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In this paper, support vector machines (SVMs), least squares SVMs (LSSVMs), relevance vector machines (RVMs), and probabilistic classification vector machines (PCVMs), are compared on sixteen binary and multiclass medical datasets. Particular emphasis is put on the comparison among the commonly used Gaussian radial basis function (GRBF) kernel, and the relatively new generalized min–max (GMM) kernel and exponentiated-GMM (eGMM) kernel. Since most medical decisions involve uncertainty, a postprocessing approach based on Platt’s method and pairwise coupling is employed to produce probabilistic outputs for prediction uncertainty assessment. The extensive empirical study illustrates that the SVM classifier using the tuning-free GMM kernel (SVM-GMM) shows good usability and broad applicability, and exhibits competitive performance against some state-of-the-art methods. These results indicate that SVM-GMM can be used as the first-choice method when selecting an appropriate kernel-based vector machine for medical diagnosis. As an illustration, SVM-GMM efficiently achieves a high accuracy of 98.92% on the thyroid disease dataset consisting of 7200 samples.

The study's primary objective was to assess teachers' perceptions of innovative science, technology, engineering, arts, and mathematics (STEAM) teaching in China and Ghana. Three research goals were established to meet this primary research purpose; precisely, the study attempted to; find out how STEAM teaching practice has been implemented China and Ghana; the perceived values and challenges teachers face during the practice of STEAM education in China and Ghana and the suggestions of development of STEAM education in China and Ghana and implications for the rest of the world. The research approach included document gathering, document sorting by relevance, noting thematic concepts, grouping the concepts into a set of categories, and constructing major themes from these categories. After analyzing the associated ideas and codes, sub-themes, and themes culled from many sources of literature, we interpreted each subject in connection to its conceptual codes and sub-themes in the context of STEAM education. The research included five instructors from five schools in Accra, Ghana, and Zhejiang Province, China. The study found that both Ghana and China alluded the fact that the practice STEAM to a certain degree. Teacher in Zhejiang province indicated their preference for discipline specific STEAM integration while teachers in Ghana supported the content-specific STEAM approach out of the four main approaches used. Both countries revealed that they derived benefits from STEAM. This includes adding to relevance to learning material; helping students develop preparedness for college; empowering students; encouraging students to go beyond the comfort zone; helping students discover their interest and many others. Teachers from two countries further opined some challenges they faced with integrating STEAM in their teaching and learning. This also compose of; difficulty for schools’ leaders to change; difficulty for educators to develop their own level of comfort; difficulty in finding time in the curriculum for project-based learning; difficulty in collaborating with other colleagues; inconsistent access to resource; Colleagues teachers’ perception of STEAM integration, difficulty in balancing district or city initiatives and many more. The study revealed the need for improvement in STEAM integration in both countries by ; gaining support from building school leaders; developing community partnerships; speaking with educators who are already integrating STEAM; taking advantage of STEAM professional development opportunities, sharing their success stories.

To make radial basis function (RBF) networks efficient for large-scale learning tasks, the parallel technique provides a promising way for the construction of multicolumn RBF network (MCRN), where the task to be tackled is decomposed into several subtasks and RBF networks designed for these subtasks are integrated as a parallel structure to obtain the final model. This paper focuses on presenting an efficient design method for MCRN based on improved generalized hybrid constructive (GHC) learning algorithm such that desired performance and high efficiency are guaranteed. Specifically, a minimum difference scheme is firstly introduced to divide a large-scale dataset into several parts with similar distribution. For each subtask, a compact RBF network is designed by improved GHC learning algorithm. By fully considering the inherent properties of RBF networks, a K-nearest potential centers based criterion is established to calculate the output of testing sample. Experiments on some benchmark classification problems are conducted to verify the advantages of the proposed method. In terms of training and testing efficiencies and classification accuracy, it is shown that our model is superior over some existing ones.

In order to study the influences of microstructures on corrosion behavior of double-side friction stir welded 6082Al thick plate. The corrosion behavior was firstly clarified by potentiodynamic pol...

The recognition of license plates is very important for intelligent transportation systems. Generally, the performance of an intelligent recognition algorithm is greatly affected by different shooting angles, illumination conditions and backgrounds of the license plate images. This paper presents a sequence recognition approach for intelligent recognition of Chinese license plates. Firstly, a spatial transformer network (STN) is employed to adjust the inclined and deformed license plates such that all the plates have a uniform orientation and thus are easier to be recognized. Then, an improved convolutional neural network (CNN) is designed to extract sequence features of the rectified license plates. The features of different convolutional layers are integrated as input to a bi-directional recurrent neural network (BRNN), where the character segmentation is not needed. Finally, the recognition is accomplished by the BRNN and connectionist temporal classification (CTC). Due to the lack of adequate Chinese license plates, an effective training method is presented in which the network is pre-trained by sufficiently enough synthetic license plates and is fine-tuned by our collected real Chinese license plates. The experimental results show that our model achieves better recognition accuracy and lower average edit distance than some existing methods.