Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.
Electronic healthcare services are becoming an increasingly essential form of information and communication technology (ICT) that enables the fast and smooth delivery of health care, specifically in countries with scarce resources such as Pakistan. A better understanding of factors contributing to the adoption of electronic health care is needed, yet this remains an under-researched phenomenon. Grounded in the united theory of acceptance and use of technology, this article attempts to fill the gap by proposing and empirically testing the contribution of trust, privacy, task-technology fit, and personal innovativeness of patients’ intentions to adopt electronic health technology. A survey questionnaire was used to collect data from 353 patients in major hospitals in Islamabad, Pakistan. This study used Partial Least Square Structural Equation Modeling for the analysis. Results indicate that the intention to adopt electronic health technology is determined mostly by effort expectancy, social influence, facilitating conditions, task-technology fit, trust, privacy, and personal innovativeness in information technology. The study concludes with several managerial implications and future research directions, which give further opportunities to researchers and practitioners in the field of e-health technology.
Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey.
At present, the problems of high energy consumption and low efficiency in electrocatalytic hydrogen production have limited the large-scale industrial application of this technology. Constructing effective catalysts has become the...
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