A Fast Image Retrieval Method Designed for Network Big Data

Yang, Jiachen; Jiang, Bin; Li, Baihua; Tian, Kun; Lv, Zhihan

doi:10.1109/tii.2017.2657545

Cited by 47 publications

(30 citation statements)

References 48 publications

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“…Binding of protein-sites and the specially targeted proteins are two important moves to understand the concept of biological activities [9]. A lot of techniques that gives high-throughput have recently purposed that try to enumerate the similarity between proteins motifs and protein [10]. In spite of the strong achievement, these techniques have some limitations and go down towards the strict classification of motifs.…”

Section: Related Workmentioning

confidence: 99%

“…Gene mapping has been considered as one of the challenging tasks for researchers who belong to the field of bioinformatics and data analysis. Previously such tools are developed which are used for gene analysis and gene mapping for example MAPMAKER [10]. MAPMAKER has been applied to the construction of linkage maps in a number of organisms; including the human beings [13].…”

Section: Related Workmentioning

confidence: 99%

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Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

Ali¹,

Yasin²,

Draz³

et al. 2018

ijacsa

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Abstract-Due to the rapid growth of data in the field of big data and bioinformatics, the analysis and management of the data is a very difficult task for the scientist and the researchers. Data exists in many formats like in the form of groups and clusters. The data that exist in the group form and have some repetition patterns called Motifs. A lot of tools and techniques are available in the literature to detect the motifs in different fields like neural networks, antigen/antibody protein, metabolic pathways, DNA/RNA sequences and Protein-Protein Interactions (PPI). In this paper, motif detection is done in tumor antigen protein, namely, cellular tumor antigen p53 (Guardian of the protein and genome) that regulate the cell cycle and suppress the tumor growth in the human body. As tumor is a death causing disease and creates a lot of other diseases in human beings like brain stroke, brain hemorrhage, etc. So there needs to investigate the relation of the tumor protein that prevents the human from not only brain tumor but also from a lot of other diseases that is created from it. To find out the gap between the motifs in the tumor antigen the GLAM2 is used that detects the distance between the motifs very efficiently. Same tumor antigen protein is evaluated at different tools like MEME, TOMTOM, Motif Finder and DREME to analyze the results critically. As tumor protein exists in multiple species, so comparison of homo tumor antigen protein is also done in different species to check the diversity level of this protein. Our purposed approach gives better results and less computational time than other approaches for different types of user characteristics.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

Ali¹,

Yasin²,

Draz³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…O BJECT categorization in images has been an active and fundamental research topic in various industrial areas, such as automated visual inspection [1], fast image retrieval [2] and intelligent surveillance [3]. Recently, contextual information, a sort of available and complementary information that provides rich positive details for target data, can enhance the accuracy of object categorization models.…”

Section: Introductionmentioning

confidence: 99%

CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts

Yan

Qiu

et al. 2020

IEEE Trans. Ind. Inf.

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“…13 The LSTM model has many successful applications in different fields, including linguistic modeling, speech recognition, machine translation, multimedia related audio and video data analysis, picture title modeling, and medical-related protein secondary structure sequence. 14,15 However, few studies have applied the LSTM model to construct traffic flow prediction network, especially for the research on traffic big data, no progress has been made.In this paper, we construct a novel prediction network with the LSTM model based on machine learning, which includes network structure design, network training, and prediction process implementation. In addition to the minimum prediction error as the goal, we present a typical machine learning with the deep learning network.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Big data‐driven machine learning‐enabled traffic flow prediction

Kong

Jiang

et al. 2018

Trans Emerging Tel Tech

Self Cite

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Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. The key challenge of traffic flow prediction network is how to construct an adaptive model relying on historical data. Existing big data‐driven traffic flow prediction networking approaches mainly use shallow learning, and there are unsatisfying for many realistic applications, which inspire us to rethink the traffic flow big data prediction problem with deep learning. In this paper, we propose a novel prediction approach based on machine learning. In addition to the minimum prediction error as the goal, we present the long short‐term memory model, which is a typical machine learning algorithm with deep learning network. This method is applied into the real‐world traffic big data from performance measurement system. Experimental results show that the proposed machine learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network.

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A Fast Image Retrieval Method Designed for Network Big Data

Cited by 47 publications

References 48 publications

Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

Motif Detection in Cellular Tumor p53 Antigen Protein Sequences by using Bioinformatics Big Data Analytical Techniques

CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts

Big data‐driven machine learning‐enabled traffic flow prediction

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