardiovascular and Cerebrovascular Disease datasets demonstrate that our proposed algorithm outperforms related state-of-the-art methods.

Among a number of ensemble learning techniques, boosting and bagging are the most popular sampling-based ensemble approaches for classification problems. Boosting is considered stronger than bagging on noise-free data set with complex class structures, whereas bagging is more robust than boosting in cases where noise data are present. In this paper, we extend both ensemble approaches to clustering tasks, and propose a novel hybrid sampling-based clustering ensemble by combining the strengths of boosting and bagging. In our approach, the input partitions are iteratively generated via a hybrid process inspired by both boosting and bagging. Then, a novel consensus function is proposed to encode the local and global cluster structure of input partitions into a single representation, and applies a single clustering algorithm to such representation to obtain the consolidated consensus partition. Our approach has been evaluated on 2-D-synthetic data, collection of benchmarks, and real-world facial recognition data sets, which show that the proposed technique outperforms the existing benchmarks for a variety of clustering tasks.

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Trust is critical to the success of e-commerce. Despite the richness of e-trust literature, it is lack of empirical research on this area. So in this paper, we made a review on the e-commerce trust studies, both the literature and empirical studies. Basing on relevant trust literatures we presented a web trust-inducing model and proposed four hypotheses. This model consists of four dimensions, namely graphic design, structure design, content design and social-cue design. In order to test the original model and hypotheses, an online survey was conducted. The results of this study provide support for a majority of the original design features. Finally we identified 12 trust-inducing features, and by applying them, the e-commerce merchant might anticipate fostering optimal levels of trust in their customers. The revised model we present is a reasonable starting point for developing a friendly interface for inducing trust in e-commerce.