Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices objects. The flourishing IoT leads to an unprecedented data explosion, traditional storing or processing techniques have the problem low efficiency, if are used maliciously, security loss may be further caused. Multicloud high-performance secure computing platform, which combines multiple cloud providers for processing, distributed multicloud platform ensures some extent. Based on task...

Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we that cluster centers cannot be determined automatically selected may fall into a local optimum random selection parameter cut-off distance dc value. To overcome these problems, novel algorithm based on DPC & PSO (PDPC) is proposed. Particle swarm optimization (PSO) introduced because its simple concept strong global ability, which can optimal solution in relatively few iterations. First, to solve effect...

Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of surveys, a variety classification techniques have been successfully applied to astronomical data processing. However, it difficult select an appropriate method practical scenarios due different algorithmic ideas characteristics. Here, we present second work mining series - review techniques. This also consists three parts: systematic overview current...

As one of the most important techniques in data mining, clustering has always been highly concerned. Most algorithms have encountered challenges, such as difficulty cluster centers selection, artificial determination number clusters K, low accuracy clustering, and uneven efficiency different sets. Considering chosen, a new algorithm fast selecting initial is proposed this paper. Generally, are those points with higher density, smaller radius threshold far away from each other, method uses...

ABSTRACT Clustering is an effective tool for astronomical spectral analysis, to mine clustering patterns among data. With the implementation of large sky surveys, many methods have been applied tackle spectroscopic and photometric data effectively automatically. Meanwhile, performance under different characteristics varies greatly. aim summarizing algorithms laying foundation further research, this work gives a review spectra in three parts. First, are investigated analysed theoretically,...

The extensive growth of data quantity has posed many challenges to analysis and retrieval. Noise redundancy are typical representatives the above-mentioned challenges, which may reduce reliability retrieval results increase storage computing overhead. To solve above problems, a two-stage pre-processing framework for noise identification reduction, called ARIS, is proposed in this article. first stage identifies removes noises by following steps: First, influence space (IS) introduced...

The gas-phase metallicity is a crucial parameter for understanding the evolution of galaxies. Considering that number multiband galaxy images can typically reach tens millions, using these as input data to predict has become feasible method. However, accuracy estimates from relatively limited. To solve this problem, we propose measurement residual network (GPM-ResNet), deep learning method designed photometric DESI. parameters are labeled with values, which were obtained through...

The cluster number can directly affect the clustering effect and its application in real-world scenarios. Its determination is one of key issues analysis. According to singular value decomposition (SVD), characteristic directions larger values likely represent primary data patterns, trends, or structures corresponding main information. In analysis, information structure are related itself. may correspond clusters, their different clusters. Based on this, a singular-value-based detection...

Ancient Chinese architecture (ACA) has rich historical and cultural value, the preservation of ACA can be enhanced by designing an efficient multi-label image classification model. However, most existing methods mainly focus on convolutional neural networks to learn local features ancient architectural images, which cannot retain positional information each part fail leverage semantic context effectively. This limitation leads confusion when dealing with similar structures. To solve these...

Prototypes help to explain the predictions of deep classification models for time series. However, most learn prototypes by randomly initializing an uncertain number low-discriminative prototypes, which may lead unstable and unreliable results. To address these issues, we propose a new class Discriminative Prototype Learning Network (DPL-Net), learns appropriate class-discriminative thus improving performance. Specifically, proposed Initialization Mechanism (PIM) introduces proximity metric...

Abstract Carbon stars are chemically peculiar with high carbon abundance, showing strong molecule bands and rare emission lines in their spectra. This paper explores the stellar activity of by identifying line features An outlier detection method based on morphological feature extraction interval representation is used to identify 88 targets presenting from 3546 star Of these, 55 present Balmer series emissions, 35 show forbidden ([O i ] λ 6301 Å, [O iii ], [N ii [S only 2 spectra that not...

The missing satellite problem remains a central issue of the Lambda cold dark matter (ΛCDM) model. On small scale, number observed dwarf galaxies is still fewer than predicted by existing theories. Therefore, finding fainter in deeper images crucial for refining theoretical framework. In this study, we propose an end-to-end automatic identification scheme size and faint based on Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys photometric images, provide batch galaxy...