A Learning to rank framework based on cross-lingual loss function for cross-lingual information retrieval

Ghanbari, Elham; Shakery, Azadeh

doi:10.1007/s10489-021-02592-z

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…In Formula (10), d pj represents the expected value of the input mode p output unit j of the 3D medical image overlapping area information, and the weight w ji is modified according to Formula (11), the correction formula is as follows:…”

Section: F I G U R E 6 Flow Chart Of Three-dimensional Medical Image ...mentioning

confidence: 99%

“…It represents the times of data issued by medical science and publishers, and is a medical database for literature information such as electronic newspapers, periodicals and books 10 Medical electronic publishing objects include electronic books and electronic regulations, and so on. In the process of information retrieval, the tools used can carry out the retrieval of directories and keywords, and information retrieval plays a decisive role in information sharing 11 The interactive 3D medical image surface reconstruction information contains both vocational and general educational information. The surface reconstruction information of evidence‐based motion 3D medical images is mainly studied based on clinical sports medicine 12 .…”

Section: Information Classification Of Overlapping Regions In 3d Medi...mentioning

confidence: 99%

“…In the process of information retrieval, the tools used can carry out the retrieval of directories and keywords, and information retrieval plays a decisive role in information sharing 11 …”

Section: Information Classification Of Overlapping Regions In 3d Medi...mentioning

confidence: 99%

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Research on 3D medical image surface reconstruction based on data mining and machine learning

Hua

Liu

Yin

et al. 2021

Int J of Intelligent Sys

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Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that

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Section: F I G U R E 6 Flow Chart Of Three-dimensional Medical Image ...mentioning

confidence: 99%

Section: Information Classification Of Overlapping Regions In 3d Medi...mentioning

confidence: 99%

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Research on 3D medical image surface reconstruction based on data mining and machine learning

Hua

Liu

Yin

et al. 2021

Int J of Intelligent Sys

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Narrowing the language gap: domain adaptation guided cross-lingual passage re-ranking

Chen¹,

Zhang²,

Zhang³

2023

Neural Comput & Applic

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Supervised ranking approach to identify infLuential websites in the darknet

et al. 2023

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The anonymity and high security of the Tor network allow it to host a significant amount of criminal activities. Some Tor domains attract more traffic than others, as they offer better products or services to their customers. Detecting the most influential domains in Tor can help detect serious criminal activities. Therefore, in this paper, we present a novel supervised ranking framework for detecting the most influential domains. Our approach represents each domain with 40 features extracted from five sources: text, named entities, HTML markup, network topology, and visual content to train the learning-to-rank (LtR) scheme to sort the domains based on user-defined criteria. We experimented on a subset of 290 manually ranked drug-related websites from Tor and obtained the following results. First, among the explored LtR schemes, the listwise approach outperforms the benchmarked methods with an NDCG of 0.93 for the top-10 ranked domains. Second, we quantitatively proved that our framework surpasses the link-based ranking techniques. Third, we observed that using the user-visible text feature can obtain comparable performance to all the features with a decrease of 0.02 at NDCG@5. The proposed framework might support law enforcement agencies in detecting the most influential domains related to possible suspicious activities.

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A Learning to rank framework based on cross-lingual loss function for cross-lingual information retrieval

Cited by 3 publications

References 39 publications

Research on 3D medical image surface reconstruction based on data mining and machine learning

Research on 3D medical image surface reconstruction based on data mining and machine learning

Narrowing the language gap: domain adaptation guided cross-lingual passage re-ranking

Supervised ranking approach to identify infLuential websites in the darknet

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