Titlebook: Advanced Intelligent Computing Technology and Applications; 19th International C De-Shuang Huang,Prashan Premaratne,Abir Hussain Conference

cinder

Neferti X. M. Tadiar,Angela Y. Davisr time. Online data-stream outlier detection can indeed be more difficult and challenging. This is because new data points are continuously arriving, and the outlier detection algorithm must process them in real-time. Our idea is to use online evolving spiking neural network classifier and dynamic o

Aggrandize

https://doi.org/10.1007/978-1-4039-8261-2r system. With the development of social production, the electricity consumption in people’s daily life, factories and enterprises is continuously increasing, it also increases the difficulty of electric load forecasting. Traditional methods are difficult to analyze the huge and complex electricity

Explicate

方舟

https://doi.org/10.1007/978-1-4039-8261-2f cancer can be predicted by analyzing lncRNAs. However, lncRNA is characterized by a limited amount of data samples and a large number of expression levels of gene features, where there exist much redundancy. It results in difficulty in cancer predicting. To solve the problem, this paper proposes a

爆炸

https://doi.org/10.1007/978-1-4039-8261-2 propose a new algorithm based on the manifold tangent space, called the manifold tangent space-based 2D-DLPP algorithm. This algorithm embeds the covariance matrix into the tangent space of the SPD manifold and utilizes Log-Euclidean Metric Learning (LEM) to fully extract feature information, thus

Canyon

Advanced Intelligent Computing Technology and Applications978-981-99-4752-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

宪法没有

https://doi.org/10.1007/978-981-99-4752-2Evolutionary Computation and Learning; Swarm Intelligence and Optimization; Information Security; Theor

Grievance

是剥皮

FRAUD

https://doi.org/10.1007/978-1-4039-8261-2er fit the non-convex distribution of data (MKTL, Multi-core K-means Transfer Learning). The experimental results show that MKTL achieves the best average accuracy in 3 datasets. Compared with the original methods (kNN, TCA, GFK, JDA), the performance of MKTL is improved by 2.5 ~ 12.8 percentage in high computational efficiency.