scholarly journals Automatic trend detection: Time-biased document clustering

2021 ◽  
pp. 106907
Author(s):  
Sahar Behpour ◽  
Mohammadmahdi Mohammadi ◽  
Mark V. Albert ◽  
Zinat S. Alam ◽  
Lingling Wang ◽  
...  
Keyword(s):  
Document Clustering ◽  
Trend Detection ◽  
Detection Time

Improving Mycobacterium spp. detection in water: a new methodological proposal

2004 ◽  
Vol 4 (2) ◽  
pp. 103-106
Author(s):  
R. Santos ◽  
S. Gonçalves ◽  
F. Macieira ◽  
F. Oliveira ◽  
R. Rodrigues ◽  
...  
Keyword(s):  
Water Samples ◽  
Recovery Rate ◽  
Opportunistic Infections ◽  
Environmental Water ◽  
Environmental Water Samples ◽  
Sample Treatment ◽  
Detection Time ◽  
Fast Detection ◽  
Enrichment Step ◽  
Mycobacterium Spp

In recent years, non-tuberculous mycobacteria (NTM), once considered merely environmental saprophytes, have emerged as a major cause of opportunistic infections. There is no evidence of human-to-human transmission but they have been found in several environmental water samples. It is, therefore, of the utmost importance to develop methods of rapidly and accurately detecting non-tuberculous mycobacteria in water samples. To obtain a maximum recovery rate and a reduction of Mycobacterium spp. detection time in water samples, different decontamination, enrichment procedures and antibiotics supplements were tested before the inoculation into the Bactec® system. The proposed method of sample treatment (decrease in the decontamination time, followed for a peptone pre-enrichment step and an aztreonam and cefepime supplement) before the inoculation into the Bactec® system proved to be a good option for reliable and fast detection of Mycobacterium spp. in water samples.

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

Deep Multi-view Document Clustering with Enhanced Semantic Embedding

2021 ◽  
Author(s):  
Ruina Bai ◽  
Ruizhang Huang ◽  
Yanping Chen ◽  
Yongbin Qin
Keyword(s):  
Document Clustering ◽  
Semantic Embedding

scholarly journals Correction: Huet, R.; Johanson, G. 1,1-Difluoroethane Detection Time in Blood after Inhalation Abuse Estimated by Monte Carlo PBPK Modeling. Pharmaceutics 2020, 12, 997

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1037
Author(s):  
Raul Huet ◽  
Gunnar Johanson
Keyword(s):  
Monte Carlo ◽  
Pbpk Modeling ◽  
Detection Time

The authors wish to make the following corrections to this paper [...]

scholarly journals First-detection time of a quantum state under random probing

2021 ◽  
Vol 103 (2) ◽  
Author(s):  
David A. Kessler ◽  
Eli Barkai ◽  
Klaus Ziegler
Keyword(s):  
Quantum State ◽  
Detection Time

Rapid, highly sensitive and quantitative detection of interleukin 6 based on SERS magnetic immunoassay

2021 ◽  
Vol 13 (15) ◽  
pp. 1823-1831
Author(s):  
Xiaomei Wang ◽  
Li Ma ◽  
Shijiao Sun ◽  
Tingwei Liu ◽  
Hao Zhou ◽  
...  
Keyword(s):  
Interleukin 6 ◽  
High Sensitivity ◽  
Quantitative Detection ◽  
Detection Time ◽  
Highly Sensitive ◽  
Sandwich Method

We have developed a SERS magnetic immunoassay method based on the principle of sandwich method for rapid and quantitative detection of IL-6. The developed SERS method has the advantages of high sensitivity and detection time is only 15 min.

ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

2021 ◽  
Vol 13 (2) ◽  
pp. 1-27
Author(s):  
A. Khalemsky ◽  
R. Gelbard
Keyword(s):  
Big Data ◽  
Visual Presentation ◽  
Trend Detection ◽  
Dynamic Visualization ◽  
Segmentation Process ◽  
Key Points ◽  
Wide Range ◽  
History Of ◽  
The Individual ◽  
Over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.

Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling

2021 ◽  
Vol 172 ◽  
pp. 114652
Author(s):  
Nabil Alami ◽  
Mohammed Meknassi ◽  
Noureddine En-nahnahi ◽  
Yassine El Adlouni ◽  
Ouafae Ammor
Keyword(s):  
Neural Networks ◽  
Topic Modeling ◽  
Document Clustering ◽  
Text Summarization ◽  
Arabic Text ◽  
Arabic Text Summarization ◽  
Unsupervised Neural Networks
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