identification methods
Recently Published Documents


TOTAL DOCUMENTS

1545
(FIVE YEARS 390)

H-INDEX

57
(FIVE YEARS 8)

2022 ◽  
Author(s):  
Qiang Lai ◽  
Hong-hao Zhang

Abstract The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted k-shell degree neighborhood identification method (KSD), the degree k-shell identification method (DKS), and the degree k-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.


Antibiotics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 86
Author(s):  
Yuki Uehara

Staphylococcal cassette chromosome mec (SCCmec) typing was established in the 2000s and has been employed as a tool for the molecular epidemiology of methicillin-resistant Staphylococcus aureus, as well as the evolution investigation of Staphylococcus species. Molecular cloning and the conventional sequencing of SCCmec have been adopted to verify the presence and structure of a novel SCCmec type, while convenient PCR-based SCCmec identification methods have been used in practical settings for many years. In addition, whole-genome sequencing has been widely used, and various SCCmec and similar structures have been recently identified in various species. The current status of the SCCmec types, SCCmec subtypes, rules for nomenclature, and multiple methods for identifying SCCmec types and subtypes were summarized in this review, according to the perspective of the International Working Group on the Classification of Staphylococcal Cassette Chromosome Elements.


2022 ◽  
Vol 11 (1) ◽  
Author(s):  
Shinpei Matsuda ◽  
Hitoshi Yoshimura

Abstract Background Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical consideration during the coronavirus disease 2019 pandemic is that there is no contact between the subjects and personal identification systems. The aim of this study was to organize the recent 5-year development of contactless personal identification methods that use artificial intelligence. Methods This study used a scoping review approach to map the progression of contactless personal identification systems using artificial intelligence over the past 5 years. An electronic systematic literature search was conducted using the PubMed, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore databases. Studies published between January 2016 and December 2020 were included in the study. Results By performing an electronic literature search, 83 articles were extracted. Based on the PRISMA flow diagram, 8 eligible articles were included in this study. These eligible articles were divided based on the analysis targets as follows: (1) face and/or body, (2) eye, and (3) forearm and/or hand. Artificial intelligence, including convolutional neural networks, contributed to the progress of research on contactless personal identification methods. Conclusions This study clarified that contactless personal identification methods using artificial intelligence have progressed and that they have used information obtained from the face and/or body, eyes, and forearm and/or hand.


2022 ◽  
Author(s):  
Núbia Rosa Da Silva ◽  
Victor Deklerck ◽  
Jan Baetens ◽  
Jan Van den Bulcke ◽  
Maaike De Ridder ◽  
...  

Abstract Background: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on the transversal section. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results: We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multiview image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the singleview methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. Conclusions: Additional images from the non-transversal sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learningbased wood species identification methods to avoid an overestimation of the performance.


Author(s):  
Sowmya HK ◽  
R. J. Anandhi

The WWW has a big number of pages and URLs that supply the user with a great amount of content. In an intensifying epoch of information, analysing users browsing behaviour is a significant affair. Web usage mining techniques are applied to the web server log to analyse the user behaviour. Identification of user sessions is one of the key and demanding tasks in the pre-processing stage of web usage mining. This paper emphasizes on two important fallouts with the approaches used in the existing session identification methods such as Time based and Referrer based sessionization. The first is dealing with comparing of current request’s referrer field with the URL of previous request. The second is dealing with session creation, new sessions are created or comes in to one session due to threshold value of page stay time and session time. So, authors developed enhanced semantic distance based session identification algorithm that tackles above mentioned issues of traditional session identification methods. The enhanced semantic based method has an accuracy of 84 percent, which is higher than the Time based and Time-Referrer based session identification approaches. The authors also used adapted K-Means and Hierarchical Agglomerative clustering algorithms to improve the prediction of user browsing patterns. Clusters were found using a weighted dissimilarity matrix, which is calculated using two key parameters: page weight and session weight. The Dunn Index and Davies-Bouldin Index are then used to evaluate the clusters. Experimental results shows that more pure and accurate session clusters are formed when adapted clustering algorithms are applied on the weighted sessions rather than the session obtained from traditional sessionization algorithms. Accuracy of the semantic session cluster is higher compared with the cluster of sessions obtained using traditional sessionization.


2022 ◽  
Author(s):  
Yifei Yu ◽  
Oscar Alvarez ◽  
Vishwa Patel ◽  
Chaoqun Liu

2022 ◽  
Vol 2146 (1) ◽  
pp. 012012
Author(s):  
Fang Wang

Abstract With the advancement of the times, computer technology is also constantly improving, and people’s requirements for software functions are also constantly improving, and as software functions become more and more complex, developers are technically limited and teamwork is not tacitly coordinated. And so on, so in the software development process, some errors and problems will inevitably lead to software defects. The purpose of this paper is to study the intelligent location and identification methods of software defects based on data mining. This article first studies the domestic and foreign software defect fault intelligent location technology, analyzes the shortcomings of traditional software defect detection and fault detection, then introduces data mining technology in detail, and finally conducts in-depth research on software defect prediction technology. Through in-depth research on several technologies, it reduces the accidents of software equipment and delays its service life. According to the experiments in this article, the software defect location proposed in this article uses two methods to compare. The first error set is used as a unit to measure the subsequent error set software error location cost. The first error set 1F contains 19 A manually injected error program, and the average positioning cost obtained is 3.75%.


2022 ◽  
Vol 31 (1) ◽  
pp. 35-46
Author(s):  
Dakun Sun ◽  
Zhenyu Li ◽  
Xu Dong ◽  
Xiaofeng Sun

Sign in / Sign up

Export Citation Format

Share Document